Was doggedness part of artificial intelligence success story?

Posted by Sukant Khurana
February 8, 2018

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Was doggedness part of artificial intelligence success story?

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by Harsh Singh, Raamesh Gowri Raghavan, and Sukant Khurana

Artificial intelligence (AI, also machine intelligence, MI) is the term used for intelligence displayed by machines. The term “Artificial Intelligence” is used when the machine is not a slave to basic commands of a program, it leaves them way behind and is structured to learn from experiences thus being analogous (not homologous) to biological systems, like spiders, octopuses, and humans etc.

AI is now becoming an integral part of our ecosystem, from solving the toughest problems known to humans to providing individuals, governments, and corporations to royally screw up in ways previously unimaginable without this technology.

Photo by Jace Grandinetti on Unsplash

After very promising predictions of AI accomplishing feats humans do, from its early days in 1960s AI encountered some setbacks; the US congress thought that other projects coming up at that time were far better than AI. The next few years were termed as “AI winter” a period when obtaining funding for AI projects was difficult. The founders could see its growth in future and they didn’t lose hope in it. By 1985 the market for AI had reached over a billion dollars. Near the end of 1987 the growth of AI was curbed again but soon the use of AI could be seen in logistics, data mining, medical diagnosis and other areas. The success was due to increasing emphasis on solving specific problems, new ties between AI and other fields. Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov on 11 May 1997.

Advanced statistical techniques which are also known as deep learning now work through an access to large amounts of data and faster computers outperforming humans in several ways, for example in the more obvious facets of AI in a Jeopardy Quiz show exhibition match, IBM’s question answering system, Watson, defeated the two greatest Jeopardy champions, Brad Rutter and Ken Jennings, by a significant margin. In our everyday life, social media, maps, most financial transactions at high end have some or the other use of AI. In 2017, Future of Go Summit, AlphaGo won a three-game match with Ke Jie, who at the time continuously held the world number 1 ranking for two years. The number of software projects that use AI within Google has changed from a “sporadic usage” in 2012 to more than 2,700 projects.

Success of AI has been because of shear doggedness of researchers during the period of poor support, who understood what the politicians and the industry leaders of their time failed to realize. Now, it is a different story, where AI has become the buzzword of success and now people fail to see the artificial stupidity and infringement of privacy that gets wrapped along with artificial intelligence in the same bag and they swallow it without questioning.

Where are we in our progress of AI: close to human intelligence or not there yet?

In 1950, Alan Turing came up with a procedure to test the intelligence of an agent now known as the Turing test. This procedure gave freedom to almost all the major problems of artificial intelligence to be tested. However, it is a very difficult challenge and at presents all agents fail.

For example, performance at draughts (i.e. checkers) is optimal, performance at chess is high-human and nearing super-human (see computer chess: computers versus human) and performance at many everyday tasks (such as recognizing a face or crossing a room without bumping into something) is sub-human.

A quite different approach measures machine intelligence through tests which are developed from mathematical definitions of intelligence. Examples of these kinds of tests started in the late nineties devising intelligence tests using notions from data compression. Two major advantages of mathematical definitions are their applicability to nonhuman intelligences and their absence of a requirement for human testers.

A derivative of the Turing test is the Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA). As the name implies, this helps to determine that a user is an actual person and not a computer posing as a human. In contrast to the standard Turing test, CAPTCHA is administered by a machine and targeted to a human as opposed to being administered by a human and targeted to a machine. A computer asks a user to complete a simple test then generates a grade for that test. Computers are unable to solve the problem, so correct solutions are deemed to be the result of a person taking the test. A common type of CAPTCHA is the test that requires the typing of distorted letters, numbers or symbols that appear in an image undecipherable by a computer.

While it is difficult to say where we would be with AI in five years but certainly we would not have been here if it were not for the shear doggedness of early researchers. A case in point for good (not all) academic research. Current applications include autonomous vehicles, medical diagnosis, proving mathematical theorems, search engines (such as Google search), online assistants (such as Siri or cortana), image recognition in photographs, spam filtering, and targeting online advertisements etc. We ourselves hope to develop new kinds of AI and apply them to areas previously unexplored and we owe our debt to the early researchers in the field.

Photo by Erik Odiin on Unsplash

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Harsh Singh was a researcher working in Dr. Sukant Khurana’s group, focusing on Ethics of Artificial Intelligence.

Raamesh Gowri Raghavan is collaborating with Dr. Sukant Khurana on various projects, ranging from popular writing of AI, influence of technology on art, and mental health awareness.

Raamesh is an award winning poet, a well-known advertising professional, historian, and a researcher exploring the interface of science and art. He is also championing a massive anti-depression and suicide prevention effort with Dr. Khurana and Farooq Ali Khan. You can know more about Raamesh at: https://sites.google.com/view/raameshgowriraghavan/home and https://www.linkedin.com/in/raameshgowriraghavan/?ppe=1

Dr. Sukant Khurana runs an academic research lab and several tech companies. He is also a known artist, author, and speaker. You can learn more about Sukant at www.brainnart.com or www.dataisnotjustdata.com and if you wish to work on biomedical research, neuroscience, sustainable development, artificial intelligence or data science projects for public good, you can contact him at skgroup.iiserk@gmail.com or by reaching out to him on linkedin https://www.linkedin.com/in/sukant-khurana-755a2343/.

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