Habs Elstree: ENIGMA 2025

COMPUTER SCIENCE SOCIETY 2024/25

ENIGMA HABERDASHERS’ ELSTREE SCHOOLS Computer Science Society STEM Magazine

The Coming Wave • Paradox of Human Intelligence • Brain-Computer Interfaces • Conceptual Clash In Physics Has Google’s Quantum Chip proved the multiverse exists? Can you solve Alan Turing’s Puzzle?

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A long time ago

INTRODUCTION in a Quantum Realm Far Far Away...

Dear Reader, We are delighted to release the Computer Science Society’s 2024/2025 edition of ENIGMA. The articles featured here show the absolute best of what HABS’ STEM students are capable of, even from the youngest of years. With articles ranging from the 1,000 km/hr Hyperloop to Brain Computer Interfaces, take your pick and dive into some incredibly detailed and impressive pieces.

ENIGMA would not have been possible without the hard work of the HABS Marketing Team, and of course, everyone who took the time to write the incredible pieces this magazine features. We hope you enjoy!

Yours algorithmically, Aaron Varma, Yunfei Fan, Pasha Soneji, Kayan Shah, Samira Glynn Chairs, Computer Science Society

Quantum Supercomputers >

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ARTICLES

How to create an unbeatable AI player for strategy games

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How HCIs Shape our Digital Experiences

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The Paradox of Human Intelligence

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The Impacts of Virtual and Augmented Reality on Society

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Physics Concepts and its Landscape with AI 18

The Tower of Hanoi

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Bridging the Divide : The Conceptual Clashes in Modern Physics

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Quantum Mechanics... What is it, and what is its significance?

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The Special Theory of Relativity

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AI And the Age of Uncertainty : Book Review on The Coming Wave

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Secrets of Nuclear Reaction

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Explaining Retrieval Augmented Generation (RAG)

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The Future of Brain Computer Interfaces

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Hyperloop : An Impossible Promise?

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Bias of AI

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Polymorphism

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BRAIN

HABS ELSTREE ENIGMA

Are BCI’s Our Future?

What are they?

COMPUTER INTERFACES

Usage

BRAIN COMPUTER INTERFACES ARE A

BCI’S HAVE ALREADY FOUND POTENTIAL

SYSTEM THAT ALLOWS DIRECT

IN THE MEDICINE INDUSTRY, SUCH AS IN

COMMUNICATION BETWEEN THE BRAIN

CONTROLLING PROSTHETICS. SOME EVEN

AND AN EXTERNAL DEVICE, LIKE A

SPECULATE THAT THEY COULD BE USED

COMPUTER OR ROBOTIC LIMB, BYPASSING

FOR REHABILITATION PURPOSE, HOWEVER

THE BODY'S USUAL OUTPUT PATHWAYS

LIMITED TRIALS HAVE HINDERED ITS

(MUSCLES, NERVES). BUT COULD THEY BE

PROGRESS FOR CLINICAL USE.

ANY USE FOR MANKIND OR HAS IT POSED AN ENOUGH THREAT TO OUR ABILITY TO

FURTHERMORE, WITH OVER 5000 - 1000

THINK FREELY?

USD PRICE RANGE AND CONCERNS OVER RELIABILITY, THERE IS NOT YET ADEQUATE INCENTIVE FOR COMMERCIAL INTERESTS SUCH AS IN GAMING OR SENSOR TECHNOLOGY.

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"My brain is in the cylinder, and I see, hear, and speak through these electronic vibrators.”

H.P. LOVECRAFT. HIS NOVEL, THE WHISPERER IN THE DARKNESS

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LAKSH PATEL - SFM3

HOW TO CREATE AN UNBEATABLE AI PLAYER FOR STRATEGY GAMES [PYTHON] MINIMAX ALGORITHM

PROGRAMMING >

To visualise how the Minimax Algorithm works,

INTRODUCTION W

imagine that you are playing Tic-Tac-Toe. You take the current game-state, and place it at the top of a tree. Then, from it, you draw arrows, with each

hat’s more fun than having something

possible move following on placed underneath

constantly beat you? This article will guide you

(based on whose turn it is). You continue building

into coding your own bot into playing any

the tree until Player 1, who we will call the

game of your choice, with a near 100% (if you

Maximising Player, has won, Player 2, who we will

do it right) success rate.

call the Minimising Player, has won, or, the game has been drawn. Then, next to it, write +10 if the Maximising Player won, -10 if the Minimising Player

The code that I share in this article is

won, or 0, if it is a draw.

gravitated towards games where each player places a piece of some sort on a board, one at a time, and if this is you want to test it out, I would recommend starting with a straightforward game like Tic-Tac-Toe, and then doing something more complicated, but equally simple, like Connect 4. Despite that, you could transform the same principles into working for more complicated games, even as far as Chess.

Hopefully, you can see, that the Maximising Player wants the score to be as high as possible, as a score of +10 means they have

N

ow, there is another aspect to the

won. Likewise, the Minimising Player wants

Minimax Algorithm called the Heuristic

the score to be as low as possible. So, start to

Function (as if it couldn’t get simpler!). Think

trace back up the tree. If a game-state has two

of this as being like the evaluation bar in

options below it, one with a score of -10, and

online Chess Games: it takes in the board,

the other with a score of +10, and it is the

inspects it, and then returns a value, such

Maximising Player’s turn, they will want to

that a positive value means that the

choose the move in which they have the

Maximising Player’s position is better, and a

highest score, and so, you can write +10 next

negative value meaning that the Minimising

to the game-state above it.

Player’s position is better. The magnitude of

Continue doing this, paying attention to

the value, how positive or negative the value

whether it is the Maximising or the Minimising

is, shows the magnitude of how much a

player’s turn, working upwards until you get to

player is winning. For example, in two

the last layer before the current game-state. If

different game-states, if the Heuristic

it is the Minimising Player’s turn, for example,

Function returns -6 and -3, in both cases the

look at which score is the lowest, and then

Minimising Player is in a better position, but,

play it.

for the game where the value is -6, they are

So how does this help us?

This function, essentially, saves time. You wouldn’t need a Heuristic Function for a game as simple as Tic-Tac-Toe, as you can easily traverse every single combination of moves, from the very start to the very end, but for a more complicated game, you can just tell your code to stop after, let’s say, 5 moves into the future, and then use the Heuristic Function to assign it a score.

This is the only real thing that can affect the ability of your AI. The more accurate the Heuristic Function is, the more accurate the computer can judge the game, the more accurate the moves are that it makes. No pressure.

winning by a greater margin.

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CREATING THE BASIC FUNCTIONS L

Again, not much explanation required, but just returning what moves a player in this situation can possibly make. This requires you to be familiar with your game’s rules, for example in Tic-Tac-Toe you can place it anywhere that there is an empty space, however in Connect 4, you can only place it in a fashion that follows gravity, where there has

et’s start coding, shall we?

to be a piece (or the bottom of the board) underneath it.

There are some essential functions that you will need, and I am afraid for this bit, you might have to think for yourself.

Making a Human Move

One of the first and most obvious thing is that you establish a board that you can play on. In the code above, I created a 7x6 board, that is used in Connect 4, as a 2D array. Of course, depending on what game you play, you can change the dimensions and nature of the board.

You must also create a function that can print the board, in whatever

Using the functions we have made previously, we can ask the human

format you want, so that your user can actually see what is

player to make a move, validate it, and then, once a reasonable move

happening. You may want it to print row-by-row, have labels for each

has been made, insert it onto the board.

row and column, or whatever, it’s up to you. This function does nothing else, except for displaying the board. If you want, you can

Checking if the Game is Over

develop this into updating some sort of GUI.

Making a Move

One of the first and most obvious thing is that you establish a board that you can play on. In the code above, I created a 7x6 board, that is used in Connect 4, as a 2D array. Of course, depending on what game you play, you can change the dimensions and nature of the board.

Create functions which returns Boolean values whether either player has won, or the game has been drawn.

You must also create a function that can print the board, in

HEURISTIC FUNCTIONS

whatever format you want, so that your user can actually see

Now is the time to make the Heuristic Function. Beforehand,

what is happening. You may want it to print row-by-row, have

decide which factors you want the Heuristic Function to take

labels for each row and column, or whatever, it’s up to you. This

into account. For example, when I made a Othello Reversi AI

function does nothing else, except for displaying the board. If

Player (great game, you should check it out), the factors I chose

you want, you can develop this into updating some sort of GUI.

were the number of pieces each player had, the mobility of the player (the number of moves it could make), and the number

Seeing What Moves You Make

of edge pieces and corners a player had captured (which is important to the game).

Here, I show you how to code for one of the factors (number of pieces), but for the rest, you would just repeat the whole block and adjust depending on what the factor is.

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text

First, we assign the Maximising Player’s and the Minimising Player’s value for the factor to 0. Then, depending what the factor is, we adjust those values, in this case, just adding one for every piece they have. Then, if both of those factors are not 0, to avoid a Zero Division Error, we apply the following formula:

The weight essentially signifies how important the value is, and tends to be in the range of 0–2, but you can adjust that value as much as you want (here I chose 1.2 after testing a range of values, and observing that 1.2 worked the best).

Clearly, if the factor is more important, you want the weight to be bigger, as it has a larger effect on the heuristic value.

IMPLEMENTING THE MINIMAX ALGORITHM Now we get into the fun(ish) bit, how to actually code the Minimax Algorithm.

In order to do this, it requires two functions, and some nifty aspects of dynamic programming, but luckily for you, it is not game-specific, and you can essentially use the same code for whatever game you are making, thanks to the functions we created earlier.

And… take a breath.

This may seem complicated, but it is essentially doing the exact same thing you did earlier with the Tic-Tac-Toe tree, so I would recommend looking at the code, and try to visualise this process. There are a few things that I should point out, however:

The reason why I set the bestScore for the Maximising Player to be -1,000,000 to begin with (and for the Minimising Player, +1,000,000) is that this is an absurdly large number, such that I am confident that my Heuristic Function cannot produce a value to these extremes.

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Furthermore, if a player had won, for example the Maximising

This code makes the human play first, then the computer, and then

Player, I would return a value of 1,000,000 — depth, as this would

the human, and so on, so forth. If you want, you can add a coin flip or

be a number large enough such that the computer will naturally

rock-paper-scissors algorithm to decide who plays first, and/or wrap

try and go for this route, and I subtracted the depth, so that if

the whole thing in a while loop so that every time after a game has

there are 2 situations where the player can win, one with a

ended, the game starts again (making sure to reset the board), that is,

depth of 3 (after 3 moves), and another with a depth of 4 (after 4

until you throw your laptop at the wall after losing for the billionth

moves), it would prioritise the situation in which they win faster.

time, to your unbeatable AI player.

Notice that more nodes are added to the tree only when the depth is less than five (five moves into the future) , otherwise it just resorts to our Heuristic Function. You can adjust this value however you want, as when you increase it, the moves become more accurate, but it takes exponentially more time to travel through all the options.

N.B. As previously mentioned, for Tic-Tac-Toe, you do not need to create a Heuristic Function, and you can get rid of the IF statement, as the computer can easily travel through every option, and you will have an AI that is, literally, unbeatable.

THE MAIN LOOP Now, the only thing that we have to do is get our game started, and make sure everything that we have created ticks in the right order.

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DIYA KOHLI -

L6 MO

HOW HCIS SHAPE OUR DIGITAL EXPERIENCES UI/UX DESIGN>

INTRODUCTION A

s the field of computer science and

WHAT ARE HCIS?

H

CI must account for the user’s behaviours, needs, abilities etc through conducting

usability tests (discussed later). The overall task must be clearly defined, as well as maps or structure plans created to guide the development process. The most crucial element is the interface, where interaction type and physical characteristics of the interface are defined,

technology grows exponentially, us humans

although they are subject to change by the user’s requirements. Lastly, the context of the

need to adapt and learn with it. Whilst the

system and user must be considered to provide a more holistic experience, for example its

technological singularity remains a slightly

performance without network connection or its visual appearance in different lightings. HCI

dystopian theory, computers’ intelligence and

principles are the foundation of various digital fields such as User Experience Design (UX)

capabilities are evolving rapidly, meaning that

and Interaction Design (IxD), which focus on the overall human experience with a product

the way in which humans interact and use

or specifically examining the interface whereupon the interaction between human and

these systems must in turn change while

computer take place.

maintaining control over them. To facilitate this, fields such as HCI and UX play a vital role in helping to understand these complex technologies, as well as integrate them more effectively into our daily lives so we can leverage their capabilities and allow everyone in society to benefit from them.

The field of Human-Computer Interaction (HCI) focuses on how humans (the users) interact and engage with computers, through interfaces, hardware, and engineering. It is a multidisciplinary study involving subjects like

At the crux of HCI, usability and user efficiency are the primary goals when it comes to

computer science, cognitive sciences,

designing and operating computer systems. In order for a product to succeed, it must include

ergonomics, and design engineering. The 4

highly engaging features as well as being usable and easy to operate, for the diverse

key elements of HCI include:

possibility of users. According to the Nielsen Norman Group, a leading UX organisation,

The user

usability depends on how well a product’s features accommodate users’ needs and contexts.

A goal-oriented task

Some heuristics for usability from Jakob Nielsen include error prevention, visibility of system

The interface

status and minimalist design. The aim of optimal usability is to guide users through the least

The context

labour-intensive route of the system, in order to reduce their cognitive load.

One key example of reducing cognitive load is through limiting the options or decisions necessary to complete a task. Having too many choices for the user can cause a cluttered display on the interface as well as overwhelm them, making their response time slower and the overall usability decreases. This is demonstrated in the Hick-Hyman Law which states that the time it takes to make a decision (reaction time RT) increases with the number n and complexity of choices:

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Where a is the time not involved with decision making and b is the

Human-in-the-loop (HITL) is a collaborative approach integrating

cognitive processing time per option. This proves that the more

human input and expertise into the lifecycle of AI systems and machine

stimulus to choose from, the longer it takes the user to decide

learning. We can see HCI is heavily depended upon in this approach, as

which to interact with, hence limiting the usability of the system.

humans have a direct interaction and influence on complex AI applications, therefore require highly efficient and understandable HCI. Essentially, humans participate in training, evaluation and operation of the AI model, providing feedback and guidance on improvements.

Unlike the recently established ‘HABA-MABA trap’ (Humans Are Better At vs Machines Are Better At), HITL aims to enhance the accuracy and reliability of AI through embedding human and machine capabilities together in one system.

Where a is the time not involved with decision making and b is the cognitive processing time per option. This proves that the more stimulus to choose from, the longer it takes the user to decide which to interact with, hence limiting the usability of the system.

Another way of improving the usability of HCI is the GOMS model (Goals, Operators, Methods and Selection) proposed by Card et al (1983). It is a cognitive model of a general framework of representing how a user performs tasks by breaking down the task into its components. GOMS aims to improve the efficiency of HCI through identifying and eliminating unnecessary user actions, similar to reducing cognitive load.

Furthermore, GOMS adapts a hierarchical structure when executing tasks, whereby operators can establish sub-goals which in turn get accomplished by sub-methods. Unsurprisingly, artificial intelligence has had a growing impact on the fields of HCI and UX. As AI continues to revolutionise society and transform a plethora of sectors and industries, HCI ensures AI systems are user-friendly, intuitive and responsive to the user’s needs, therefore allowing a wider range of customers and businesses to utilise it. Examples of AI where HCI has been incorporated are voice assistants and gesture recognition. Voice assistants like Siri and Alexa encompass conversational AI, using natural language processing (NLP) and deep learning techniques, all of which are facilitated by the voice interaction type, allowing for hands-free communication and human-like conversations, making its usability highly effective.

Human-in-the-loop (HITL) is a collaborative approach integrating human input and expertise into the lifecycle of AI systems and machine learning.

We can see HCI is heavily depended upon in this approach, as humans have a direct interaction and influence on complex AI applications, therefore require highly efficient and understandable HCI. Essentially, humans participate in training, evaluation and operation of the AI model, providing feedback and guidance on improvements. Unlike the recently established ‘HABA-MABA trap’ (Humans Are Better At vs Machines Are Better At), HITL aims to enhance the accuracy and reliability of AI through embedding human and machine capabilities together in one system.

Another model similar to GOMS is the SOAR model, proposed by Laird et al (1987). It is a general cognitive architecture of human intelligence and is used for creating AI systems with intelligent behaviour. Whilst SOAR hasn’t been applied extensively in HCI research, it has the potential to answer questions not addressed or ignored by GOMS model, hence suggesting that introducing this model will be a pivotal step the future of HCI.

The future of HCI may be shaped by many advancements in technology as it continues to grow and strengthen its importance in society. We are already seeing innovations in wearable technology such as smartwatches and fitness trackers, where HCI is a fundamental element of its usability, beyond the traditional touch-based interfaces through motion sensing, voice commands and gesture recognition. Another possibility of an area where HCI will thrive is Brain-Computer Interfaces (BCI).

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BCIs acquire and analyse brain signals, to then translate them into commands that are relayed to output devices that carry out a desired action. They are a type of neural interface which provides a direct communication between the brain and external devices. They are a shift from traditional HCI input systems (touch, keyboard, mouse etc) and instead gather input from neural signals inside humans, enabling an emergent interaction type as well as a deeper connection between humans and computers.

REFERENCES Arafat, Md Shahriare Hossain. “AI and Human-Computer Interaction.”

Medium, 21 Feb. 2024, medium.com/@Shahriare/ai-and-humancomputer-interaction-481e39f7d032.

Artemis Skarlatidou, and Carol Iglesias Otero. “Design Approaches and Human–Computer Interaction Methods to Support User Involvement in Citizen Science.” UCL Press EBooks, 4 Feb. 2021, pp.

55–86, https://doi.org/10.2307/j.ctv15d8174.11. Accessed 23 Nov. 2023.

Nevertheless, even in today’s society, the symbiotic relationship between humans and computers is burgeoning. From smartphones to household machines, computers are becoming increasingly integrated into our lives and along with it, the way we interact with them becoming more important. Human-Computer Interfaces aim to bridge the gap between the complexity of these technologies and how we understand and maximise their capabilities. Whether this may be through enhancing usability or employing AI systems, HCI will no doubt have a prominent influence on the future of technology and innovation.

Gerlach, James H., and Feng-Yang Kuo. “Understanding HumanComputer Interaction for Information Systems Design.” MIS Quarterly, vol. 15, no. 4, Dec. 1991, p. 527, https:// doi.org/10.2307/249456. Accessed 12 Apr. 2020.

Google. “What Is Human in the Loop.” Google Cloud, 2025, cloud.google.com/discover/human-in-the-loop.

I Scott Mackenzie. Human-Computer Interaction : An Empirical Research Perspective. Amsterdam Etc., Morgan Kaufmann, 2013.

Interaction Design Foundation. “What Is Usability?” The Interaction Design Foundation, 1 June 2016, www.interaction-design.org/ literature/topics/usability.

Kanade, Vijay. “What Is HCI (Human-Computer Interaction)?

Meaning, Importance, Examples, and Goals.” Spiceworks, 22 July 2022, www.spiceworks.com/tech/artificial-intelligence/articles/whatis-hci/.

Kieras, David E. “An Overview of HumanComputer Interaction.”

Journal of the Washington Academy of Sciences, vol. 80, no. 2, 1990, pp. 39–70. JSTOR, www.jstor.org/stable/24531047, https:// doi.org/10.2307/24531047.

Nielsen, Jakob. “10 Heuristics for User Interface Design.” Nielsen Norman Group, 24 Apr. 1994, www.nngroup.com/articles/tenusability-heuristics/.

Prasad, Manjusha, et al. HCI in Mobile and Wearable Computing).

Shih, Jerry J., et al. “Brain-Computer Interfaces in Medicine.” Mayo Clinic Proceedings, vol. 87, no. 3, Mar. 2012, pp. 268–279, pmc.ncbi.nlm.nih.gov/articles/PMC3497935/, https://doi.org/10.1016/ j.mayocp.2011.12.008.

“What Is Human Computer Interaction?” Figma, www.figma.com/ resource-library/human-computer-interaction/.

Yablonski, Jon. “Hick’s Law.” Laws of UX, 2022, lawsofux.com/hickslaw/.

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neven.

Google’s Vice President of Engineering at Google.

Founder and manager of the Quantum Artificial Intelligence lab.

Could there be a multiverse?

Wil low , Qu ant um

Ch ip

There is nothing cooler in the universe than quantum computers. Or should I say the multiverse? yes, you heard me. the spiderverse series could be real.

A quantum chip refers to a hardware component which processes information exponentially faster than a normal computer, utilizing characteristics such as superposition and entanglement. it represents data using qubits which can simultaneously or either be 0 or 1. according to google, 105 qubit willow solved a computational task in under 5 minutes which would take over 10 septillion years for a normal computer to solve. quantum ai founder hartmut neven claims such a fast solver would be near impossible to occur under one human timeframe, and such algorithms would proceed in alternate realities using superposition, which allows a quantum system to exist in multiple states simultaneously.in one universe, the qubit holds 0, whilst in another reality, it holds 1.

However, this is just a speculative claim, and nothing has yet been proved.

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KARIS LAU - U5 ALPHA

THE PARADOX OF HUMAN INTELLIGENCE: RISKING YOUR HEARTBEAT TO HARD DRIVES AI >

INTRODUCTION I

magine if you could choose between an

experienced doctor who has memorized each procedure and would help you calculate each risk statistically in the blink of an eye, or a doctor with a few years of experience who would need 3 weeks to inform you of your diagnosis. How about a doctor who knows every language in the world and can help you in any way possible, or a doctor who requires a translator at hand to communicate? AI is a rapidly evolving landscape of technology and

FEAR OF THE UNKNOWN, LOSS OF CONTROL AND LACK OF HUMAN INTERACTION

T

he fear of the unknown is a significant barrier to the widespread adoption of AI in

medicine, stemming from concerns about reliability, data privacy and human interaction.

Many healthcare professionals worry that AI systems may produce inaccurate results or make decisions without human oversight, potentially jeopardizing patient safety. For example, a survey conducted by the American Medical Association found that 75% of physicians expressed concerns about the reliability of AI algorithms in diagnosing diseases, fearing that a misdiagnosis could have severe consequences. Additionally, issues related to data security and patient confidentiality heighten apprehensions; the misuse of sensitive health data by AI systems can lead to breaches of trust between patients and healthcare providers. A notable case is the backlash faced by companies like IBM Watson, which, despite its advanced capabilities, has struggled to gain traction in clinical settings due to skepticism about its recommendations.

has shown much potential in revolutionizing aspects of medicine, yet despite AI improving efficiency and accuracy, there is a limit as to how willing patients are to risk their heartbeats to hard drives.

Many healthcare professionals worry that reliance on AI could undermine their clinical judgment and decision-making authority, leading to a scenario where algorithms dictate patient care. Many question the humane balance between statistics and emotions and wants. For instance, a study published in JAMA Network Open highlighted that 60% of

AI IN MEDICINE

physicians felt that AI could erode their role in the patient care process, fostering anxiety

The integration of AI in medicine offers

situations, such as diagnosing cancer or managing chronic diseases, where nuanced

substantial benefits backed by evidence from

understanding and empathy are crucial.

about becoming obsolete. This loss of control is particularly concerning in high-stakes

various studies. For instance, a study published in Nature demonstrated that AI

Moreover, the shift towards AI-driven solutions can diminish the essential human

algorithms for diagnosing skin cancer

interaction that forms the foundation of patient-centered care. Patients often value the

outperformed dermatologists, achieving an

personal connection with their healthcare providers, and the introduction of AI could lead

accuracy rate of 95% compared to 86% for

to a more transactional experience. For example, telehealth platforms that utilize AI for

human experts. A report from the McKinsey

initial assessments may reduce face-to-face consultations, which can leave patients feeling

Global Institute indicated that AI could potentially reduce healthcare costs by up to 20% through improved efficiency and optimized resource allocation. By streamlining administrative tasks and enhancing clinical

less supported and more isolated. Research indicates that a significant portion of patients prefer in-person interactions when discussing sensitive health issues, as the human touch fosters trust and emotional support. This growing concern about losing the relational aspect of healthcare can make both providers and patients hesitant to embrace AI technology, ultimately hindering their potential benefits in improving medical outcomes.

decision support, AI not only improves diagnostic precision but also allows healthcare providers to focus more on patient care, ultimately leading to better health outcomes and patient satisfaction. These case studies highlight the significant impact of AI, assisting in diagnostic accuracy, personalising treatment, and reducing costs in the healthcare sector.

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ETHICAL DILEMMAS AND RESPONSIBILITY

REFERENCES Zou, F.-W. et al. (2020) Concordance study between IBM Watson for

AI's potential to challenge ethical norms raises another layer of fear

oncology and real clinical practice for cervical cancer patients in

that stems from human intelligence. Questions about accountability

China: A retrospective analysis, Frontiers in genetics. Available at:

—who is responsible when an AI system makes a mistake?—

https://pmc.ncbi.nlm.nih.gov/articles/PMC7105853/

complicate the narrative surrounding AI implementation. The

#:~:text=Watson%20for%20Oncology%20(W

capacity for moral reasoning inherent in human intelligence leads to

FO)%20is,is%20suitable%20for%20Chinese%20patients. (Accessed:

a heightened awareness of the ethical implications of deploying AI

09 January 2025).

technologies.

Singla, A. et al. (2024) The state of ai in early 2024: Gen ai adoption spikes and starts to generate value, McKinsey & Company. Available

Moreover, the fear of creating machines that could inadvertently

at: https://www.mckinsey.com/capabilities/quantumblack/our-

perpetuate biases or make unethical decisions leads to calls for

insights/the-state-of-ai (Accessed: 09

stringent regulations and oversight. While such measures are

January 2025).

necessary for responsible AI development, they can also slow down the pace of innovation. The desire to ensure ethical standards can

Polygraph. Council on Scientific Affairs (no date) JAMA. Available at:

lead to bureaucratic inertia, preventing organizations from fully

https://pubmed.ncbi.nlm.nih.gov/3735653/ (Accessed: 09 January

exploring the capabilities of AI.

2025).

CONCLUSION Although AI is a strong tool to use for research and sometimes diagnosis, at this point, AI has not been developed enough to consider more holistic medicines. Furthermore, the concept of AI is so complicated in terms of ethics and responsibility, so although AI could help with more statistical and data analysing aspects, the idea of patients awaiting for a machine to inform them of their soon to be death still remains.

15

ADVAIT TRIPATHI - 10M1

THE IMPACTS OF VIRTUAL AND AUGMENTED REALITY ON SOCIETY TECHNOLOGY >

INTRODUCTION V irtual Reality (VR) and Augmented

APPLICATIONS OF VR AND AR

V

R and AR are applied in numerous ways already integrated into the daily lives of billions

of people around the world. AR is commonly seen in apps like 'Pokémon Go' and on Instagram and Snapchat filters whilst an example of VR in day to day lives could be through video games. In the field of health care, we are seeing VR used in training doctors and

Reality (AR) have revolutionized the ways in

nurses with surgical simulations, whilst helping patients recover from trauma with therapy.

which we interact and perceive the digital

Meanwhile AR helps with vein visualization and surgical navigation. In education VR allows

universe, immersing humans in ways which

students to conduct virtual experiments and explore key historical events safely from the

blur the lines between the physical and virtual

classroom or from home, which helped the educational system through the years of the

world. These technologies have rapidly

Covid-19 pandemic. Perhaps the most significant impact of VR and AR on an industry is the

evolved from futuristic visions to daily

Gaming industry, where VR offers gamers realistic and immersive game experiences where

applications in life, profoundly shaping the

players can finally feel what it is like to be inside their favourite game. AR allows mobile

modern lifestyle. The historical journey of VR

gamers location-based gaming which gives players a greater feeling of control encouraging

and AR has shown a timeline of innovation

players to invest more time and effort into the game.

which has led to the expansion of the

Though there are numerous good things about VR and AR it

technology into fields such as gaming,

may have negative impacts on society, as being in a virtual

healthcare, and education. In this essay we

world different to our own for too long may see a disconnect

will explore the vast impact VR and AR had on

between people and their relationships. Though nothing is

society, industries and individuals making

proven yet doctors believe VR and AR could trigger abnormal

vivid the transformative powers of the

brain function and rewire the brain to think in potentially

technology.

harmful ways. It has been found that using VR for a prolonged period may cause fatigue and cybersickness.

Before delving into the complexities, it is a common misconception that AR and VR are

Yet social impacts of VR and AR are not all that bleak, they

same, though they are completely different.

(The Sensorama by Morton have the potential to impact the lives of those with Heilig in 1956) disabilities and change them for the better. AR can overlay

AR overlays digital content onto the real world whereas VR immerses the user into a virtual

audio descriptions for the environment a person is in, helping people with visual

world.

impairments navigate and interact with their surroundings. AR can create interactive learning spaces for those with cognitive disabilities making education more fun and effective. VR allows people with mobility challenges to experience virtual environments that may be impossible for them to access in the real world. VR and AR hold the power to close

AR and VR technologies have been around for

the accessibility gap providing new ways for people to interact with the world around them,

decades however grown into the limelight

if designers keep people with disabilities in mind, they can create a much more equal world

after the turn of the century. VR dates to the

to live in.

1960's when the first VR headset was created, and in the 1990's the 'Virtual Boy' was released

by Nintendo and became the first VR technology publicly available. In 1970 the US Air Force used AR for training simulations whilst the first AR technology publicly available was the 'Google Glass' released in 2013.

16

ETHICAL CONSIDERATIONS

CONCLUSION

When it comes to ethical considerations in technology there are

In conclusion, the profound impacts of VR and AR reverberate

several important aspects to consider. Due to the nature of

through society, unveiling an interplay of benefits and challenges

information, they collect, AR technologies raise significant privacy

that shape our future in technology. We see this in areas like

concerns. Users may be at risk to data theft or spoofing. Immersive

accessibility, health care, education and gaming. As the AR/VR

technologies like VR also pose risks like Identity theft as without

industry charts a path of exponential growth and innovation, the

proper safeguarding data about the user's body, behaviour and

impacts on society highlight the need for conscientious design,

environment could be under significant risk. Another concern is

ethical frameworks, and user-centric approaches. By using the

whether AR and VR technologies can distort or manipulate the

transformative powers of VR and AR while addressing the social

user's perception of reality.

implications through inclusive design methods, we can create a future where immersive technologies enrich human experiences, foster empathy, and bridge societal divides in a digitally augmented landscape.

REFERENCES

(No date) AR and VR in Education. Available at: https://vectiontechnologies.com/solutions/industries/education/ (Accessed: 31 May 2024).

Cross

Director, R.J. et al. (2023) VR risks for kids and

teens, U.S. PIRG Education Fund. Available at: https://pirg.org/edfund/resources/vr-risks-forkids/#:~:text=Using%20VR%20may%20trigger%20abnormal,60%2C”%2 0said%20Professor%20Mehta. (Accessed: 03 June 2024).

Druzhinin, A. (2019) The history of AR and VR, Medium. Available at: https://arvrjourney.com/the-history-of-ar-and-vr-3faea3f1e94b (Accessed: In 2024 the AR/VR industry is expected to make US $40.4 billion yet

31 May 2024).

by 2029 it is predicted this number could reach US $69 billion,

Dutertre, A. (2023) The ethical challenges of AR/VR, Medium. Available

growing at an annual rate of 8.97%. This is not the only statistic at

at: https://medium.com/@alex24dutertre/the-ethical-challenges-of-ar-

which the industry is growing, there was a surge in startups in the

vr-a5333594f909 (Accessed: 03 June 2024).

AR and VR market over 2023, this reflects the broader shift towards

Mishra, S. and Agarwaal, P. (2023) Top 5 applications of Augmented

the immersive technologies in recent years. As companies in the AR

Reality (AR) and virtual reality (VR), ET Edge Insights. Available at:

and VR industry lighter VR headsets, AI integration, and smart

https://etinsights.et-edge.com/top-5-applications-of-augmented-reality-

glasses to improve their products, there are still challenges

ar-and-virtual-reality-vr/ (Accessed: 31 May 2024).

upcoming. Like technology limitations, privacy concerns and user

Recovery, O. (2023) Virtual reality: The impending revolution and risky

adoption barriers, assessing these issues is crucial for growth, as

consequences, Omega Recovery. Available at:

fixing even one of these could lead to the expansion of AR and VR

https://omegarecovery.org/virtual-reality-the-impending-revolution-

technologies to even rural areas around the world.

and-riskyconsequences/#:~:text=The%20Good%20and%20Bad%20of%20VR%2 0Technology&text=However%2C%20some%20risky%20consequences %20should,engaged%20in%20the%20real%20one. (Accessed: 03 June 2024).

Sokołowska, B. (2023) Impact of virtual reality cognitive and motor exercises on Brain Health, International journal of environmental research and public health. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10002333/ (Accessed: 03 June 2024).

Statista (no date) AR & VR - worldwide: Statista market forecast, Statista. Available at: https://www.statista.com/outlook/amo/arvr/worldwide#:~:text=The%20AR%20%26%20VR%20market%20is,US% 2462.0bn%20by%202029. (Accessed: 03 June 2024).

Thampan, A. and Razak, A. (2023) ‘Evolution of Augmented Reality (AR) and Virtual Reality (VR)’, International Journal of Research Publication and Reviews, 04(02). doi:10.55248/gengpi.2023.

17

ARKANSH PANDEY - 10J1

HOW DO PHYSICS CONCEPTS RELATE TO THE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE, AND WHAT IS THE SIGNIFICANCE OF QUANTUM COMPUTING IN THE AI LANDSCAPE? AI is a massive emerging tool for businesses because of the exciting potential which it has

QUANTUM / AI >

INTRODUCTION Q

uantum Computing and AI are two of

for the future. It is already appearing in many industries and companies which use it not only to automate their services and data sorting, but to make better business decisions. The scope and potential of AI in our future is undeniable; it is only a matter of how advanced it will get and how quickly we will adapt to its new challenges.

CONCEPTS OF PHYSICS USED IN AI

the most impactful developments of our

modern era. This is because they are able to complete tasks more efficiently than the resources we have now. Therefore, as the two resources are put together, it will allow humans to explore new, uncharted territory in the world of business and human development as a whole. In this essay I will be exploring the significant influences which fields such as physics are having in the AI industry right now and what the emergence

Physics concepts play a crucial role in the development of Artificial Intelligence. Physics

of quantum computers means in terms of the

provides rules and models that govern physical systems and interactions, which have been

boost which it may give to AI development.

instrumental in explaining natural phenomena and engineering machines. However,

ARTIFICIAL INTELLIGENCE

traditional physics can only help us to a certain extent, and in the internet era tasks such as image and speech recognition cannot be advanced by the traditional studies of physics. This is

Artificial intelligence is intelligence exhibited

where AI, particularly deep neural networks, has excelled by introducing a data-driven

by computer systems. It is a field of computer

computational framework. The combination of physics and AI concepts can overcome the

science that develops and studies software

challenges faced by both fields, extending AI's development and advancing research in

and data that enable computers and

engineering and physical science. Physics-inspired models, such as quantum neural nets and

machines to use learning and intelligence in

physics-inspired machine learning methods, have the potential to address challenges related

order to succeed in given tasks. AI is made

to data quality and availability in AI. These approaches can benefit AI and machine learning by

from transformers which are the neural

deep learning methods. In particular, concepts like Quantum Physics in the form of Quantum

networks of the AI model allowing them to

Computing, Electrostatics, in the field of data sampling and Thermodynamics in the field of

learn for its own and access deep learning and

prediction accuracy are the most useful in AI advancement by offering models and

Machine Learning to advance to new heights.

computational rules that enhance machine learning. Quantum computers, based on quantum physics, introduce qubits and superposition, which allow AI systems to process data

AI leverages branches of science and

more efficiently than classical computers. Quantum computing is set to revolutionise AI by

mathematics that enable it to make better

increasing speed and accuracy, enabling tasks like data analysis and image generation to be

predictions with the least error rate and by

completed faster and more efficiently. However, challenges such as error rates in quantum

utilising the least amount of computational

systems remain. The combination of AI and quantum computing could lead to huge

power. In today’s context, heavy reliance on AI

advancements in fields like finance, cybersecurity, and scientific research.

means that error rates have to be extremely low in order to inspire confidence in the power of AI. Similarly, low computational power means lesser environmental impact and lower cost, which is important to ensure better use of AI.

18

QUANTUM PHYSICS

Q

THERMAL DYNAMICS

uantum physics is the branch of physics that deals with the

behaviour of matter and energy at tiny scales, such as atoms and subatomic particles. It was developed in the early 20th century to explain phenomena that classical physics could not describe.

T

Quantum mechanics can describe many systems that classical physics cannot. This is because classical physics can describe many aspects of nature at an ordinary (macroscopic and microscopic) scale but is not sufficient for describing them at very small, sub microscopic (atomic and subatomic) scales.

hermodynamics is essentially the study of the random

behaviours of particles for example when particles diffuse through a liquid. This can be compared to how AI looks at pixels in a picture and makes a decision about what the pixels are representing. This technique can be seen in diffusion models like DALL-E by ChatGPT, which implement this method by looking at the static pixels which follow the picture and then trace the random movement of the pixels back to the original picture. This technique is utilised by AI models to create imagery allowing us to tell the model what we want the generated image to look like.

ELECTROSTATICS For many years, the scientific fields of quantum physics and computer science were separate sections in the academic community. Modern quantum theory developed in the 1920s to explain the wave–particle duality observed at atomic scales, and digital computers emerged to replace human computers for tedious calculations. Both disciplines had practical applications during World War II; computers played a major role in wartime cryptography and code breaking, and quantum physics was essential for the nuclear physics used in the Manhattan Project. This led to more experimentation and research into how the two subjects could be merged to create new science, and eventually scientists such as Benioff and Feynman drafted the first academic papers on the subject of quantum computing.

Just as the bit is the most basic concept of classical computers, the qubit is the fundamental unit of quantum computers. The term qubit (literally ‘quantum bit’) refers to a mathematical model and to any physical system that is represented by that model. A classical bit exists in either of two physical states, 0 or 1. A qubit is also described by a state, and two states often written |0⟩ and |1⟩ serve as the quantum counterparts of the binary 0 and 1. However, the quantum states |0⟩ and |1⟩ can exist as |0⟩ or |1⟩ or a linear combination of the two states, meaning somewhere in between the two states. This unique property of quantum physics is known as superposition and is the building block of all quantum computers.

Quantum computing makes tasks exponentially quicker due to the fact that it needs several less qubits than classic computers need bits to perform a task. This is due to the superposition explained above.

Electrostatics is the study of electric charges. Charge densities are continuous objects that have different amounts of charge in different areas. A place with a high charge density would exert greater force on electrons than areas with low charge density. Interestingly, the distribution of electrons in space looks very similar to the probability density curve, which is useful in the context of AI. Specifically, one such class of AI models - Poisson Flow Generative Models (PFGM) simulate probability distribution as charge density. This is particularly useful in the field of sample data generation, as these models can generate new training data for AI models.

19

T

his is done by visualising each piece of data as an electron, this

is done because it is too complex to pick out a specific piece of data for training because it is so vast and not likely to find a realistic set of scenarios in the data manually. Therefore, as each data is visualised as an electron, they repel each other outwardly to form the data set into a hemisphere; this makes it easier to pick out a specific data point because we see the trajectories that map the points on the hemisphere back down to the original data set giving us new data to use as training for AI models.

CHALLENGES FOR AI AND HOW QUANTUM COMPUTING IS USEFUL

Q

CHALLENGES FOR QUANTUM COMPUTERS AND NEED FOR FURTHER RESEARCH

Q

uantum systems currently have a very high error rate due to

the fact that it is very difficult to predict whether the qubits will land on a 1 or 0 and therefore quantum computers have not been able to be implemented much so far in industries but looking at the vast amounts of research and development going into the sector by companies such as IBM and Google and universities such as MIT and Princeton, it seems that these issues will be resolved in our lifetimes and quantum computers will begin to play a huge role in our lives.

There are a few different methods of resolving and correcting the uantum Computing can bring AI to a whole new level due to

issues that quantum computers make that are being implemented

the speed of processing which can be accessed by AI models

or experimented so that the quantum computer can eventually

allowing them to perform more tasks with higher speed. This would

become a usable product: For example, you can Quantum Error

solve the limitations of current AI models which are only able to

Correction methods which use a unique code like the Shor code or

perform rudimentary tasks without knowledge of what they are

the Surface Code which take into account the unique challenges

doing. Additionally, Quantum Computers allow for better and

which are posed by the quantum properties such as superposition

quicker analysing of data, so AI doesn’t waste as much time in

and entanglement. Additionally, Fault-Tolerant Quantum Computing

processing.

manipulates quantum circuits in order to allow them to function even after some components fail, these systems also rely on

The transformer, as mentioned before, is a neural network based on

Quantum Error Correction to detect and correct errors before they

the human brain allowing AI models to learn and progress at an

accumulate.

unprecedented rate, currently transformers and AI models are limited by the processing speed of classical computers meaning that

Researchers have also been exploring methods of actually improving

they aren’t able to progress as quickly and as far as they can.

the qubit quality in order to minimise qubit decoherence (when

However, if transformers could be run on quantum computers, then

qubits lose their quantum properties). This can be achieved by

AI advancement would be taken to new heights.

isolating qubits from external noise or using higher quality materials.

However, it is most essential that greater control is held over qubit

Quantum computers are going to revolutionise the way AI performs

states and interactions to make sure there are no drifts in qubit

due to the fact that qubits do not operate simply with 1s and 0s. The

behaviour. Additionally, Quantum Gates, which are the operations

properties of superposition mean that quantum computers can use

that manipulate qubits, must be enhanced by improving control

many less qubits compared to a classical computer do bits in order

mechanisms and shortening the time taken to perform these gates

to complete the same task at the same speed. Therefore, when a

so that there is as less exposure to noise as possible.

quantum computer with several thousand qubits will run an AI model the potential of machine learning in the model will be

Noise can also be mitigated by noise extrapolation which measures

substantially more than what classical computers allow for currently.

the level of noise in the system and then apply corrections to mitigate its effect, decoupling can help by applying a series of pulses to qubits which ‘decouples’ them from environmental noise.

Quantum computers are already kept at extremely low temperatures to reduce thermal noise, but these systems need to be further stabilised by reducing physical vibrations and lapses in the cryogenic environment.

Increasing the number of physical qubits can create logical qubits which have inbuilt error correction, improving accuracy. Whilst these logical qubits are less prone to errors, they do require a significant overhead in physical qubits which means that custom architectures must be built to support this large number of qubits while optimising the layout and connectivity of the qubits to remove error.

20

I

n my opinion, the most interesting form of error management

SUMMARY

T

in quantum computers is hybrid quantum-classical computing by combining quantum computations with classical computers’ postprocessing to compensate for errors which happen during quantum computations. An example of this is the VQE (Variational Quantum Eigensolver) algorithm where a quantum computer evaluates a function, and a classical computer adjusts the parameters and improves the result despite the presence of noise.

o conclude, Physics concepts support AI development by offering

models and computational rules that enhance machine learning by finding new training data more efficiently and developing methods of picture recognition. Quantum computing, based on quantum physics, introduces qubits and superposition, which allow AI systems to process data more efficiently than with classical computers. Quantum computing is set to revolutionise AI by increasing speed and accuracy,

These are all methods which have been researched and are being put into practise currently and are vastly improving the performance of quantum computers and bringing them one step closer to the quantum advantage. The quantum advantage is the point at which quantum computers get better at performing a certain task than classical computers, and when this point is passed, AI models will be able to be run on Quantum computers and its new potentials explored.

enabling tasks like data analysis and image generation to be completed faster and more efficiently. However, challenges such as error rates in quantum systems remain big issues to be solved. The potential combination of AI and quantum computing could unlock powerful advancements in fields like finance, cybersecurity, and scientific research; and looking at the rapid progress being made in the fields of Quantum Computing, Artificial Intelligence and Machine Learning supported by Physics concepts, it seems that the combination of the two huge developments will bring about a revolution in the way companies go about their business and also the way we live our lives.

REFERENCES Tom Garlinghouse, ‘Researchers discover an abrupt change in quantum behaviour that defies current theories of superconductivity’, Princeton University, Department of Physics, January 19, 2024, https://www.princeton.edu/news/2024/01/19/researchers-discover-abrupt-change-quantum-behavior-defiescurrent-theories

SPECIFIC EXAMPLES OF HOW QUANTUM COMPUTERS WILL REVOLUTIONISE AI

Q

uantum computers have not been put into implementation

very much yet currently due to the fact that the quantum advantage has not yet been met. However, quantum computers will get more

‘Quantum Entanglement’, Wikipedia, 8 September 2024, https://en.wikipedia.org/wiki/Quantum_entanglement

Brian Clegg, ‘What is Shrödinger’s Cat?’, Science Focus, BBC Science Focus Magazine, https://www.sciencefocus.com/science/what-is-schrodingers-cat

‘Superposition’, Quera, https://www.quera.com/glossary/superposition

Matt Swayne, ‘Top 18 Institutions Leading Quantum Computing Research in 2024’, The Quantum Insider, May 16, 2022, https://thequantuminsider.com/2022/05/16/quantum-research/

useful as hardware improves and quantum computers are able to complete tasks more efficiently with lower error rate; some examples of its use cases will be to go through data quickly and perform tasks more efficiently for companies. Quantum Computing will bring AI to a whole new level as mentioned previously due to the speed of

‘Surface Codes’, Quera, https://www.quera.com/glossary/surface-codes

Member of Caltech Faculty, ‘What is Quantum Physics?’, Caltech Science Exchange, https://scienceexchange.caltech.edu/topics/quantum-science-explained/quantum-physics

‘Quantum Computing’, Wikipedia, 5 September 2024, https://en.wikipedia.org/wiki/Quantum_computing

processing which can be accessed by AI models allowing them to

Ryan O’Connor, ‘How Physics Advanced Generative AI’, Assembly AI, April 19, 2023,

perform more tasks with higher speed. This would solve the

https://www.assemblyai.com/blog/how-physics-advanced-generative-ai/

limitations of current AI models which are only able to perform rudimentary tasks without knowledge of what they are doing.

Additionally, Quantum Computers allow for better and quicker analysing of data, so AI doesn’t waste as much time in processing. It can finally also be used for financial modelling, encryption and cyber security, drugs research and batteries.

Beth Stackpole, ‘Quantum Computing: What leaders need to know’, MIT Management Sloan School, January 11, 2024, https://mitsloan.mit.edu/ideas-made-to-matter/quantum-computing-what-leaders-need-to-knownow

Jacob Roundy, ‘Explore 7 future potential quantum computing uses’, Tech Target, 10 February, 2023, https://www.techtarget.com/searchdatacenter/tip/Explore-future-potential-quantum-computing-uses

Bhoomi Gadhia, Ram Cherukuri and Kristen Perez, ‘Physics-Informed Machine Learning Platform NVIDIA Modulus Is Now Open Source’, Nvidia Developer, 23 March, 2023, https://developer.nvidia.com/blog/physics-ml-platform-modulus-is-now-open-source/

Chipmaker Nvidia has already started implementing AI and ML in scenarios such as weather forecasting, clean energy solutions,

‘Artificial Intelligence’, Wikipedia, 8 September 2024, https://en.wikipedia.org/wiki/Artificial_intelligence

greenhouse gas emissions mapping and also learning to model car and industrial parts; This has imminent use in our world today and is a development which will be further accelerated by the advent of AI running on quantum computers.

21

AKSHARA SHIVAKUMAR- 9 ANSUZ

THE TOWER OF HANOI COMPUTATIONAL MATHS >

INTRODUCTION T

The aim of the game is to get from an initial state to a target state by moving a single disk from the top of the peg to the top of another possible one while obeying the following rules: Each time only 1 disk is moved Only the topmost disk can be moved At any moment, a larger disk cannot reside on a smaller one

here is no doubt that the world is

mathematical. Mathematics is an integral part in the solution of many problems and for this reason, it is recognised as one of the most important educational courses in the new curriculum. Problem solving has been a crucial aspect of teaching and learning mathematics. Not only can students improve their thinking and ability with problem solving, but can further apply procedures and deepen their conceptual understanding. Many occupations require adequate problem

Written by Lucas:

solving skills like architecture and engineering

D'après une vieille legend indienne, les brahmes se succedent

because there is a need to design buildings

depuis bien longtemps, sur les marches de l’autel, dans le

that are not only pleasing and functional but

Temple de Bénarès, pour exectuter le dèplacement de la Tour

ones that meet the strict safety requirements.

Sacrée de Brahma, aux soixante-quatre étages en or fin

For example, in psychology research, many

garnis de diamants de Golconde. Quand tout sera fini, la Tour

mathematical puzzles are used as a tool to

at les brahmes tomberont es ce sera la fin du monde!

investigate how humans develop their

That is, the Holy Tower of Brahma is to be found in a

problem solving ability. One such puzzle is

temple in the Indian city of Benares. It has 64 disks, each

called the Tower of Hanoi, which I will explore

of pure gold and embossed with diamonds on a

in detail in this paper.

cylindrical slate. The disks are to be transferred by the

THE LEGEND OF THE TOWER

temple priest from the initial stele to one of two other

The original description from the leaflet that accompanied the puzzle was The Tower of Hanoi problem proposed by Lucas over a 100 years ago, contains deep, foundational mathematical truths, which requires knowledge of the basic if not fundamental properties of odd and even numbers. It is a mathematical puzzle that involves transferring a certain number of disks to a goal position in the fewest moves possible.

steles although it is not stated to which stele the disks will be moved nor how often a disk is moved but this is believed to be one a day in the legend. When the task is completed the priests will collapse and so will the tower meaning the world will end. It is also stated that before the priests complete their task the temple will crumble into dust and the world will vanish in a clap of thunder.

The problem now is to determine how worried we must be about when the world will end.

given a certain number of disks (typically 3) of

ITERATION AND RECURSIVE PROBLEM SOLVING

mutually different diameters placed in order

Iteration is a programming technique that uses loops (for, while etc.) to repeatedly execute a

on the first peg.

set of instructions until a condition is met. It is the repetition of a block of code using control

Initially you start with 3 vertical pegs and are

variables, typically in the form of for, while or do-while loop constructs.

22

Recursion is a programming technique where a function calls itself within its own definition to solve a problem. A recursive function breaks down a problem into smaller, self-similar subproblems, then solves the subproblems in the same way as the original problem.

Recursion is a technique that enables a function to save its local variables and parameters on an activation stack, which is then popped off when the function exits or the base case is reached. For any problem that can be solved via iteration, there is a corresponding recursive solution and vice versa. Similar algorithms are used in both approaches, however recursive algorithms tend to be more efficient as it can be more elegant and easier to understand for certain problems, like solving the Tower of Hanoi. In this case, the solution to this problem is conceptually easier to implement via recursion, and harder to implement via iteration. Breaking the problem down into a smaller version of itself would be the best approach. Recursive algorithms also naturally matches the puzzle’s structure: to move a stack of discs, you must first solve smaller instances of the same problem. Even though problems like Tower of Hanoi are naturally recursive problems with easy implementations using recursion, an iterative solution could be equally as efficient since there would be no overhead associated with the activation stack. Hence, the Tower of Hanoi puzzle is a great tool to understand and further implement the two key types of problem solving used extensively in computer

INDUCTIVE PROOF TO ARRIVE AT THE MINIMUM NUMBER OF MOVES TO SOLVE TOWER OF HANOI PUZZLE

I

f we were to use the minimum number of moves to move the

disks so that all n disks end up on a specified different peg, we need to find a mathematical way to count them. To move n disks, we must first move n-1 disks out of the way and then move the largest disk and again move n-1 disks on top of the largest.

Now to move 1 disk it takes exactly 1 move and to move 2 disks we move the smallest one, the bottom one then the smaller one again, so 3 moves at a minimum. To move 3 disks, we must move 2 disks, so three moves, then the bottom one which is another 1 move, and 3 more moves to move the two disks to the target peg adhering to the rules, making up 7 moves. This is illustrated in the table below. This is a beautiful example of recursion, and use of induction to prove the formula is a great application of this principle. Suppose we want to move n disks. Name the number of moves required to move n-1 disks M(n-1). Using this we can describe the number of moves for n disks which is M(n-1) twice +1. We can call this M(n). The table below captures M(n) for 1 – 4 disks which is 1, 3, 7, 15 respectively, suggesting this is an exponential series. Solving this series mathematically, we can arrive at a hypothesis that M(n) = 2ⁿ - 1. This is proved by induction below.

programming. It helps with honing the problem solving skills as well

Disks

as conceptually appreciating both the problem-solving methods.

MATHEMATICAL INDUCTION

M

Moves 1

1

athematical induction is a proof technique used to establish

the truth of a statement for all natural numbers . It involves proving

2

1+1+1= 3

3

3+1+3 = 7

4

7+1+7 = 15

a base case (the statement is true for the first number) and an inductive step (assuming the statement is true for a given number, proving it's true for the next number). Typically, this technique helps derive formulas for problems which require recursive algorithms to compute. The Tower of Hanoi puzzle is a classic example where mathematical induction can be used to prove the minimum number of moves required to solve the puzzle for any number of disks, and where patterns repeat and build on earlier steps.

…

…

n-1

M(n-1)

n Analysis of Algorithm: M(n) = M(n-1) + 1 + M(n-1) = 2 × M(n-1) + 1 Claim: M(n) = 2ⁿ - 1 Now we are using induction proof to prove our recursive algorithms Proof: Claim: M(n) = 2ⁿ - 1 Thus, our predicate p, the basis of our induction proof, of n is the claim that M(n) = 2ⁿ - 1.

P(n) = M(n) = 2ⁿ - 1 Base case: n = 1: M(1) = 1 = 2¹ - 1 = 2ⁿ - 1 We have counted manually that the moves it takes for 1 disk is 1 move which is what we get using this formula. However, this doesn’t mean that it is proved.

23

Induction step: We must prove: P(n)

→ P(n+1)

→ M(n+1) = 2ⁿ⁺¹ - 1 Assume: P(n) is true → M(n) = 2ⁿ - 1 If M(n) = 2ⁿ - 1

REFERENCES (PDF) An Evolutionary Approach to Tower of Hanoi Problem Australian Senior Mathematics Journal vol. 29 no. 1

M(n+1) = ?

hanoi.dvi

When we did our analysis of the problem we found that we must

AlgoDaily - Problem Solving With Recursion vs. Iteration

first move n disks out of the way and then back again which is 2 ×

(PDF) Improving Solving Problem Ability with Tower of Hanoi Puzzle

M(n) and then add the 1 larger disk at the end, so this becomes:

(PDF) A Review of Mathematical Conjectures: Exploring Engaging

2 × M(n) + 1

Topics for University Mathematics Students

From the induction hypothesis we know what M(n) is, 2ⁿ - 1. Then

The Tower Of Hanoi Myths And Maths (2013, Birkhäuser): Free

we substitute it into the formula and do a bit of algebra.

Download, Borrow, and Streaming : Internet Archive

= 2 × (2ⁿ - 1) + 1 = 2 × 2ⁿ - 2 + 1 = 2ⁿ⁺¹ - 1 Thus, we have finished our proof. We have successfully proved that if P(n) is true so is P(n+1). If we go back to the base case, this proves that for all n ≥ 1, M(n) = 2ⁿ - 1.

A NICE SURPRISE

T

he mathematical mystery is solved by inductive proof and yet there

is a mystical element to this. If one is still wondering or anxious about when the world will end and the priests will complete their task of moving all 64 disks, here is the calculation.

A rough estimate of this number is: Using the formula 2ⁿ - 1 will give us the number of moves and days as we are still assuming that 1 move is made per day. To find out the number of years we must simply divide this amount by 365. The logarithm is there just to find out the number of digits in decimal base.

That is when the number is expressed as a decimal it is 17 digits long.

Given that the earth is around 4.5 billion years old, it would take 10,000,000 times that long for the priests to complete their task. This means a rough estimate is 45,000,000,000,000,000 years before the task is completed. Hence, it appears that scientifically the ‘the temple and tower will fall down’ could happen trillions of years before the task is completed.

24

SOLITAIRE.

ou leave y e r o f e b is h t get I hope you may do in the o t g in h t e m o s will give you it s a , w o r r o tom train. uzzle. p e ir a lit o s e h t how to do u o y ll e t o t t zzle to u p "It is jus e h t o d o t am trying I if , s lp e h it d ter still to I fin t e b r o is h t e of pieces lik s d in k r u o f lours. e o s c u r u o f in s e r the squa h it w d r a o b a use on the s y a t s s y a lw a Each piece aken. You t is it il t n u r same colou nd you a 's X r u o f ly n start with o ] on at the m e h t [ e v a h must still areful of c y r e v e b t s end so you mu e got b o t ’s O 12 e r ere a them. But th r this all e b m e m e r o t s d rid of. One nee the time.”

Italian in y a lid o h e ic a very n e v a h ll a u o y at: y a I hope w a r a f y r e ot be v n ll a h s I . d n la r Switze néer Club Mediterra psos-Corfu Greece See : https://www.cityam.com/letter-from-alan-turing-explaining-the-formula-towin-solitaire-sells-for-136000-at-auction Alan Turing’s letter about Solitaire, written during the final years of his life, offers an intruiging mathematical view of computing puzzles. In this letter, he describes a version of the card game he played, known as "The Turing Solitaire," with its rules.

YOURS, ALAN TURING

25

ARAV MATHUR - 11H2

BRIDGING THE DIVIDE: THE CONCEPTUAL CLASH IN MODERN PHYSICS PHYSICS >

INTRODUCTION M

WHY WOULD THIS NOT WORK? Despite the success of quantum mechanics when explaining sub-atomic interactions, its theories break down when describing gravity, suggesting a missing piece in our theories of the universe. This is because quantum mechanics uses hypothetical gravitons to quantize gravity

odern physics relies upon the principles

causing mathematical inconsistencies leading to non-renormalizable infinities, which is why

of general relativity and quantum mechanics

we turn to Einstein’s theory of general relativity when attempting to understand the concept

to comprehend the universe. Quantum

of gravity. General relativity uses the principle of spacetime to explain gravity and can be

mechanics is the study of how things move

visualised as a huge elastic net that has been stretched out. When you introduce a massive

and work at the smallest, most fundamental

object onto the net, it weighs the net down, creating a dip, causing any smaller objects

level, and helps us to understand how

introduced to the dip to roll towards the more massive object because of the slope created.

individual particles interact and behave to

Fundamentally, this is the concept of gravity.

make the universe work. Quantum concepts challenge our intuitive understanding of the universe by exposing the fact that particles in the universe behave in ways that are far more complicated and nuanced than our day-today experiences might suggest.

For example, according to Heisenberg’s uncertainty principle, if you were to shrink a car down to the size of an electron, you would only be able to calculate its speed, or its position precisely, not both of those values

Einstein’s theory of general relativity portrays events in the universe as deterministic, where

simultaneously. Or imagine an electron –

each cause has an equal, specific effect that can be precisely calculated, whereas quantum

surely it has to be in one specific, observable

mechanics portrays events in the universe as probabilistic, where the behaviour of individual

position – say Position A or Position B? In the

particles can never be determined with complete certainty. The outcomes of quantum

quantum realm, this electron can be in

calculations are governed by probability distributions, so we are only dealing with the

superposition, where it exists in both positions

probabilities of certain events happening until we actually perceive them to happen.

simultaneously. Only when this electron is

Quantum principles state that at very small scales, particles (even if in a vacuum) experience

actually observed, does our perception of the

spontaneous energetical fluctuations due to Heisenberg’s uncertainty principle, and when this

probabilities of its different states collapse

idea is applied to spacetime, incorporating these fluctuations would go against the

into one definite position or state. These

fundamentally smooth structure required by general relativity. For example, at extreme

concepts contradict our logical understanding

moments, such as the initial moments of the Big Bang, in theory, these energetical

of the universe as these phenomena are not

fluctuations would magnify to an extent which would introduce a randomness onto the

observable at a macro level, so we do not

Planck scale which would undermine the fundamental principle of predictability required by

experience them.

general relativity.

There is no uncertainty when we are trying to

When scaling general relativity down to incorporate its calculations in understanding how the

observe both the position and speed of a car

universe works at a quantum levels, we similarly have little success. We end up with infinity

simultaneously, in the same way that there is

values for certain calculations, most importantly, those to do with gravity. When we describe a

no chance that before being observed, the car

particle using general relativity, its energy is concentrated in an extremely small volume, and

might exist in two different positions

the magnitude of the concentration of this energy curves spacetime immensely, resulting in a

simultaneously.

gravitational singularity; where the curvature of spacetime is infinite. This would mean that according to general relativity, even elementary particles would collapse into a tiny black hole if their gravitational effects are fully incorporated.

26

Furthermore, when general relativity is applied to subatomic

This would mean that what we experience as space and time could

particles, we also have another major problem. As distances shrink,

instead be a high level illusion emerging from quantum rules that we

the strength of gravity hugely increases, which means that at the

don’t yet understand. And any attempts to understand this would just

Planck scale, gravity becomes as strong as the other fundamental

be our attempts of approximations of a more profound framework of

forces, resulting in the equations of general relativity to break down

reality. The breakdown of relativity at the Planck scale should be a

due to infinite energy densities and infinite curvature.

signal that gravity is a concept that dissolves when zooming into the universe beyond the resolution that we experience it in our day to day

These problems show us the fundamental incompatibility of the

lives, which means that the next breakthrough in modern physics will

two principles because of the clashes that occur when trying to

not be yet another advancement in string theory or attempts to

explain events that occur at a macro scale with concepts that are

reconcile these two theories, but instead will result in us looking back

used to explain the behaviour of individual particles.

at our two previous ‘breakthroughs’ as primitive, while completely

OPINION

transforming our outlook on the universe by providing us a singular,

I

complete framework.

believe that the incompatibility between quantum mechanics

and general relativity is not merely a mathematical failure but instead an indication that our current frameworks are only approximations of a deeper reality. While these two theories are incredible successful in explaining their own subsets of the universe, attempting to understand the quantum world using general relativity and the macro world using quantum mechanics result in failure. I believe that our lack of a unified theory does not stem from mere mathematical inconsistencies, but instead from a conceptual misalignment in the way we use physics to understand the universe. The reason why we experience these non-renormalizable infinities when scaling quantum mechanics up, and general relativity down, is because they are two distinct, incomplete representations of a greater, more fundamental framework.

The problem with gravity is not that it cannot be quantized, but instead that it is being attempted to be used in a way in which it is inapplicable. Spacetime could be an information structure, an emergent theory as opposed to a fundamental concept, which would mean that the fabric of our reality is not made from matter or energy alone, but instead is comprised of fundamental bits of information; similar to how a computer simulation is built from binary machine code. If this is true, then gravity and quantum mechanics would merely be a large-scale side effect of the processing of this information.

REFERENCES Demystifying Quantum Physics: A Beginner’s Guide | by Chris Ferrie | Medium

Quantum Theory and the Uncertainty Principle - The Physics of the Universe

Special and General Relativity - The Physics of the Universe

Introduction to quantum mechanics - Wikipedia

Quantum mechanics | Institute of Physics (iop.org)

What is the theory of general relativity? | Space Quantum Theory: The Einstein/Bohr Debate of 1927 | AMNH

Relativity versus quantum mechanics: the battle for the universe | Physics | The Guardian

27

ISSIE RIDGWAY -10 AESC

QUANTUM MECHANICS -- WHAT IS IT, AND WHAT IS IT’S SIGNFICANCE? QUANTUM >

INTRODUCTION A

PREVIOUS MODELS OF THE ATOM Previously, scientists believed that atoms followed the structural theory proposed by John Dalton, who thought that electrons consisted of a positive ‘dough’ with electrons stuck to it (this was called the plum pudding model, where electrons where randomly surrounding the

‘quanta’, is the smallest discrete unit

nucleus). However in 1913, Neils Bohr revised this model suggesting that electrons orbited the

(e.g. a particle) of a phenomenon. Quantum

nucleus at different energy levels and specific distances from the nucleus (what we now know

mechanics is the fundamental theory that

as shells). This then allowed him to explain that since certain chemicals burn with certain

describes the behavioural nature at and below

coloured flames, the pattern of energy realised by the electrons must be the same for every

the scale of atoms.

atom of that element (due to the different energy levels). This means that electrons cannot be

While we would typically assume, based on

arranged at random and must have fixed levels of energy.

quantum experiments that Quantum

There are many central concepts that helped to form out understand of quantum physics.

mechanics only involves the smallest parts of our universe (such as electrons and photons), quantum phenomena are all around us, acting on every scale.

Whether you are making some toast in the toaster, turning on a fluorescent light or simply using your compute, you are surrounded by quantum phenomena.

Niels Bohr, Quantum Physicist

WAVE PARTICLE DUALITY The wave particle duality theory states that waves can exhibit particle like properties, while particles can exhibit wave like properties. Albert Einsteins theory that light, which is considered as a form of electromagnetic wave, must also be thought of as particle-like. This lead French physicist Louis de Broglie to propose that electrons and other discrete bits of matter (which have previously only been conceived as material particles) must have wave-like properties (e.g. wavelength and frequency). The experiment that was done to prove this is called the ‘double-slit experiment’ . In this, electrons are beamed at two holes in a wall, with another wall behind it. With out previous knowledge of electrons, we would assume that the The idea of Quantum physics arose in the late 1800s and early 1900s when scientists noticed that classical physics could not be used to

electrons would then land on the wall behind exactly where the other holes were. However, this is not the case. If we think of electrons as subatomic particles with wave-like qualities, the waves would go through the two slits and then interfere with each other.

explain some of the behaviours of subatomic particles.

Due to the inferences of the two waves the electrons actually do not land where we would expect – on the wall behind the slips-, but behind the centre wall (as seen on the diagram).

The line showing the wave intensity shows that actually the electrons would gather behind the centre wall. This experiment proves that electrons have wave like qualities, due to the area that they gather once beamed at two holes. This theory (wave particle duality), is just one key concept in quantum mechanics.

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UNCERTAINTY PRINCIPLE The uncertainty principle states that the momentum and position of a particle cannot be precisely determined at the same time. Now this sounds silly to use, because it is easy to know the speed and position of a car. But the uncertainties that this principle is talking about are far too small to be observed. The principle says that the product of the uncertainties in position and the velocity is equal to/ greater than a tiny physical quantity or constant (h/(4π). Where h is Planck’s constant (6.6 × 10−34 joules per second). It also says that any attempt to precisely measure the velocity of a subatomic particle (like an electron), will knock it around in an unpredictable way, so that a simultaneous measurement of it’s position has no validity.

WHY WERE THESE DISCOVERIES SO IMPORTANT? This new knowledge has had profound impacts on science and technology today. The understanding of quantum mechanics has lead to the development of things like lasers, light emitting diodes, transistors, medical imaging. electron microscopes, and many modern day devices. Without the discovery of quantum mechanics, our phones would not exist today!

QUANTUM SUPERPOSITION:

Q

uantum superposition is the ability of a quantum system to act

as if it is in multiple states at the same time until it is measure. This is a property that applies to all waves. The Schrödinger’s cat experiment, was originally used to point out the absurdity of superposition, however, it is now used to illustrate the phenomena.

In this experiment, a cat is placed in a seal box, along with a vile of poison that will kill the cat if an event (such as the decay of a radioactive atom) were to happen. From outside, whether the cat is dead or alive is unknown, so therefore the cat is in a state of superposition and is both dead and alive at the same time (because it is not until the box is opened that the state of the cat can be known).

This theory can also be carried over to the double-slit experiment

REFERENCES

(which is an example of superposition in real life). This is because it

What Is Quantum Physics? Quantum Physics in Simple Terms - Caltech Science Exchange

demonstrates that light can be classified as both and a wave and a particle simultaneously until it is observed in a specific way.

Quantum Mechanics Essay - 609 Words | Bartleby

Figure 1: Models of the atom over time - What is radioactivity? - OCR 21st Century - GCSE Combined Science Revision - OCR 21st Century - BBC Bitesize

Figure 2: Models of the atom over time - What is radioactivity? - OCR 21st Century - GCSE Combined Science Revision - OCR 21st Century - BBC Bitesize

Figure 3: Double-slit experiment - Wikipedia

Figure 4: Double-slit experiment illustrating the wave behavior of light. | Download Scientific Diagram

Wave-particle duality | Quantum Mechanics, Electrons, Photons | Britannica

What Is Quantum Superposition? - Caltech Science Exchange

Uncertainty principle | Definition & Equation | Britannica

29

KATIE WELSH - 10A

THE SPECIAL THEORY OF RELATIVITY PHYSICS >

INTRODUCTION S

o, what is special relativity? Special

relativity, in short, is the scientific theory of the relationship between shape and time. Albert Einstein proposed this theory in 1905 which specified many different concepts we see in physics today such as time dilation, length contraction and relativistic mass. Einstein produced these concepts and postulates

Once she reaches the planet, she will turn around and head back to earth at the same speed.

Because Bo is travelling at such a significant percentage of the speed of light, time will pass slower for her, because the light rays will take a longer time to reaching her which will slow time down. To calculate by what factor time will pass slower for Bo, we can use the Lorentz Factor. At 86.6% the speed of light, the Lorentz Factor is 2, meaning that time passes twice as slow upon the spaceship as it does on earth (2). To find out how long this journey would take on the spaceship, we can use the formula speed= distance/time. So, speed = 3/0.866x the speed of light, which equals 3.46 years passed on the spaceship. To find out the amount of time, passed on earth, we can use the equation for time dilation. This is that time dilation= Lorentz Factor x the time that passes on the spaceship. So, 1.998 (to be precise) x 3.46= 6.91 years passed on Earth. From this we can see that time has passed much faster on Earth than it has on the spaceship. Not just this, but the person on earth will look biologically older than the person on the spaceship.

through a series of elaborate thought experiments and contemplating many constants in physics such as the speed of light and Planck’s constant. Special relativity has 2 main postulates: 1.

The principle of relativity: the laws of physics are all the same for people travelling at a constant speed no matter how fast they are going. 2. The constancy of the speed of light: the speed of light in a vacuum in always 300,000,000 regardless of how fast the light is moving or how fast the object is moving.

These postulates affect our perception of space,

TIME DILATION

RELATIVISTIC MASS Relativistic mass is the mass of an object perceived by an observer, which increases as the object increases in speed (3). If an object is traveling at exactly the speed of light, its relativistic mass would be infinite (which is why no object can ever reach the speed of light) as shown by the formula for relativistic mass, which is shown below. An object always has a rest mass which is represented by the symbol m0 and a relativistic mass which is shown by the formula m.

RELATIVITY OF SIMULTANEITY This phenomenon is the idea that if two events happen simultaneously, the event is not absolute, but it depends on the observer's reference frame. The laws of classical physics stated that two events happening simultaneously are absolute and happen at the same time for

T

everyone, no matter your reference frame. Special relativity disproved this theory. In special he first key concept that arose from the

special theory of relativity was time dilation.

This is the concept that time moves differently for people in different reference frames. The twin paradox was used to explain this (1). Two twins called Bo and Frankie are both born at the same time and have just turned 20. Bo decides to jump on a spaceship and travel to a distant planet 10 light years away at 86.6% of the speed of light.

relativity, there is no such thing as ‘now’ and there is no absolute present which is applicable for everyone, everywhere. Essentially, the relativity of simultaneity states that if two events happen at the same time in one reference frame, the time that those events happen is not absolute. We can use a thought experiment designed by Einstein to understand this concept further (4). Envision a train, which is moving at a constant speed. There is an observer on the train and one on the platform that is moving relative to the train. Two lightning bolts strike the train’s rear and its front. The observer on the train will see the lightning bolts strike at exactly the same time. However, the observer at the front of the train moving relative to it, will see the lightning bolt at the front of the train before the one at the rear, because the light rays of the bolt at the back of the train must travel further to reach the observer than those of the bolt at the front of the train.

30

This thought experiment is based on the fact that the observer in the train is in the middle of the train, and since light travels at the same

REFERENCES

speed in all directions, the light rays will meet at the same time,

1. https://www.youtube.com/watch?v=yuD34tEpRFw

meaning that they will perceive the strikes to be at the same time.

2. https://ffden-2.phys.uaf.edu/webproj/211_fall_2014/Jackson_Page/

However, the situation would be different if the person on the train was sitting right at the front in 1st class. If this was the situation, then the light rays from the lightning bolt at the front of the train would have a shorter distance to travel to the person than the rays from the bolt would at the back. This would mean that the bolts would strike at different times for the observer. This is also the case for if the person was sitting at the back of the train. As we can see, it is all

jackson_page/page3.html 3. https://www.einstein-online.info/en/explandict/relativistic-mass/ 4. https://www.nationalgeographic.com/science/article/einsteinrelativity-thought-experiment-train-lightning-genius 5. https://phys.libretexts.org/Bookshelves/Relativity/

dependent on where you are and how you are moving relative to the

Spacetime_Physics_(Taylor_and_Wheeler)/06%3A_Regions_of_Spac

situation.

etime/6.01%3A_Light_Speed-

CASUALITY AND LIGHT SPEED LIMIT

_Limit_on_Casualty#:~:text=More%20generally%2C%20one%20even

T

his key point of special relativity states that no object with mass

t%20cannot,zooming%20particle%20of%20any%20kind.

can exceed the speed of light, because it would require infinite energy. Light is the fastest moving thing in our universe, meaning that it is faster the communication. This makes instant communication impossible because it will never catch up with the speed of light. One lightyear is the distance that light can travel in one year. Let’s use the spaceship analogy again. Bo is currently 5 light years away from Frankie. Since the speed of light is finite, the light will take 5 lightyears to travel from Bo’s position to Frankie’s. By the time the information has reached Bo, Frankie will be a long way away because that information will have taken 5 years to reach Frankie. This means that Frankie will be seeing Bo as 5 lightyears away from her, because that’s how far the distance is between them as she sees it, when in reality Bo is actually much further away than 5 lightyears.

To conclude, special relativity is an extremely complex and difficult topic to comprehend, let alone understand and these are just a few of the topics and postulates that are involved in the theory.

31

HAMZA MEHRALI - 10C1

AI & THE AGE OF UNCERTAINTY: BOOK REVIEW OF MUSTAFA SULEYMAN’S THE COMING WAVE AI >

INTRODUCTION I

recently read a profound book on the

future of artificial intelligence by Mustafa Suleyman, CEO of Microsoft AI and cofounder of DeepMind. This book aims to wake readers up to the stark reality of the serious change that will come about in the next few years, with Suleyman suggesting that the AI revolution may be more consequential for our species than any prior technological revolution.

Like with all innovation, the exciting prospects, and the future they hold are coupled with dangerous newfound implications—the likes of which have never been seen before with this kind of tech. The reason this time of innovation and progress is referred to as the “coming wave” in this book is because of the risks it will undoubtedly pose, with the problem of its containment being, in his opinion, the “defining challenge of our era.”

Failure to do so, from his rather disturbing perspective, could lead to our unfortunate demise. Already, we are familiar with the products of years of research and experimentation in the form of LLMs like ChatGPT, Gemini, or Copilot. What are currently forms of AI that mostly help with menial work-related tasks will very quickly, predicts Suleyman, become core components of our lives—organising our routines, operating businesses, and managing fundamental government services.

AN OUTLINE OF WHAT THE REVOLUTION LOOKS LIKE:

T

his AI revolution we are just stepping

Price collapse and widespread availability

into will lead to advancements in an array of

of tech:

fields. Whether through DNA printers or

The release of ChatGPT-4 in March 2023

robot assistants, these seemingly far-off

seems lost to the past now, with the

wonders of the tech world are not as distant

prospect of ChatGPT-5 already drawing

as once believed.

closer. OpenAI CEO Sam Altman suggests

Whilst we are presently unaware of it,

the same price collapse in this industry,

Suleyman strongly argues that the coming

estimating that the cost to use a given

decade will be defined by this wave of

level of AI falls roughly ten times every 12

powerful yet rapidly proliferating tech. He

months.

gives the example of the jaw-dropping collapse in the cost of sequencing DNA,

What was seen as pioneering and futuristic

which has dropped a millionfold in under

just three years ago is now a common

twenty years—a thousand times faster than

aspect of the lives of millions. In the next

Moore’s Law. Suleyman believes that a

few years, the boundaries of what AI is

similar chain of events will unfold in the AI

capable of will continue to be broken

industry.

again and again, paving the way for innovation in so many different areas.

This has already been seen since the release

AI could combat major global issues such

of ChatGPT and other LLMs, with years of

as poverty, disease, and climate change far

extremely expensive, groundbreaking

more effectively than any human can. It

research already amalgamating into game-

may well even play a role in the future of

changing chatbots used by hundreds of

warfare, taking cyberwarfare and

millions of people daily around the world.

autonomous weapons to a whole other level.

32

RISK VS REWARD

F

rom all this, it seems that AI could be a phenomenal tool for

manufacturing the ideal world—if it stays on our side.

The classic dystopian forecasts of AI world domination and its goal to wipe out mankind may be quite far-fetched. But to a lesser extent, AI going rogue would have to be a serious consideration according to Suleyman. That is because we are currently at the stage of artificial intelligence, far off from the prospect of reaching AGI (Artificial General Intelligence)—the stage where AI can successfully outperform humans in all cognitive skills.

We are fast approaching somewhere in the middle: ACI (Artificial

BILL GATES REVIEW (EDITORS NOTE)

B

ill Gates has also produced a review for the Coming Wave,

naming it as his favourite book about AI. You can see a short extract of his review below:

When people ask me about artificial intelligence, their questions often boil down to this: What should I be worried about, and how worried should I be? For the past year, I've responded by telling them to read The Coming Wave by Mustafa Suleyman. It’s the book I recommend more than any other on AI—to heads of state, business leaders, and anyone else who asks—because it offers something rare: a clear-eyed view of both the extraordinary opportunities and genuine risks ahead.

Capable Intelligence) and will most likely see the implementation of AGI at some point in our lifetimes. What it holds in store for us we can only speculate about.

What we do know for certain is that we must be ready to meet and adapt to these challenges that we will definitely face as AI grows more powerful and intelligent.

MY FINAL TAKE

S

uleyman doesn’t want us to fear AI, but to accept its incredible

power. If we manage it wisely, it could be the greatest tool humans have ever created. If not, Suleyman strongly believes it may well become the most dangerous. When managed properly, AI could be the most important invention of our species.

The author, Mustafa Suleyman, brings a unique perspective to the topic. After helping build DeepMind from a small startup into one of the most important AI companies of the past decade, he went on to found Inflection AI and now leads Microsoft’s AI division. But what makes this book special isn’t just Mustafa’s firsthand experience—it’s his

“Containment is the defining challenge of our era.” -

deep understanding of scientific history and how technological revolutions unfold. He's a serious intellectual who can draw meaningful parallels across centuries of scientific advancement.

“Containment is the defining challenge of our Most of the coverage of The Coming Wave has focused on what it has

era.”

Mustafa Suleyman

to say about artificial intelligence—which makes sense, given that it's one of the most important books on AI ever written. And there is probably no one as qualified as Mustafa to write it. He was there in 2016 when DeepMind’s AlphaGo beat the world’s top players of Go, a game far more complex than chess with 2,500 years of strategic thinking behind it, by making moves no one had ever thought of. In doing so, the AI-based computer program showed that machines could beat humans at our own game—literally—and gave Mustafa an early glimpse of what was coming.

33

- DRISHAAN PANDEY - 7J

SECRETS OF NUCLEAR REACTION NUCLEAR POWER >

INTRODUCTION N

uclear reaction is slowly reforming our

future: a renewable source of energy, limited waste, the perfect answer to our problems.

However, it could also spell out our demise; destructive weapons that cause calamities all over the world and lay waste to civilisation. That is why nuclear reactions should be handled with care; they have the power to create and destroy.

Nuclear reactions are a key part of nanoscience, as well as nuclear physics and chemistry. They are so powerful, yet they occur on such a small scale! Nuclear reactions can help fill in the gaps of our knowledge of the universe, and they will be key to expanding our knowledge soon. They have many applications in the modern world, ranging from medicine, to power, to weaponry.

There are two main types of nuclear reaction: fission and fusion. Quite briefly, they are near opposites; whilst fission is the splitting of an atom nucleus, fusion is the combining of atom nuclei. Both release monumental amounts of power, however fusion releases approximately four times the energy of fission. That is why we are looking to fusion as a new, reliable energy source.

DETAIL >

THE PHYSICS BEHIND NUCLEAR REACTIONS F ission: Nuclear fission occurs when a

Fusion: Nuclear fusion, on the other hand,

neutron, called the incident neutron, hits a

occurs when two light atomic nuclei hit

fissile nucleus (a nucleus that can undergo

each other and combine to form a heavier

fission). The nucleus is excited by the

nucleus. It must take place at an extremely

incident neutron, splitting into two nuclei

high temperature; to occur on Earth, it

and releasing some more neutrons. These

must be over 108 °C. Two nuclei will always

neutrons in turn collide into other

have what is called a ‘mutual electrical

surrounding fissile nuclei, causing more

repulsion’. This is a force that pushes the

fission, more neutron release and eventually

nuclei apart whenever they move too close

a chain reaction.[1]

to each other. However, when the

Some of the best examples of fissile nuclei

temperature breaches 100,000,000°C,

are the Plutonium isotope Plutonium-239

Tritium nuclei have enough energy to

(239Pu) and the Uranium isotope Uranium-

overcome this force. They then get pushed

235 (235U), which are the most active and

together by an attractive nuclear force,

fissile isotopes. They also easily absorb low-

and if the conditions are right, fusion

energy neutrons, making it easy for them to

occurs.[4]

split and for nuclear fission to take place.[2]

Nuclear fusion must occur in plasma,

However, these isotopes are not in

another state of matter: an extremely hot,

abundance. Only 0.7% of the Earth’s natural

electrically charged gas that is full of

uranium sources are 235U, meaning that a

charged ions. Plasma is the only state of

large sum of Uranium must be mined, which

matter that can withstand such extreme

could lead to harm for the environment and

temperatures, which is why fusion must

miners being largely exposed to dangerous

take place within it. Plasma makes up 99%

radioactive elements. 239Pu is not a natural

of the universe, which explains why so

isotope, which means that a large amount of

much fusion occurs in space. However,

energy needs to be used to synthetically

plasma is extremely rare on Earth, and

produce it. However, it can be produced by

even when we see glimpses of it, it is only

the natural, abundant Uranium isotope

in things like lighting and Aurora Borealis

238U, meaning that it is unlikely that we run

(the Northern Lights), making it very hard

out of our resources any time soon.[3]

to harness. This is one of the things that

34

make it hard to synthesise fusion on Earth.[5]

The most common nuclei that are used for nuclear fusion are two hydrogen isotopes – Hydrogen-2 (2H or D) and Hydrogen-3 (3H).

These are commonly known as Deuterium and Tritium. They are so common in fusion because of their compatibility, as they readily fuse together in the right conditions to form Helium. Deuterium has one neutron and Tritium has two, meaning when they undergo fusion, 3 neutrons are put together. However, Helium only has 2 neutrons, meaning the other, high velocity neutron, escapes and can transfer energy very well, which could be harnessed for power.[6]

Deuterium is an the more abundant isotope of hydrogen that can be found in most of the world’s water; 1 in 6420 hydrogen atoms are deuterium, and when this is put into context, we have a steady supply of deuterium that is virtually inexhaustible. Tritium, on the other hand, is a radioactive isotope that decays within 12 years, which is, relative to other elements and isotopes, a very small half-life. As well as this, tritium is very rare in nature, and for all fusion purposes, must be produced synthetically. This takes fuel, energy and money, although it is possible that the production of tritium can be merged into the plant itself, which might save on some energy.[7]However, it must be considered that the energy released from just one gram of deuterium-tritium fusion is the equivalent of the amount of energy taken from around 9000 litres of oil.[8]

Einstein’s General and Special theories of Relativities, Niels Bohr’s original atomic structure and James Chadwick’s discovery of the neutron, to name a few. However, possible one of the most significant breakthroughs in nuclear science was made in 1934, when a man called Enrico Fermi achieved the first controlled self-sustaining nuclear Fission reaction- the first step towards a nuclear future.[9] [10]

Humans have been dealing in practical fission for over 70 years, and we have made many outstanding advancements. The first example of a major fission reaction happened in 1945, when the Manhattan Project resulted in the first nuclear explosion, on the 16th of July. This eventually led to the bombing of Hiroshima and Nagasaki, showing how destructive nuclear energy can be. Thankfully, we haven’t had any large-scale nuclear bombing in warfare since then, but the world still has a substantial nuclear arsenal; the power peaked in 1986, reaching over 60,000 bombs during the Cold War. The first power plant to produce usable electricity through atomic (nuclear) fission was EBR-1 (Experimental Breeder Reactor 1), which powered four 200-watt lightbulbs on December 20th, 1951. In the modern day, we can power over 1 gigawatt of electricity; just one example of how much we have advanced over the last 70 years. The first commercial fission plant was commissioned in 1960 and lasted until 1992; it was called Yankee Rowe.[11]

APPLICATIONS AND EXAMPLES OF THE REACTIONS

F

ission and fusion reactions release monumental amounts of

energy, and humans have discovered many applications to harness this energy. However, whilst we have made significant developments in the field, we have only seen the tip of the iceberg in this fascinating realm.

Fission One of the main applications of nuclear fission on Earth is in a nuclear power plant. The plant’s purpose is to generate electrical energy from a controlled nuclear fission reaction; a relatively clean, renewable source of

HISTORY OF NUCLEAR REACTIONS

H

electricity. The reactor core used in the reaction contains over 200 rods filled with small pellets of 235U, to provide a controlled chain reaction

umans have been investigating nuclear physics and

that generates power. The reactor core also contains many control rods,

radioactivity since the 1700s. From Bohr to Rutherford, Curie to

made of various materials, such as boron, cadmium and indium. These

Einstein, there have been many scientists who have made important

absorb some neutrons, creating a reaction that does not get out of

discoveries in this field. Theoretical physicists have been exploring

control; a safe way to generate power.[12]

the realms of fission and fusion for over two centuries, and we have

Another important application of nuclear fission is in bombs; nine

also begun to act on these ideas; we have made massive leaps in

countries now have a nuclear arsenal. A nuclear explosion kick-starts a

these mysterious fields.

rapid nuclear reaction, and the energetic neutrons escape in an explosion of light, sound and shockwaves, and power more than any average

In 1789, Martin Klaproth discovered the first radioactive element:

explosion.[13]

Uranium. However, its radioactive capabilities were only uncovered in 1896, by French physicist Henri Becquerel, who discovered it by

Fusion

accident. After this, Marie and Pierre Curie began testing with other

The most important example of nuclear fusion is in stars; the closest to us

radioactive elements such as Radium, to attempt to find a cure for

is the sun. The sun is made up of hydrogen and helium atoms in an

Cancer. This process is still used as a treatment today; Radiotherapy

incredibly hot plasma. These conditions are perfect for fusion to occur,

uses radioactive elements to try and kill cancer cells. In 1902, Ernest

and fusion is the reason for the light and heat we get from the sun. When

Rutherford learnt how to manipulate the elements using alpha

the Deuterium and Tritium molecules fuse together, they form Helium

particles: the first steps towards full sized reactions. Over the next 30

molecules. The high-energy is expelled, and it is received on Earth as

years or so, many significant discoveries were made in this field;

warmth and light.

35

COMPARING THE TWO REACTIONS

B

oth reactions have their pros and cons. Whilst fission is a very

reliable source of energy, it produces waste as well. Although the amount is quite low – only 25-30 tonnes a year – the waste is highly radioactive, and a big hazard for our health and for the environment.

As well as this, whilst nuclear power plants are quite eco-friendly and releases more energy than other forms of power generation, it costs high amounts of money, and the radioactive elements required are not inexhaustible. As well as this, many populations do not understand nuclear power, and this causes opposition and the reason why nuclear energy is not very widespread.

CONCLUSION

I

t is likely that nuclear reaction will play a big part in our lives in the

near future. Once we find a cost-effective way of simulating fusion on

Fusion, on the other hand, has virtually no waste or impact on the

Earth, there will be near unlimited prospects for it. Fusion will easily

environment. As well as this, hydrogen is the most abundant element

replace fossil fuels, and although it may be many years until we reach this

in the known universe, meaning that we have an almost inexhaustible

level, it is a good target to look to.

supply of fuel. However, there is not much known technology to simulate such high temperatures, and if there was, the cost would be monumental. Whilst fission has the possibility of going out of control, fusion is self-regulating, meaning it will not get out of control.

BIBLIOGRAPHY Bibliography

1. Andrea Galindo. (2022). What is Nuclear Energy? The Science of Nuclear Power.

Available at: What is Nuclear Energy? The Science of Nuclear Power | IAEA 2. (2025). Physics of Uranium and Nuclear Energy. Available at: Physics of Uranium and Nuclear Energy - World Nuclear Association 3. (N.D). Why Uranium and Plutonium? Available at: Why Uranium and Plutonium?​

4. Andrea Galindo. (2022). What is Nuclear Energy? The Science of Nuclear Power.

Available at: What is Nuclear Energy? The Science of Nuclear Power | IAEA 5. (N.D). About Plasmas and Fusion. Available at: About Plasmas and Fusion | Princeton Plasma Physics Laboratory 6. (N.D). DOE Explains...Deuterium-Tritium Fusion Fuel. Available at: DOE Explains...Deuterium-Tritium Fusion Fuel | Department of Energy 7. M.R. Gordinier , J.W. Davis , F.R. Scott , K.R. Schultz. (2004). Nuclear Fusion Power.

Nuclear Fusion Power - ScienceDirect 8. (N.D). DOE Explains...Deuterium-Tritium Fusion Fuel. Available at: DOE Explains...Deuterium-Tritium Fusion Fuel | Department of Energy 9. (2024). Outline History of Nuclear Energy. Available at: Outline History of Nuclear Energy - World Nuclear Association 10. (N.D). What is nuclear energy?. Available at: What is Nuclear Energy? - Nuclear Industry Association 11. (2024). Outline History of Nuclear Energy. Available at: Outline History of Nuclear Energy - World Nuclear Association 12. (2019). NUCLEAR 101: How Does a Nuclear Reactor Work?. Available at: NUCLEAR 101: How Does a Nuclear Reactor Work? | Department of Energy 13. (2014). Science Behind the Atom Bomb. Available at: Science Behind the Atom Bomb - Nuclear Museum​

36

S ’ T I , O N ai!

Is it a bird? is it a plane?

aI TOOLS COULD BE USED TO FIGHT CLIMATE CHANGE, IT COULD ALSO BE A KEY CONTRIBUTOR IN DESTROYING IT,

could ai save the world???

ai has exceptional data pattern recognition and analysis which lead to high hopes for a more sustainable environment by reaching more informed decisions or detecting methane levels. despite this, critics argue the 800kg of raw materials required to build a measly 2kg of computers is more trouble than its worth.

According to earth.org, the average co2 emissions by a car in one’s lifetime is 126,000 lbs. training an AI MODEL emits 626,600 LBS. however, TECHNOLOGY SUCH AS SATELLITE IMAGERY AND MACHINE LEARNING HAVE PROVED SIGNIFICANT IN REMOVING plastic from the oceans and preventing illegal logging. over 32 billion waste items across 67 waste categories in 2022 alone were recovered and recycyled.

37

RAVJOTH BRAR - 11M1

EXPLAINING RETRIEVAL AUGMENTED GENERATION (RAG) ARTIFICIAL INTELLIGENCE >

INTRODUCTION A rtificial Intelligence, commonly

referred to as AI, is one of the most

promising and rapidly developing fields of the 21st century. As Large Language Models (LLMs) such as Chat-GPT become more integrated in our day-to-day lives their relevance will only increase. LLMs have greatly influenced how we interact with technology, and with the introduction of Apple Intelligence, there is no telling just how far this will go. Currently over 77% of businesses are either using or exploring the use of AI in their business to optimise efficiency and workflow. But LLMs are not perfect, in fact they are far from it. They can hallucinate, miss crucial details, or, more often, misunderstand the prompt. Either way, we must do something to fix this issue.

In this article, I will be discussing a widely recognised technique to solve this problem – Retrieval Augmented Generation (RAG), which is already implemented in many topperforming LLMs such as Chat-GPT.

WHAT IS RAG?

R

AG is a technique that essentially

HOW DOES RAG WORK?

R

AG is comprised of two main

intelligence that have the potential to

components; the retriever, and the

impact society. Essentially, connecting the

generator.

dots between words and meanings, providing you with results that are actually

The retriever model’s aim is to extract

constructive.

information relating to the prompt from the training data. It does this through the use of semantic search methods, the most commonly used being cosine similarity. A normal key-word search is not as useful as other methods of search as they can often miss the context or intent behind the search.

Semantic searching however improves by attempting to grasp the query’s meaning taking into account context. This then retrieves content that is relevant in meaning, not just in word match.

enhances the LLMs ability to process text, giving an external information retrieval

Let’s take the example of the prompt: “Most

system. This is particularly useful and

promising fields of artificial intelligence in

impactful as it means that the model can

the coming century relating to societal

access the Web at any point and pull data

change”. A basic keyword search may focus

from there. This results in more accurate

on the words; “promising”, “fields”,

data enabling the LLM to give a more in-

“intelligence”, “century”, “societal”, “change”.

depth and detailed response. This improves

When compiling these words together it is

the overall performance of the LLM making

clear how a basic search can easily focus on

it much more effective, thus knowing how

the wrong components of the query, and

RAG works will certainly give an advantage

thus provide an irrelevant response, not

to you, yes you in the coming world.

actually answering the query. On the other

The diagram above displays a traditional cosine similarity graph

hand, with semantic search the LLM understands that you’re asking the LLM to state the most promising fields of artificial

38

COSINE SIMILARITY & WORD EMBEDDINGS

C

osine similarity excels here as it is extremely precise at finding the

exact information required which makes it the perfect method. Cosine similarity is a metric that evaluates how similar two documents are, regardless of their size. It does this by calculating the cosine angle of the between the two non-zero vectors. In the diagram above, it is a 3D representation of a graph with 3 axes each representing a different topic: McDonalds, Popeyes, and the Mona Lisa. There are four documents in the graph represented by the circles, the distance between these points is then calculated using cosine similarity, which measures how closely related the documents are to each topic based on the angle between them. The smaller the angle, the closer linked documents. In this graph, we can see that the Mona Lisa document has the largest cosine distance/angle, this conveys that the Mona Lisa is a different topic altogether due to its distance between the other two documents.

THE GENERATOR COMPONENT

T

he secondary component of an RAG system is the generator.

The generator’s predominant aim is to actually craft the coherent and informed AI responses. It does this by utilising the famed transformer architecture which Google pioneered in their revolutionary AI paper titled ‘Attention is all you need’. This architecture essentially uses context and data to predict what is the most likely words to follow them by looking at all of the prompt at the same time. By doing this, it allows the model to generate profound statements. In essence, the retriever component acts as a search engine, compiling all the relevant information and facts, and the generator strings it all together to eventually form the AI’s response.

APPLICATIONS AND ETHICAL USE

T

he combination of both the retriever and generator make RAG

systems novel and particularly useful for certain applications. Whilst RAG models are limited in Generative text content, due to their ability to

At this point, you may be wondering how the points of the documents

hallucinate and amplify any existing social stereotypes in the training

are decided, well fret no longer, your requests have been answered.

data, they have many popular uses such as in image generation. RAG

This is done by another very famous tool which is necessary for all NLPs

systems are extremely intriguing to analyse as their image outputs can

(Natural Language Processing), it is called word embedding. Word

interpret user prompts very differently to how other AI image generators

embedding is a vector representation of a word in high-dimensional

may, resulting in new (and often interesting) AI images. Despite the

space. In general, the specificity of the dimensionality of the word

overwhelming urge to automate everything with a variant of AI, it is

represents the total number of features that are encoded in the vector

important to be considerate and ethically just in what we utilise AI for;

representation. Effectively, the more words that are encoded, the more

ensuring our usage of AI is moderate and carefully considered will result

precise the representation of the words are. These values are typically

in a more equitable and better society for all. Let us ensure the future we

assigned to words through one-hot encoding. This is where each word

craft together is one for all of humanity.

in the vocabulary that is being represented is given a binary vector.

BIBLIOGRAPHY

Then it is mapped out on the graph.

[Gao et al] Retrieval-Augmented Generation for Large Language Models: A Survey - https://arxiv.org/abs/2312.10997 (Accessed on the 18th of January 2025) [Merritt] What Is Retrieval-Augmented Generation, aka RAG? - https:// blogs.nvidia.com/blog/what-is-retrieval-augmented-generation/ (Accessed on the 16th of January 2025) [Varkey, 2023] The ELI5 Guide to Retrieval Augmented Generation https://www.lakera.ai/blog/retrieval-augmented-generation (Accessed on

Alternatively, TF-IDF can be used which is a cross between keyword

the 17th of January 2025)

and semantic searching. This is more advanced and more useful. TFIDF is a numerical statistic that reflects how important a word is to a document. As the LLM processes more words, the TF-IDF matrix becomes more detailed and weights certain words as more important.

Then this can either be directly put into a keyword search for the more important words, or these specific words can then be selected through cosine similarity to further determine what the prompt is actually asking for.

39

ARKANSH PANDEY - 10J1

FUTURE OF BRAIN-COMPUTER INTERFACES ARTIFICIAL INTELLIGENCE >

WHAT ARE BCIS (BRAIN COMPUTER INTERFACES)?

B CIs are devices that can process brain

activity and send signals based off this

activity to external devices which will then perform actions based off these signals. This essentially allows a user to control external devices such as phones or laptops with just their thoughts.

HOW DO THEY WORK?

B

CIs work around the electrophysiology of

the brain’s neural network, every time our brain

The use cases for such devices are immense, potentially helping patients with paralysis or motor diseases regain motor functions through this BCI technology.

makes a decision or even thinks it sparks electrical chemical signals. “This phenomenon is located in our nervous system; more specifically, in the gaps between neurons, known as synapses, as they communicate back and forth.” (“Brain Computer

There are two different types of BCIs: Invasive

Interfaces (BCI), Explained - Built In”)

and Non-invasive. Invasive BCIs are directly connected to the brain tissue via surgical procedures and are most appropriate for patients looking to overcome severe conditions due to the risks associated with the procedures. Non-invasive BCIs involve wearing an electrical device on the head of the patient, they produce weaker signals since they are not directly connected to the brain tissue and is therefore better suited for use in virtual reality, video games and robotics.

In order to capture this brain activity, BCIs place electrodes proximal to these synapses. These electrodes are known as EEG and detect electroencephalogram waves. They detect voltages, measuring the frequency and intensity of each “spike” as they fire.

That information is then fed through local computer software, where it’s translated in a process called neural decoding. This is where a variety of machine learning algorithms and other artificial intelligence agents take over, converting complex data sets collected from brain activity into a programmable understanding of what the brain’s intention might be.

USES AND APPLICATIONS

B

CIs are somewhat of a sci-fi concept to most of the world with it being a relatively new

advancement. However, with the investment of tech tycoons like Elon Musk the sector is expected to grow rapidly over the course of the next few decades. BCIs have actually already been implemented in certain areas and their main potential applications can be grouped into two uses: neurorehabilitation and direct control of assistive technologies.

40

NEUROREHABILI-TATION

B

CIs have already helped victims of paralysis by supplying a neural

feedback loop that rewires the brain. BCIs are also capable of restoring movement, mobility and autonomy for quadriplegic or partially paralyzed and disabled patients, heightening their quality of life. The feedback loop involves EEG sensors which are placed on the scalp to monitor brainwave activity. The brainwaves are displayed on a screen or

readings at the same time as acoustic readings of a subject speaking a set of words then training a deep learning model with this information.

This allows it to correlate EEG waves from the brain to syllables or letters that the subject is trying to speak. Over a period of time of training, the model will be able to identify the words that a user is thinking of and then correlate it to words then output it into a third-party device. This technology would not only be helpful to patients of paralysis but ablebodied patients as well, making communication online more efficient.

translated into visuals. The person gets real-time feedback—for example, a game character moves correctly only when the brain is producing the desired pattern while the patient plays a game. Over time, the brain "learns" to produce healthier or more regulated activity. This method has been used successfully for ADHD and epilepsy, and the next stage of development for the treatment is for partial paralysis.

A study by the IEEE noted that subjects with neurological conditions such as epilepsy, ALS, cerebral palsy, brainstem stroke, spinal cord injuries, muscular dystrophies or chronic peripheral neuropathies may be able to be treated by BCIs. If not completely cured by the devices, their quality of life would certainly be improved by these devices in the form of assistive devices which could perform routine tasks using just their

In more chronic cases, robotic devices and limbs are integrated into this system so the robotic limbs themselves are controlled by the BCIs. This is done as the BCIs pick up signals directed towards the nonfunctioning limb directly from the brain, bypassing sites of injury or disease, and sending them directly to the robotic limb giving them direct control

thoughts. In the specific case of epilepsy, BCIs would be able to easily identify spikes in brain activity and therefore be able to predict when seizures will happen more effectively and even provide targeted stimulation to the regions of the brain affected in order to prevent the seizures fully.

over this new limb.

Similarly, BCIs can help paralysed patients communicate with others as the devices allow patients to type or speak through a third-party device.

A team from Stanford University found that its brain chip could hack 62 words per minute, which is on pace with natural conversation. The study featured a non-verbal patient who suffered amyotrophic lateral sclerosis (ALS) and a pre-programmed vocabulary of 125,000 words, marking “a feasible path forward for using intracortical speech brain-computer interfaces to restore rapid communication to people with paralysis who can no longer speak.” This is done by recording EEG

Finally, the use of BCIs is hypothesised to make it possible to control or ease mental health conditions. Research at California Institute of Technology has theorised the possibility that psychiatric conditions such as bipolar disorder, OCD, depression and anxiety could be eased by BCIs. Neurofeedback therapy already exists involving a form of non-invasive BCI to treat more pedestrian conditions such as migraines, ADHD, fatigue, and burnout; however, this process could be revolutionised in the future with use of more invasive BCIs within the brain to treat more effectively as it can pick up brain signals much clearer.

41

DIRECT USE OF ASSISTIVE TECHNOLOGIES

A

n amazing use scenario is with drones: in January 2025, a study

was published from Jackson State University showing a user control a drone through a BCI. This feat was achieved using a non-invasive BCI called an EEG headset which measures electroencephalogram waves from the brain. These readings were then processed and classified, using a deep learning (machine learning) algorithm, into 4 different directions in which the drone should move. The Department of Defence has already funded research on BCIs for hands-free control of drones.

And the Federal Aviation Administration has started investigating how to medically certify pilots who may one day use BCIs to control airplanes, all signalling how a telepathically controlled air force could be the future of modern warfare as we know it.

ISSUES

T

here are some significant issues which need to be addressed and

resolved in order for the technology to progress and become a leading technology in our future lives.

Some companies such as Elon Musk’s Neuralink have tested their invasive BCIs on real test subjects. Noland Arbaugh was their first human patient having been diagnosed with quadriplegia after a diving accident, within a month of the surgery to implant the chip, 85% of the threads implanted in his brain had retracted and become unresponsive. This meant it was much slower and more unresponsive at times making it less effective at completing tasks.

Another possible issue experienced with these devices is how dangerous the surgical procedures can be, specifically with invasive BCIs. This is because they are being implanted into the brain tissue, invasive BCI can damage nerve cells and blood vessels, hence increasing the risk of infection. Additionally, the natural defence system of the body may reject

Video: https://www.youtube.com/watch?v=PR3KPwkeyQc&t=60s

the implant, treating it as a foreign entity (biocompatibility concern).

Another safety concern of invasive BCI is the possible formation of scar

A use which would impact our lives the most is the development of

tissue after surgery, a consequence that may gradually degrade the

BCIs to access and control electronic devices in the home such as

quality of the acquired brain signals necessary for the BCIs to effectively

smartphones, lights, virtual assistants, and messaging apps. Companies

function.

such as Neuralink and Synchron have already managed to allow their users to control video games and social media applications with their

With non-invasive BCIs, the most common issues occurring in tests is

thoughts. In studies by the University of Malaga and Valladolid, users

inefficient functionality due to weak signals detected and low signal-to-

have exercised control of social networking apps, email administration,

noise ratio, this is because the signals from neurons in the brain get much

virtual assistants, and instant message services without physical motor

weaker the further from the brain the EEG detectors are and get mixed

skills. Dimming the lights or changing the channel on a TV are

up with neuron signals from unwanted parts of the brain (noise). This can

examples of how BCIs can be adapted in the home.

mean they don’t function as well with external devices and don’t produce

Similarly, another use in our daily lives would be wearable headsets

the desired effect of the user.

with non-invasive BCI technology built in. For example, the company Neurable has built a pair of headphones with 12 EEG sensors to detect

Finally, BCIs require heavy regulatory approval, from the FDA and other

brain activity and feed it to a mobile app, showing the user how

organisations, given the risk and unfamiliarity of this technology. In spite

productive they have been and helps to avoid burnout and fatigue. The

of this, the FDA did approve Neuralink in May 2023 for human testing.

picture shows the Neurable BCI headset readings of brain activity on a

However, as developments become more advanced, the restrictions

subject allowing you to see when you are productive and not in a

imposed will inevitably become more of an issue as questions about

period of work.

ethics and privacy are raised.

42

These questions have led to the creation of neuroethics: a growing field

We have already seen examples of BCI technology integrated with day-

which looks at the ethical, social, and legal implications of

to-day devices such as headphones and phones. This ecosystem will

neuroscience. With the advent of BCIs, neuroethics is beginning to

only become more extensive as the technology becomes more

focus on subjects such as cognitive liberty (freedom of thought,

integrated with our daily lives, we could see AR glasses like Meta glasses

ensuring that corporations or powerful individuals cannot influence

or VR headsets like the Apple Vision Pro integrated with BCI technology.

people’s minds) and mental privacy and neurodata (ensuring that

It sounds like a science fiction world far off, but it may become reality

sensitive data from within an individual’s mind is kept private). As BCIs

sooner than you would think. Video of BCI using Apple Vision Pro

grow, much like AI, we will need to address the ethical concerns of how

powered by Nvidia

it may be misused and can be an extremely dangerous power to have over someone.

Finally, BCIs will give us a deeper insight into how the brain functions and is structured, a concept which we don’t have the greatest understanding of as of now. Deep learning algorithms used to decode brain signals learn rapidly and better themselves on their own increasing their knowledge of the brain and its functions. Using these algorithms, scientists can also gain a deeper understanding of the brain, allowing us to understand psychiatric conditions better and the

THE FUTURE

T

he future looks bright for BCIs as astounding developments are

being made in our time and the sector looks to be getting increasingly

structure of the brain better as well.

BIBLIOGRAPHY Brooke Becher, ‘Brain Computer Interfaces (BCI), Explained,’ BuiltIn, 7th May 2025, https://builtin.com/hardware/braincomputer-interface-bci Rachel Tompa Ph.D., ‘Why is the human brain so difficult to understand? We asked 4 neuroscientists.’, Allen Institute, 21st April 2022, https://alleninstitute.org/news/why-is-the-human-brain-so-difficult-to-understand-we-asked-4-neuroscientists/

advanced as we look to the future.

‘A Year of Telepathy,’ Neuralink, 5th February 2025, https://neuralink.com/blog/a-year-of-telepathy/

Companies such as Neuralink as Synchron have connected Brain-

Medicine, 27th April 2021, https://medicine.washu.edu/news/stroke-recovery-device-using-brain-computer-interface-receives-

computer interfaces with AI models such as Nvidia and Open AI, deep

Tamara Bhandari, ‘Stroke-recovery device using brain-computer interface receives FDA market authorization,’ WashU

fda-market-authorization/ Francisco Velasco-Álvarez, ‘Brain–Computer Interface (BCI) Control of a Virtual Assistant in a Smartphone to Manage

learning models are used with BCIs to make for efficient pattern

Messaging Applications,’ Departamento de Tecnología Electrónica, Universidad de Málaga, 26th May 2021, https://

recognition in EEG waves and interpret waves to find out what each

Víctor Martínez-Cagigal & Eduardo Santamaría-Vázquez & Roberto Hornero, ‘Controlling a Smartphone with Brain-Computer

pattern means. These models can train themselves over time to find patterns and be able to recognise them the next time they arise making the BCIs faster and better. With thought-to-text, AI models can enhance BCIs so that they can predict words based off patterns in the user’s previous activity much like predicted text on keyboards but

www.mdpi.com/1424-8220/21/11/3716

Interfaces: A Preliminary Study’, University of Valladolid, June 2018, https://www.researchgate.net/ publication/325803835_Controlling_a_Smartphone_with_Brain-Computer_Interfaces_A_Preliminary_Study Joseph N Mak & Jonathan R Wolpaw, ‘Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects,’ IEEE Rev Biomed Eng., PubMed Central, 3rd May 2010, https://pmc.ncbi.nlm.nih.gov/articles/PMC2862632/#S13

Joshua I Glaser & Ari S Benjamin & Raeed H Chowdhury & Matthew G Perich & Lee E Miller & Konrad P Kording, ‘Machine Learning for Neural Decoding,’ eNeuro, PubMed Central, 27th August 2020, https://pmc.ncbi.nlm.nih.gov/articles/PMC7470933/ #:~:text=Neural%20decoding%20is%20an%20important,traditional%20linear%20methods%20for%20decoding.

instead with your thoughts.

‘Implanted Brain-Computer Interface (BCI) Devices for Patients with Paralysis or Amputation - Non-clinical Testing and Clinical

Synchron is developing technology which may be able to bypass the

Services Food and Drug Administration Center for Devices and Radiological Health, 20th May 2021, https://www.fda.gov/

dangers of open brain surgery to implant invasive BCIs. They can implant the device through an endovascular procedure where the BCI is fed through the jugular vein in the neck via stents and then is released at the site in the brain where it is required to work (the motor cortex). Over a 90-day period after insertion, cells in the vessel wall grow around the ‘stentrode’ (BCI), integrating the device into the body and

Considerations Guidance for Industry and Food and Drug Administration Staff,’ U.S. Department of Health and Human

media/120362/download

Brittany Loggins, ‘What Is Neurofeedback Therapy?,’ Very Well Mind, 17th December 2024, https://www.verywellmind.com/ neurofeedback-therapy-definition-techniques-and-efficacy-5193195

Mokhles M. Abdulghani & Arthur A. Harden & Khalid H. Abed, ‘A Drone Flight Control Using Brain Computer Interface and Artificial Intelligence’, International Conference on Computational Science and Computational Intelligence (CSCI), 2022, https://american-cse.org/csci2022-ieee/pdfs/CSCI2022-2lPzsUSRQukMlxf8K2x89I/202800a246/202800a246.pdf

‘Science & Tech Spotlight: Brain-Computer Interfaces,’ U.S. Government Accountability Office, 8th September 2022, https:// www.gao.gov/products/gao-22-106118

reducing the main risk of blood clots around the device. So far, Synchron have tested this on 10 patients and met the required safety guidelines from the FDA. This exciting technology could make BCIs much more accessible and safer for people in the near future.

Alicia Howell-Munson & Walter T Piper & Theresa Guarrera & David Stanley & Davide Valeriani & Michelle Lim & Ramses E. Alcaide, ‘Detecting Focus States in Office Environment with Neurable EEG Headset,’ BCI Meeting 2023, Graz University of Technology Publishing House, https://davidevaleriani.it/pub/BCIMeeting2023.pdf

Rafeed Alkawadri, ‘Brain–Computer Interface (BCI) Applications in Mapping of Epileptic Brain Networks Based on Intracranial-

EEG: An Update,’ Frontiers in Neuroscience, 27th March 2019, https://www.frontiersin.org/journals/neuroscience/articles/10.3389/ fnins.2019.00191/full

Baraka Maiseli & Abdi T Abdalla & Libe V Massawe & Mercy Mbise & Khadija Mkocha & Nassor Ally Nassor & Moses Ismail & James Michael & Samwel Kimambo, ‘Brain–computer interface: trend, challenges, and threats,’ Brain Informatics, PubMed Central, 4th August 2023, https://pmc.ncbi.nlm.nih.gov/articles/PMC10403483/ #:~:text=Safety%20concerns%20can%20generally%20be,increasing%20the%20risk%20of%20infection.

Dingguo Zhang, ‘New Horizons 21 - Decoding Speech using Invasive Brain-Computer Interfaces based on Intracranial Brain Signals (dSPEECH)’, Department of Electronic & Electrical Engineering, Centre for Bioengineering & Biomedical Technologies (CBio), 31st December 2024, https://researchportal.bath.ac.uk/en/projects/new-horizons-21-decoding-speech-using-invasivebrain-computer-int#:~:text=BCIs%20may%20use%20invasive%20or,with%20acceptable%20performance%20at%20present.

‘Brain-Computer Interface: No Open Brain Surgery Required,’ CNET, 24th September 2023, https://www.cnet.com/videos/braincomputer-interface-no-open-brain-surgery-required/

43

SHAY PATEL 11C2 AND AARAV RAJPUT 11H2

HYPERLOOP: AN IMPOSSIBLE PROMISE? TECHNOLOGY >

INTRODUCTION I

The ability to achieve this requires pumps to vent out air, and a sealant coating to maintain the vacuum properties. Of course, the pressure differential will exert a huge force on the tube, given by the formula:

t was 12 years ago that the concept of

Hyperloop was published by Elon Musk. The

Here, 𝑃1 = atmospheric pressure (101300Pa), and 𝑃2 = pressure inside the tube (100Pa). It is the

rush for development which followed

circumference of the tube that is equal to the area boundary between the two pressures at any

brought a surge of excitement with the

one point, and with Musk’s specification suggesting the tube’s

thought of travelling over 600mph being an

diameter would be 3.5miv, 𝐴 = 3.5𝜋. Plugging these values back into the force equation:

irresistible prospect. Yet since then, it has found little success beyond passenger testing, bringing about the rise and fall of Hyperloop One and speeds which are struggling to exceed 400mphi. In the public

It is clear the tensile strength of the tube would have to be immense, bringing into question

eye, Hyperloop is an airplane brought down

whether Musk’s initial intentions of a steel tube would be possible, not least without concrete

to the ground. In reality, it is an airplane

reinforcement.

struggling to take off.

HOW DOES THE HYPERLOOP WORK?

T

here are three main components which

go into the construction of Hyperloop: Vacuum Tubes, Magnetic Levitation (Maglev), and Linear Induction Motors. As with any high speed system, the key to success involves minimising energy loss, and for vehicles, this energy loss is a result of air resistance and friction. And of course, there is no better way to eliminate air resistance than simply removing air. Hypothetically. The original publication of Musk’s Hyperloop Alpha paper suggested an internal pressure of 100Pa inside the tube, through which small pods could travel, each one accommodating 28 peopleii. With the tube being almost a vacuum, the air resistance could be decreased by almost 90%iii.

What doesn’t seem a heedless interpretation of science fiction, however, is the use of Maglev technology. Maglev has been well established to function at high speeds with a flawless safety record of 0 fatalities in 60 yearsv. Electromagnetic suspension (EMS) has been used on Japan’s SCMaglev, relying on the simple concept of the repulsion of like poles; the underside of the train has the same pole as the track from which it is levitating. This enables the complete removal of friction between the track and the train. In fact, recent developments have led to the rise of Electrodynamic suspension (EDS), whereby superconducting magnets are used, inducing currents within the track to maintain repulsion and keep the pod floatingvi.

The third key components is reliant on Linear Induction Motors which provide the necessary propulsion. Unlike standard motors, which spin a rotor to generate motion, linear motors use an electromagnetic field to propel the pods forwards in a straight line, relying on tilt angle for steering much like an airplanevii. It is through the careful control of the magnetic forces that smooth acceleration and deceleration can be achieved, ultimately removing the need for wheels, brake pads, and fuel-burning engines.

It is through these theoretical mediums that the promise of hyperloop has been conveyed, with only recent developments by the China Aerospace Science and Industry Corporation (CASIC) showing practical promiseviii. The engineering challenges associated with each element cannot be understated, yet like many prospective commercial projects, it remains the most gatekept from public, giving rise to premature excitement and unjustified predictions.

44

ENGINEERING CHALLENGES OF THE HYPERLOOP

H

Cost and Infrastructure challenges – if safety regulations were to be met and effective designs were developed, Hyperloop would still face large economic challenges. Billions would need to be spent if hundreds

yperloop’s failure lies in the structural challenges that occur in

of kilometres of vacuum- sealed tunnels would need to be built. This

building a safe and effective system. Whilst hypothetically Hyperloop

would be significantly more expensive compared to any other forms of

appears to be a sound idea, its

land travel such as high speed rail. Some companies have proposed

inevitable failure lied within this failure. Below are a few of the

that Hyperloop systems can be made underground or be elevated to

challenges that the

reduce land acquisition costs, however, there will be long and

design faces with some basic proposed solutions, however, many of

expensive legal processes and this could hinder the development of an

these procedures are either too costly or haven’t been discovered.

already stalling idea.

Majority of these solution are hypothetical, thus reflecting why Hyperloop has been unable to succeed as a project.

Maintaining a Low-Pressure Vacuum – a low pressure vacuum is required in order to avoid air resistance and friction, as mentioned above, however in a long tube it is extremely difficult to create a near vacuum environment. As it stands, the longer the tube, the higher difficulty to maintain the environment without regular leaks, hence meaning that long journeys will be very difficultix. In order to attempt to contain these leaks, engineers will need explore advanced sealing technologies that and employ redundant vacuum pumps in order to maintain stability.

Emergency Evacuation Systems – If there is a safety breach or other issues inside the hyperloop pod/tube there will need to be an escape mechanism for passengers. However, these are significantly harder to implement as passengers are in sealed tubes inside sealed pods.

Passengers will be trapped if there is a loss of power or depressurization. Whilst possible safety measure include emergency airlocks, escape pods and side exits at regular intervals, due to the low pressure environment, that is already difficult to make, there will be considerable challenges to implement these without significantly ramping up costs.

Safety at extreme speeds – due to hyperloops proposed speeds of over 600mph, any small obstacle can pose a major issue. Any risks are amplified due to Hyperloop pods being extremely close to the grounds. One major safety concern is that of a tube breach: in the case of this event, air would rush in at supersonic velocities, which can lead to dangerous pressure wavesx. Engineers will need to install emergency pressure equalization systems in order to prevent these dangerous waves and use reinforced tubes to prevent structural

CONCLUSION

failures and avoid the use of emergency systems.

Whilst there may be solutions to structural and engineering problems hyperloop faces, these will require large amounts of innovation to

Passenger comfort due to acceleration – due to high speeds,

develop successfully and vast costs in order to be implemented. These

acceleration and deceleration can cause major discomforts if not

solutions have already been considered but require extensive research

carefully controlled. Sharp turns and sudden stops can exert several

and funding and therefore can’t be carried out. Therefore, despite

G-forces on passengers due to fast changes in motion. Passengers

Hyperloop’s idea appearing to rejuvenate travel by land, in reality it will

will experience high centripetal forces, expressed by:

be extremely difficult to develop in the coming decade and will not be able to take flight without significant scientific breakthroughs in engineering.

BIBLIOGRAPHY i https://www.independent.co.uk/tech/hardt-hyperloop-elon-musk-europe-b2609728.html

where m = mass of the passenger, v = speed of the Hyperloop pod and r

ii https://docs.hardt.global/what-is-hyperloop/hyperloop-product-specifications

= radius of the turnxi. This suggests that if Hyperloop is moving beyond

iv https://www.hyperloopdesign.net/vacuum

supersonic speed, passengers may experience over 5Gxii, similar to that of a fighter jet pilot, and can lead to a loss of consciousness.1 Routes will need to be designed with long, gradual curves alongside smooth deceleration to ensure a comfortable ride without the loss of consciousness.

iii https://eurotube.org/hyperloop/

v https://www.jrailpass.com/blog/maglev-bullet-train vi https://www.techrxiv.org/users/660107/articles/850128-a-reality-check-on-maglev-technology-for-thehyperloop-transportation-system-status-update-after-a-decade-of-development

vii https://www.linearmotiontips.com/why-use-a-linear-induction-motor-and-will-they-come-to-drive-a- hyperlooponeday/#:~:text=Linear%2Dinduction%20motors%20application%3A%20HyperloopCtext=Linear%20inducti on%20motors%20could%20soon,back%20to%20where%20it%20started. viii Faster than a plane: Hyperloop race speeds up as China tests ‘flying train’ system | RailTech.com ix Realities and uncertainties of the hyperloop - PierNext x Hyperloop faces challenges as it attempts to get back on track | ASCE xi Centripetal Force xii How does Virgin Hyperloop work? | Virgin

45

ISAAC COLLIS – 10R2

BIAS OF AI ARTIFICIAL INTELLIGENCE>

INTRODUCTION T he industry surrounding Artificial

Intelligence has boomed over the last

decade, reaching a revenue of $94.41 billion in 2024 ​(Howarth, 2025)​. The efficiency and availability of AI makes it extremely useful in many sectors across the global market, from online chatbots, to patient diagnosis.

However, due to this heavy reliance on AI, it can have severe negative impacts if not used with caution, such as threatening data privacy, and discriminating against minority groups.

FIGURE 1 - COMPARING FALSE TEST RESULTS IN AFRICAN-AMERICAN AND CAUCASIAN COMMUNITIES. (ANGWIN & LARSON, 2016)

THE CAUSE

T

here are a variety of reasons why AI may demonstrate a bias, most notably due to skewed

test data. Artificial Intelligence algorithms are made by using large amount of training data, with

Several courts across America use an Artificial

which it can categorise information (unsupervised learning) and associate characteristics of the

Intelligence algorithm called COMPAS to

data with given answers (supervised learning). However, the creation of data sets which are used to

identify the likelihood of criminals becoming

train AIs may contain an underrepresentation of reality, portraying only one demographic group.

repeat offenders using information such as

This means the AI has a poorer understanding of other groups and produces more inaccurate

criminal records and demographic

results. For example, Amazon used an AI-based hiring tool to help make the recruitment process

information, and the decision of the

more efficient, collecting resumes over a 10-year period to use as test data. However, a large

algorithm plays a large role in deciding the

majority of these came from male applicants, making the AI favour male-oriented language,

punishment for criminals. However, in 2016,

causing it to discard any female candidates. Another example is Google, which teaches its AI with

several reports of inaccuracies in the system

data from thousands of published books. As AIs based their information on human-generated

were identified, resulting in unjust decisions,

data, the bias of humans is replicated in the decision making of AI. Figure 2 shows the

biased against minority groups. For example,

consequences of this, with a case from 2016 where Black people in a Google image search resulted

an investigation by Julia Angwin for the

in pictures of criminals, and White people showing results of people smiling.

ProPublica ​(Angwin & Larson, 2016)​took data from over 7,000 cases and found that African Americans were twice as likely to be falsely labelled as high-risk than White people.

Similar trends have been identified with gender, as Dr Melissa Hamilton found that the COPMAS assessment was half as accurate with women as with men: 25% of women classified as ‘high risk’ re-offended, whereas

FIGURE 2 – LEFT: GOOGLE IMAGE SEARCH OF ‘THREE BLACK TEENAGERS’. RIGHT: GOOGLE IMAGE SEARCH OF ‘THREE WHITE TEENAGERS’. (SINI, 2016)

52% of high-risk men reoffended.

46

Another feature of AI algorithms which make them susceptible to bias is due to a mechanism they use known as ‘positive feedback’. This means they reimplement the feedback it receives from previous outcomes to learn and strengthen its source of knowledge. However, as the morality

SOLUTIONS

O

n a small scale, bias as a concept is essential to the proper

of AI is limited, it could just as easily reinforce previous stereotypes it has functioning of AI, and doesn’t pose any issues, as it allows these made. Since 2018, an AI software called PredPol has been used

algorithms to perform educated guesses based on patterns it has

throughout America to assist the police force in predicting the location

recognised. However, when the bias is against certain groups of people, it

of crime. The algorithm initially started identifying neighbourhoods

becomes a much greater issue. In particular, it may influence court cases

containing Black and Latino people as having the highest chance of

and crime detection, which is an unacceptable ethical flaw in society. As

crime, due to their previous crime rate records from its training data. As

well as this, it may make racist and untrue claims, which can spread

a result, more arrests would happen in this area, as police became more

damaging misinformation. As humans are so reliant on AI, what solutions

focused on that area. This information is then fed back into the system,

are there to deal with the consequences that come with AI effectively?

reinforcing its premade ideas about those areas being more dangerous.

AI software typically operates as a ‘black box’, which refers to the hidden As a result of the AIs attention to those areas, it is less likely to identify

nature in which AIs make decisions. Due to the complex structure of AI

crimes which occur outside those areas, despite being mainly equal in

algorithms and their attempt to imitate the human brain, it is difficult for

number and severity. This loop greatly magnifies any initial bias in the

humans to identify flaws in the logic of an AI model to mitigate any bias

system and plays a significant role in the targeting of minority groups.

out of the system. As a result, many AI models have moved to a more

The Markup and Gizmodo analysed over 5 million predictions made by

‘transparent’ method of implementation (Figure 4) by publishing their

PredPol, and identified that in Indianapolis, Black and Latino

source code online and providing clear documentation. This allows for

neighbourhoods were targeted up to 400% more than neighbourhoods

better accountability and enables users to identify and prevent biases in

which contain mostly White people. For example, two neighbourhoods

AI applications, limiting any discrimination that may otherwise arise. The

in New Jersey, only a mile apart, demonstrated vastly different amounts

importance of transparency is illustrated in the Ethical AI Guidelines of

of crime predictions, simply due to the percentage of White residents in

The Documentary Film Industry in September 2024. ​(The Gaurdian, 2024)​

those areas, as shown in Figure 3 ​(The Markup & Gizmodo, 2021)​ Governments also implement ethical AI guidelines, with the Vatican releasing a set of principles in February 2025 ​(The Independent, 2025)​.

These highlight that AI shouldn’t replace humans altogether, but they should work cooperatively. This is an effective solution as it allows the benefits of AI efficiency and reliability to assist decision making, will allowing human intellect and morality to correct mistakes and misjudgements that AIs make due to unintentional biases.

FIGURE 3 - EACH RED DOT REPRESENTS 100 CRIME PREDICTIONS IN THESE TWO NEIGHBOURHOODS IN NEW JERSEY. (THE MARKUP & GIZMODO, 2021) The causes of bias in AI discussed so far have largely been unintentional, and simply a result of the nature in which AI is developed. However,

While there are many strategies people can implement to reduce the

there have been many scenarios where data in AI has been purposefully issues of bias with AI, it can never be truly erased, and there will always skewed, and algorithms maliciously implemented to favour certain

exist some prejudice. This is due to the inherent nature of AI, relying on

groups of people. A popular example of this was Microsoft’s chatbot Tay, human data which may be distorted, using feedback learning techniques which had to shut down within just 24 hours of its release. It was

that reinforces pre-existing stereotypes, and containing complex

designed to imitate human behaviour and conversations by following

processing algorithms, making it very difficult to implement moral

similar speech patterns to users on Twitter. However, users on these

neutrality. As a result, many people are advocating for the removal of AI

platforms started to repeatedly and deliberately communicate with the

from public service positions, such as law enforcement and healthcare

bot using offensive language, reinforcing this behaviour into the model.

systems, and even for the banning of AI, due to the violations of human

As a result, it began repeating these phrases, making racist and

rights and morality that come with it. The NAACP, an organisation

discriminatory comments.

combatting racism, are greatly concerned about the evidence of AI biases in predictive policing models and are requesting people of authority to regulate its usage ​(NAACP, 2024)​.

47

CONCLUSION

I

BIBLIOGRAPHY

n conclusion, it is evident how the fast-growing nature of AI has

come with the significant issue of poorly implemented algorithms reinforcing biases. This has been supported by various case studies which show the significant impact this has on targeted minority groups.

While various people argue for the banning of AI, I believe a hybrid

Angwin, J. & Larson, J., 2016. Machine Bias. [Online] Available at: https://www.ibm.com/think/topics/shedding-light-on-ai-biaswith-real-world-examples [Accessed 10 February 2025].

solution of AI operated tasks and human intervention is an ideal solution. Artificial Intelligence is vital to the functioning of modern society, and in sectors such as the police force, it can reduce workload by up to 40%. With the identification of biases by humans, the loss of accuracy from AI can be avoided, and with the development of

AP News, 2025. Vatican City offers AI guidelines. [Online] Available at: https://apnews.com/article/vatican-artificial-intelligenceethics-pope-risks-warnings-231b4b7b8ed6a195ec920f1f362c15e2 [Accessed 10 February 2025].

transparent algorithms and law-binding government legislations, many of the issues with AI can be avoided. Furthermore, the time saved on accurate crime predictions, or determining the likelihood of recidivism, humans in public roles can spend more time performing their duties to

Ashe, J., 2020. Algorithmic Bias and Fairness. [Online] Available at: https://www.youtube.com/watch?v=gV0_raKR2UQ [Accessed 10 February 2025].

a greater extent, such as preventing more crimes or evaluating more crime cases.

Grasso, C., 2024. Black Box vs Explainable AI. [Online] Available at: https://blog.dataiku.com/black-box-vs.-explainable-ai [Accessed 10 February 2025].

Howarth, J., 2025. Artificial Intelligence Statistics. [Online] Available at: https://explodingtopics.com/blog/ai-statistics [Accessed 10 February 2025].

NAACP, 2024. Artificial Intelligence in Predictive Policing. [Online] Available at: https://naacp.org/resources/artificial-intelligence-predictivepolicing-issue-brief [Accessed 10 February 2025].

Sini, R., 2016. 'Three black teenagers' Google search. [Online] Available at: https://www.bbc.co.uk/news/world-us-canada-36487495 [Accessed 10 February 2025].

The Gaurdian, 2024. Documentary producers released AI guidelines.

[Online] Available at: https://www.theguardian.com/film/2024/sep/13/documentaryai-guidelines?

[Accessed 10 February 2025].

The Independent, 2025. Pope Francis issues AI warning. [Online] Available at: https://www.independent.co.uk/news/world/europe/aivatican-pope-francis-deepseek-b2688259.html [Accessed 10 February 2025].

The Markup & Gizmodo, 2021. Crime Prediction Software. [Online] Available at: https://themarkup.org/prediction-bias/2021/12/02/crimeprediction-software-promised-to-be-free-of-biases-new-data-shows-itperpetuates-them [Accessed 10 February 2025].

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49

YUNFEI FAN - SFH3

POLYMORPHISM IN MACHINE LEARNING AND OOP: TEACHING ALGORITHMS TO BE CHAMELEONS PROGRAMMING >

INTRODUCTION P

olymorphism is defined as the "provision

of a single interface for entities of different types [1] and it is one of the four pillars of Object Oriented Programming. It can be used in programming to allow objects of different classes to respond to a common function in a different behaviour. For example, the len() function in Python could be used to find the number of elements in an array or the number of characters in a string. As a result, it is polymorphic because len() behaves

After numerous attempts did the AI finally manage to stand up and went on to adapt to various environments such as blocks trying to knock them off or walls forcing them to turn in Figure 2.

different depending on the type of object being passed into it.

FIGURE 1: THE FOUR PILLARS OF OOP Machine Learning is everywhere in the news.

FIGURE 2 : THE AI’S FIRST "STEPS" [2]

FIGURE 3 : THE END PRODUCT

OOP EXAMPLE

It is an algorithm which "trains" Artificial Intelligence to learn from their past experiences and examples. It does this by con- tinuously performing an action (or

As shown in Listing 1, the code defines A series of classes.

receiving training data) and recording which

The first class, animal, is called the parent class. It defines

action is more "successful" than others.

the category that the sub-classes (Dog, Cat, Cow) lies in.

Subsequently, it gains insights from its

The sub-classes are a subset of the parent class. Object-

previous outcomes which means in the

Oriented Programming and its significance in Machine

future their actions become more

Learning. In depth, OOP consists of four core concepts as

sophisticated and ac- curate as they build

shown in Figure 1 :

from their past successful actions and prevent making the same mistakes.

1. Abstraction

For example, a simulation by AI Warehouse

2. Inheritance

taught the AI how to walk by controlling its

3. Encapsulation

limbs. Initially, the AI struggled, where in the

4. Polymorphism

first 100 attempts, the AI could not even stand up and could only crawl in Figure 1(before the rule was set to punish the AI for

LISTING 1: OOP EXAMPLE

touching the ground).

50

CONCEPTS OF OOP

A

bstraction refers to the removal of the unnecessary information

from an algorithm to simplify the programme and hide its complexity.

It could either be data abstraction such as removing information on how data is stored or structured, or process abstraction. Data abstraction could be used to simplify usage of complex data, protect

Also notice how the function start_engine() returns different strings other than the model of the car. For the tesla car, "Electric motor started silently" is output whilst for BMW, the exact same function returns a different string, "Engine started." Therefore, this is OOP as it allows objects of different classes to use the same function with different results. This makes the program much more efficient than the alternative:

data integrity or encapsulate data. Process abstraction refers to when the internal operations of processes are hidden and focusing only on its function or syntax.

Inheritance is defined as the "mechanism that allows a class to inherit properties and behaviours from another class". This means that code can be reused often as well as establishing a hierarchy in code which makes it easier to manage and represents a clear relationship between classes. As demonstrated in Listing 1, classes are defined as shown. The parent class is defined as Animal, whilst sub-classes of the class Animal can be shown as the parent class is contained inside the brackets of the objects Dog, Cat and Cow.

LISTING 3: OOP IN CARS Polymorphism plays a significant role in Machine Learning. As introduced briefly, polymorphism allows functions to react differently depending on different variable types whilst maintaining the same interface. This is an essential principle in object-oriented programming, which "organizes software design around data, or objects, rather than functions and logic".

EXAMPLE : STATISTICAL MODELLING

One example of a machine learning algorithm is statistical modelling. This means the program observes previous patterns on the "training data" used to give the program a sense of the relationship between variables and allow it to make predictions based on these patterns. Some data may require an alternative method depending on its advantages over others.

For example, one of the most simple statistical models is the Linear Regression Model. This model means that data is fitted using a straight line (i.e. with equation y = mx + c), hence assumes that data follows a linear relationship. On the other hand, other methods such as the Random Forest Model combines multiple decision trees from different section of the data before calculating an average of all predictions, which makes it more suitable for non-linear data. In this example, polymorphism would be used to allow different model types to be used interchangeably through a common interface. This design pattern

LISTING 2: OOP IN CARS This is an example of inheritance. "self" represents the instance of the class being used. Notice how the parent class is Car and the sub-classes are GasCars and ElectricCars which fall under the "car" category. As a Gas Car is part of the Car class, the brackets specify which parent class the GasCar inherits from, so brackets are formed around the Car. The parent class defines the default purposes of the functions used for those actions in the context of a generic car

promotes code reusability, modularity, and extensibility, making it easier to experiment with different algorithms.

In a regression Machine Learning model, an abstract class may be used to define the functions needed to operate and manipulate the data. An abstract class refers to a blueprint for other classes that cannot be instantiated, however, can have other classes inherit and extend from its structure. When working with multiple regression methods, the following code will ensure functions can be used for multiple regression methods such as fit() and predict() whilst ensuring they serve a different purpose tailored to the method being used.

51

This is because the method to fit() a Linear Regression would be

In the hidden layer, the function would perform the weighted sums and

different to fitting a Random Forest model. Therefore, this ensures that

the activation function such as sigmoid. The output layer would then

the interface is easier to program and manage, as well as making

convert the final hidden layer output into a prediction.

models easier to use interchangeably.

NEURAL NETWORKS - YET ANOTHER EXAMPLE Polymorphism and OOP are also used in neural networks. A neural network is a computational model inspired by the human brain, used in artificial intelligence to process data and make predictions. Originally proposed by Donald Hebb in 1949, suggested a machine learning technique reminiscent of the operations of how to human brain processed data. It does this by having three distinct layers :

Input Layer Hidden Layer Output Layer

For a neural network aiming to identify images, the input layer would contain the characteristics, or raw data, of the image and passes it on to the next layer. Each node would represent a pixel's raw data such as its RGB value ( a pixel is the smallest identifiable component in an image).

The hidden layer would process the input using weights and biases, then performs an activation function. The activation function allows the model to look at complex patterns in data such as colour gradients and contrasts, outlines which help identify the picture whilst taking 'rules' into account. For example, if it has detected an living mammal, it may detect the image as a face because there would be contextual data stored that helps identify the animal, such as the texture of a zebra, or the facial proportions of a human. The output layer would then converts the final prediction or result into a label.

LISTING 4: BASE CLASS FOR SINGLE LAYER NEURAL NETWORK

BIBLIOGRAPHY Cowan D. Polymorphism Warehouse A. AI Learns to Walk (deep reinforcement learning). 2023.

What is object-oriented programming? Explain OOP in depth.

Vukovic S. What is object-oriented programming? Explain OOP in depth. 2024.

Exploring Inheritance in Object-Oriented Programming. 2024.

Gillis AS. What is Object Oriented Programming? 2023. TechTarget.

FIGURE 4 : NEURAL NETWORK DIAGRAM The NeuralNetwork() is a base class. This acts as the template for all classes to inherit from - i.e., it provides common attributes and functions for all child classes. Polymorphism would then be used so that different layers would have the same interface for functions but

The Power of Polymorphism. 2023 Neural network (machine learning).

Ali M. Introduction to Activation Functions in Neural Networks.

Bostoen J. simple-neural-network Github.

Thorben. Abstraction in Programming: A Beginner’s Guide. 2023.

perform different operations. For example, for the think(self, inputs) function, when acted upon the input layer, it would receive the data and pass it on, possible with data normalisation.

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53

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