Artificial Intelligence

GENERAL ARTICLE

Artiﬁcial Intelligence∗ The Big Picture

Deepak Khemani

In the first week of the year 2020, we got the news that AI now outperforms doctors in detecting breast cancer. This is in line with a continuous stream of news coming from the world of diagnosis and has lent credence to the sentiment that AI is poised to overcome humankind. However, some perceptive observers have commented that recent advances are largely due to the massive increase in both availability of data and Deepak Khemani is a computing power. Moreover, it is only a narrow task of clas- professor at IIT Madras. He sification that has led the news blitz. Classification can be has been working in AI for thought of as a stimulus-response process. Human intelli- over thirty years, with a focus on knowledge representation gence is much broader. In particular, humans often display and problem solving. He is a stimulus-deliberation-response cycle. There is much that the author of the text book, A goes on in the “thinking” phase that was the original aim of First Course in Artificial AI before the data and speed started dominating applications. Intelligence, and has three online courses on Swayam. The second of the two-part article on AI traces the evolution Hiscurrentfocusisto in the field since the Dartmouth conference, and takes stock implement a contract bridge of where we are on the road to thinking machines. playing program that reasons like a human expert. The term Artificial Intelligence is attributed to John McCarthy (1927–2011) who along with Marvin Minsky (1927–2016), Natha- niel Rochester (1919–2001) and Claude Shannon (1916–2001) organized a summer conference in Dartmouth College. Much of research in AI had its seeds in the 1956 Dartmouth conference. Pamela McCorduck, in her delightful book Machines Keywords Who Think, observed that “several directions are considered to Search, knowledge, logic, language, machine learning, agents. have been initiated or encouraged by the Workshop: the rise of symbolic methods, systems focussed on limited domains (early Expert Systems), and deductive systems versus inductive sys-

∗Vol.25, No.1, DOI: https://doi.org/10.1007/s12045-019-0921-2

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tems”. The proposal also included the use of neuron nets – “How can a set of (hypothetical) neurons be arranged so as to form concepts”, and the use of natural language.

GOFAI

McCorduck says that the greatest impression at Dartmouth was made by “two vaguely known persons from RAND and Carnegie Tech...a significant afterthought.” The two people were Herbert Simon (1916–2001) and Alan Newell (1927–1992) working at Carnegie Tech and RAND Corporation, who played a major role in setting up AI research in what was later called the Carnegie Mellon University. Along with J. C. Shaw (1922–1991), also from RAND, they had already developed a program called the Logic Theorist (LT). “It was the first program deliberately engi- neered to mimic the problem solving skills of a human being”. It went on to prove several theorems in Russell and Whitehead’s Problems given at the celebrated Principia Mathematica finding shorter and more el- end of the chapter in egant proofs for some! Simon, a Nobel Laureate in Economics, math books are easier to confirmed in his 1996 book Models of My Life that “a paper coau- solve because we know Journal of Symbolic Logic the method we are thored by LT was rejected by the on expected to employ. Not the grounds that it was not a new result”. It is also interesting so when the problem is to note that Simon wrote to Bertrand Russell (1872–1970), an au- posed in later life, thor of the original proof, that “in general, the machine’s problem- outside the context of the chapter. solving is much more elegant when it works with a selected list of strategic theorems than when it tries to remember and use all the previous theorems in the book.” A repository of knowledge is a key to intelligent behaviour. That retrieving the right chunk of knowledge from memory is not easy for humans either is illustrated by the following high school problem, given in the book How to Solve It: Modern Heuristics (Michale- wicz and Fogel, 1999). The authors say that problems given at the end of the chapter in math books are easier to solve because we know the method we are expected to employ. Not so when the problem is posed in later life, outside the context of the chapter. Try the following problem yourself: Given a triangle ABC and an

44 RESONANCE | January 2020 GENERAL ARTICLE interior point D in the triangle, show that the sum of the lengths of the segments AD and DC is less than the sum of AB and BC. The statement by Simon is revealing of a major problem in AI – how to retrieve the relevant piece of knowledge from the vast repository that the memory may contain. Indeed, an active area of interest in AI is Memory Based Reasoning which, like humans, aims to exploit knowledge and experience for problem solving. One feature that separates experts from novices is the ability to do this. The chess psychologist Adriaan de Groot (1914–2006) con- ducted several ground-breaking experiments in the cognitive processes that occur in the brains of strong chess players. His most startling result was that grandmasters found a good move during the ﬁrst few seconds of contemplation of the position, drawing attention to “the role of memory and visual perception in these processes, and to how strong players, especially grandmasters, used experience with past positions to expediate the process of ﬁnding a move”.

Simon and Newel had laid the foundation of Classical or Sym- Symbolic AI is bolic AI when they put forth the Physical Symbol System Hy- concerned with writing pothesis, which says that processes acting upon symbol systems programs which ﬃ themselves are symbol are su cient to create artiﬁcial intelligence. A symbol is “a per- systems operating upon ceptible something that stands for something else”. Road signs, data structures, also numerals in mathematics, and letters of an alphabet, are exam- symbol systems. ples. Symbol systems refer to composite structures of symbols, for example, words and sentences in a natural or a formal language. Processes are realizations of algorithms acting upon symbol systems. Symbolic AI thus is concerned with writing programs which themselves are symbol systems operating upon data structures, also symbol systems. This approach has also been called Good Old Fashioned AI (GOFAI) specially in the light of the bottom-up approaches in which intelligent behaviour emerges from a collec- tion of simple elements, like neurons, often through a process of evolution and learning. It is sometimes said that while in classical AI, symbols stand for elements, individuals and also concepts, in neural networks, it is not clear how such things are represented.

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In artificial neural networks, also called sub-symbolic systems, representation is somehow encoded in the weights of the connec- tions between neurons. Further, they are not localised as in symbols, but are distributed across the network. Such networks are also called connectionist networks in which information is stored in patterns across neurons. A striking feature is that when parts of the network are damaged, specific information is still not lost. A point to ponder here is that even the artificial neural networks are implemented as computer programs, and so deep down are somehow symbolic.

CHESS

Games like chess have Games like chess have long held a fascination for AI researchers long held a fascination because, on the one hand, they are considered to be hallmarks for AI researchers of intelligence and genuinely complex problems, while, on the because, on the one hand, they are other hand, they are easier to implement in terms of input, output considered to be and representation. The paraphernalia required is minimal, and hallmarks of intelligence I have known chess enthusiasts on a walk playing a game men- and genuinely complex tally simply by saying their moves aloud. Luminaries like John problems, while, on the other hand, they are von Neumann (1903–1957) and Alan Turing have pondered over easier to implement in chess. Alex Bernstein (1936–2010) from IBM, who was present terms of input, output at Dartmouth, was already working on chess. and representation. It was quite apparent that a program that searches through future moves would be confounded by the explosion in the number of possibilities in chess. (See the section on searching below.) It is estimated that there are about 10120 distinct chess games. Compare this to the estimated 1075 or so number of fundamental particles in the entire universe, and it is clear that the game cannot be completely analysed. The British grandmaster, David Levy, in 1968 scoﬀed at the idea of a computer program beating In 1997, the then world him at chess and wagered that none would do so in the next ten champion Garry years. He did, narrowly, win his bet, but machines were rapidly Kasparov lost a six-game improving. In a couple of decades, in 1997, the then world cham- matchtoIBM’sDeep Blue machine. pion Garry Kasparov lost a six-game match to IBM’s Deep Blue machine. In 2006 Levy converted, and became a champion of AI,

46 RESONANCE | January 2020 GENERAL ARTICLE even going as far as to publishing a book predicting that humanoid robots will become human companions in the future. The oriental game of Go by the same measure is even harder, but as described later, even here, a machine has beaten the reigning world champion. It must be noted that we still do not know the outcome when both players play these games perfectly, called the mini- max value. Only that a machine has beaten a champion. We do know that a smaller game like Cross & Noughts, played on a 3×3 board, is always a draw when both players play optimally. Also present at Dartmouth was Arthur Samuel (1901–1990) who impressed everyone with his checkers playing program. The thing that caught public imagination was that Samuel’s program could learn from experience. It improved as it played games, eventually beating its own creator. This, in public imagination, evoked the fears of Frankenstein’s monster (Mary Shelley, 1818) — of crea- tures that would overwhelm humankind. Recently, these fears have been echoed by some scientists, Stephen Hawking being the most eminent amongst them. But Samuel’s program was a simple machine learning algorithm, which essentially learnt which piece combinations and arrangements were good and which were not. The idea of machines learning from experience has been with AI ever since.

Machine Learning

Machine learning (ML) is what has brought AI into public cog- Machine learning is nizance at this moment. This has happened because of a conflu- what has brought AI into ence of circumstances. First is the exponentially growing com- public cognizance at this moment. puting power available (remember Moore’s law). Algorithms that were conceived fifty years ago and considered unscalable are back in fashion now. Second, the emergence of the Internet, which has shrunk the world, and rendered physical distances meaningless. Social networks and commerce are no longer confined to geo- graphic regions. Third, the ever-ongoing activity on the Internet is leaving a digital trace all around us, and if machine learning feeds on anything, then it is data! Thus, we have areas of re-

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search like Data Science mushrooming, and terms like Big Data being bandied about. Many of the advances in extracting stuff from data, which is what machine learning is all about, are being driven by commercial concerns. Sellers want to figure out what you might be persuaded to buy next. But e-commerce is not the only ML application area. ML is also being used in mining the data obtained from thousands of pa- tients to identify the causes and symptoms of diseases. NBC News reports that a child in Mississippi was diagnosed for the Mowat–Wilson syndrome in a matter of seconds by an app called Face2Gene (Figure 1) that works with a facial image taken on The artificial neural the phone. Face2Gene’s system uses a machine learning algo- network (ANN) rithm, meaning it learns from every new face it scans. The more community has data it acquires through its use, scientists hope, the more accurate responded to the challenges introduced by the diagnoses will be. An added advantage of such applications humungous amounts of will be that doctors over the world can benefit from the knowl- data by building more edge gleaned from shared learning from the data of numerous pa- powerful systems, often tients, especially for rare diseases. It is not a surprise that medical referred to by the name ‘deep learning systems’. schools are collaborating with computing researchers to improve medical treatment together. The artificial neural network (ANN)community has responded

Figure 1. Facial recog- nition app Face2Gene is being used by doctors to diagnose rare diseases. Courtesy FDNA from the NBC News article. How Machine Learning Is Revo- lutionizing the Diagnosis of Rare Diseases by Jane C. Hu http://www.nbcnews.com/story line/the-big-questions/how- machine-learning- revolutionizing-diagnosis- rare-diseases-n700901

48 RESONANCE | January 2020 GENERAL ARTICLE to the challenges introduced by humungous amounts of data by building more powerful systems, often referred to by the name ‘deep learning systems’. The earliest system built by Frank Rosen- blatt (1928–1971), called the Perceptron, had only one layer. It was short-lived and its demise was triggered by a book the same name by Marvin Minsky and Seymour Papert (1928–2016), which showed that it was essentially a linear classifier. This means that it could identify which of the two classes an element belongs to, only if one could draw a straight line separating the two sets of data. Subsequently, it was shown in the mid-eighties that with a hidden layer, and an algorithm called Backpropagation, one could build neural networks that could learn non-linear classifiers as well. Recent work on deep neural networks (DNN) has demon- strated that more hidden layers result in faster learning in practice. Another example that highlights the spectacular success of ML is the program called AlphaGo (Figure 2) that for the first time beat the human world champion in the hitherto unconquered game of Go. This came as a surprise to many because the considered opin- ion was that Go, which is played on a 19 × 19 board, is too complex for machines. Thinking is much more than that being able to Clearly, a machine that can learn from data and do what you want, make sense of data. The is a dream for many. The burning question is: Can machine learn- ability to imagine is ing cover the entire gamut of intelligent behavior? crucial for intelligence. Thinking is much more than that being able to make sense of data. The ability to imagine is crucial for intelligence. Just think of the

Figure 2. Picture from 2016 Popular Mechan- ics article by Jay Bennet. https://www.popularmechanics .com/technology/a19863/googles- alphago-ai-wins-second- game-go/

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folks who conjure up imaginary, even magical, worlds in stories; the musicians who string together notes into a melody; the painter who recreates an imaginary or real-world onto a canvas; the friend who can empathize with you; even the fraudster who spins a fancy tale to rob you of your money. Machine learning and data science are the ﬂavour of the day because we are currently overwhelmed both with huge amounts of (big) data and the computing power to crunch it. But it was not always so.

The Age of Reason

Around the time of the Dartmouth, real computers were rare, and they were huge ‘mainframe’ machines that filled rooms. A decade earlier, the president of IBM Thomas Watson had asserted, “I think there is a world market for maybe five computers.” Ken Olsen, the founder of Digital Equipment Corporation, said in 1977, “There is no reason anyone would want a computer in their home.” Even when PCs came, there were few and far between. In 1990, my office PC had a hard disk with capacity 20MB. Given the smaller and slower machines, the scientists in the mid- dle of the last century were focused on conceptual matters relat- ing to thought, and preoccupied with devising algorithms to work on human-generated representations. The larger effortofSimon and Newell was to try and unravel how humans solve problems, and try and imitate them. Their program General Problem Solver (GPS) was a pioneer in the use of heuristics in search and adopted a human-like approach to problem solving. This program, and the Forabriefperiodinthe associated theoretical framework, had a significant impact on the early eighties, rule-based subsequent directions in cognitive psychology. It also introduced expert systems were the use of productions or rules as a method for specifying cogni- touted as the silver bullet for building AI systems. tive models. A rule is essentially an For a brief period in the early eighties, rule-based expert systems if-then statement, for example, “if the patient were touted as the silver bullet for building AI systems. A rule has yellow eyes. is essentially an if-then statement, for example, “if the patient has yellow eyes, do a test for jaundice”. The basic idea was as follows. Capture the problem-solving knowledge from a domain ex-

50 RESONANCE | January 2020 GENERAL ARTICLE pert in the form of rules. Thus, the expert has only to articulate the domain knowledge. A separate program, called the inference en- gine, would then pick the relevant rules and propose the relevant actions or inferences to construct the solution. Several systems were built, for example, MYCIN, for diagnosing and identifying bacteria causing severe infections, and Prospector, used for de- ciding where to dig for oil exploration. However, it eventually became evident that it was very hard to elicit the knowledge of experts into the form of rules, and the pendulum of AI shifted away.

Searching for Solutions

A fundamental ability needed for problem-solving is the abil- A fundamental ability ity to imagine the future and visualize the consequences of the needed for decisions an autonomous agent is considering. In the AI commu- problem-solving is the ability to imagine the nity, we refer to this as problem-solving using search. A classic future and visualize the example is the way many of us play a game like chess, and not consequences of the surprisingly, the way the programs that beat us also do. And that decisions an autonomous is, to project the moves in our imagination, a kind of mental sim- agent is considering. In the AI community, we ulation. The computation goes as follows, “if I play move X, the refer to this as opponent can respond with Y and Z, and then I can ...”. This problem-solving using process continues till resources permit, and then one has to take search. a call. At that point, programs consult an ‘evaluation function’ that encapsulates the knowledge of the game and, given a board position, computes a number signifying how good the position is. Traditionally this kind of knowledge was gleaned from human experts, either directly or by learning from their past games. But recently the program AlphaGo Zero has shown that the evaluation function can be learnt by simply playing against itself. The dif- ference between human players, at least the non-expert ones, and machines is that humans tend to be selective and, more impor- tantly, unsystematic, while machines thoroughly search through the entire space of possibilities. And they do not make silly mis- takes. Games are multiagent scenarios. Searching can be a useful tool

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when the agent is operating solo as well. We often illustrate this approach using puzzles that involve selecting an appropriate sequence of actions. Consider an instance of a water jug puzzle – “You have three jugs with capacity 8, 5 and 3 litres. The 8-litre jug is ﬁlled with water. The 5 litre and 3 litre jugs are empty. You are required to measure 4 litres of water”. Clearly, an agent which can do ‘mental’ simulations of its actions can work out the solution.

Early research was Early research was focussed on such trial-and-error first princi- focussed on ples methods. The realization dawned pretty soon that the number trial-and-error first of possibilities can explode dramatically. Consider a tiny world principles methods. The realization dawned in which an agent has three choices at any point in time. If the pretty soon that the agent is to look ahead and mentally peer into the future, then the number of possibilities possibilities increase exponentially with depth. There are 3 states can explode to consider looking one move ahead, 9 at the next level, then 27, dramatically. 81, 243, 729, and so on. The search tree depicted below (Fig- ure 3) soon becomes too large to fit in a page. I like to call this ‘monster’, that AI is combating, CombEx, for combinatorial explosion. Every time one removes a node for inspection at a given level, many more sprout in its place. Quite like the monster Hydra that Hercules was fighting in Greek mythology. And yet, just as Hercules prevailed over Hydra, so are search methods making progress against CombEx. And a tool that we have added to the AI armoury is knowledge. Heuristic functions encapsulate thumb rule knowledge to help guide the search, and algorithms like A* can search through large spaces finding optimal solutions to problems. An application where such methods have been hugely successful is the task of sequence alignment in the domain of biology. The task is to take two sequences of let-

Figure 3. The search tree.

52 RESONANCE | January 2020 GENERAL ARTICLE ters A, C, G, and T, representing the four nucleotide bases of a DNA strand – adenine, cytosine, guanine, thymine – and align them by inserting gaps in some places to maximize the match in the remaining. A* based methods do this optimally, even when the input sequences are hundreds of thousands of characters in length. In the domain of planning actions, modern algorithms In the domain of like GraphPlan and its derivatives can construct plans of hun- planning actions, dreds of actions, something that was not thinkable in the last cen- modern algorithms like GraphPlan and its tury. derivatives can construct plans of hundreds of actions, something that Natural Language Processing was not thinkable in the last century. Building systems that can understand natural language has always been a goal of AI, and various approaches have been tried out. The simplest is to construct parse trees of sentences and identify the categories of the words and phrases, and thus, hopefully, the meaning of the sentence. But this is not as straightforward as it seems. When Susumu Kuno gave a sentence to his parser at Harvard in 1963, the computer program came up with multiple parse trees! The sentence was – “Time flies like an arrow”. Try out different ways of reading this sentence yourself! Is ‘time’ a noun, as we normally perceive it to be? Or could it be a verb? Is some fly trainer trying to time her subjects as they practice a dash? Or could it be an adjective? Are there certain kinds of flies called ‘time flies’, like fruit flies? Incidentally, is that referring to a fruit that can fly?! Making sense of language is not so straight forward. One promising approach was developed by Roger Schank and his team of doctoral students at Yale University starting in the mid-seventies. Their theory, called the conceptual dependency (CD) theory, was designed to uncover the underlying conceptual base or the meaning of linguistic utterances. Schank designed a small set of logical predicates which he claimed were enough to construct ‘conceptual structures’ which were the meaning of the linguistic utterances. One conceptual action for example was called ATRANS, which stood for ‘abstract transfer or transfer of possession’. Thus,

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the linguistic verb ‘buy’ could be conceptually represented by two reciprocal ATRANS actions, one of money and the other of the object being sold. Some of the earliest approaches to machine translation, another desirable task given the multitude of languages that humankind uses, were based on constructing mappings from words of one language to another. Again, we soon discovered that the richness of human languages makes this task difficult as well. It seems that Western spy agencies spent huge amounts of money towards building automatic translation systems around that time. But as The Yale school the joke goes (see The Economist, Oct 16, 1997 – ‘A gift of approach to translation tongues’) when the sentence “The spirit is willing but the flesh was more promising. is weak” was given as input, the Russian translation produced by They said that since the The CD representation was the program was “The vodka is good but the meat is rotten”. not specific to any Economist article goes on to say that this story is probably a myth, particular language, it but it does illustrate the difficulties machine translation faced. could be used as in intermediate The Yale school approach to translation was more promising. representation, or They said that since the CD representation was not specific to interlingua , between the any particular language, it could be used as in intermediate repre- two languages. sentation, or interlingua, between the two languages. But that is not the last of the difficulties of processing natural language. Sentences like “He walked towards the terminal” and “She shot the boy with the gun” are inherently ambiguous, and cannot be disambiguated in a standalone manner. The context has to be brought in.

The Scruﬃes vs. The Neats

A clear picture emerges that the approach followed in GOFAI is based on symbolic representations and processes acting upon the representations. Different emphasis on each aspect resulted in two different flavours in AI research. One school of thought, exemplified by the Yale group, believed that representation is paramount. It is the representation that em- bodies what an intelligent agent knows about the world, and that

54 RESONANCE | January 2020 GENERAL ARTICLE is what is important. The processes or algorithms that act on these representations are secondary, almost to the point of being ad hoc in nature. This group of researchers came to be A clear picture emerges known as ‘scruffies’. There focus was on building and demon- that the approach strating working systems, rather than unearthing and describing followed in GOFAI is based on symbolic some fundamental processes behind intelligence. representations and Central to representation was the notion of a ‘schema’. Drawn processes acting upon the representations. from folk psychology, a schema is a mechanism for structuring Different emphasis on knowledge. Marvin Minsky, from MIT, presented schemas in each aspect resulted in the form of ‘frames’ which describe part sub-part relationships, two different flavours in as also the class sub-class relationships along with inheritance of AI research. property values. Frames laid the foundation of the object-oriented approach to programming. Roger Schank evolved the notion of scripts designed to capture stereotypical situational information for the purpose of story understanding. A script sets the context by providing the background knowledge employed by a knowledgeable listener. A program knowing a restaurant script, for example, would know what typically happens in a restaurant and would fill in the unsaid details in a tiny story like “Ramesh went to the canteen. He ate a masala dosa” and answer “yes” to questions like “Did Ramesh order a dosa?” or “Did Ramesh pay money in the canteen?”. Of course, these answers may not necessarily be factually correct. For example, Ramesh may have had an account with the canteen requiring monthly settlement.

The ‘neats’ on the other hand focus on the algorithms, and as- The ‘neats’ focus on the sume that the data on which they will act upon will be supplied algorithms, and assume by the user. This is exempliﬁed by well-known algorithms like that the data on which AlphaBeta they will act upon will used to play any board game – just plug in the rules be supplied by the user. of the game, or the well-known search algorithm A* which fo- cuses on guaranteeing an optimal path, and can be used to solve any problem that can be posed as a graph search, or a theorem proving system that can determine what necessarily follows as a logical conclusion of a given set of premises. Observe that there is no emphasis on any speciﬁc problem being solved. The algorithms are general purpose. It is also notable that neural networks in general, and deep neural networks in particular, also fall in the

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The simplicity and ambit of such neat general purpose approaches. genius of the Turing test is that it requires the machine to not only Logic, Language and Knowledge converse in natural language, but also be The simplicity and genius of the Turing test is that it requires knowledgeable about the world, have the ability to the machine to not only converse in natural language, but also make an inference and be knowledgeable about the world, have the ability to make an draw conclusions from inference and draw conclusions from what is being discussed. what is being discussed. However, given the recent advances with systems like Alexa interacting with humans in natural language, it may appear that machines will soon be able to fool many of us most of the time into thinking that we are interacting with a human at the other end. Automated voice-based help systems, in restricted domains, are a clear motivation. Some people have, however, questioned the emphasis on language in judging intelligence. After all humans are prone to anthropomorphizing. If it nods or speaks like a human, it must be like us. Hector Levesque has proposed an alternate approach to testing for intelligence that requires less judgement on our part. These are pairs of sentences called Winograd schemas, named after the scientist who first proposed them. The two sentences differ in one or two words only and give rise to ambiguity that can only be resolved using world knowledge, and not by su- perficial processing based on learning. The example first given by Terry Winograd is – ‘The city councilmen refused the demonstra- tors a permit because they [feared/advocated] violence.’ Given a sentence with only one of ‘feared’ or ‘advocated’, resolving the ambiguity of the word ‘they’ can only be done if one has sufficient world knowledge. We have not said much about logic, but suffice it to say that the machinery of logic has been built to draw conclusions that necessarily follow. The classic example almost all logic textbooks be- gin with is known as the Socratic argument: Given that “all men are mortal” and “Socrates is a man” it necessarily follows that “Socrates is mortal”. Recent work on logic has also laid a foundation for the class/sub-class taxonomies proposed by the scruffies. The frame-based systems were susceptible to illogical inferences,

56 RESONANCE | January 2020 GENERAL ARTICLE since they were hand-coded. Nothing stopped a knowledge base creator from asserting that a whale is a large fish, when in fact it is a mammal. The advent of the internet and the need for software agents to communicate with agents across the world wide web, has seen the emergence of special logics known as ‘description logics’ which form the basis of ontologies that capture the logical basis of taxonomies. We have referred to the challenges posed to natural language processing by homonyms, and the consequent ambiguity of sentences. No less a problem to processing languages is due to syn- onyms, hypernyms and hyponyms when we consider the task of The idea of conceptual making inferences. If different words meaning the same thing are structures is that one to be used in knowledge representation, then for each avatar of the creates a canonical language-independent underlying concept, should we devise separate rules of inference? unambiguous unique Consider the rule “if you love someone, you care for them”. Do representation that we need a separate rule when we use the phrase “dote on” or “are serves as the meaning of crazy about” instead of “love”? Just imagine the explosion in the a sentence. The idea of semantic parsing of a number of rules that one would need. natural language The idea of conceptual structures is that one creates a canonical sentence is to map it to such a conceptual language-independent unambiguous unique representation that ser- structure. ves as the meaning of a sentence. The idea of semantic parsing of a natural language sentence is to map it to such a conceptual structure. One needs also to distinguish conceptual actions as distinct from linguistic verbs. For example, the English word “kill” is a verb, but conceptually it is not clear what action it refers to. In Schank’s CD theory, this is modelled as a state (of being dead) resulting from some unspecified action. Thus, Boris could have killed David by strangling him, or simply by not living up to his expectations. This approach went out of fashion after the eighties, partly because it had served as the proof of concept but no one had the resources or the willingness to manually craft the knowledge structures. Perhaps in the next buzz cycle of AI, and AI does go through such surges, semantic representation will marry machine learning?

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What Next?

If an AI is to control a physical robot or a virtual companion con- vincingly, it must have the cognitive abilities that humans display.

Address for Correspondence At the very least, the AI agent must have a model of the world in Deepak Khemani which it exists, a model of itself in its model of the world, an Department of CS&E ability to reason with its representations, and have a memory of IIT Madras, Chennai 600 036, experiences. Robots to explore other planets autonomously or India. Email: driverless cars of the future will need to have this capability too, [email protected] if they are to be called thinking machines, and not just smart ones.

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