Enigma - Computer Science, Physics & Engineering Magazine - Issue 2

ENIGMA Computer Science, Physics & Engineering Magazine

2022/23 issue 2

Extended Research Project Excerpts

The Peculiar Nature of Dark Energy

How do you train Language Models?

Mark I to COBOL: Grace Hopper’s Unsung Legacy

Devyanshu Sinha

Faraz Ahmad

Rohan Vadgama

Committee Introduction Dear Reader, Taking lead of Turing Society from Yash Shah last year, I looked to continue to allow Habs Boys and Girls to develop their passion in Computer Science and Technology. The industry is ever-growing, with innovations every single year that are impacting society daily. The rise of AI chatbots, generative models, the evolution of CRISPR for high cholesterol and the rapid advancements in developing smart cities are just some examples worth mentioning. Turing Society provides a platform to allow such emerging revolutions and discoveries to be discussed, through talks and presentations to debates and quizzes! Habs Turing Society is delighted to present the second edition of the school’s Computer Science, Physics and Engineering magazine: ENIGMA. This edition provides an extensive selection of topics from Quantum Technology to genetic engineering. As of now most industries are heavily intertwined with Computer Science and Technology. Hence no matter which profession or industry you are looking to delve into, I would highly encourage you to appreciate and engage with the flourishing industry of technology and computer science.

ENIGMA would not have been possible without my co-editors, Faraz, Rohan and the Habs Marketing team, as well as the talented Turing Society committee: Salman, Ishani, Suren, Ayza and Margi. Thank you for your contributions to making this magazine as well as it could possibly be. Additionally, a thank you to Mr Franks and Mr Ovia who have guided us and the wider computing society significantly throughout the year.

Lastly, thank you to all contributors of the magazine for your time! It is inspiring to see such a passion for Computer Science and technology within our own student body.

I hope you enjoy reading the second edition of the Computer Science, Physics and Engineering Magazine of the Haberdashers’ Schools, ENIGMA. Yours Sincerely Devyanshu Sinha, Chairman of Turing Society

TuringDash Project: The weekly HabsDash is by no means an easy process, so we looked to automate the sector by implementing an over engineered integrated system that no doubt took far more effort than any other feasible solution using computer vision, body-pose recognition, Raspberry Pi’s, binary encoders, and Excel macros.

Dear Computing Society, I’m thrilled to reflect on the journey we’ve embarked on this year. Exploring emerging tech and industry has been fantastic and our endeavors into this year’s provisions from Turing to Virtual Reality to TEDx and beyond have been nothing short of successful.

We’ve explored Natural Language Processing, Sentiment Analysis, Computer Vision, Proportional Integral Derivative based motion control and even got our hands on some drones. However, its only looking up from there! We’ve got a budget bigger than ever before, invested into new VR technology and are beginning to provide robotics education to local schools. From hardware to software, the TuringDash project was just a start. As such, we are eager to introduce more and more integrated systems and projects for all STEM enthusiasts within Habs!

If you share our passion for technology and look to one day transform the world, feel free to reach out and let us know. As the industry grows ever more expansive, we need bigger and bigger ideas! Although seemingly ambitious, I hope one day some of you can nurture a snippet of inspiration you’ve gained from Habs to shape our world at the forefront of STEM. We need innovators and inventors now more than ever and so I wish you the best of luck for the future.

Best wishes, Faraz Ahmad, Chairman of Habs Computing Societies

Wanting to improve our selfie taking abilities, we turned to technology, implementing a backflipping PID controlled aerobic contraption that (sometimes) tries to attack my face using Python, OpenCV, Haar Cascades, and Facial Recognition.

Contents Page 6

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The Blockchain Revolution

The Peculiar Nature of Dark Energy

Showing referees the red card

By Parsa Zamani (11J2)

By Aaryan Doshi (L6S1)

By Amelie Supran (L5 Aleph)

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Quantum Technology: A Generational Asset For The Future

Are general adversarial networks (GANs) able to emulate human creativity?

By Sohan Patel (9M1))

By Faraz Ahmad (L6R2)

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How do you train Language Models?

By Ravjoth Brar (9M2)

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The Mathematical Fioundations of Quantum Computing

By Devyanshu Sinha (L6M2)

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Beyond the microprocessor: An exploration of microprocessor architecture and performance

By Rohan Vadgama (L6C1)

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What are the effects of social media on society?

By Rohan Shinn (9S1)

From Mark I to COBOL: Grace Hopper and her unsung legacy in Computer Science

By Ishan Visvanath (8C1)

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War Games: How gaming entered the arena of real life

By Eleni Limond (U4 Aleph)

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The positives and negatives of genetic engineering in conservation biology

By Ishan Visvanath (8C1)

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Refereces

It’s incredible to see generative AI’s progress.

Being unable to find a unique photo for the front page, this editions cover is AI generated!

The Blockchain Revolution Parsa Zamani (11J2) Possibly the greatest leap forwards in the development of man was the invention of currency.

We went from trading products to buying items.

Being able to buy anything we wanted with money meant that individuals could specialise. Rather than needing to provide all their own food by trading and farming, workers could now provide services, grow a specific crop and focus on one job. With the money earnt from their jobs, workers could buy all their necessities and feed their families. The culmination of the results of this revolution led to our economy today, where most employees provide services, such as medicine or entertainment, or

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work in research. The food that we all consume and essentials that we require are provided by a small number of multi-national corporations, meaning that everyone else can work jobs that either maintain our current state as humans or seek to improve it. Day by day our lives improve, be it from new technologies or new scientific discoveries, but at the dawn of the third millennium we might once again be at the feet of a new revolution: that of blockchain and crypto currencies.

What is a Blockchain?

A blockchain is a database or ledger that is distributed and shared among the computers in a network. Unlike a typical database that stores data in tables, blockchain stores data in blocks.

When a block is filled up, a new block is created and is joined to it like a chain. This means that an irreversible timeline of data is created, and each block is given a timestamp of when it was added to the chain. Effectively a blockchain allows data to be recorded and shared, but it cannot be edited. Since its innovation it has been used for the creation of cryptocurrencies to be able to produce records of transaction that cannot be deleted, destroyed or altered.

The process of transaction is such: a new transaction is entered and then sent to the network of computers around the world. The network is called ‘peer-topeer’ because each computer is used as a node for sharing files within the group. The network of computer than solves equation to make sure that the transaction was valid. Once the transaction has been validified it can then be stored in a block.

The block is added to the chain, completing the transaction and making it permanent (Figure 1).

This process makes a cryptocurrency trustworthy, secure, fast and cheap, giving it an intrinsic value as a form of money.

in multiple locations. Whereas in a traditional database or even a bank, all the information can easily be destroyed or changed by a single action, in blockchain, any changes in a single node are cross-refenced with all the other nodes, so any alterations are easily found and corrected. For information to be altered, 51% or more of the nodes have to have the same information changed on them, a task which is almost impossible considering the immense number of devices that are part of the blockchain. Even if a hacker managed to change over half the nodes, users would realise the drastic change to the chain and change to a new version of the blockchain without the changes , plummeting the value of the hacked chain. Therefore, taking part in the network is far more economically incentivised than attacking it.

Cryptocurrency Although cryptocurrencies can be linked to some illicit activities and users are hard to track down by governments the myriad of benefits it provides for user are immeasurable. Perhaps the most profound benefit, is allowing the 1.7 billion adults who do not have access to a bank account due to living in a less economically developed country. Many people in these countries which rely solely on cash who are at risk of attacks over money will be free of the constant threat of unnecessary violence by using a cryptocurrency rather than cash.

With many practical applications of blockchain already being implemented in areas other than cryptocurrency, we can look forward to a future of completely online transactions which are secure and trustworthy. With transactions cost reduced to near nothing alongside the ability to make them at any time without limits, we can ensure a new future of trade which may upturn the way we work and live together when coupled with the rise of online work and automated technology.

Figure 1: Simplified Blockchain Architecture Another important feature of crypto is the fact that it is decentralised. This means that rather than all the data being stored in one location it is stored

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Quantum Technology: A Generational Asset For The Future

By Sohan Patel (9M1) Quantum technology is a type of technology that uses quantum mechanical principles like entanglement, superposition and tunnelling. This is a futuristic idea, however right now, huge companies are developing this technology including Microsoft, IBM and Google. Over the recent years, this idea has grown significantly in popularity. This is because it provides humanity with many benefits that we have never seen before and can help us grow into our future and help us solve upcoming problems.

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Examples of quantum technology include: •

Quantum Computers

•

Simulators

•

Sensors

Quantum Computers Classical (current) computers rely on bits with values of either 0 or 1. Quantum computers will use quantum bits, also known as qubits which can have both 0 and 1 at the same time, which is known as superposition. This allows Quantum Computers

to be in more than one state at a time. Explained mathematically, a quantum system can be in two states: State A or State B. The superposition allows both States A and B to coexist simultaneously.

This then allows information to be processed more efficiently than classical computing, which can only have one state at a time. Quantum superposition can also allow functions to be executed faster which will be particularly useful for several businesses and industries in the future as it will allow productivity to increase. Recently, there has been a computational experiment with quantum computers. Chinese and Singaporean physicists have solved mathematical equations like the one below:

3x + y = 2 x + 2y = 0 The simultaneous equation above can be done in a few minutes by hand however quantum computers currently are able to generate answers in less than seconds. The same will be done with more complex equations that will help us understand the world around us, in the future. They will also reduce research costs which will make time for more experiments, whilst reducing the need for animal and human tests. This is unseen in classical computers, which shows us the advancement humanity has made and will make in the future. Developers have said that complex algorithms will help solve supply chain logistics, finance, data analysis and weather forecasting. This will make human life easier in the long term whilst creating new jobs.

The Physics behind Quantum Technology Quantum Mechanics is the basis of quantum technology. Its main principles are influential for quantum computing:

complex math grows significantly.

Quantum Superposition: Superposition is beneficial for the development of this technology because it allows it to be more powerful and be capable of more things.

Quantum Tunelling: Quantum tunnelling in general is described as the ability for a particle to potential energy barrier that is higher than the particles kinetic energy. Particles in quantum mechanics behave as a wave, the wave diminishes as it reaches a barrier however there is still a possibility for part of the wave to pass through the barrier. This phenomenon is easier explained by the following:

Figure: Tunnelling allows Quantum Tech to complete functions that classical technology won’t be able to complete.

Qubits must be particles with quantum properties such as atoms and ions. These are manipulated using things like magnetic fields which change the state of the particle which enables them to perform different functions. When a calculation is performed on a computer, different operations are performed on the qubit. Overall, the manipulation of particles allows us to break the confines of modern-day physics.

Quantum entanglement: The link between particles or quantum objects. The link can be strong even if very far apart (Ghosh 2018).

Entanglement is key for computing at this level because it can be used to make calculations faster and easier for computers. As you entangle more qubits together, the ability of the computer to do ENIGMA: Computer Science, Physics & Engineering Magazine |

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The Application of Quantum Technology Quantum technology isn’t just a futuristic idea, as it is being used right now. For example, in medicine Magnetic Resonance Imaging (MRI) scans use quantum technology to perform their function which is to use radio waves to see inside images of the body to diagnose people. MRI scans use quantum mechanic’s properties of manipulating particles with magnetic fields. It uses strong magnetic fields to create strong signals which is used for MRI.

1.5 trillion times faster than a classical computer.

However, it won’t only revolutionise technology, but jobs and employment. They will open a whole new field of research and jobs, bring investment to businesses and help the economy.

There are many more areas of life sciences where quantum technology will be beneficial for. For example, Quantum Computing will make drug discovery and design much easier and more optimal. This will benefit humanity vastly. However, it can also help make new medicines and products.

Challenges That being said, there lay many challenges for the future of quantum computing. The term ‘decoherence’ is used to describe the loss of information from a system into the environment. Qubits are sensitive to their surrounding environment. As such, small technicalities can cause them to lose some of their quantum properties which means they are susceptible to decoherence.

Also, quantum computers need to be held in cool environments, in fact they are mostly operated at -273°C, this is because heat can cause errors in qubits.

This means that they are expensive as they are difficult to keep in a functioning state. This is why all the big companies are in demand for them: if not many companies can afford them, they will be able to get ahead of others and be more productive.

Why it Matters Quantum computers will matter a lot for the future of technology. They will create technology unlike any other and will boost our lifestyle a lot in terms of health whilst helping us understand the physical world around us through the calculations of complex equations. They are also extremely fast, in fact

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Figure: The estimated impact on computational efficiency that quantum computing can bring

The quantum computer at Chalmers with the outer shielding of the dilution refrigerator removed.

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How do you train Language Models?

By Ravjoth Brar (9M2) Language models are used all over the world from ChatGPT to hiring applications. To fully understand how they work, we need to discover how the Artificial Intelligence inside the language model is developed. Before I begin to discuss how one would train a language model, let me define Artificial Intelligence which is predominantly used in language models. Artificial Intelligence (AI) is defined by the Encyclopaedia Britannica as, “The ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings”. And AI is most commonly implemented in language models. The first step to train a language model is to find or gather data. This is becoming increasingly difficult as the majority of data available on the Internet, sadly, may be in

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the form of toxic thoughtless tweets or messages.

This proposed training data would negatively affect the language model as the outputs could convey these toxic and potentially stereotypical messages. Also, this situation could be worsened by the public then posting these results (which has become more common in recent years) on social media to gain attention. This is then caught up by another language model further perpetuating current stereotypes which has the potential to become an endless loop. An upcoming issue for language models in the future is us running out of data to train these language models on. To counter this, Swayamdipta (2022) says that, researchers have theorised that it may be possible to train a language model several times using the same data.

This would fix the problem and would not result in our personal data being used in language models.

Companies are trying to save money and get better representation in their data by using synthetic data to train their AI models. However, so far, these experiments have not been successful. Next, you must tokenise your training data. This is a crucial step in Natural Language Processing (NLP), which enables the AI to understand the human language in the training data. There are multiple different ways you could go about this. The most common method is tokenizing data based on a space. The space acts as a delimiter and separates the words.

For example, the sentence “I am happy”, would be tokenised to “I-am-happy”. On the other hand, you could tokenise based on the characters themselves.

For example, the sentence “I am happy” would then be tokenised into “I-a-m-h-a-p-p-y”. This process must occur to allow the AI to form a vocabulary out of said tokens so that it can interpret the training data. Alternatively, to speed up the process the AI can form a list of the most frequently occurring words/letters depending on which tokenisation method you are implementing.

Afterwards, you must choose the infrastructure you want the language model to be based on. There are three main types of infrastructures for language models. The first is an Encoder-only model. In an Encoder-only model, you must know exactly how many numbers/words you want your language model to produce as an output. Due to this, an Encoder-only model doesn’t need a huge amount of parameters (a quantitative representation of how much space a language model will take) thus reducing its cost. The second type of models is a Decoder-only model. In a Decoder-only model, the output can be vastly different lengths and can produce coherent text-based answers. However, it cannot infer and look forward. The original GPT released in 2018 was a Decoder-only model and had 150 million parameters. Lastly, there is an Encoder-Decoder model which takes in the input prompt passing it through an encoder to create a context vector which represents the language model’s understanding of what the prompt is asking.

From there, the decoder takes this context vector and uses it to come up with a response to it. This

is greatly beneficial as it keeps the decoder’s ability to have answers with different lengths but drastically reduces the inefficiency with the encoder.

Nowadays most language models such as the latest ChatGPT-3, Microsoft Bing and Google Bard all use a form of an Encoder-Decoder model. The main downside to Encoder-Decoder models is that, as the input is passed through an encoder to create a context vector, long input sequences can be difficult to process. Also, Encoder-Decoder models use a lot of parameters thus becoming more expensive to establish it due to having to pay more to have more parameters. After choosing a model you must train the language model by letting it process through all of the training data. While the model is being trained, the model learns to predict the next word in a sequence given the previous words. This is how it begins to formulate responses. These predictions can be measured in many ways, the most common being perplexity and BLEU (BiLingual Evaluation Understudy). Perplexity measures the average log likelihood of the test set normalised by the number of words. The average log likelihood is how good the model fits into a dataset and the number of words is a factor as the model must be taught how to be concise and still convey a clear message thus a lower perplexity score means better performance.

BLEU measures the similarities between the predicted translation and the referenced translation.

A good BLEU score means that your encoder is working very well as the context vector is similar to the referenced translation of the prompt. Lastly, the language model is ready to be deployed into the market once it has gone through the relevant EU rules and regulations.

Figure: Diagram showing the EU’s process of authenticating AI systems before they can be publicly accessed

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The Mathematical Foundations of Quantum Computing

By Devyanshu Sinha (L6M2) Introduction The circuit model in the context of classical computation consists of applying logic operators to bit sequences, which allows us to carry out algorithms. These may then provide solutions to problems ranging from straightforward sorting to combinatorics and graph traversal. There are some problems that may have a higher degree of time complexity, which indicates that finding a solution will take more time.

Computers can solve two different types of problems: those that can be resolved in time O(nk) or faster, and those that can only be resolved in time O(kn) or slower. These classes are referred to as the P class and NP class respectively. For clarity, O can be represented here as “in proportion to.” O(nk) refers to polynomial time, where the time taken to obtain a solution increases at a polynomial rate as the input size (or you could say problem complexity) increases, contrasting with O(kn) which refers to exponential time, where the growth rate of the time against input size is exponential. Figure 1 portrays the patterns of time complexity between different classes of problems computers solve. Two relevant problems are factorising

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and searching. Factoring falls under the NP class since it can be solved classically in sub exponential time. This is as when the number of digits in prime numbers increase, the time it takes to factorise the prime number increases at a near exponential rate.

As an example, encryption uses large semiprimes (which are the product of two prime numbers) to protect data because they are too challenging for conventional computers to factorise. Large here refers to a number with more than 100 digits. For comparison, factoring an RSA-250, a semiprime with a length of 250 digits used in data encryption, would take 2700 years using the number field sieve algorithm, the most effective classical approach for factorization. Searching through data has a speed of O(n), where n refers to each piece of data.

Searching therefore falls into the P class.

Classical computers are digital information processing machines that manipulate information or data. However, they can only take in information

Figure 1: Graph representing the time complexity patterns for different classes of problems

through bits. A bit is a single digit of the binary number system and, Chris Bernhardt (2019) mused (p.15): “the basic unit of classical computing.” Binary is a number system with base 2, meaning that a bit can only be represented with the number 1 or 0, or in two different states. This means that the manipulation of bits is limited to a few logic gates. However, quantum computers operate differently to classical computers as they exploit the rules of quantum mechanics. What differentiates quantum computing from classical computing is that quantum computers can take in information in the form of quantum bits, otherwise known as qubits.

In the book Quantum Computing: A Gentle Introduction, Reffiel and Polak, (2011) mused (p.9) noted: “Quantum bits are the fundamental units of information in quantum information processing much the same way that bits are the fundamental units of information for classical processing.” An example which can demonstrate how a qubit works is an electron. All electrons have magnetic fields, placing an electron in a magnetic field will cause the electron to align with that field. This is similar to how a compass needle lines with the magnetic field of the Earth. As the lowest amount of energy is needed to obtain this movement, this is the lowest energy state which can be called spin down. You can also rotate the electron so that it is facing directly opposite the magnetic field. This is otherwise known as spin up. However, as you need a lot of energy in order to rotate the electron so that its magnetic field is opposite the magnetic field it is in, spin up is regarded as a highest energy state. Returning to the compass analogy, if you take out the glass cover of the needle and manually twisted the needle till it faced opposite to where it was facing, significant force is required to perform the action causing a lot of energy to be used up. This is why spin up is regarded as the highest energy state.

So far the qubit is a lot like the classical bit. It consists of two states, spin up and spin down, which can correlate to the 1 and 0 of the classical bit. However, what fully separates the qubit from the classical bit is that the qubit can be in any proportions of both states at once. This is as the electron is spinning in all directions at once, instead of just up and down. This phenomenon is

Figure 2: Bloch Sphere representation of a qubit

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called superposition. However, when you proceed to measure the value of the qubit, it will be in either of two states: 1 (spin up) or 0 (spin down) which are identical to a bit. Another analogy to explain superposition is flipping a coin to obtain the ‘heads’ sign or the ‘tails’ sign. When you flip a coin it can either land on the ‘heads’ side or the ‘tails’ side. However, when the coin is in the air it is flipping and spinning in different directions till it lands. The duration of time when the coin is in the air could symbolise the qubit before it is measured as it is in a proportion of landing on either side, and when the coin has landed on a surface that could demonstrate the qubit when it is being measured. If there are 4 classical bits, they can be in one of 2^4 configurations at a time. However, due to the property of superposition the qubits obtain, if there are 4 qubits then they could be in all 16 different combinations at once. This number can grow exponentially with each extra qubit. The expression to demonstrate the number of configurations the qubits can attain and hence how much more power qubits can gain when compared to classical bits is 2^n, where n is the number of qubits. This expression demonstrates the power of quantum computing as if there are only 300 qubits, the power of those 300 qubits will be equivalent to 2^300 classical bits.

Another peculiar property qubits obtain is called entanglement. Entanglement is a close connection between two qubits that causes each of the qubits to react to a change in the other’s state instantaneously, no matter how great the distance is between the two qubits themselves. As an example, if you measure one qubit and it appears as a spin up, the other qubit entangled to it will automatically appear as a spin down when you go to measure it. Due to entanglement, when you measure one entangled qubit you can directly deduce the properties of the other entangled qubit without any measurement of that qubit.

The result is that by using specific quantum algorithms, several problems can be solved with better time complexity than the most efficient classical algorithms.

The implementation of Vectors and Matrices within Quantum Computing Vectors are used to describe quantum states in the context of quantum computation. As previously demonstrated, the Bloch sphere is a helpful tool for visualising this. A qubit in the Bloch sphere (Figure 3) is initialised to |0>. On the Bloch sphere, different vectors will correspond to different points, but some of the most significant vectors are shown in Figure 4.

Figure 3: Bloch Sphere representation of a qubit initialised to |0>

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Figure 4: Important vectors on the Bloch sphere

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Measuring a qubit is the process whereby the state of a qubit is observed to be either |0> or |1> (which is analogous to a bit being 0 or 1). A qubit is measured when its state is observed to be either |0> or |1>, which is equivalent to a bit being either 0 or 1 (or ON or OFF). When a quantum circuit’s output needs to be extracted, measurement is required. The qubit may be in a non-deterministic (random) state during this extraction which is not a definite |0> or |1>, but it must “collapse” to one of the two states mentioned in order to be measured. When a state collapses, there is a probability that the state could end up as |0> or |1>.

Matrices are the next essential component of quantum computation. This is as quantum gates act on qubits as linear transformations, which allows them to be represented as matrices. The unitary matrix is the most significant type of matrix. Unitary matrices make up all quantum gates. For a matrix A, A is unitary if A-1= A+.

In other words, this means that the inverse of matrix A must be equal to its conjugate transpose (this can also be written as AH). Figure 5 illustrates this property using an example.

Figure 5: Example displaying the property of unitary matrices The importance of unitary matrices becomes visible when they are applied to vectors existing in a particular vector space (which means a set in which such vectors exist) called a Hilbert space. The Hilbert space is a vector space over the field ℂ, and a special operation called the inner product exists within Hilbert spaces (Portugal, 2019). An inner product is the conjugate transpose of a vector |a>, multiplied by a vector |b>. This returns a scalar as shown in Figure 6.

Figure 6: Example demonstrating the inner product Normalisation is a condition which all vectors within the Hilbert Space must follow. The condition is that for a vector, the inner product of itself must be equal to 1. This is demonstrated in Figure 7.

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Figure 7: Example illustrating normalisation

Basic Quantum Gates and Their Circuits It is now possible to comprehend the significance of normalisation and unitary matrices in relation to quantum processing. A quantum circuit uses quantum logic gates to conduct operations on qubits in the same manner that a classical logic circuit uses logic gates to perform operations on binary values.

Different unitary matrices are represented by these quantum logic gates. Importantly, a qubit obeying the normalisation constraint will still maintain its normalised state when a unitary matrix is applied to it.

A simple circuit using the Pauli X gate, a frequently used gate, is illustrated in Figure 8. When a qubit (q) is introduced into the circuit, a Pauli-X gate (X) is applied to it. When this happens, the qubit’s state is measured and collapses (whenever a measurement takes place, the symbol shown in the black square is used). The measurement’s outcome correlates to a certain output bit, in this case the bit 0.

Figure 8: Quantum circuit which uses the Pauli X Gate

This transformation can be seen on the Bloch Spheres in Figure 9. Take a qubit with a starting value of |0>.

The Pauli-X Gate’s unitary matrix is . The vector displayed on the Bloch sphere reverses direction when it is applied to the qubit. As a result, it is possible to conceptualise quantum processing (with a single qubit) as a series of transformations that involve rotations around the Bloch sphere. For example, the |0> vector, changes with a single transformation of the X gate to become the |1> vector, as illustrated by Figure 9.

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Figure 9: Quantum circuit which uses the Pauli X Gate

With a classical bit, measuring |0> always responds in an output of 0, and the same is true for |1> and 1.

However, there are very few situations where deterministic operations would be carried out on a quantum computer. As a result, there are also very few circuits, like the one described above, where a qubit would only ever be in the |0> or |1> state.

A qubit will frequently be in a state that is a superposition of the |0> or |1> basis vectors in order to execute more complicated tasks. Consequently, the qubit can be defined as follows: where x and y are non-zero and contained within . When a qubit is measured whilst it is in superposition, it collapses to either 0 or 1. The probability of it collapsing to either state is given by: p(|x>) = |<x|a>|2 , where x = 0, or x = 1. Figure 10 shows a Bloch sphere where a different gate known as the Hadamard gate (or H gate for short) with the matrix has been applied to |0>.

Figure 10: Qubit transformation from Hadamard Gate

After applying the H gate, the qubit can now be described by the superposition . Using this equation, the probabilities of getting either 0 or 1 is 0.5. The Bloch sphere could be used again to intuitively grasp this concept. The qubit is in a state which is equidistant from both basis vectors after going through the H gate, hence the 50/50 chance of the qubit being 1 or 0 when measured. The Hadamard gate transforms |0> to |+>, and |1> to |-> (for which the vector is ).

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Such superpositions are very significant in quantum circuits and are referred to as “uniform superpositions”.

A uniform superposition is a superposition where the probability of measuring a 1 and the probability of measuring a 0 is equal.

Two other important gates which have not been explicitly mentioned in this article are the Pauli Y and Pauli Z gates, which are represented by the matrices:

On the Bloch sphere, both gates can be visualised as 180° rotations about each of their respective axes.

Implementation of multiple qubits and their property of entanglement The tensor product is used to represent the combined state of a circuit’s multiple qubits, as follows:

The equations above are valid ways of writing the collective state of the qubits. The four 4-dimensional vectors |00>, |01>, |10> and |11> (which are the tensor products of the 0 and 1 basis vectors) are very frequently used for this purpose due to their ability of forming an orthonormal basis.

The dimensions of the matrix are made consistent in a circuit like the one in Figure 5 by taking the tensor product of all the matrices acting on all the qubits at any given time. In this case, this would be the same as taking the tensor product of the identity matrix and the unitary matrix for the Hadamard gate (because the qubit is unchanged). However, in this case, the states can be addressed independently since they are not yet entangled and there are no multi-qubit gates: |00> → |+0>.

Figure 11: Quantum circuit with qubits not entangled

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However, circuits will often use gates which require multiple qubits for an input. The most important gate for two qubits is the controlled gates. These are gates which apply a matrix (and hence transformation) to a target qubit only if the value of another control qubit is 1. As an example, Figure 12 demonstrates the controlled Pauli X (CNOT) gate (a commonly used gate in quantum circuits involving multiple qubits) and how its 4x4 matrix is generated. As shown in the bottom right illustration of Figure 12, when the control qubit is 0, both qubits stay the same. However, when the control qubit is 1, the transformation causes the control qubit to stay one whilst simultaneously also causing the target qubit to switch states. Every possible qubit combination in this case is put along the rows and columns of the matrix, and whenever the qubits in the column corresponds to the qubits along the row, the intersection is given a 1, with all other intersections which incorrectly transform the qubits given a 0. This causes the formation of the CNOT 4x4 matrix.

Figure 12: CNOT gate

When the value of the control qubit is |0> or |1> , the gates operate as predicted. However, if the control qubit is in superposition, there may be a different effect. Consider the following circuit (Figure 13) where the smaller blue dot serves as the control and the blue circle with a cross as the target:

Figure 13: Quantum circuit with the control qubit in susperposition

The collective state of the qubits would be |0 + > following the application of the Hadamard gate. However, after this, q0 is in superposition whilst serving as q1’s control qubit as part of the CNOT. The result of applying the CNOT is the new state . This is a state that cannot be expressed as the tensor product of two other vectors. As a result, the state is entangled and there is a 50% chance of measuring either one of |00> or |11>. Alongside, when either of the qubits are measured, the states of both qubits collapse.

Therefore, if q0 is measured as |0>, it is clear that q1 also has a value of |0>. The same is true for measuring |1> for both qubits. However, it is not possible for the final state of the qubits to be different (i.e., |0> and |1>) in this specific circuit.

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ERP Excerpts The Extended Research Project (ERP) forms a cornerstone of the Habs diploma, tailored to Year 12 students. Over the course of the year, students are granted an opportunity to immerse themselves in an area of expertise that they are significantly passionate about, allowing them to express their critical thinking and analytical skills through a piece of academia that exceeds the curriculum.

The following excerpts are intended to spark inspiration for your own STEM related endeavours and other ideas that go beyond the classroom.

The Peculiar Nature of Dark Energy

By Aarvan Doshi (L6S1)

Blurb: The ΛCDM (cosmological constant cold dark matter) cosmological model has experienced great success over the past few decades. As part of my project I evaluate this cosmological model and analyse its predictions about the future evolution of our universe. At first glance, its predictions seem to entail a universe that asymptotically reaches a static state, however, upon unravelling the weird and wonderful quantum mechanical origin of dark energy, we are able to uncover the truth about the universe’s evolution.

In the following extract I talk about the predictions that the ΛCDM model makes, and what it actually means for this dark energy to “borrow” energy from the vacuum of space itself.

The cosmological constant, Λ was first added by Einstein to his field equations of general relativity in order to achieve a static universe – it acts as an anti-gravity force, used to prevent the universe from collapsing in on itself. Upon Hubble’s discovery of the expanding universe, Λ was eventually scrapped until the late 20th century, when redshift measurements proved the universe’s accelerated expansion. A mysterious force labeled “Dark Energy” guaranteed a positive cosmological constant.

To understand the ramifications of this so-called “dark energy”, we must consider the density parameter, Ω, a value used to calculate the curvature of spacetime. The density parameter can be calculated through the relationship:

Where H and G are the Hubble and Gravitational constants respectively. The equation above can be simplified to a ratio of the universe’s average energy density (ρ) to the critical energy density (ρcritical), that is the mass-energy required for a flat universe, whereby: If Ω = 1, the universe is flat ρ = ρcritical If Ω > 1, the universe has positive curvature ρ > ρcritical If Ω < 1, the universe has negative curvature ρ < ρcritical

Figure 1: Graph of ΩΛ against ΩM

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The diagonal line on the graph above shows the relationship ΩΛ (the density parameter from the cosmological constant) + ΩM (the density parameter from regular matter) = 1. The circle represents the value of ΩΛ and ΩM based on current observations. It centers around the point (0.3,0.7), giving ΩTotal to be approximately 1, suggesting that the universe is flat.

In order to see what is really going on, we must take a closer look at the true nature of dark energy through the second Friedmann equation:

Where ä is the expansion rate, a is the expansion scale factor, ρ is density and p is pressure. We can see that in order for the expansion to be outward, must be positive. Just like normal matter, the energy density of dark energy must be positive, hence we can deduce that the pressure, p of dark energy must be negative in order to cancel out the initial negative.

This concept of negative pressure may seem ambiguous – just as a positive pressure would require work to compress, a negative pressure would require work to expand. Therefore, from the Friedmann equations, we can infer that as the universe expands, more dark energy is created as work is done on the universe, hence it must be true that the later universe will be substantially dominated by this dark energy as the normal matter dilutes. In other words, the density parameter from the cosmological constant is constant over time. However, as normal matter dilutes, its density parameter decreases.

Analyzing Figure 1, we see that over time the point corresponding to our current observations (0.3,0.7) would move to the left. The universe will have negative curvature. Such a universe would perpetually expand, and it closely resembles a De Sitter Space - De Sitter space is a maximally symmetric solution to Einstein’s field equations in general relativity that describes a universe with a positive cosmological constant, driven by dark energy. We have now fulfilled the first condition – a perpetually expanding universe.

It is now important to clarify what actually causes the cosmological constant. The most successful contender is vacuum energy, which can be described as quantum fluctuations that arise from Heisenberg’s uncertainty principle, which states:

Where is uncertainty in position and is uncertainty in momentum and ħ is the reduced Planck’s constant. To understand this concept better, we must understand the basics of quantum mechanics and the wave function, ψ. Quantum mechanics posits that before measurement, a particle exhibits a wave-like form called the wave function. This wave function can be used to calculate certain properties of its corresponding particle, like its momentum and location, however, there will always be an uncertainty in either variable. Wavelength determines momentum according to De Broglie’s formula .

Figure 2 shows how the superposition of multiple waves of different wavelengths forms a wave function with a more definite (localised) position as the square of the wave function determines the probability distribution of the position of the particle.

However, this precision in position comes at the expense of momentum as a range of different wavelengths are used to form the final wave function, demonstrating the reason behind Heisenberg’s uncertainty principle.

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Figure 2: Superposition of wave function Mathematically, we would say that momentum space wave function (probability distribution for measuring specific momenta) is the Fourier transform of the position space wave function (probability distribution for measuring specific position). This is represented through the equation:

Where and are the momentum and position space wave functions respectively. The variable k is proportional to -ikx momentum. e represents a Fourier wave and is an application of Euler’s identity (eio = cos(0) + i sin(0)). The integral shows that we are taking the superposition of all Fourier waves to form our final probability distribution.

Examine the following position space wave function:

Figure 3: Simplified position space wave function

We can see that a given particle has an equal probability of being found at any point between -a and a and a 0 probability of being found anywhere else. The height, is so that the total area under the wave function is 1. Now we can use the Fourier transform equation. and are constants, hence are ignored in this simplification. The limits become -a and a as all other probabilities are 0. The integral becomes:

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Using Euler’s identity and plugging in limits, this can be simplified to . If a is sufficiently small (i.e. we are very certain about the position of the particle), we can use a small angle approximation (sin(ka) = ka). The k cancels and we are left with a constant 2a (multiplied by another constant, C, related to those that we ignored previously). The momentum space wave function is now simply a non-zero horizontal line, . In other words, k (hence momentum) can take a large range of values (there is a large uncertainty in k) as the probability distribution is even across a large range of values for k. Note that we cannot measure an infinite range of values for momentum as the small angle approximation is not exact (the line is not perfectly horizontal – it will eventually go down to 0). This is Heisenberg’s uncertainty principle – by increasing our certainty in one variable, we have decreased our certainty in another.

The same reasoning applies to an energy-time uncertainty principle, which goes as:

Therefore, it is possible for something to be created from nothing in empty space, so long as this increase in energy annihilates back into nothing in a time that is so short that it is immeasurable. Hence, the uncertainty principle is still obeyed.

These are known as vacuum fluctuations, and it’s thought that these fluctuations, which give rise to vacuum energy, are what drive the universe’s expansion - Vacuum energy is dark energy. This makes sense – as the vacuum expands, more vacuum fluctuations occur. This lines up with the constant energy density of dark energy.

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Are General Adversarial Networks (GANs) able to emulate human creativity?

By Faraz Ahmad (L6R2)

Blurb: The implementation of emerging technologies in various forms of artificial image synthesis highlights the prospect of such generative models. In particular, in my ERP, I discuss and implement a form of generative models knows as Generative Adversarial Networks whilst evaluating their performance alongside their successors in the sector of AI generated imagery. The objective of the network was to train an image generator capable of fabricating realistic looking images that were entirely synthetic.

The snippets below introduce the topic of GAN’s and evaluates the output of a Deep Convolutional GAN to illustrate generative AI’s power in emulating human creativity.

Introduction The introduction of generative models at the turn of the century offered a means of generating synthetic data through replicating an underlying pattern within a given training dataset. The utility of generative models appeared in their ability to generate unique images that were both realistic yet diverse in comparison to their training data, beneficial in the fields of data augmentation and imputation. A specific generative model, the Generative Adversarial Network (GAN), conceptualised

by Goodfellow et al. (2014), consists of 2 competing “adversarial” neural networks. The objective of the network was to train an image generator capable of fabricating realistic looking yet entirely synthetic images.

The GAN Architecture In the context of GANs, a preliminary understanding of the networks goal would be wise: the ‘generator’ is tasked with creating synthetic data that is indistinguishable from real data, whilst a secondary ’discriminator’ network attempts to distinguish this generated data from real data.

Game Theory: Declaring the game Goodfellow et al. (2014) presents GANs through a competitive environment which is best analysed through game theory.

GANs compete in a two-player zero-sum minimax game such that the success of one network directly contributes to the other sides loss. Strategically in such games, each player should make moves that minimises the advantage given to the other side – similar to chess.

The generator is evaluated through the metric of fooling the discriminator. Therefore, should the generator create an image that the discriminator cannot distinguish from real images, the generator has succeeded in its role. Subsequently, due to the nature of the game, the discriminator has lost. However, should the discriminator differentiate the fake image, the generator has lost.

Both networks compete against each other in this “adversarial” manner, allowing them to learn over time until a successful generator is formed.

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The Discriminator For ease of interpretability, the following terms have been abbreviated:

Real and generated images are both input into D() and are consequently classified. D(G(z)) and D(x) therefore represent the outputted classification of images from the discriminator. In the context of GANs, a perfect discriminator is defined as one that is able to independently classify D(G(z))=0 and D(x)=1.

An overview of this whole process for a perfect discriminator model is as follows:

Figure 1: Ideal discriminator outcome (authors own)

The generator tries to fool the discriminator and therefore anticipates the discriminator incorrectly classifying synthetic data (Figure 2). Since the generator is unable to directly manipulate D(x), this aspect is disregarded.

Figure 2: Ideal generator outcome (author’s own)

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The Generator Creating Images The generator functions through creating a set of features on an image that are most likely to fool the discriminator.

Conceptually, this is through experimenting with the location, textures and colours of shapes, which allows the generator to effectively map inputs into a realistic output image.

Defining a sample space Assuming a hypothetical series of training images parameterised by height, h, width, w, and colour channels, r, an n-dimensional vector space can be created, denoted Vi, where n=hwr. By extension, any image with matching dimensionality can be represented in this sample space through a vector of its pixel’s location and colour (Kana, 2020). Over time, G() is required to shape an input into an image that aligns with the underlying distribution and complexity of training data.

Consequently, the generated image should match Vi’s dimensionality.

A point plotted in Vi therefore represents an image, with each dimension’s value representative of certain features (i.e. a pixel’s colour). A simplified 2-dimensional representation of this concept is illustrated:

2D representation of sample space vi (author’s own)

The position of each point, P, within Vi affects the arbitrarily generated output as illustrated below, for some features X and Y (Figure 3). Intrinsically, upscaling Vi’s dimensionality will lead to more complex generated features.

Figure 3: Output image from vi as position P varies for some feature X and Y

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Training a Model A GAN can be implemented utilising the MNIST dataset (Figure 4) to illustrate its functionality. First introduced by Lecun et al. (1988), the MNIST dataset consists of 60,000 pre-processed images of handwritten digits, ranging from 0 to 9.

Importantly, it allows for fast training times as the images possess low spatial dimensions of 28x28 (WxH) with 1 colour channel. Therefore, in this implementation, vi is defined as a 28x28x1 dimensional vector space.

Figure 4: MNIST Dataset Visualisation (author’s own)

Deep Convolutional GAN Radford, Metz and Chintala (2016) introduce convolutional layers within a highly successful variation of GANs, subsequently forming the Deep Convolutional GAN (DCGAN). Alone, these convolutional layers capture hierarchical representations of training images to increase the network’s ability to learn more complex representations. As such, we will utilise their guidelines in forming a stable GAN, with the purpose of emulating the high-fidelity and diversity of outputs represented by the MNIST dataset above.

Both networks will be trained independently over a set number of epochs. Initially the discriminators parameters are frozen, and the generator is trained. After which, the discriminator is unfrozen and trained whilst the generator is frozen.

This adversarial training is passed back-and-forth until both networks have met an optimum level of development, signified through the stabilisation and convergence of the model.

Discriminator Architecture: DCGAN They advised an architecture that utilises a series of convolutions to reduce the spatial dimensions of an inputted image to a singular prediction output from a sigmoid function. The discriminator is a probabilistic classifier, such that the range of the networks final output must be between 0-1, that is to say a probability. Therefore, a sigmoid function is utilised where more positive inputs tend to 1 and more negative inputs tend to 0. Whilst detrimental in hidden layers, these asymptotes help squash outliers, leading to more confident predictions. With this in mind, an alternative discriminator network can be created, based off the principles of convolutional layers.

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Representation of Features In the discriminator, layered convolutions act as feature detectors, capable of capturing a hierarchical representation of images. This includes both low-level features such as edges and shapes (Figure 5) alongside high level features such as intricate details (Figure 6) to determine the discriminators classification.

Figure 5: Examples of low-level features (Nanos, 2023)

Figure 6: Examples of high-level features (Nanos, 2023)

Generator Architecture The generator expands the spatial dimensions of a noise vector input (1x1) to a 28x28 pixel output in one colour channel to match vi’s distribution.

For this, transposed convolutions are utilised, functioning as the inverse of a standard convolution. The filters within transposed convolutions learn to create patterns resembling the target data distribution, acting as feature generators.

These collectively contribute to the formation of highly realistic images through capturing a hierarchal representation of training data over time.

DCGAN Evolution The DCGAN generator provides great success emulating human capabilities. Through hierarchical representations, the benefits of convolutions are evident, ultimately leading towards higher-fidelity outputs proven by the 79% success rate in fooling an actual person. Alongside, due the representational capacity of the network being utilised far more effectively, mode collapse is alleviated through more stable training.

DCGAN Generated Samples

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The DCGAN was trained through 100 epochs, however, due to such network advantages, training was much more effective.

Should the model converge in local extrema, the generator may prioritise a limited number of outputs, as illustrated by the high-fidelity but low-diversity outputs in the Vanilla GAN. Yet, by the 10th epoch, largely coherent yet noiseless images were being generated via the DCGAN (Figure 7). Through the convolutional representation of features, modal collapse in generated samples was avoided, allowing for diverse explorations of the underlying data distribution, best visualised as follows:

Figure 7: Visualisation of early training stages of vanilla GAN vs DCGAN (author’s own)

Emulating Complex Human Creativity To convey the potential of the DCGAN, a similar architecture was trained off the CIFAR10 Dataset, consisting of higher resolution 32x32x3 coloured images. In which, the generator performed adequately in comparison to the training data (Figure 8), illustrated through a high recall and some identifiable objects.

Figure 8: Direct comparison of real (Left) vs Generated Data (Right) from the CIFAR10 Dataset (author’s own)

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However, the majority of photos remain unclassifiable, with some outputs resembling mere abstract shapes. As proven, to generate even marginally higher resolution images, more innovative approaches are required.

That being said, artificial image synthesis, resources availability, and architectural developments have come a long way since the DCGAN. Such advancements have subsequently allowed for the generation of far more impressive visual content.

Notably, via learning thematic relationships between captioned training images, Open AI’s DALL-E 2 can create conditionally generated images based off a user’s input:

Intuitively, as larger corporations support and encourage the developments of generative models, we can continue to anticipate the emergence of higher-resolution generator outputs.

For instance, Stability AI’s Stable Diffusion model utilised 32 servers, each equipped with 8 A100 GPUs capable of storing up to 40GB of RAM each. Alongside, the combined total training time of each GPU amounted to an incredible 150,000 hours. In comparison to the DCGAN implementation which utilised a peak of 5.5GB of RAM in 3 hours, these networks possess unfathomably large representational capabilities.

Conclusion Current architectures excel at replicating patterns within training data but lack the conceptual understanding and emotional depth that humans can express through a creative means. However, all forms of art, whether generated or real, ultimately stem from human capabilities. As such, to expect models to create art even slightly beyond the scope of human capability would be impossible, requiring the generation of images that surpass humanities collective understanding. Therefore, whilst future innovations may anticipate further advancement, as the threshold of utility for generative models is approached, computational and conceptual limitations will certainly arise. Regardless, these models illustrate incredible potential, be it in augmenting or merely complementing human creativity. Yet, they may unfortunately never serve as a comprehensive substitute for the depth of human artistic expression.

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Beyond the Microprocessor: An exploration of microprocessor architecture and performance

By Rohan Vadgama (L6C1)

Blurb: The microprocessor is one of the greatest engineering and computing revolutions of the last 50 years, bringing us into the current era of mass computing. Yet, as we continue to seek for ways to further performance, the challenges facing their development only seem to become greater. My ERP addresses these issues and also explores alternative computing methods, such as supercomputing and massively parallel computing, cloud computing as well as quantum computing.

The extract below delves into the architecture and performance of our current chips, as well as highlighting those aforementioned issues that will no-doubt face their continued evolution.

Architectures One of the key benefits of microprocessors is that they allow for all the main processing parts in the “decode” and “execute” parts of the von Neumann processor cycle to be physically close together, reducing the time for these instructions to be sent and hence increasing power efficiency (due to a lower energy loss) and performance (while also allowing for a smaller package size to be used in more portable devices) This revolution really began with the release of the Intel 4004, the first programmable chip. However, it could only execute 8-bit instructions (Furber, 2017), and due to the bottleneck of the 4-bit data bus, these instructions would take two cycles to load. Furthermore, the 4004 did not actually use a traditional von Neumann architecture like most modern machines. It had a separate memory space for instructions and data, unlike the von Neumann model where they would be stored in the same memory. This was more akin to the Harvard architecture (Kann, 2021), one which in its original form is generally not used in modern microprocessors (they tend to be much closer to the von Neumann architecture).

Memory can only be accessed once per clock cycle and the Harvard. Architecture has two distinct memories for data and instruction which can be used in the same cycle. As such, this architecture does have certain advantages, such as the ability to execute instructions in one clock cycle, as opposed to two cycles for the von Neumann architecture. However, Harvard is not exactly efficient, as it requires the memory to permanently be split into two physical portions for instructions and data.

If there is not exactly the same ratio of instructions to data as there is physical memory space available to each, there will be some inevitable wasted memory; unlike with the von Neumann architecture, which is more “dynamic” in designating the amount of memory required for data and instructions. Not only this, but the splitting of memory into “instruction” and “data” sections means that there are now twice as many memory addresses to keep track of, which could potentially decrease the processor’s efficiency.

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Instruction Sets However, there is not simply the distinction between computer architectures (von Neumann and the Harvard architecture), but also between the “instruction sets” of the microprocessor. There have fundamentally been two main types – CISC (Complex Instruction Set Computers) and RISC (Reduced Instruction Set Computers). The main purpose of the CISC instruction set is to complete a task as efficiently as possible (in terms of machine code length), with the fewest lines of assembly language (Chen, Novick and Shimano, 2000) by building hardware that is capable of running “complex” or specialised instructions.

These are designed to meet the requirements of a high-level programming language which are closer in style to regular English, and generally easier for a programmer to digest. For example, one to perform multiplication, which the hardware would come prepared with, and would avoid the need for the programmer to call any load/store functions in assembly language and also reduces the work needed for the compiler to translate high level instructions into binary.

In turn, this helps to increase the “code density” (Blem, Menon and Sankaralingam, 2013), i.e. the ratio between the instructions themselves and the processes that they perform. On the other hand, RISC computers use only simple instructions that can be executed in one processor clock cycle (Langfield and Duddell, 2019). This reduces the physical complexity of the chip and hence the transistor count required (a transistor is essentially a tiny “switch”, used to build logic gates to perform calculations, cache and registers), reducing the required hardware space and allowing more room for general purpose registers. While it may seem as though it is less efficient, each instruction only takes one clock cycle - and so it will take a similar amount of time as to execute the multi-clock cycle instruction on a CISC system. Furthermore, the time taken for a RISC program to execute will be proportional to the number of instructions, allowing this time to be estimated, however this is not possible on CISC due to the varying instruction complexities (some may use more clock cycles than others).

A more real-world application of these two instruction sets can be seen with ARM (Advanced RISC Machines) and x86 processors – the former of which is based on RISC while the latter is based on CISC. Generally, the reduced complexity of the ARM chip allows it to be more efficient at the same performance level, and we have seen exceptional results through one of the breakthrough pioneers of this technology, Apple. In the article, “Apple’s M1 Pro, M1 Max SoCs Investigated: New Performance and Efficiency Heights” (Frumusanu, 2021), we see a very detailed dive into the performance vs energy efficiency of the recent, ARM based Apple M1 Max chip compared to a similarly-specced competing system with an Intel i9-11980HK (CPU). One of the clearest and most representative examples of this is shown in Frumusanu’s use of the Cinebench23 multi-threaded test, where the M1 delivered a score of 12375 while using 34W, while the i9 had a slightly higher score of 12830 but used 82.6W, amounting to more than double the power consumption.

Moore’s Law and the challenges facing development While the industry’s shift towards ARM processors does represent a big turning point in microprocessor history, the potential for future evolution is not as clear-cut as it may seem, linking to another key concept – Moore’s Law, which was proposed in 1965 by Gordon E. Moore. In general, increasing the transistor count on a microprocessor will allow for more clock cycles and hence increase its performance (El-Aawar, 2015). Moore’s law suggests that the transistor count roughly doubles every two years, often through the reduction in the “process node” used (which represents the pitch between two corresponding transistors) (LaPedus, M, 2018). This allows for more transistors and performance in the same area (increasing transistor density), all while reducing the latency of data transfer between them, which therefore increases performance efficiency.

Although this exponential growth has largely held true over the years, many have commented on how this rate may not be as fast nowadays, especially as Burg, D and Ausubel, J (2021) allude to in their research article. One of the key avenues towards this ideal exponential growth in transistor count comes from shrinking the physical transistor size (associated with the process node), yet as examined with Intel processors over the last 20 years, the rate of transistor size reduction has severely slowed down, as shown in the graph to the left, to an almost linear rate.

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Burg and Ausubel, 2021

Rupp and Selberherr, 2010

One of the key reasons for this reduction in exponential growth comes from the economic issues with shrinking a process node down even further (Rupp and Selberherr, 2010). Not only may it cost the chip producers more to advance to a new processor generation (through research and development of new fabrication methods) but also the manufacturing costs required to actually create the new fabrication facilities designed for the new node are greater, alongside the increased costs to produce and test each chip. Rupp and Selberherr (2010) shows another very clear, exponential trend between costs to maintain the fabrication facilities over time. As the costs to advance new chips increases at such a fast rate, this makes it simply not economically feasible for the exponential growth in performance through Moore’s law to co-exist, as else the fabrication costs will simply outweigh the profit gained from selling the chips to consumers, and operating at a loss would naturally not allow for the necessary resources required for Research and Development and hence further enhancement.

Not only are there the economic issues that come with shrinking a node down further, but also the physical issues that come with the miniaturisation of transistors, demonstrated clearly through the major thermal limits encountered (Krishnan et al., 2007). Flipping a bit or changing the state of information, represented by a transistor, will lead to an exceedingly small amount of energy dissipation.

This is demonstrated by the Landauer principle (Bormashekno, 2019), derived from the laws of thermodynamics, which states the minimum energy release necessary for this is equal to kBTln2, where kB is Boltzmann’s constant and T is the temperature of the environment around it. The principle has been proven experimentally in multiple contexts, as demonstrated in the article “Experimental test of Landauer’s principle in single-bit operations on nanomagnetic memory bits” (Hong et al., 2016), where a memory bit represented by a nanomagnet had its state flipped, and a laser probe was used to measure an amount of energy that was released. Increasing transistor density will, therefore, increase the energy output of a chip per unit area, which requires more cooling to maintain – as high heat will cause a processor to thermal throttle and reduce its performance in an attempt to cool down. Unless new methods of cooling are produced, we may reach a point where cooling the chips requires more energy than powering the processor itself. Furthermore, there is the mere fact that we are approaching a hard limit in terms of physical transistor sizes, as the largest atoms have a diameter of 1nm, while many modern process nodes such as TSMC’s latest 5nm are seemingly dangerously close. However, the “5nm” term is more of a marketing one to refer to an improvement over the past generation, while the true “contacted gate pitch” (being the distance from one transistor’s gate to another) for “5nm” advertised chips is 51nm (IEEE, 2021). Despite this, the physical size barrier may still become a major issue in the near future, and hence raises the question of what potential future advancements microprocessors can have – or even of potential alternatives.

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End of ERP Excerpts

What are the Effects of Social Media on Society?

By Rohan Shinn (9S1) Social media has had a profound impact on society in the past decade. With the advent of platforms such as Snapchat, Twitter and TikTok, people are now able to connect with one another and share content on a scale that was previously unimaginable.

Multiple studies have found that there is a strong link between heavy social media and health. Some of the ways social media can physically impact your health are irregular sleep, eyesight and bad posture.

However, mental health is more affected by social media than physical health. Some of the ways social media can negatively impact your mental health are depression, anxiety, and even suicidal thoughts. Surveys show that 61% of men and 50%

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of women are or have been depressed as a result of heavy social media use. Feelings of inadequacy about your life and especially your appearance are also common as a result of using social media in excess. These feelings occur as our brains create an environment in which we are constantly comparing ourselves to others. Our brains also pay remarkably close attention to ourselves and compare to what we see, so we fit in socially. In fact, a study by the University of Pennsylvania found that people who use social media excessively are a lot less likely to produce innovative ideas. On the other hand, social media used in proportion positively affects mental health due to its increased connectivity. It can ease stress, anxiety and depression, boost self-worth, provide comfort and joy, prevent loneliness and

even add years to your life. One of the most significant effects of social media on society is the way it has changed the way we communicate. In the past, people would communicate primarily through face-to-face interactions or through phone calls and emails. However, with the proliferation of social media platforms, people are now able to communicate instantly with one another from anywhere in the world. This has had a number of consequences, both positive and negative.

The Positives On the positive side, social media has made it easier for people to stay in touch with one another, even if they are physically far apart. It has also made it easier for people to form and maintain relationships and network easily, as people can keep in touch with one another through messages, photos and videos. Another way this increased connectivity has been used is how communities of like-minded people sharing similar interests have been created.

It helps many people to find their voice and share their expertise, making the world a more diverse place, with more opinions and views found on social media. It can also help people to grow or advertise their businesses.

The Negatives On the other hand, social media has largely contributed to the spread of misinformation. With the ability to share information instantly with a large audience, it is easy for false or misleading information to spread quickly. This can have serious consequences, as people may make decisions based on misinformation, leading to harmful consequences. For example, misinformation about COVID-19 has led to some people refusing to wear masks or get vaccinated, which has contributed to the spread of the disease.

Another negative effect of social media is that many teens say social media lacks emotional connection, gives people a license to be hurtful, diminishes faceto-face interaction skills and is a large distraction from their goals. Many feel social media gives people a license to do and say whatever they want as they cannot be punished in any way for it. Many people now prefer to communicate through social media rather than in person, which has led to a decrease in the number of face-to-face interactions

people have daily. This has had a few negative consequences, including a decline in social skills, an increase in social isolation, and a decrease in the quality of relationships. Another negative effect of social media on society is the way it has contributed to the erosion of privacy. With the ability to share information instantly, it is easy for people to overshare personal information on social media.

This can lead to a loss of privacy, as people’s personal information is made available to a large audience. Additionally, social media companies often collect and use people’s personal information for targeted advertising, which can be a violation of privacy.

A final negative effect of social media on society is the way it has contributed to the increase in cyberbullying. With the ability to communicate anonymously, it is easy for people to harass and bully others online. This can have grave consequences, including depression, anxiety and even suicide.

Concluding Remarks In conclusion, social media is a powerful tool that has had a significant impact on society in the past decade. While it has made it easier for people to connect and share information, build communities of like-minded people, and grow their businesses, it has also had several negative effects, including a decline in face-to-face interactions as people prefer to talk online, the spread of misinformation, the erosion of privacy as people feel safer sharing things behind a screen, not fully grasping the consequences of sharing personal information to strangers. It also negatively impacts people’s behaviours, making them more narcissistic, lazy and violent. This can be very dangerous when people attempt dangerous “trends” and has led to children dying by attempting things like the “milk crate challenge” or “the blackout challenge.” Social media acts as a large distraction to teens now, this is because of social media cues like notifications drawing individuals’ attention away from the task they originally pursued. Due to mobile access to social media these distractions can occur frequently. Surveys have found that in the U.S., 95% of teenagers own smartphones and, in the UK, 72% of 14-year old’s and 96% of people aged 16-24 own smartphones. This means most of the population are constantly distracted by their smartphones, leading to a decline in mental health, face-to-face interactions and innovative ideas across the world.

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Showing Referees The Red Card

By Amelie Supran (L5 Aleph) The Video Assistant Referee in football, or VAR, is a technology that allows refereeing assistants to examine video playback of an incidence in a match as soon as it occurs and advise the referee on their decision. Since the 2018 World Cup, VAR has established itself in football leagues across the world and footballers, managers and fans tend to have strong opinions on whether they are supporters of its use or against it.

The role of technology to make decisions in sport isn’t just limited to football. Tennis has Hawkeye (we’ve all heard the beep when the ball skims the top of the net), rugby, cricket and American football all use equipment to detect what referees can’t see, haven’t seen or can’t be certain about. For this article, I am going to concentrate on football

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and predict how VAR may just be the start of the ‘beautiful game’ fundamentally changing how it’s played in the future due to technological innovation.

A Sky Sports survey revealed that 67% of football fans believe VAR makes watching football less enjoyable. Former Liverpool striker Stan Collymore said that it is ‘taking the spontaneity out of the game’.

Other criticisms include that referees rely too much on the technology, the flow of the game is disrupted and sometimes VAR gets it wrong. However, there are also a lot of benefits. The referee can make more informed decisions and avoid controversy, there is greater player discipline and there is a feeling of excitement for fans when they are waiting on the VAR feedback. With VAR here to stay, could

this be the start of technology taking on more of a refereeing role, perhaps to such an extent that we won’t need human referees at all?

The first change we could see is the linesman being replaced, probably with technology similar to that used in tennis. When the equipment detects an infraction, for example off-side, the referee will be alerted and they will take the appropriate action.

This would happen in real-time so causing no delay to the game or the need to use VAR. With the advancement of robotics, the next step would be when the main human referees are replaced by androids on the pitch. Their ability to move faster than a human means they will always be in the right position, the technology will enable them to make decisions with marginal error, the rules will be installed in their software, they can replay video instantly, their computation of an incident can be projected onto the screen in the stadium for transparency, and they will be hard for players to argue with!

With the addition of AI, the androids can have records of each footballer, learn their style of play and history as players, contributing to their decision making. They can also communicate with the other referees – the linesman – so their decisions are made as a collective in seconds. And instead of a red card, they can project a red light around the player who has received the penalty – much more dramatic! Can you also imagine watching the game through the android referee’s eyes as what they are ‘seeing’ could be televised and fans would feel they are on the pitch during a game. This would be a truly immersive viewing experience.

Figure: Computer vision aided AI system for real time performance analysis

If we take this further, there may come a time when even an android isn’t needed. The pitch itself could become ‘intelligent’ and sense what is happening and with the help of 100’s of precision cameras around a stadium, a central system will be able to assess everything going on in a game and make choices about play. The pitch itself will referee the game! There will probably need to be a loud speaker telling players what is happening and if they refuse to follow the directives, the system can penalise the offending team.

Of course, no technology is perfect, and the risk of hacking into the system could be a real issue.

If a person managed to manipulate the software to favour their team, this could cause major concerns.

Fans that were against non-human referees might try and break the machinery as a protest against the technology that has replaced real people. There is the chance that the hardware can break mid-game creating a problem for the rest of the match. It also needs to be considered that the technology would probably be expensive and this might mean poorer clubs across the world won’t be able to implement these new advancements.

If this new technology became popular in football maybe other sports would copy. Boxing matches could have androids that could withstand being punched and you’d no longer need the judges, and diving contests would be marked by intelligent processors. Could we be heading for a future Olympics without one human referee or judge?

Technology in refereeing sport helps make more accurate decisions that are objective and without the possibility of human error. However, what will be interesting is that even with the many positives for fairness in a competition using innovation, would fans ever accept there being limited or no human input? Would viewers of sports want their teams to be ‘ruled’ by technology no matter that it may be better at the job, then a human? How far will people want their sports to be changed as technology advances? My feeling is that if this was to ever happen, it will need to be a very slow process if fans were to accept a new way, so in the near future our human referees will still be the ones handing out the red cards.

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Grace Hopper

From Mark I to COBOL: Grace Hopper and Her Unsung Legacy in Computer Science

By Ishan Visvanath (8C1) In the vast landscape of computer science, we often credit major discoveries and inventions directly with their inventors, or those who are publicly associated with their steps to bring an idea to launch. However, it is often those working tirelessly on their own or in the background, whose contributions transcend into time and revolutionise the future of the field. In this article, I will discuss an example of one of these people: Grace Hopper, a female mathematician, computer scientist and naval officer. With her pioneering spirit and unmatched dedication, she was able to break-through substantial gender boundaries to leave a mark on the industry which

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she dedicated countless decades of her life towards. In this article, I will discuss some of the most significant feats across her life’s journey, and how it plays a part in the world that we live in today.

For one, during the midst of the second world war from 1939-1944, during some of her first years in the US navy, Grace Hopper played a significant role in building and co-developing the Mark I computer: An electromagnetic powered, sequence calculator which could work out complex mathematical equations, such as calculating the trajectory of missiles, which proved crucial in the war effort against the Nazis. What made the Mark I so

significant was its potential in data analysis, being able to read and interpret data inputted from hole punched cards, which forever simplified the process of feeding data into a computer. Most of all, it laid the foundation for computational concepts that could be harnessed into data-understanding abilities. Over time, as computing technology became even more advanced and powerful, Grace’s invention led directly to the founding of innovative data analysis software, which took the idea of the Mark I even further by producing valuable insights and being able to uncover patterns and trends within data. In the modern day, this is evident in various industries, including Financial tools which can analyse market trends over time to Supply Chain tools which are able to track inventory and monitor logistics for different companies. These examples alone represent the great value in Grace’s discoveries and how even early in her career she was able to harness her position to create an influence on the world that we live in today. Funnily enough, the term for ‘debugging code’ was in fact invented by Hopper around this period of time, whilst she was building a developed version of the Mark I computer, when a moth got stuck inside a slot which caused a malfunction!

Another significant milestone in Grace Hopper’s career took the form of her invention of the first ever compiler, known as the A-O system. In simple terms, the compiler allowed computer programs to write the code for their machines in complex computer languages, instead of low-level machine code, and then translate the code for them so the machine could understand it. The compiler was particularly unique because before it was developed, computer programmers had to write code in a different coding language directly into the machine which was extremely tedious and time consuming. As a result of her invention, this issue was majorly eradicated because programmers could write their code in coding languages that they understood without worrying about making it compatible with machine specifics which could take months, if not years. Even today, the impact of this invention can still be seen all around us, with software development such as mobile apps and complex software systems such as Windows or MacOS only being made possible by the

compiler which is able to translate the programming language into one which is recognisable for a phone or a laptop.

Nonetheless, despite Grace Hopper’s high-level work with both the Mark I set of machines as well as the invention of the compiler, today, she is most known for her development of the Common Business-Oriented Language (COBOL) in the late 1950’s. COBOL was invented whilst Hopper worked at the Harvard University Computation lab and was a high-level programming language that was especially made for the average person who worked or was from a non-tech background. Mainly for applications in the business industry, Hopper and her team of support staff created COBOL to resemble everyday English so it would be simple for anybody to learn and use it to code. In fact, COBOL was one of the world’s most significant tech developments of all time because it bridged the gap between tech and non-tech working people to open up the world of programming to a much wider audience. As a result, this helped to significantly elevate businesses’ use of technology and computer science because it helped them to handle large-scale data processes and businessoriented applications such as financial transactions without deeply experienced coders in their ranks.

Today, the effects of this programming language can be seen all across the world in businesses and enterprises which hire basic developers and teach them to program on COBOL based systems thus helping to enhance their daily operations. As well as this, COBOL systems have long been utilised by government agencies and departments who can count on the language’s stability and reliability to be the foundation of different applications such as tax systems and identity management.

In conclusion, Grace Hopper’s vast impact on Computer Science reaches far beyond the constraints of the industry itself, and has shaped the world that we live in. Through her work on the Mark I, the invention of the compiler, and the development of COBOL, she continues to inspire a new generation of scientists and researchers to take her findings even further and to fight against the odds to follow their passions.

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War Games: How Gaming Entered the Arena of Real-life Conflict

By Eleni Limond (U4 Aleph) ‘The relationship between Western citizens and war is characterised today by physical distance and virtual intimacy” - Kaempf and Renic, 2022 We live in a polarised world. Technology, which brings us closer and made many things easier, has also made us more vulnerable to manipulation and societal destabilisation, from spread of fake news to dependency on tech for our basic existence. And it has drastically changed the way war plays out.

Propaganda is nothing new, but now we have more powerful and persuasive platforms, one of which is digital gaming. This is why we should take a closer look at gaming’s influence - that of one of the most popular forms, ‘First-Person Shooters’ (FPS).

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Video gaming has come a long way from its origins in William Higinbotham’s ‘Tennis for Two’ in 1958.

Photo-realistic worlds can be realised through use of world-creation development environments such as Epic’s Unreal Engine, engaging and shaping the consumer’s consciousness. The launch of ‘Atomic Heart’ by Mundfish brought a new dimension to the conflict in Ukraine. Backed by Russian entrepreneurs, its release on 21 February this year was greeted mostly with enthusiasm by gamers, who love the expansive world which pushed the

earlier iteration of Unreal Engine 4 to its limits, but with concern by the Ukrainian government, who couldn’t fail to notice the significance of the release date exactly one year after the Russian President Vladimir Putin signed a decree recognising two eastern Ukrainian regions of Donetsk and Luhansk as independent entities, kickstarting the war.

is projected to reach US$ 384.90bn in 2023, and to continue growing by more than 7% a year. Popular media has always been a place of propaganda.

Video games are ‘a contested political space in which governments and corporations, journalists and activists, and players of every stripe, are competing to tell stories and shape perceptions about the world…playing an increasingly important role in shaping the world-view of its participants and the politics of their societies.’

The notion of the “military-entertainment complex” originated in Hollywood, where movies and TV productions are ‘contractually supervised by the DoD Entertainment Media Unit within the Office of the Secretary of Defense at the Pentagon.’ There’s going to be bias, and the ‘American’ view of the world propagated across the English-speaking world.

Figure: Atomic Heart Game Cover As a classic FPS equipped with impressive world creation, Atomic Heart is yet another game in a popular genre, shot through with scripting that at times is just awkward. The game takes place in an alternative historical setting of 1955, where the Soviet Union, after winning WW2 early, following earlier discovery of advanced polymers, becomes the world’s first superpower and centre of scientific innovation. The studio denies claims that a connection exists between the company and the Russian government adding that “we do not comment on politics”, yet the game glorifies communist soviet symbols and contains robotic ballerinas seemingly modelled on the former Ukrainian president, Yulia Tymoshenko. However it is hard to avoid the political significance during a time of attempted annexation and ‘Sovietisation’ of a sovereign country. Will raising concerns and attempting to dissuade people from buying the game pump up its popularity?

Gaming is a multi-billion-dollar industry and its influence is significant. The Video Games segment

Games have been used by the US army for recruitment ‘America’s Army’ is a series of FPS developed and published by the U.S. Army, to inform, educate and recruit prospective soldiers.

Equally the Chinese People’s Liberation Army developed a non-public version of ‘Glorious Mission’ for a similar purpose. The storyline focuses on contended Senkaku Islands, northeast of Taiwan and administered by Japan, but claimed by China.

These FPS games are rooted in real-world conflict and are therefore political.

Figure: Glorius Mission Game Cover Conflicts which are in recent living memory may be insensitively handled and add to political tensions. ‘Six days in Fallujah’ is a game based on an American-led offensive in the Iraq war in 2004 following US marines. Key to the criticism is that it ENIGMA: Computer Science, Physics & Engineering Magazine |

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takes a real-life battle, which raised questions around the validity of American armed forces being there due to the now discredited reason of ‘weapons of mass destruction’, and possible use of White Phosphorus, and makes this into entertainment. It places the player on the American side and sees all others as targets – polarising it into ‘them and us’.

“I am disgusted that this is something that will be producing profit when people like me suffered the consequences of this war and will have to watch people play it for fun,” Abdulelah, 28, told CNN. “I just can’t get past the inhumanity.”

FPS need adversaries, and in a fantasy setting this can be pure entertainment (if you like shooting stuff) but in rendering real-life conflicts into games this begins to become problematic, as it is an informed public which (in democracies) hold governments to account. Instead, games can polarise opinion, possibly resulting in more hardline attitudes.

Games can rewrite the ‘Rules of Engagement’ which define lawful engagement and appropriate use of force and instead become a place where the player does what they must to survive, killing as many opponents as possible. Highwire Games and publisher Victura, creators of the game, say it is misunderstood, and that they’re merely using gameplay – the way players interact with a video game – to teach history. The game takes the player straight into urban warfare, based upon the reallife testimony of those who fought. But, is it simply reinforcing Islamophobic narratives?

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Within games, especially FPS, but also within gaming communities, gaming has become political to the point that Europol’s (2021: 90) EU Terrorism Situation and Trend Report, noted ‘the use of video games, gaming platforms and forums, as well as gamer channels (as instrumental in) spreading rightwing extremist propaganda has been a growing trend.’

Is there a better way to create FPS games which builds more consensus, offering different perspectives and roles, or leave conflict to the realms of fantasy and historical battles outside of living memory? Or will we continue to see more games like Atomic Heart, which immerse the player in alternative realities, directed by those with the money and leverage to shape public opinion?

Still from video game ‘Atomic Heart’

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The Positive and Negatives of Genetic Engineering in Conservation Biology

By Ishan Visvanath (8C1) In the modern day, the delicate balance of organisms and their ecosystems is becoming more crucial than ever, with the species extinction rate 1,000-10,000 times larger than the healthy natural rate. After desperate attempts from scientists to slow down the causes of these adverse environmental happenings, such as nuclear power, and CCS (Carbon Capture and Storage) technology which aims to capture wasted carbon and store it underground, conservation biology is in need for an organic and renewable method to provide organisms with the nutrients and minerals required to protect themselves from predators and sustain

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life, where natural methods are not always possible.

This possibility comes in the form of genetic engineering, a phenomenon which is the process of altering an organism’s genetic composition in order to change its structural, physiological and behavioural characteristics. In this article, I will outline the possibilities of genetic engineering to conserve populations and ecosystems, and go in depth behind its positives and negatives.

For one, genetic engineering presents the possibility of protecting the agricultural industry which is being threatened by the impacts of climate change in the future. With the rate of global warming since 1981

twice as fast as the natural rate per decade, the issue of climate change is becoming more pressing than ever. In the long-term, this will lead to an increasing number of extreme weather patterns, such as droughts, floods and storms, which will thus contribute to a significant plateau in crop and agricultural yields all across the world due to environmental instability. This would be a massive issue, since it will lead to a substantial increase in food insecurity, which will affect economic and social levels across the world, particularly in countries which grow these crops, over Asia, Africa and some areas of Europe. For instance, in India alone, the agricultural industry represents about 18% of the country’s GDP, employing 151 million people, which is approximately 33% of all employed Indian citizens. As a result, genetic engineering is becoming one of the only methods to protect these organisms, by arming them with the essential minerals and DNA needed to protect them against diseases, tolerate harsh weather conditions and yield higher crops with less resources. Potentially, this could be one of the biggest advantages of the phenomena, which is already proven with humans, through various vaccines and medication which can fight different physiological issues inside the body.

Nonetheless, one of the biggest problems we have with genetic engineering are the ethical issues which are raised. This is because, in a sense, the entire premise is to inject foreign bodies and substances into an organism to alter its DNA, which controls the way an animal or plant behaves. This issue has been brought up by various natural protection groups, such as the RSPCA, who have raised multiple statements going against new legislation on genetic technology, discussing how it is a step back from equality and equal rights for all living beings. One of their main points is that there is no way to accurately test the technology and medicine for genetic tests, so using them on animals could produce some dangerous side-effects that we aren’t prepared for. This comes in light of recent news on animal testing, which results in over 1 million animal deaths and severe injuries in the US alone, every single year, through forms of medical training, and unproven chemical and drug tests.

On the contrary, genetic engineering provides a more full-proof and efficient way to selectively breed

organisms, which involves choosing two organisms of a species with desirable characteristics to mate, to pass these desirable characteristics on to their offspring. The advantage of this form of breeding is that providing organisms with these traits, such as strength, or speed will help them to live a longer life, which thus increases the life of their own offspring and in turn helps to strengthen the survival rate of an entire species. However, the downfall of this method is that it is simply not possible to control which traits and characteristics will be passed down, and often the exaggerated traits of an organism’s parents can lead to a variety of life-long mental and physical issues in the child such as heart disease, cataracts and cancers. An example of this is the substantial hearing problems in dalmatian dogs, 30% of which have been affected in some way by deafness, as a result of many years of selectively breeding children.

Evaluating the positives and negatives of selective breeding, we can help to boost the positives, and eliminate some of the major negatives through the use of genetic engineering.

This is because, with the characteristics of organisms enshrined in their DNA, changing their DNA makeup through Genetic engineering will allow us to directly control the characteristics which are being passed down, and to the extent that these are present. This is extremely significant, especially when dealing with endangered species, since we can provide them with the functionalities needed to survive without the side-effects. As a result, genetic engineering is already being used to alter and add components to Plant and Fungi DNA in the lab to help them to survive for longer and adapt better to their environment.

In conclusion, the field of genetic engineering presents a complex yet promising path towards efforts in conservation biology. Whilst the potential benefits are phenomenal, such as increasing agricultural yields and strengthening the population of a species in an ecosystem, ethical considerations cannot be ignored with the potential consequences altering DNA of organisms can present to natural systems. Nonetheless, as science and technology continue to evolve, applications of genetic engineering hold the possibility to lead to a brighter future - A world full of more conservation, augmentation and sustainability.

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13. LaPedus, M (2018) Nodes Vs. Nodelets [online].

Available at: https://semiengineering.com/nodesvs-node-lets/ (Accessed 19 February 2023) 14. Rupp, K and Selberherr, S (2010). The Economic Limit to Moore’s Law. [online]. Available at: https:// ieeexplore.ieee.org/abstract/document/5610740 (Accessed 5 February 2023) What Are The Effects of Social Media

12. https://study.com/academy/lesson/media-effectsdefinition-effects-beliefs.html#:~:text=Two%20 core%20effects%20of%20media,subject%20 of%20a%20news%20story 13. https://www.womenshealthmag.com/health/ a38603617/blackout-challenge-tiktok-2021/

1. https://resources.uknowkids.com/blog/whysnapchat-should-terrify-you

From Mark I to COBOL: Grace Hopper and Her Unsung Legacy in Computer Science

2. https://smartsocial.com/post/effects-of-snapchatteens#:~:text=Teens%20may%20easily%20 feel%20dissatisfied,issue%20with%20 Snapchat%20is%20cyberbullying

1. https://www.techtarget.com/searchdatamanagement/definition/data-analytics#:~:text=Data%20 analytics%20(DA)%20is%20the,of%20 2. https://www.wallstreetmojo.com/financial-analysis-tools/ 3. https://towardsdatascience.com/what-is-supply-chain-analytics-42f1b2df4a2 4. https://en.wikipedia.org/wiki/Debugging#:~:text=The%20terms%20%22bug%22%20and%20 %22,were%20%22debugging%22%20the%20 system 5. https://www.techtarget.com/searchitoperations/ definition/COBOL-Common-Business-Oriented-Language 6. https://www.spiceworks.com/tech/artificial-intelligence/articles/what-is-cobol/

3. https://theorion.com/89932/opinion/social-mediaalgorithm-designed-to-be-toxic/#:~:text=Social%20 media%20portrays%20a%20false,that%20 can%20even%20turn%20fatal 4. https://www.leehealth.org/health-and-wellness/ healthy-news-blog/mental-health/are-youaddicted-to-social-media#:~:text=Using%20 social%20media%20can%20lead,in%20 neurological%20and%20physiological%20 functioning.

5. https://temismarketing.com/blog/how-socialmedia-affects-time-management/#:~:text=on%20 time%20management-,Social%20media%20 can%20also%20have%20some%20negative%20 effects%20on%20time,looking%20at%20 other%20people’s%20profiles 6. https://medium.com/@productivitybee/7ways-how-social-media-can-ruin-your-lifee5b7d980395d#:~:text=Social%20media%20 has%20a%20plethora,many%20issues%20 with%20social%20media 7. https://www.headspace.com/mindfulness/ negative-effects-of-social-media 8. https://temismarketing.com/blog/is-socialmedia-destroying-creativity/#:~:text=Social%20 media%20can%20be%20a,to%20conform%20 to%20group%20norms 9. https://www.frontiersin.org/articles/10.3389/ fpsyg.2021.711416/full#:~:text=Social%20 media%20distraction%20refers%20to,social%20 media%20can%20occur%20frequently 10. https://www.livingbyexample.org/how-is-socialmedia-changing-human11. https://www.toppr.com/guides/essays/essayon-social-media/#:~:text=Topics%20and%20 Ideas-,Disadvantages%20of%20Social%20

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Media,your%20privacy%20like%20never%20 before

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War Games: How gaming entered the arena of real-life conflict 1. https://academic.oup.com/isagsq/article/2/1/ ksab045/6513408 2. https://history-computer.com/tennis-for-two-complete-history/ 3. https://www.nbcnews.com/tech/tech-ws/atomicheart-video-game-draws-scrutiny-ukrainians-russia-ties-rcna72714 4. https://www.thegamer.com/atomic-heart-mundfish-denies-harvesting-data-russia/#:~:text=The%20developer%20behind%20the%20upcoming,for%20the%20Federal%20Security%20 Service 5. https://www.statista.com/outlook/dmo/digital-media/video-games/worldwide#:~:text=Revenue%20in%20the%20Video%20 Games,US%24365.60bn%20in%202023 6. https://www.brookings.edu/techstream/videogames-are-the-new-contested-space-for-publicpolicy/ 7. https://en.wikipedia.org/wiki/Military%E2%80%93entertainment_complex 8. https://en.wikipedia.org/wiki/Fallujah,_The_Hidden_Massacre#:~:text=But%20there%20is%20 hard%20evidence,it%20to%20flush%20out%20

insurgents 9. https://www.ign.com/articles/six-days-in-fallujah-iscomplicated-and-painful-for-those-connected-tothe-real-events 10. https://edition.cnn.com/2021/10/09/us/six-days-infallujah-iraq-video-game/index.html 11. https://en.wikipedia.org/wiki/Rules_of_engagement 12. https://www.youtube.com/watch?v=VF0c9SwewpA 13. https://home-affairs.ec.europa.eu/system/ files/2022-02/EUIF%20Technical%20Meeting%20 on%20Video%20Gaming%20October%20 2021%20RAN%20Policy%20Support%20paper_en.pdf 14. The Imperial war Museum London is hosting an exhibition until end of May 2023 on the theme of war as interpreted through video games. [online].

Available at: https://www.iwm.org.uk/events/wargames The Positives and Negatives of Genetic Engineering in Conservation Biology 1. https://www.climate.gov/news-features/understanding-climate/climate-change-global-temperature#:~:text=January%2018%2C%202023-,Highlights,0.18%C2%B0%20C 2. https://www.theglobaleconomy.com/India/Share_ of_agriculture/ 3. https://www.peta.org/issues/animals-used-for-experimentation/animal-testing-101/ 4. https://www.open.edu/openlearn/science-maths-technology/science/biology/the-selective-breeding-dogs#:~:text=Many%20develop%20 disease%20including%20heart,b 5. https://www.yourgenome.org/facts/what-is-selective-breeding/#:~:text=Selective%20breeding%20 makes%20use%20of,one%20generation%20 to%20the%20next

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