1 What is AI, what is an AI technique, which problems need AI attention?

Bhushan Trivedi

Introduction

AI or Artificial intelligence has many definitions associated with it. Read a new book and get a new definition. The AI, in simplest terms, is a way of solving difficult problems. The meaning of difficult problem is not which is difficult because of the logical requirements, but difficult because of the design of the computer itself. For example it is very easy for my four year old daughter to see a few samples of cars and then classify a vehicle as a car but can a normal computer program do that? Many of us have observed that when we meet a friend after years and find him grown in all dimensions, we are still in a position to recognize him. Researchers are working on programs which can recognize simple photos and take decisions based on the same. As long as the face is similar to the face the have seen to a large extent, they do not have any issues. The problem starts when there is a significant difference between two photos of a same person may be taken at different age or under different backgrounds. The researchers are finding difficulties in writing such programs due to obvious reasons as there is no algorithm known for solving these problems. My definition of difficult programs is these programs which are hard due to the design of the computer systems themselves. Humans can solve those problems as their minds are better equipped at solving such problems.

Here are some more examples. When a doctor can examine and diagnose a disease in few minutes why a robot (or a computer program) is not yet designed to do that? When an averagely intelligent person can learn to play games like table tennis, why a robot is not yet designed to do so? If I tell you that I went to the movie last night and I liked ShahRukh’s performance, will you conclude also that I went to a theater, I bought tickets, took my seat, spend my time viewing the movie and return back? In fact you may also additionally conclude that I am a fan of Shahrukh and may be a movie is “Happy New Year” as it is just released. What do you think if a computer program is told to listen to my statement and reason from it? ¹

“1  One may conclude learning from the functioning of brain and many intelligent people failing in designing a machine which can mimics simplest of functions of brain that the human brain is really a great invention and only the God can construct that!”

The problems that need special attention

Why it is hard for computers to solve these problems? Let me repeat that the computers are not designed to solve such problems. Humans have analog inputs, have a processor (the brain) which is very slow but runs highly parallel algorithms to solve problems (1010 neurons with on an average 1000 connections to other neurons, total 1014 connections) which are typically vision related, audio related and requires highly parallel processing. They possess common sense (somebody said that common sense is highly uncommon but is still possible for most to reason as I mentioned earlier in the text about the movie), they can work with incomplete information (I did not mention the name of the movie or the theater). They can also work with unstructured information (how have I recognized my friend after a long duration? I do not really know that myself!) Human brain stores information in a way that it is easier to have association and access information using that association. For example if I ask you the name of an actor who is tall, aged over 70 and with spectacles, all most all of you will immediately respond that I am talking about Amitabh Bachhan. If I ask my daughter a KG question, name a TV serial with a cat like alien being a friend of a human boy, she may immediately recall him to be Doremon. A guest may play few bars of music of a popular song and you may immediately recall it to be “Awara Hun” or “Indiawale”. All these are examples of how we use association in retrieving information. Our brain stores information in a form that it is easier for us to retrieve information in this fashion. Conventional databases, renowned for their ability to crunch data, are not good at accessing data using associations.

The AI or Artificial Intelligence is to write computer programs which can mimic human brain problem solving capabilities. Elaine Rich, in her book “Artificial Intelligence” puts it as “AI is the study of programs at which, at the moment, people are better”. One more author puts it as “AI is about writing intelligent programs”. One more definition is “AI is about building entities which can understand, perceive, predict and manipulates like humans do”. The last definition is little more interesting as it is also talking about entities which can act like humans and not merely programs. The robots which are confined in science fictions till now, AI is a study of methods of bringing them to real world. So far, we know, it is not really possible. You may also wonder why that is so. In fact, to solve any problem, we first of all must know what the real problem is. Let us try to understand this point little further. Let us again jump back to the definitions of AI. I used word “difficult programs” while somebody else used word “intelligent programs”. Do you consider brushing the teeth or picking up a chalk stick is an intelligent task? Unless you want your robot to do so, these tasks do not seem to require the use of intelligence. In fact when you start building your robot, you may have to decide the exact pressure to apply to pick up the tooth brush or a chock stick, a slight miscalculation results into breaking it. The brushing process also is required to decide the direction and pressure of the movement of the tooth brush, which, however trivial it look like, an extremely hard  problem to  solve even when the human head is considered stationary. Thus, I stick to the word “difficult programs” and not “intelligent programs”.

To make the long story short, AI is about solving problems which are difficult to solve by conventional methods of computing as those methods are not designed to act like human brain. It is clear from above discussion that if we ever want the computer programs or Robots (with computer programs running within) to be successful in performing tasks that we have discussed so far, we need to design our computer systems or programs differently. AI is the study of designing computer systems and programs where they can perform more like humans and solve similar problems.

Why study AI

AI is not new discipline. In fact it begun in 60’s when many researchers begun to take interest in learning how human brain functions and try predicting the human behavior from their perceived model of brain and try mimicking human behavior using automated programs. Many models ae presented till now and many research papers have given many researchers their Ph Ds and many people like me earned their living by teaching AI as a subject. The ultimate objective of building a human like machine is not yet been met. It is not likely to be met in foreseeable future as well.

Does that mean that we should not study AI? Or AI is a dead discipline? No. Let us understand why. Though study of AI so far could not exactly achieve the goal it started with (and still perceives to achieve), it has contributed revolutionary solutions in the field of computer science. For example we have programs which can find traffic related parameters from visual images and provide intelligent traffic control. There are programs which can recognize people’s faces to certain extent and provide search options like “Give me all photographs where this face appears in”. Signature recognizing programs are common. Solutions which look at PDF  files and  convert them to  document  files by classifying each character of the PDF image are available. Access control systems which use biometric measures like thumb impression, retina scanning or face recognition for authentication are already available. Expert systems which may not be able to exactly work like a doctor but an assistant are available. Thus, AI has achieved many things. Game playing programs are in huge demand today. Most, if not all, use one or the other form of AI.

All in all, AI has provided answers to many challenging problems and likely to provide even without achieving what it set out to. Our aim in the series of discussion is to learn how AI attempts to solve problems, what the obstacles are and how they are overcome, and why some problems are not yet solved².

“2  So you can solve one of them and get your Ph D degree or a patent!”

AI techniques

The techniques that we need to use to solve the difficult problems are known as AI techniques. There are a few common characteristics of AI problems and the AI techniques must attempt to handle them. Here is a list.

1. In most cases, the AI problems have no algorithm. For example signature or face recognition problems. We can recognize anybody’s face or signature but if asked how we have recognized somebody’s face we cannot answer.

2. In  many  other  cases,  the  process  has  simple  algorithm  but  with  many  permutations  and combinations. For example a travelling salesmen problem itself is not hard  but getting an optimum answer is beyond the scope of even the best of computer systems with reasonable number of cities to travel. Another example is of game like chess. A normal chess program has on an average 50 moves from both sides. The average branching factor of the search tree is about 35. Looking at this complexity, if a chess program checks for every conceivable option once the opponent has played its first move, even with the best of the computing system in place, cannot take its first step in the opponent’s lifetime.

3. In many other cases, we do not want best answers but answers which are acceptable. For example if we are planning to go to a movie we do not decide the best movie or the best theater or the best actor or actress. We would go to any movie with some of our expectations (like a good reasonable story, or some entertainment etc) is satisfied. Such answers are harder to get by conventional computing systems.

4. Conventional programs do not usually have learning component. A human can learn (almost) anything if taught properly. If we have such capacity in a programming system, we do not need anything else3.

5. Another important requirement of many AI program is called explanation facility. If a human doctor diagnoses me with malaria for example, I would invariably ask “How can you say so doctor?” A doctor may respond back saying “because your red blood cell count is less than required”. A computer program’s response can be quite interesting. If a computer program respond like “You have malaria because the variable xyz has value > 0.8 while pqr has value < 0.3 and all the values of abc is below 0.5” probably none of us use that system again.

AI has tried to answer solutions to almost all of above problems and many more in a successful way. It has to find its own techniques for solving these problems. Those techniques are called AI techniques. Let us try to understand what these AI techniques are and how they can actually help solve these problems.

“3  It is good that such intelligence is not present in computer systems. Otherwise programmers are not needed so you won’t be studying this and people like me who earn their living by teaching this become jobless.“

Heuristic based search

The AI technique that we discuss first is called heuristic based search. Most of the problems humans solve are of the kind where if an optimized solution is sought, it would be impossible to solve. For example a game like chess has many alternatives to considered in true sense impossible for any human being to explore in real time. A human player actually considers only a very small subset of all the moves and evaluates them. Also, he does not consider more than a few moves down. For example he might think what opponent will do with the move that he is planning to play, how he will be responding to that and so on for a few moves. He does not consider the entire game. Number of moves (they are called plies in game playing parlance) are decided based on the state of the game, the possibilities of fork (when opponent Night can take two different directions, one which threatens the King or Queen and other one can capture some other important piece. We can save only one of them at any given point of time usually), the possibility of check (when our king is under attack), and so on. Human players develop their own rules (we call it knowledge) to play games like chess over a period of time. They also use their knowledge to determine better move from alternatives available at any given point of time. Using these rules, they eliminate most of the not-so-useful options and can avoid overwhelming combinations of moves to choose from.

Humans use this method of searching using heuristic in most of the problem solving that he does. For example when a doctor examines the patient, he does not check him for all possible cases of diseases and does not offer all possible diagnostic tests to determine actual disease. A doctor may have some knowledge about where the patient lives, the work that he does, other important attributes of a patient (for example whether he is living a stressful like or leaves near a dirty place etc) and so on. He also has some idea about the atmosphere when the patient is reporting, for example if he is reporting in a rainy season, amount of mosquitoes in the area, the predominant types of diseases in that area and so on. He also is aware of the patient history and probably knows a bit about his family history to learn about probable disease the patient has. From this knowledge he might have formed rules and use them to try fixing his attention to test if the most probable disease is the culprit. For example in rainy season and the patient leaving in an area with many mosquitoes, he might check for malaria first if the patient complaints about body ache and headache. During summer he might think the other way round and check for throat infection and so on. The rules formed by such experts help them zero in on the right diagnosis quickly. They do not need to search systematically checking for all possible causes and combinations of causes.

In fact, a human is considered expert if he has such domain knowledge. If one thinks little further about any expert’s working style, they can clearly see two different types of skill sets present with every expert. One is, obviously general problem solving skills by which all experts tackle the problem. The other and much more important is this domain knowledge. The heuristics are the rules of thumb which represent expert’s knowledge about the domain. An expert with better heuristics is a better expert.

Expert’s heuristics represents their power and their ability to diagnose. Computer based expert systems which tries to mimic experts must  have these heuristics as part of  their system. Not only storing heuristic related information but to use them in making decisions is also an important part of every AI system.

So the first important attribute of an AI system is the set of heuristics which can help reduce the search to a real time search and solve the problem. Any technique which enables incorporating heuristics into the system and allows using them in the process of reducing number of options and finding solutions in real time is an AI technique.

Sometimes the heuristic is denoted by other names, for example in the domain of Genetic Algorithms the heuristic is known as a fitness function. A fitness function indicates how the solution is fit as per the definition of the user. Fitter solutions are kept and others are thrown out. The process is repeated until the final set of solutions with required attributes is found. In the process of simulated annealing the heuristic is known as objective function.

Knowledge representation and inference

Another important part of any AI system is the method to represent the knowledge about the system and a process which allows the manipulation of that knowledge for decision making. For example many current systems, especially big data systems require to process text based information coming from social media websites like Facebook and Twitter. Take an example of a product based company. The company works hard to analyze twits and posts their users make over the social media and guess if any comment is about the product (or the rival product). Twits like “This bike is awesome, I ran it for 18 hours and it had no problems!”, or “I feel very comfortable even after 12 hours of riding” can tell a lot about the product than a conventional customer feedback form. On the contrary, “This washing machine is awful, it breaks down every month”, or “This service center always gives busy response to calls and do not respond to mails” are warning signs. A good program which can analyze the texts the user is posting, find out if that post has any relation to the product of the company, and if so, find if that comment is positive or negative can be extremely handy for the customer relationship department. Unfortunately, the biggest hurdle in this case is a good method for analyzing text, storing it in a form which can help us deduce something easily. There are many methods proposed which can solve it for one problem but fails to take account another. For example, a human can read all these twits and also gather (looking at other information which an automated program is not designed to look at) that most problems reported is about a typical washing machine model or associated with a typical branch of service station (by finding out which washing machine that customer is using and which service branch is providing service to that customer). Can an automated system do so? It is hard for any system to solve the problem in a different way than it is originally designed to. One of the biggest challenges here is the representation of the knowledge in a form where such processing is possible. In fact this is also an example of humans inferring from their knowledge to build on the knowledge that they have. For example they look at the details of the twits by customers and infer that the brand y model x has a recurring problem called z. This is an addition to the knowledge that they have. The knowledge representation must also allow inferring and adding to the knowledge storage (called knowledgebase). Any technique which allows the system to process and infer from existing knowledge is an AI technique.

This inference and storage and retrieval of knowledge abilities also indicate an important attribute of human problem solvers, they learn from their experience and be better at any anything they practice regularly. An AI technique which can help the system learn from the past experience is an AI technique.

There are quite a few AI techniques used for learning. A big class of systems use neural network based systems for learning. Neural network based systems mimic the function of human brain using the similar physical structure in which the brain is organized. The neural network system use methods known as BPNN (Back propagation neural networks), Markov Models, Hopfield Networks and a few more. Another area where the learning is equally important is known as genetic algorithms which are based on patterns of solutions used for the ecosystem for survival. They also have a strong learning component.

One excellent tool to represent knowledge using these models is Metlab. Libraries to represent BPNN and other neural network models are available in Metlab and a programmer can easily write programs like signature recognition using Metlab.

The other learning approach used in practice use predicate logic based approach. A language called Prolog was solely designed based on the predicate logic and is quite powerful in inferring from old knowledge. It uses predicate logic form for storing the knowledge and thus enables the program to infer from old data. Let us take an example to understand.

Suppose following predicates are given.

1.  mother (Devaki, Krishna),

2. brother (Kansa, Devaki)

3. mama (X, Y) if brother (X,Z) and mother(Z,Y)

One can prove mama(Kansa, Krishna) using prolog using three predicates defined above. There are two types of knowledge in Prolog. The first two statements represent facts and the third represents a rule. A fact is a simple knowledge about a relation (a relation called mother between two entities called Devaki and Krishna for example)between a few (in our case two) entities. The rule is another type of knowledge. A rule contain variables which can assume values (for example the rule mama(X, Y), both X and Y are variables which can assume different values like Kansa and Krishna).

One more similar method for knowledge representation is called frames. They are the first version of object oriented knowledge representation. Many current languages, most notably Java and C++ can support knowledge representation using frames. For example we may have a class frame called Student and object frame called Ramesh who is an actual student and can specify that Ramesh is an object of type Student. A class frame has attributes (for example frame Student might have some attributes like total number of objects (known as cardinality in AI) represented in object oriented programming languages using static members. An object frame like Ramesh has attributes represented by non-static non-global members of a class.

One of the growing areas of application of AI is use of software robots. These robots (sometimes called bots) are software mobile entities. They travel from one machine to another and gather information and interpret them. The author of this module has developed a system during his Ph D work which used mobile agents which can go to target machines, gather intrusion related information and comeback. The sender collects information from all such mobile agents and deduces if there is an intrusion somewhere in the system or not. Such systems are quite handy for using behavior based heuristics to figure out if the user has malicious intentions or not.

Another form of knowledge representation is to use some kind of entity relationship diagram. Semantic net, conceptual dependency and a few other methods are used to represent knowledge. In fact the object based representation can also be used in conjunction with and actually used with such knowledge representation schemes. Such knowledge is represented in form of a graph where nodes represent entities while the arcs represent the relations between them. Each node as well as relation can be represented as objects. In fact special languages which provide direct representation using frames where proposed and used to a small extent. However, most designers prefer to use general purpose languages like Java.

The techniques which allow the knowledge representation for manipulation and inference are also AI techniques.

In fact the requirements that we listed are all which the users can see on the surface. To support these requirements, the computing systems do need to provide many other requirements. An AI technique, in general, should help in knowledge representation, inference, learning and programming with heuristics.

The requirement of learning is obvious for knowledge extension. In fact one important branch of AI deals with automates the learning process for information gathering. That information is to be used for further processing. That process of automated learning is known as machine learning.

Another area where learning is more important is known as case based reasoning. Whenever a human receives a problem, he would try to match that with other problems he has already solved and try using the same approach with required modification if he finds a perfect or a near match.

Reason with incomplete information

In fact many AI programs need something more. For example most humans can reason with incomplete information. They are able to guess and work. There is a set of problems known as constraint satisfaction problems which demand such ability. For example we may get a new mobile with many new features. We can assume something and try, if fail, we assume something else and try. Usually, based on our previous experience, we can assume most things right and learn to use the mobile in a very short period of time. Let us take this problem to little deeper level. Suppose we want to find out how to set alarm using a new mobile. We will try to find settings first. (That is based on our experience of having similar features under setting). Suppose under setting we find following menus; Phone, Contacts, Logs, Connections, Accounts and More, which one will we choose? Most of us choose Phone as the clock is more likely to be part of it than any other menu. Suppose we cannot find it under phone, we may try a menu called More as it may be the next best option. What we are doing is assuming something (for example the phone menu designer is following some logical sequence) and checking for it. It is quite possible that a setting does not contain the clock or alarm related information and it is somewhere else. In most cases we will be able to find alarm related information assuming some constraints and following the search process accordingly.

Another example is processing user inputs in form of voice or spoken commands. Sometimes the voice input matches with two items. The decision about which item to choose is decided based on context. We sometimes face the same problem when the mobile call is not coming clearly and we cannot listen to the other party’s voice cleanly. We may miss words or sometimes entire sentences but still can communicate based on our ability to judge the missing word or sentence.

Another example is the domain all of us loves be engaged with, games. A simple game of cards depends heavily of our ability to assume what the other player’s card values are. We do not have that information but we are able to assume and proceed with the information that we have.

Thus the ability to continue despite incomplete information using assumptions is an important requirement of an AI program. A technique which enables the program to  work with  incomplete information is an AI technique.

Fault tolerance

Another important attribute an AI system sometimes requires is fault tolerance. We do not only work with incomplete information but incorrect information as well. For example if you ask a school boy a question like “Name the fish which is bigger than  shark and have lungs”. Most school kids would respond back “I think you are asking for Whale, but it is not a fish, it is a mammal”. We provided wrong or incorrect information but a school kid can not only produce correct answer, also help us learn about our mistake.

Let me quote one more example. I was looking for a friend who told me over phone that he lives in a society called “Ratlam”. While looking for him, I asked a panwala nearby “Where can I find a society called Ratlam?”. The panwala responded “Sir, there is a society called Satnam nearby, you better check there.” Is there something which surprises us? If you are an AI teacher or researcher, you are. I have provided completely wrong information but I got correct answer (I could locate my friend actually from that society). How could that panwalla’s brain detected that? It is due to the fact that both Ratlam and Satnam look different, they sound similar. Can our AI based programs get that? Some programs actually are able to do so and exhibit fault tolerance. Not all programs require this attribute though.

Let us quickly recap what we have learned. AI is a discipline where we learn how to write difficult programs. The difficulty of such programs lie in their ability to act like humans, use heuristics to search when there is no direct solution available or search blindly would result into inordinate amount of time. The programs need explanation facilities sometimes and sometimes fault tolerance. Sometimes ability of the program to work with insufficient information also matters. One of the important attribute of many programs which need AI support is learning. Neural network based learning methods are very popular today.

The techniques which enable the program to have all of above abilities are called AI techniques. We will study many of them in due course.