Artificial Intelligence (Overview )

Is AI dangerous or useful?

This question is currently the subject of extensive debate. The aim here is not to repeat well-known opinions, but to shed light on the basics of the technology that you are almost certainly unaware of. Or do you know where AI gets its intelligence from?

For a quarter of a century, I have been developing ‘intelligent’ IT systems and I am astonished that we ascribe real intelligence to artificial intelligence at all. That’s exactly what it doesn’t have. Its intelligence always comes from humans, who not only provide the data, but also have to evaluate its meaning before the AI can use it. Only then, AI can surprise us with its impressiv performance and countless useful applications in a wide variety of areas. How does it achieve this?

In 2019, I started a blog series on this topic, which you can see an overview of below. In 2021, I then summarised the articles in a book entitled “Wie die künstliche Intelligenz zur Intelligenz kommt” (in German). See below a list of blogposts which form the basis of the book.

While the book is in German, the blogseries is available both in German and English.

Latest Posts about AI

English Posts

German Posts

Earlier Posts on AI (basis of the KI-book)

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?

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.

So where is the intelligence?

The considerations reveal what distinguishes natural intelligence from artificial intelligence:

AI only shows its capabilities when the task is clear and simple. As soon as the question becomes complex, they fail. Or they fib by arranging beautiful sentences found in their treasure trove of data in such a way that it sounds intelligent (ChatGPT, LaMDA). They do not work with logic, but with statistics, i.e. with probability. But is what appears to be true always true?

The weaknesses necessarily follow from the design principle of AI. Further articles deal with this:

Leave a Reply

Your email address will not be published. Required fields are marked *