There's a lot of buzz about artificial intelligence at this time and the term AI seems to be widespread. But what is it exactly? To clarify these things, I just want to make a quick explanation on this topic.
First of all, let's look at the definition to avoid confusion. We have to go back to the earliest and hence purest definition of AI from the time when it was first coined. The official idea and definition of AI was first coined by Jay McCartney in 1955 at the Dartmouth conference. Of course those plenty of research work done on AI by others, such as Alan Turing before this, but what they were working on was an undefined field before 1955. Okay so here's what McCarthy proposed:
"Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find out how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve
Translation, in essence, AI is a machine with the ability to solve problems that are usually done by us, humans, with our natural intelligence. A computer would demonstrate a form of intelligence when it learns how to improve itself at solving these problems. To elaborate further, the 1955 proposal defines seven areas of AI.
Today they're surely more but here are the original seven.
The Original Seven Aspects of A.I. (1955):
- Simulating higher functions of the human brain.
- Programming a computer to use general language.
- Arranging hypothetical neurons in a manner enabling them to form concepts.
- A way to determine and measure problem complexity.
- Abstraction: Defined as the quality of dealing with ideas rather than events.
- Randomness and creativity.
After 60 years, I think realistically we've completed the language, measure problem complexity, and self-improvement to at least some degree. However
randomness and creativity is just starting to be explored. This year we've seen a couple of web episode scripts short films and even a feature-length film co-written or completely written by AI. That don't really make sense but here's a few snippets for your entertainment anyway.
Okay, so in the definition you heard the word intelligence. What is the intelligence? Well, according to Jack Copeland who has written several books on AI, some of the most important factors of intelligence are:
- Generalization learning, that is, learning that enables the learner to be able to perform better in situations not previously encountered.
- Reasoning, to reason is to draw conclusions appropriate to the situation in hand.
- Problem solving, given such and such data find X.
- Perception, analyzing ask and environment and analyzing features and relationships between objects, self-driving cars are an example.
- Language understanding, understanding language by following syntax and other rules similar to a human.
Okay, so now we have an understanding of AI and intelligence. To bring it together a bit and solidify the concept in your mind of what AI is, here's a few examples of AI: machine learning, computer vision, natural language processing, robotics, pattern recognition, and knowledge management.
There are also different types of artificial intelligence in terms of approach for example, the strong AI and weak AI. Strong AI, is simulating the human brain by building systems that think and in the process give us an insight into how the brain works. We're nowhere near the stage yet. Weak AI is a system that behaves like a human but doesn't give us an insight into how the brain works. IBM's deep blue, a chess-playing AI, was an example. It processed millions of moves before it made any actual moves on the chessboard.
It doesn't stop there though there's actually a new kind of middle ground between strong and weak AI this is where a system is inspired by human reasoning but doesn't have to stick to it. IBM's Watson there's an exam like humans it reads a lot of information recognizes patterns and builds up evidence to say, "Hey, I'm X percent confident that this is the right solution to the question that you have asked me from the information that I've read". If you want to know more in IBM Watson you can search it and use may materials out there.
Google's deep learning is similar as it mimics the structure of the human brain by using neural networks, but doesn't follow its function exactly. The system uses nodes that act as artificial neurons connecting information. Going a little bit deeper neural networks are actually a subset of machine learning.
So, what's machine learning them machine learning refers to algorithms that enable software to improve its performance over time as it obtains more data. This is programming by input-output, examples rather than just coding. So that this makes more sense let's use an example, a programmer would have no idea how to program a computer to recognize a dog. But, he can create a program with a form of intelligence that can learn to do so. If he gives the program enough image data in the form of dogs and let it process and learn, when you give the program an image of a new dog that it's never seen before, it would be able to tell that it's a dog with relative ease.
OK, before we finish just one last concept. Most artificial intelligence algorithms are expert systems. So, what's an expert system?
The often cited definition of an expert system, is as follows, an expert system is a system that employs human knowledge in a computer to solve problems that ordinarily inquire human expertise. Basically, it's the practical application of a knowledge database. We've arguably only just got the first proven non expert system this year. Deepmind's AlphaGo. AlphaGo is not an expert system meaning that its algorithms could be used and applied to other things.
Demis Hassabis he was the co-creator of Deep mind highlighted this in a Google Blog, quote, "We are thrilled to have mastered go and thus achieved one of the grand challenges of AI however the most significant aspect of all of this for us is that AlphaGo isn't just an "expert" system built on handcrafted rules instead it uses general machine learning techniques to figure out for itself how to win go."
He goes on quote,"Because the methods we've used a general-purpose, our hope is that one day they could be extended to help us address some of society's toughest and most pressing problems, from climate modeling to complex disease analysis."
In other words the algorithms they AlphaGo used to win go could serve as a basis to be applied to very complex problems. If you ought to know more about AlphaGo hit the search engine now and search about it.
All right, so to bring this all together and summarize all that we've learnt. Let's recap.
So, what is AI? commonly AI or artificial intelligence is a machine or a computer program that learns how to do tasks that require forms of intelligence and are usually done by humans. And the other thing to take away intelligence comes in many forms and has many different aspects. At this time, we just have many different types of AIs that are good a particular subsets of intelligence.
Anyway, I hope that clears things up as a lot of people were confused about what AI actually is so anyway thanks for reading this article. I'll bring you more about the subset of AI. Stay tune.