COVER STORY Shobhna Kunwar Humans, in their search for more and more intelligent machines, are not just trying to solve day-to-day problems but are also Of course, machines have not yet reached the level of the coming closer to solving the big mystery Robot, the problematic colleague of Dilbert. But the comic that we ourselves are. The quest for Artifi cial fi ctional episode is signifi cant in the light of the fast developing area of Artifi cial Intelligence (AI). The depiction in the comic Intelligence powered super beings may as refl ects human fears that intelligent machines may become too well end up in pushing the frontiers of our intelligent to allow themselves to be manipulated by humans. own thinking. Some time back, there was a temporary shutdown at Facebook’s AI project. The project was testing intelligence N one episode of the comic series Dilbert by Scott Adams, of two machines that had been programmed to bargain with Dilbert tells his colleague, “Our Robot was a good worker each other. But the machines seemed to outsmart their human I until we gave it Artifi cial Intelligence. As soon as it realised developers when they were detected speaking in their own it had immense strength and no soul, it started delegating.” The coded language. comic strip then shows the robot in another scene threatening Are machines then on their way to outperforming human? a co-worker to crush his head if he refuses to do his (Robot’s) This question puts AI and how it is evolving as a fi eld at the work. centre of a raging debate worldwide. 14 | Science Reporter | April 2018 What is Artifi cial Intelligence? a machine would not exhibit selective fragments of human Artifi cial Intelligence generally implies machines exhibiting intelligence but would rather behave like a human being in traits of human intelligence. The important traits of human terms of consciousness. intelligence include observing, taking inputs from the Full AI is also known as Artifi cial General Intelligence. observations, reasoning and rationalising, decision making, It is diffi cult to set a standard test to see whether a machine manipulations, learning from behaviour and patterns, has acquired the level of intelligence that a human possesses. communicating, analysing complex situations and performing Human beings reason, use strategy, solve problems, make tasks to achieve goals and targets. judgments, use commonsense, plan, learn, communicate, and At present, the subfi elds of AI like machine learning, integrate all these to achieve their goals. But apart from all autonomous systems, natural language processing, robotics and these, they are also autonomous in having their consciousness artifi cial creativity are popular areas of research. which has been a result of millions of years of evolution. To test the Artifi cial General Intelligence of machines, Strong AI and Weak AI some tests have been suggested. These are Turing Test, Coffee There are two types of artifi cial intelligence: Strong Artifi cial Test, College Admission Test and Employment Test. Intelligence (SAI) and Weak Artifi cial Intelligence (WAI). What has been actually called Strong AI is actually ‘full AI’. A full AI means a machine becoming capable of performing all intellectual tasks that a human being can perform. In this, (Source: https://towardsdatascience.com/) Turing Test: This was devised by Alan Turing, the famous mathematician, to deal with the question: ‘Can Machines Think?’ In this test, a machine and a human talk to a second human who must then judge which of the two is a machine. If the judge is unable to distinguish between the human and the machine, the machine is said to pass the Turing Test. Table 1 : Thrust Areas of Contemporary AI Research Machine Learning This is the fi eld in which computers are given the ability to learn without being explicitly programmed Autonomous Systems A system that learns on its own to perform various tasks Language Processing The fi eld of interaction between machines and human languages Robotics Robotics in entirety is not just AI but there is a fi eld of AI powered robots Artifi cial Creativity Creativity using computer. E.g. Aiva is an AI powered system that can compose soundtracks. Google’s Project Magenta is all about AI applications in arts. April 2018 | Science Reporter | 15 College Admission Test: Ben Goertzel, the mathematician who has worked extensively on Artifi cial General Intelligence, suggested this test. The test sees whether a machine can enroll itself in a university, take the same classes that human students take and successfully obtain a degree. Employment Test: Nils John Nilsson, the American computer scientist, suggested this test in which the machine is given an economically important job in which it must perform as well as or better than the level at which humans can do the same work. The tests suggested point to the disagreement as to what constitutes full AI. The tests are also challenging and there have Robots manning factory fl oors been reports of only Turing Test having been passed. Artifi cial General Intelligence therefore looks like a tough nut to crack. So far, there have been two reports of machines having Weak AI or Applied AI on the other hand, is a subset of passed the Turing Test. One was in 2014 at the University Strong AI as it involves machines being able to solve selective of Reading in the United Kingdom. In this a machine named actions that a human can perform. This requires more human “Eugene Goostman” simulated a thirteen-year-old boy at an intervention and the machine performs according to the program event. The Turing Test is said to be passed if the machine encoded in the machine. Weak AI is therefore application of manages to fool humans more than 30% of times that they are AI to selective functions. The present direction of AI research talking to a human and not a machine. The success of Eugene is towards this application aspect. however has been disputed (BBC June 9, 2014). The second machine that has been said to have cleared From Artifi cial General Intelligence to Applied AI the test is a machine developed by MIT’s Computer Science The history of AI research indicates that what is being touted and Artifi cial Intelligence Lab (CSAIL). It too was reported in as new actually began with much anticipation and focus on 2016 to have made humans believe that the sounds it produced the grand goals of developing a super intelligent machine. AI had been produced not by machines but humans (Robohub. research began in the United States with particular interest org, June 13, 2016). in developing machines that could translate from Russian to English and vice-versa. This was in the context of the Cold Coffee Test: This was suggested by Apple Inc. co-founder War with the then Soviet Union. Steve Wozniak. He said that machines could never be as intelligent as humans. He believed that a machine could not So, earlier researchers were interested in creating machines make coffee. So in this test, a robot has to go to an average that could do just any work that humans are capable of. Herbert American home, fi gure out how to make coffee. It has to fi nd Simon who is regarded as the pioneer of AI even said that the coffee machine, fi nd the coffee, mug and brew the coffee. machines will be capable within twenty years of doing any work a man can. The enthusiasm for AI with the ambitious goal of making machines as intelligent as humans lasted for nearly more than two decades among the government agencies in USA. However, the funding tap was turned off after some Strong Artifi cial disappointments in terms of realisation of grand ideas that had Intelligence fuelled the initial research (Table 2). By 1974, AI had entered its winter, a period when its research received cuts in funding and enthusiasm for research had reached its nadir. The revival of AI has taken place now with strides having been made in the applied aspect of Artifi cial Intelligence. The Weak focus now is on developing AI for solving particular problems. Artifi cial Scientists are optimistic that this approach to AI is better than the top-down approach in which the research initially was Intelligence focused on developing an artifi cial brain that would behave like a human brain. The applications that are attracting maximum attention are machine learning, robotics, autonomous systems and natural language processing. Autonomous systems are systems with Relation between Strong AI and Weak AI the ability to acquire or create content, interpret and develop 16 | Science Reporter | April 2018 Table 2 : AI Research – From Beginning to AI Winter 1950s Interest in Connectionism, Artifi cial Neural Networks, SNARC System, Machine Translation 1960s Machine translation proves to be inadequate; Automatic Language Processing Advisory Committee (ALPAC) concludes that machine translation project is not worth it due to poor performance of machines vis-à-vis human performance 1970s Connectionism, ANN abandoned; Cutback in funding; Lighthill Report by Prof. Lighthill calls AI a failure in terms of inability to achieve its grandiose aims 1980s Quiet cancellation of funding of AI research by Strategic Computing Initiative; Collapse of LISP machines market 1990s Sidelining of Fifth Generation Computing and Expert Systems Table 3 : Terms Related to History of AI Artifi cial Neural Computing systems based on biological neural networks of animals. These systems Network (ANN) are made to progressively improve their performance by analysing examples. e.g. ANN system may learn to identify images of dogs by analysing images that have been labeled as ‘dog’ and ‘not dog’.