Home Classroom Artificial Intelligence Artificial Intelligence – Part 2

Artificial Intelligence – Part 2

Moving forward

In the previous article we took a glimpse of the vast field of Artificial Intelligence. Hope you all learnt about the history and the basic background to research in this area. Let’s move forward so that we can learn about more interesting areas in AI in depth.  This article would be covering up some philosophical and scientific aspects of AI which are important to be aware of which would yield to better understanding the material which follows up in the articles to come.

 

Concepts of modeling intelligence

The concept of intelligence is a very hard thing to describe. As humans, we have the notion that we are superior and that we have the highest intelligence compared to all the other living animals on this planet. Philosophically speaking, we believe that we are intelligent and define intelligence as a property of human beings. Nevertheless, we do not know for sure, whether there are any other species that are far more intelligent than humans because, we tend to believe that man-kind is born with intelligence. All the concepts of AI are based on the fact that they are compared to human intelligence, because as humans that is the only form of intelligence that we know of and that we have experienced. Yet, we are faced with a whole bunch of questions such as what exactly is intelligence, how do we measure it, what are the factors that contribute to creating intelligence, etc? With regard to these we then face the query as to how we narrow these down. Is it being smart? Being able to think and take decisions? Act correctly to suit a specific situation? Ability to gather knowledge, remembering, recalling and reasoning it when such occasion occurs? Skill of learning things? Is it a genetically inherited aspect? This is an extremely difficult thing to define because, based on personal experience different people define intelligence differently in any way that they think is the best. In the past, researches in the area of AI also faced this situation. Ultimately, different areas of thought on intelligence were brought in to limelight. The researchers defined AI based on two main characteristics that they observed and thought which contribute a great deal in to human intelligence which are as follows.

The mental thought processes and reasoning

Behavior or actions

Based on the above two characteristics that researchers believed that intelligence can be generated the following approaches to AI were originated. The names of these approaches were mentioned in the last article but in here we go in to more detail and provide an in depth view of each one of them.

Thinking humanly – This was more linked with the thought processes and reasoning ability which generated intelligence rather that the behavioral aspect. It was believed that if we can build machines which can think and reason like humans then we have achieved some part of intelligence in those machines. The areas of interest were decision making, problem solving, learning abilities which led to researching in psychologically understanding how the human mind thinks and how the thought processes work, linking cognitive science and natural languages with AI.

Thinking rationally – The idea behind rationality is such that, to think the right thing at the right time based on what is known. Humans usually think rationally although they do make mistakes in their reasoning processes sometimes due to mistakes in thought processes, lack of information, stress and other mental factors, beliefs and emotions which results in irrational thinking. But nevertheless, this notion of rationality whether it being a human being or any other being if they can think of the right thing based on the what they know it can be considered as the best thing to think of given the circumstances. Thus, this shows a form of intelligence. In AI this approach was closely linked to studying of mental processes with the use of computational models so that it leads to rational thinking. The ideas of logic based reasoning were connected to AI with this approach.

Acting humanly – This approach was mainly trying to define AI as building machines which can perform the actions that are done by humans without human intervention in the same or closer manner. Thus, this idea is highly linked with the aspect of humans being the intelligent species of all and what they do is right because they do it with intelligence. The areas which nurtured from this aspect are humanoid robots, robots which perform day to day tasks such as cleaning, making coffee/tea, etc. This lead to linking mechanical and motor skills of humans in to machines so that they can perform the actions that the humans were better off doing. The subject areas such as Natural language processing, learning, computer vision, robotics and knowledge representation were nurtured with this approach to AI.

Acting rationally – The last approach was making agents or machines act in a rational manner so that they would be able to perform the right action and the right time. This was when the notion of ‘rational agents’ came in to the scene. This approach was highly adopted by many scientists in the field of AI because it was less linked to human centered approaches such as ‘acting humanly’ or ‘thinking humanly’ because they tend to focus on the fact that intelligence in some characteristic inherent to humans which is certainly of doubt and can lead to many philosophical and scientific arguments. Further, it was a more general approach looking at rationality and also more generalized than the notion of ‘thinking rationally’ which is dealing with logically representing the thought process which would be not very successful in highly dynamic environments and will not be perform well under uncertainty.

 

Further thoughts on AI

Although the above approaches defined what is required and intended out of AI, the areas to discover were immense. Here are some aspects which future of AI would highly depend on.

Further research was carried out and it was found out that even though the mental processes are a key to intelligence, it requires a fair amount of perceptual abilities such as vision which gives the edge to intelligent beings such as so called humans to think far better than others. Computers, on the other hand had a disadvantage when compared to humans of not being able to perceive their environments in which they operate other than by means of the data or other facts that humans themselves feed in to the machine. The area of ‘computer vision’ was then brought out in to the picture, so that researches were able to discover how humans perceive things in their environment through the ability to see using their eyes. Various researches are being undertaken in this domain of Computer Vision which spans across multiple disciplines such as Biology, Neuroscience and AI. There have been attempts to build in human like vision capabilities and perceiving systems in to robots and other machines with some success. Researchers were able to build the vision aspect to see and visualize things using image sensors and other mechanisms in machines, yet the actual goal of computer vision is yet to be discovered. It is not just seeing things from the eyes or any other visual systems that matters most, but the process afterwards which maps the images to symbols and other mental representations which generates knowledge. Still the robots and agents which act as machines with high capabilities of intelligence lack the real power of human vision system which is being able to symbolically represent the world they perceive while linking it with the other things they have perceived in the past to generate new knowledge, ideas, thoughts and reasoning. Thus, the future of intelligent systems would really benefit from the advancements in Computer vision which would enable the machines to perceive the world closer to how humans do.

The aspect of ‘consciousness’ or ‘self awareness’ which makes humans know beforehand about the world based on their past experiences is another thing which is a hard thing to replicate in a machine. It is found out that ‘Consciousness’ also contributes to human intelligence in terms of making learning more easier. For example, if we know that fire would burn the skin if it was taken too closer to our body because our parents have taught us so when we were kids or with experience we would tend not to bring harm to us using fire carelessly. This shows that we are conscious of the fact that fire is dangerous although it is useful for day to day life. But, a machine on the other hand, would not have this ability unless we explicitly insert a rule so that not to use fire too close. But still then, it is very difficult to model such characteristic in a machine because, then you have to deal with lots of minor details such as how close it can come without causing damage, how strong the fire would be to cause a damage, etc, because as the machine cannot perceive as well and use the self-awareness as we have. Some other thoughts that were brought up were the notion of ‘memes’ which are like the genes which contribute to the fact that which makes humans different and more superior to other living beings. Philosopher, Susan Blackmore wrote in her book named “The Meme machine” that humans imitate what other humans do and learn from those. This is passed on from person to person and was known as “meme” as in genes pass our heredity and actual structure of our organisms in a physical sense she argues that “memes” pass on our knowledge and ideas gained from others. This ideas have brought challenges to the field of AI if the researches are focused on building machines which are acting humanly or thinking humanly because, then they have to think of ways and means of how to model knowledge and ideas are passed on from one being to another, aspects such as ability to imitate in to the machines as well.

 

Where are we heading at?

In this article, the main intention was to give you all a philosophical and scientific background to the thought in AI so that you develop to think of this subject in a more in depth manner before we go on to details of each sub area. We have to keep in mind that the four main approaches described in the second section are very important in understanding where we are heading next. The other areas and thoughts that were presented in the third section give a good insight to where AI’s future direction would be and a brief idea of where we have to focus on. In all the other articles to follow, you would have the opportunity to get an understanding of all the sub areas of AI such as learning (which is known as ‘machine leaning’ when related to machines), logic and reasoning, knowledge representation and knowledge bases, data mining, decision making, intelligent agents, search techniques, computer vision, natural language processing and many more.

 

References

Artificial Intelligence – A modern Approach, Second edition, Stuart Russell&Peter Norvig

The Meme Machine, Susan Blackmore

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Chathra Hendahewa is a graduate student at Rutgers –The State University of New Jersey since fall 2008 and currently in her second year of studies. She is studying towards her Doctorate in Computer Science and her major research interests are Pattern Classification with application to financial systems, Machine Learning and Cognitive Sciences. She was the first ever International Fulbright Science &Technology awardscholar from Sri Lanka. Chathra graduated from Faculty of Information Technology, University of Moratuwa in year 2007. She is also a passed finalist and a multiple gold medalist of CIMA (Chartered Institute of Management Accountants –UK). She has professional experience in working as a Business Systems Analyst for more than a year at IFS R&D and has completed an internship period in developing modules for‘Sahana-Disaster Management System’. She loves reading biographies, mysteries and science fiction and listening to music during her free time.

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