- Intelligence has been defined in many ways: the capacity for abstraction, logic, understanding, self-awareness, learning, emotional knowledge, reasoning, planning, creativity, critical thinking, and problem-solving. More generally, it can be described as the ability to perceive or infer information, and to retain it as knowledge to be applied towards adaptive behaviors within an environment or context.
- Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision.
In the past six years, the technologies behind AI have rapidly progressed, allowing its development to accelerate exponentially.
The breakthrough came in 2012, when a deep neural network called AlexNet allowed computers to identify objects in real images. Computers find it difficult to recognise pictures. But since then, there have been quick improvements and machines are almost as good as humans at object recognition. They can also recognise language. “With perception solved, AI research has moved on to higher-level cognition tasks like planning and reasoning,” explains Prof. Dr. Damian Borth, Director Artificial Intelligence and Machine Learning Lab, University of St. Gallen.
A recent development, for example, is deep reinforcement learning, a neural network that interacts with its environment and gets a reward or punishment that teaches it to learn.
Notably, in March 2018 Google announced Auto ML, which allows two of the three steps in building a neural network to be done by machine. “That is exciting, as it means you can accelerate development, making the technology much cheaper,” notes Mr. Borth. “If you can industrialise the development of intelligent networks, AI will soon be a commodity technology.
Damian Borth argues that the impact will be like the internet. There will be companies that are early adopters like Amazon and Google. Those that react more slowly will lose their competitive edge.
“But we are far away from a machine that can generalise and has a consciousness,” he concludes. “These machines are self-learning systems but they are experts in their narrow domains and simple-minded compared with humans. They are very good at specific tasks but bad at others. A neural network that can recognise spoken language can’t drive cars. A neural network that can drive cars can’t play chess.”
As the technology evolves fast, organisations from many sectors are applying it to drive productivity and insight. As a first step, many organisations are deploying chat bots to answer simple client queries, freeing staff to concentrate on giving excellent service in more complex areas. Another example might be robotics, which answer client emails or employees’ questions to HR.
More specifically, IBM’s Watson AI platform has been acting as an assistant to oncologists in more than 230 hospitals and healthcare organizations, covering 13 different types of cancer. IBM has also developed a robot called CIMON® (Crew Interactive MObile CompanioN), a joint venture with Airbus, that is assisting the astronauts on the International Space Station with their experiments.
But where is this all leading? “I think we will see AI optimise industries across the board – in automotive, banking, telecommunications, manufacturing, and especially healthcare given the wealth of information and willingness to share it,” says Matthias Hartmann, Chairman of the Board of Management, IBM Germany. “Every job will be influenced by AI. That does not mean necessarily AI takes away the job; but assists.”
Over time, Hartmann anticipates that advances in AI will be matched by improvements in quantum computing, leading to great advances in areas such as medicine discovery or investment portfolio optimisation. He also foresees AI leveraging the explosion in big data to increase knowledge exponentially.
“Today you can only Google 20 per cent of the data out there. Eighty per cent of the data is held within enterprises. You need a platform to make this data accessible. My clients see lots of business advantages by applying their own data.”
Just like the Internet 20 years ago, the AI investment story is being hyped. There is a danger that AI is already being viewed too soon as the answer to many problems, causing disappointments. In fact, many of today’s applications are relatively narrow and need higher investments and iterations of improvements than the excitement suggests.
“In the long term, we do think that AI is being under-priced in healthcare, where it is improving medical diagnostics and research outcomes,” asserts Fabiano Vallesi, Next Generation Portfolio Manager at Julius Baer. “Similarly, in transportation it’s improving safety in general, enabling the basics for self-driving cars.”
But where are the interesting investment areas for today? “We think that the most attractive segment is the integrated cloud computing providers, which are building platforms facilitating access to basic AI tools,” explains Vallesi. “They will commoditise AI fast and give access to these tools for everyone. But also, the cloud software providers will be far more productive and available with AI enhanced solutions. We do not favour the hardware makers and semiconductors, as we think their stock valuations already discount their prospects.”
Here’s a brief timeline of the past six decades of how AI evolved from its inception.
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1956 : John McCarthy coined the term ‘artificial intelligence’ and had the first AI conference.
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1969 : Shakey was the first general-purpose mobile robot built. It is now able to do things with a purpose vs. just a list of instructions.
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1997 : Supercomputer ‘Deep Blue’ was designed, and it defeated the world champion chess player in a match. It was a massive milestone by IBM to create this large computer.
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2002 : The first commercially successful robotic vacuum cleaner was created.
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2005-2019 : Today, we have speech recognition, robotic process automation (RPA), a dancing robot, smart homes, and other innovations make their debut.
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2020 : Baidu releases the LinearFold AI algorithm to medical and scientific and medical teams developing a vaccine during the early stages of the SARS-CoV-2 (COVID-19) pandemic. The algorithm can predict the RNA sequence of the virus in only 27 seconds, which is 120 times faster than other methods.
Foundations of AI explained on the basis of some questions:
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Philosophy • Can formal rules be used to draw valid conclusions? • How does the mind arise from a physical brain? • Where does knowledge come from? • How does knowledge lead to action?
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Economics • How should we make decisions so as to maximize payoff? • How should we do this when others may not go along? • How should we do this when the payoff may be far in the future?
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Psychology • How do humans and animals think and act? • The three key steps of a knowledge-based agent: I. the stimulus must be translated into an internal representation II. the representation is manipulated by cognitive processes to derive internal representations III. These are in turn retranslated back into action.
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Linguistics • How does language relate to thought? • Verbal Behavior — behaviorist approach to language learning
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Neuroscience • How do brains process information? Neuroscience is the study of the nervous system, especially the brain. We are still a long way from understanding how cognitive processes actually work. The truly amazing conclusion is that a collection of simple cells can lead to thought, action, and consciousness or, brains cause minds.The only real alternative theory is mysticism: that minds operate in some mystical realm that is beyond physical science.
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Mathematics • What are the formal rules to draw valid conclusions? • What can be computed? • How do we reason with uncertain information? The main three fundamental areas are logic, computation and probability.
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Computer Science • How can we build an efficient computer? AI has pioneered many ideas that have made their way back to mainstream computer science, including time sharing, interactive interpreters, personal computers with windows and mice, rapid development environments, the linked list data type, automatic storage management, and key concepts of symbolic, functional, declarative, and object-oriented programming.
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Control Theory • How can artifacts operate under their own control?
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AI in Astronomy
Artificial Intelligence can be very useful to solve complex universe problems. AI technology can be helpful for understanding the universe such as how it works, origin, etc. -
AI in Healthcare
In the last, five to ten years, AI becoming more advantageous for the healthcare industry and going to have a significant impact on this industry. Healthcare Industries are applying AI to make a better and faster diagnosis than humans. AI can help doctors with diagnoses and can inform when patients are worsening so that medical help can reach to the patient before hospitalization. -
AI in Programming
AI can be used for gaming purpose. The AI machines can play strategic games like chess, where the machine needs to think of a large number of possible places. -
AI in Finance
AI and finance industries are the best matches for each other. The finance industry is implementing automation, chatbot, adaptive intelligence, algorithm trading, and machine learning into financial processes. -
AI in Data security
The security of data is crucial for every company and cyber-attacks are growing very rapidly in the digital world. AI can be used to make your data more safe and secure. Some examples such as AEG bot, AI2 Platform, are used to determine software bug and cyber-attacks in a better way. -
AI in Social Media
Social Media sites such as Facebook, Twitter, and Snapchat contain billions of user profiles, which need to be stored and managed in a very efficient way. AI can organize and manage massive amounts of data. AI can analyze lots of data to identify the latest trends, hashtag, and requirement of different users. -
AI in Travel & Transport
AI is becoming highly demanding for travel industries. AI is capable of doing various travel related works such as from making travel arrangement to suggesting the hotels, flights, and best routes to the customers. Travel industries are using AI-powered chatbots which can make human-like interaction with customers for better and fast response. -
AI in Automotive Industry
Some Automotive industries are using AI to provide virtual assistant to their user for better performance. Such as Tesla has introduced TeslaBot, an intelligent virtual assistant. Various Industries are currently working for developing self-driven cars which can make your journey more safe and secure. -
AI in Robotics
Artificial Intelligence has a remarkable role in Robotics. Usually, general robots are programmed such that they can perform some repetitive task, but with the help of AI, we can create intelligent robots which can perform tasks with their own experiences without pre-programmed. Humanoid Robots are best examples for AI in robotics, recently the intelligent Humanoid robot named as Erica and Sophia has been developed which can talk and behave like humans. -
AI in Entertainment
We are currently using some AI based applications in our daily life with some entertainment services such as Netflix or Amazon. With the help of ML/AI algorithms, these services show the recommendations for programs or shows.