How will Artificial Intelligence Evolve?

8/27/2019

How will Artificial Intelligence Evolve? - usm systems - Medium

How will Arti cial Intelligence Evolve? usm systems Follow Aug 27 · 6 min read

“Artificial Intelligence (AI) is Coming to Us” and it’s a small step from AI to a self-driving car that will take over our world and the “end of the days”

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dystopias that Hollywood has popularized in many recent films. Or maybe some people believe us.

The reality is a lot less dramatic, and the AI has to look at several stages of development before it gets too serious — if ever. In our recent book Beyond Genuine Stupidity — ensuring AI serves humanity, we explore the opportunities and challenges that this game-changing technology has to offer. In this article we will take the key points from the book to demystify the fundamentals of AI, provide insights into the seven stages in which it can evolve, and highlight the types of applications we will see over the next 15–20 years.

What is Artificial Intelligence and why now?

At the core of the concept of AI is the idea of developing intelligent machines — e.g. Computer algorithms act and react like humans. The exhibition includes speech recognition, natural language processing, and translation, visual perception, learning, reasoning, inference, strategy, planning, intuition and decision making. People have been working on these concepts since the 1940s, and AI has experienced a lot of bad mornings or “winters” as the AI community likes to describe them.

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In the meantime, there is a sense that AI is here and that it can become central to every aspect of human existence, from the detection and treatment of heart failure through the running companies, economies, and legal systems. There are five key elements that make AI a hot topic on the agenda for companies, investors, politicians, and citizens.

First, we are looking at the development of very efficient and intelligent machine learning tools — the main algorithms that AI systems develop for their intelligence. 2) Parallel, computer hardware processing power, and transaction speeds are accelerated and 3) cloud computing allows sharing and combining data and processing power globally. 4) At the same time companies like Google and Amazon are collecting large amounts of data needed to process AI — 5) money. The scale of opportunities provided by AI has seen these new technology companies, corporations, and venturefunded start-ups investing billions of dollars — so the game is well and truly on track.

Core Fields of Artificial Intelligence There are many different fields that fall under the broad umbrella of AI, the main ones being described below. The most common are rule-based or “expert” systems, which can apply a large number of rules on a consistent https://medium.com/@usmsystems23/how-will-artificial-intelligence-evolve-dfb46dcbbcc1

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basis. They have been around for 30 years with general applications including loans and mortgage application processing and basic medical diagnosis.

Robotic Process Automation (RPA) is the next step, as machines perform complete processes, requiring an element of rule-based decision-making and human judgment in the past — making claims in the service center, processing insurance, or checking design calculations.

Works considered close to true human intelligence include voice recognition, the interpretation of human conversations conducted in “natural” languages, and speech generation. Similarly, significant progress has been made in computer vision — enabling more accurate identification of images — with a varied application such as the detection of mishap pies on the product line by facial recognition for airport security purposes. A growing number of other AI applications include tools that can carry out advanced planning, scheduling, and tasks optimization — from manufacturing to staff rostering.

Currently AI’s hottest field of machine learning — the concept of using statistical methods to learn systems from data. It is currently gaining more attention because of its ability — using neural networks and deep learning https://medium.com/@usmsystems23/how-will-artificial-intelligence-evolve-dfb46dcbbcc1

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algorithms to develop systems with learning capabilities close to the human brain. These tools are used in a wide variety of applications, and chatbots are common to perform increasingly sophisticated customer interaction tasks by combining machine learning, natural language processing, and speech production.

Finally, robotics is widely used in everything from warehouse management and surgery to cooking burgers in restaurants. Robots usually combine several other eye fields to provide smart mechanoids.

Seven Steps in the Future Evolution of Artificial Intelligence With literally hundreds of thousands of developers and data scientists across the planet now working on AI, the pace of development is accelerating, with increasingly eye-catching advances being announced daily. This has led to a lot of confusion about what we can do with AI today and what is possible at some point in the future.

To give some clarity on the possible development path, we will look at seven different steps in the evolution of AI’s capabilities:

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Stage 1 — Rule-Based Systems — These are now around us in everything from business software (RPA) and home appliances to aviation autopilots.

They are the most common manifestations of AI in the world today.

Stage 2 — Context Awareness and Retention — These algorithms build a group of information about the specific domain they are applying to. They are trained on the knowledge and experience of the best human beings, and their knowledge base can be updated into new situations and questions arise. Chatbots that are frequently used in Frontline Customer Inquiry Handling and “RoboAdvisors” are the most common expressions that help with everything from prescribing the right oil to your motorbike to investment advice.

Stage 3 — Domain Specific Skill — These systems can develop expertise in a specific domain that extends beyond the capabilities of humans because of the amount of information they can access for each decision. We have seen their use in applications such as cancer diagnosis. Google DeepMind’s alpha is probably the most common example. The system is given a set of learning rules and a winning goal and it teaches you how to play with the human support to push it back when poor decisions are made. Go has been reported to have more motions than atoms in the universe — so you can’t

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teach it the same way with a chess play program. In March 2016, AlphaGo defeated 18-time World Go Champion Lee Sedol by four games.

The following year, Alfago Zero was created and offered no guidance or human support. Fitted only with her practice rules, she has watched thousands of Go games and developed her own strategies. Three days later she took AlphaGo and won with 100 games. Such applications are an example of when machines can acquire human-level intelligence. However, they are now limited to one domain, and now, she knows how to play Go if she begins to teach her how to detect fraudulent transactions in the Alfago Zero Accounting Audit.

Step 4 — Reasoning Machines — These algorithms have a “theory of mind” — some ability to attribute mental states to themselves and others e.g. They can learn beliefs, intentions, knowledge and how their own logic works.

Therefore, they have the ability to argue, discuss and interact with humans and other machines. Such algorithms are currently in development, but we can expect to see them in commercial applications in the next few years.

Stage 5 — Self-Aware Systems / Artificial General Intelligence (AGI) — This is the goal of most people working in the field of AI — creating systems with humans like intelligence. There is no evidence of such applications https://medium.com/@usmsystems23/how-will-artificial-intelligence-evolve-dfb46dcbbcc1

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today, although some say we can see them within five years, while others believe that we cannot really achieve this level of machine intelligence. In 2001 there were many examples of AGI in popular media, from HAL Ship Computer to A Space Odyssey and the television series “Synths” in the Humans. For decades now, writers and directors have tried to convey to the world that machines can work on an equal level with humans.

Stage 6 — Artificial Super Intelligence (ASI) — This is the concept of developing AI algorithms capable of overcoming the intelligent among humans in each domain. Obviously, it is difficult to say what capabilities are beyond human intelligence, but we can expect that ASI will address current global problems such as hunger and dangerous climate change. Such systems can also invent new fields of science, redesign economies, and develop completely new governance models. Again, expert opinions on when and where such a capability is possible will vary, but few will see it in the next decade. Films like Har and Ex Machina provide interesting depictions of the possibilities of a world where technology can overtake us.

Stage 7 — Singularity and Transcendence — The notion that the Exponential Development Path initiated by ASI will lead to a huge expansion in human capacity. We may one day grow enough that humans can connect our brains with each other and the future successor of the https://medium.com/@usmsystems23/how-will-artificial-intelligence-evolve-dfb46dcbbcc1

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current Internet. This “hive mind” allows for sharing ideas and solving problems collectively.

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