AI ∶ The Future of Humans?
What is Artificial Intelligence?
I use the term ‘AI’ to describe artificial intelligence. There are lots of programmers who don’t like the term because they think that it implies that the purpose of the programming is to make a machine equivalent to a human mind or intellect, or that the programming is meant to apply to all human beings or to all possible human beings.
If you like, you could call it ‘intelligent machine programming’ instead of ‘AI’. This would be a reasonable definition but, although it would be a useful definition, I doubt that it would mean much to most people if you told them that your programming had been created with the aim of creating an intelligent machine. In my opinion, the term ‘AI’ is more effective because it emphasises the fact that the ‘intelligence’ we are talking about is the intelligence of human beings. It’s only when you start talking about machine intelligence that people really have a problem with it.
For example, in the past there have been many attempts to create a machine that could play chess. It is not really surprising that the number of games that this machine could play was limited because the computer systems of the past were not sufficiently powerful to deal with the many different moves that a human chess player could make. The invention of chess-playing computers really started about 20 years ago when researchers came up with a program that played a good game of chess. Then more experienced computer scientists began to come up with more complicated programs that were good at many more games.
But these computer programs are still not as good as the best human chess players. Some chess players believe that, by the end of the next century, computer chess programs will be almost as good as the best human players. However, if that happens, I suspect that it won’t make a huge difference to people’s expectations about what makes a good computer programmer. People will still expect their computer programmers to be intelligent programmers who are good at working out problems, putting ideas into useful programmes and designing and implementing computer programmes that perform complex tasks.
What is the difference between AI and Robotics?
I first became interested in the topic of AI when I realised that there was a big difference between the AI research that I was working on at the time and the work that was being done in the area of robotics. I didn’t know anything about robotics when I started doing AI research, but I soon discovered that there was a whole world of AI that was very different from the sort of AI I was working on.
So, if you look in the right places, you can see that there are quite a few computer scientists who are doing very interesting work with the aim of creating robots that do useful things. There are many people who study machine learning techniques and who develop techniques for recognising speech, handwriting, facial features and so on. Some of these methods can be used to improve the performance of robots. For example, you could use a voice recognition system to tell a robot what you are asking it to do. But a lot of the things that are done by researchers in AI are designed to improve the capabilities of machines that already exist.
However, there are only a very few scientists who are trying to create an intelligent machine. This was very surprising to me when I first started working on AI. I had the impression that, if you were a good programmer, then you would have an almost automatic way of producing intelligent programs. There are so many exciting ideas and there is so much experience from so many different applications that it seemed that there were millions of intelligent programmes waiting to be created.
I soon learned that, although this was true in many cases, there are many areas of AI where people do not yet understand what is needed to create a useful intelligent machine. It’s very difficult to judge where these ‘valley-of-doubt’ areas are, but it seems to me that robotics is one of the areas where there is still not enough understanding of how to create a useful intelligent machine.
What is the measure of a ‘good’ AI?
So, I’m interested in intelligence. I want to know how much intelligence can be achieved by any computer program or by any group of computer programs.
I’m also interested in machine intelligence because I think that it’s not easy to come up with a good definition of intelligence. I think that it’s very difficult to define what makes a human intelligent. We know that human beings have certain abilities that make it possible for them to acquire knowledge. So we know that intelligent people can read, understand and remember things. We know that they can learn how to do different tasks. So we know that intelligent people have some abilities that enable them to acquire knowledge, to learn and to adapt to different situations.
We also know that human beings have some abilities that enable them to think logically and to make judgements based on logic. And we know that, when an intelligent human being is faced with a new situation, he or she is able to come up with a plan of action, a plan of how to deal with this new situation. In general, we know that human beings are able to come up with logical explanations of why things happen the way that they do and we know that they are able to judge what they think are good explanations and what they think are bad explanations.
It’s not easy to come up with good measures of machine intelligence because it’s not easy to measure something that you don’t understand. I’m going to use the idea of how much intelligence a machine can acquire. We can be sure that we can come up with definitions that will allow us to measure the ability of a machine to acquire knowledge. We can also be sure that we can define things that a machine can learn.
However, in order to be able to measure the ability of a machine to acquire knowledge and to learn, we have to know the characteristics of knowledge and of learning. We have to understand the nature of knowledge and we have to understand how knowledge develops over time. We also have to know what is the difference between knowledge and understanding.
So, although we can measure the ability of a machine to acquire knowledge and to learn, we can only do that if we can measure the things that we want to measure. We can only measure the things that we already understand. That makes it very difficult to come up with a definition of machine intelligence. We cannot give a definition of machine intelligence until we have a clear understanding of what intelligence is.
Where is Artificial Intelligence heading?
The question ‘Where is AI heading?’ is very difficult to answer. There are many different areas of AI research. So it’s difficult to say where the areas of AI that are least developed are, or where the areas that are least developed are.
There are, however, a few areas of AI that are developing fast. Computer vision is an area where a lot of progress is being made and it seems to be heading in the right direction. There are lots of interesting ideas in computer vision that can be used to develop a wide range of technologies. Speech recognition and speech understanding are areas where there is a lot of good work being done.
The work that is being done in robotics is still very limited. Robotics is an area that I have a very strong interest in. I think that, once you have a robot that does useful things, you will have a robot that is really intelligent. You will have a robot that will be able to do things in a useful way. But there is a very long way to go before a robot is able to do things in an intelligent way.
The area that interests me most is the creation of autonomous systems. If you want to create a robot that is able to do something autonomously, it’s important to get the robot to be able to think. It’s not enough to have a robot that is able to move things around. You have to have a robot that is able to make decisions and to implement decisions that it has made. But, for many years, AI research hasn’t really been concerned with developing the kind of abilities that are necessary for autonomous robots to make decisions and to do things in a useful way.
The problems in the development of autonomous robots are difficult to solve. The difficulties that you have to deal with are just about the same as you would face if you were trying to create an intelligent machine. I don’t have a good answer to the question ‘Where do you start?’. I think that, at the moment, it is difficult to start. I think that the experts would agree that the area that is least developed is the creation of autonomous robots that can do useful things.
In conclusion
AI is an area of computer science that is evolving all the time. It is a very useful area of computer science. It is a very difficult field of computer science. Many people are concerned with whether or not AI is really to be thought of as artificial intelligence. In my opinion, it’s an important topic.
I hope that I have managed to convey the excitement that many computer scientists and computer scientists experience when they study AI. It’s an area of computer science that is constantly evolving. There are a lot of exciting things that we can do, things that we can learn and things that we can do with our computer programmes.
