Artificial Intelligence 111

 

Artificial Intelligence 111

To put a context around this technology the term embraces a fundamental change in computer programming. To date computer programming has been based around a detailed understanding of the process to be computerised and a step by step translation into instructions for the computer. The general word for this is that an algorithm is constructed. This is a fancy way of saying that the problem is precisely understood and a solution can be developed.

An example would be the calculation on the interest paid at 2.5% on a principal of £1500 over 2years and 7 months. This is a precise calculation which can only give one answer and the way of calculating it is also known precisely.

Back when I was a student this was contrasted with heuristics where the precise solution is unknown but human intuition based perhaps on analogy can lead towards a solution. Heuristics can lead to rules, by which are really meant generalisations. Heuristics often doesn’t give one precisely correct answer but rather a series of options depending on the precise nature of the question

An example might be a controller of traffic lights at a multi way junction. If the lights can detect vehicles then one might say release traffic until all vehicles have passed on the major route, Conversely only stop the major route in both directions if a vehicle approaches on a minor route. Then suppose we want the lights to behave differently at rush hour. Different rules might be say let the first 50 vehicles pass on the major route before switching. So far an algorithm could be applied but then suppose some more general conditions were applied such as requirement for the lights to perform in the most optimal manner reducing vehicle delays to a minimum. Then a precise solution would be unlikely and heuristics would be needed ( Incidentally I would like to see this no doubt hideously expensive solution used )

Artificial intelligence is fundamentally different. It is based upon the study of the brain and how it works. Essentially the brain consists of neurons connected to others in what can be thought of as layers. When we learn the pathway from neuron to neuron is strengthened for a right answer and not strengthened if the answer is wrong. Artificial intelligence uses a brain like structure often called a neural net. This network can be many layers deep. As it stands the network knows nothing but is trained by giving examples. Suppose a neural net is asked to say when given a picture whether it is a cat or a dog. Many thousands of pictures are presented and the trainer tells the net whether it is a cat or a dog. If a dog then connections are reinforced in one way, if a cat then reinforced in another.

Subsequently if presented with an unknown image the net can respond either down the dog route or the cat route depending on where the neuron connections have been reinforced. The power of AI is that the network can interpolate, ie observing that the image is ( say ) dog like but not cat like. Generally AI cannot extrapolate very well.

It can be seen there are two fundamental steps. Firstly setting up the neural net and second training it on external data of known type. This process is totally different from trying give instructions to the computer which enables it  to differentiate between cats and dogs.

This a grossly oversimplified description of AI but the principle is always the same. AI needs a lot of training data. This explains why such data is so valuable. An AI system may be able to see patterns in data which a human cannot. On the other hand an AI has no common sense. Even a well trained AI can look at a dog and see something entirely different.

One intriguing way in which an AI system can be used is to unleash it on a system and just tell it when its right or wrong. The AI can slowly learn the system and may even spot hitherto unknown possibilities. AI’s are particularly good at games which have precise rules but give rise to very many possibilities like chess. A good AI can beat a chess master.

One heavily publicised possibility is self driving vehicles. As this is written there are prototypes on roads. Often this is within particular boundaries such as the City of Phoenix in the USA. Such prototypes usually have manual supervision. Tesla cars have an extremely advanced version which has the objective of only using camera “eyes” and capable of driving anywhere. Vehicles have been made available in beta test to the public but are not generally available. The self driving AI continues to be tweaked

It is important to realise that AI’s are not intelligent in the way a human is. Humans bring a lot of general learning to tasks and generally avoid the kind of silly mistakes that an AI can make. For example if a human looks at an animal picture with the name tag “Tiiddles” round its neck then the human can probably guess the animal is a cat without any more information from the picture. This is the kind of intelligence which an AI would find difficult.

Some eminent technologists are concerned about the future. They worry that an AI could invent a better version of itself and that this could escalate into a super intelligence far outstripping humans.

One doesn’t go very far without coming across the “Three laws of robotics” Conceived in science fiction by the eminent writer Isaac Asimov they are sometimes seen as the basis of a “thought experiment” when AI has advanced far enough.

The laws are-

1 A robot may not injure a human being or, through inaction, allow a human being to come to harm.

2 A robot must obey instructions given to it by a human being unless this conflicts with First Law

3 A robot must protect its own existence unless this conflicts with First or Second Law.

Although a fictional device these laws are very widely quoted. There have been various efforts to pronounce in advance ethical principles for robotics. Incidentally it should be noted these are for self mobile robots while all existing examples of AI are static “brains”

An immense amount of money and brain power are being invested in AI and the field is moving fast. After previous false dawns it does seem as though exciting tools are on the verge of development. There has been some over excited speculation of mass unemployment. All previous technological developments tell us this is highly unlikely; more leisure hopefully, but also different jobs made possible by AI tools.