Knowledge Representation Quotes

Think of ChatGPT as a blurry JPEG of all the text on the Web. It retains much of the information on the Web, in the same way that a JPEG retains much of the information of a higher-resolution image, but, if you’re looking for an exact sequence of bits, you won’t find it; all you will ever get is an approximation. But, because the approximation is presented in the form of grammatical text, which ChatGPT excels at creating, it’s usually acceptable. You’re still looking at a blurry JPEG, but the blurriness occurs in a way that doesn’t make the picture as a whole look less sharp.

The Semantic Web is not a separate Web but an extension of the current one, in which information is given well-defined meaning, better enabling computers and people to work in cooperation.

Artificial intelligence will reach human levels by around 2029. Follow that out further to, say, 2045, we will have multiplied the intelligence, the human biological machine intelligence of our civilization a billion-fold.

This is a magical time for computer programming. We are moving towards the artifical intelligence holy grail of programming the computer to simulate the human brain.

Making AI more sensitive to the full scope of human thought is no simple task. The solutions are likely to require insights derived from fields beyond computer science, which means programmers will have to learn to collaborate more often with experts in other domains.

I believe that at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted.

It's paradoxical that things that are hard for people are easy for the computer, and things that are hard for the computer, any child can understand.

In A.I., the holy grail was how do you generate internal representations.

I'd rather see artificial intelligence than no intelligence.

I became interested in AI in high school because I read 'Goedel, Escher, Bach,' a book by Douglas Hofstader. He showed how all their work in some ways fit together, and he talked about artificial intelligence. I thought 'Wow, this is what I want to be doing.'

It's fair to say that Wikipedia has spent far more time considering the philosophical ramifications of categorization than Aristotle and Kant ever did.

If you can’t program it, you haven’t understood it.

Early AI was mainly based on logic. You're trying to make computers that reason like people. The second route is from biology: You're trying to make computers that can perceive and act and adapt like animals.

One of the interesting applications of symbolic systems is artificial intelligence, and I spent some time thinking about how to create a brain that operates the way ours does.

IA is something that is proceeding very naturally, in most cases not even recognized by its developers for what it is.

But it seemed to me that as soon as you have computer storage you could put every point you wanted in - make the ones that are less relevant to your central topic, further away or allow the central topic to move as the reader proceeded.

I think what's really amazing is that given the scale of the web and getting the compute power we have today, we're starting to see things that appear intelligent but actually aren't semantically intelligent.

Curation is everything.

The big question society will have to answer is whether it wants computers thinking like humans.

Artificial intelligence is always 20 years away.

Language is too complex for a computer to understand. It's not going to be able to make sense of what people are saying en masse. We need a new type of discipline that puts together computer scientists and social scientists, who can add context to the situation.

Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the Web. It would understand exactly what you wanted, and it would give you the right thing. We're nowhere near doing that now. However, we can get incrementally closer to that, and that is basically what we work on.

Visualizing information is a form of knowledge compression.

In my teaching, I enjoyed creating models to clearly communicate my thoughts.

If you understand something well, you can represent it, describe it, embody it in several ways. Indeed, if you can only present it in one way, then your own mastery is likely to be tenuous.