Do we have to be afraid of artificial intelligence? Or will it save the world? – As is well known, we’re afraid of what we don’t understand. And at the same time, we hope that what we don’t understand will work fantastic miracles.
I’m writing this blog series because I consider it useful if you understand what types of AI there are and how they work. In this way, you’ll be able to get a more concrete idea of the dangers and opportunities of AI. The text is aimed at people with a normal educational background; you don’t need to be a special kind of nerd to follow the explanations.
How does AI work? Where are its limits? How do the various types of AI differ from each other? What is behind deep learning? Is artificial intelligence really intelligent? And if it is, where exactly is the intelligence in artificial intelligence situated, and how does it get there?
These are the questions that I’ll be trying to answer in my blog posts.
Rule-based or corpus-based?
These are the two fundamentally different methods of computer intelligence. They can either be based on rules or a collection of data (corpus). In the introductory post, I present the two with the help of two characteristic anecdotes:
With regard to success, the corpus-based systems have obviously outstripped the rule-based ones:
The rule-based systems had a more difficult time of it. What are their challenges? How can they overcome their weaknesses? And where is their intelligence situated inside them?
- The challenges for rule-based AI
- Specification of the challenges for rule-based AI
- The three innovations of rule-based AI
- Overview of the development of the two AI methods
- Rule-based AI: where is intelligence situated?
How are corpus-based systems set up? How is their corpus compiled and assessed? What are neural networks all about? And what are the natural limits of corpus-based systems?
Next, we’ll have a look at search engines, which are also corpus-based systems. How do they arrive at their proposals? Where are their limits and dangers? Why, for instance, is it inevitable that bubbles are formed?
Is a program capable of learning without human beings providing it with useful pieces of advice? It appears to work with deep learning. To understand this, we first compare a simple card game with chess: what requires more intelligence? Surprisingly, it becomes clear that for a computer, chess is the simpler game.
With the help of the general conditions of the board games Go and chess, we recognise under what conditions deep learning works.
In the following blog post, I’ll provide an overview of the AI types known to me. I’ll draw a brief outline of their individual structures and of the differences in the way they work.