The lecture will be in italic white. Any annotations we choose to make will appear in non-italic orange.
I'm happy to say that the big discovery of the last couple of years, is that it actually is possible to make this work. It's steadily expanding domain by domain, in effect sort of automating the process, delivering kind of expert level knowledge on all kinds of things and making it to if there could be something figured out by an expert, from the knowledge that our civilization has accumulated, Wolfram Alpha can automatically figure it out.
In my little corner of the world we have Wolfram Alpha which takes lots of systematic knowledge and lets one be able to compute answers from it. As that gets done more and more, more and more of what happens in the world will become understandable and predictable. When we routinely know what's going to happen, at least up to the limits of what computational irreducibility allows. Right now one still has to ask for it, what one wants to know about. Increasingly though, the knowledge we need, will be preemptively delivered when and where we need it, with all kinds of interesting technologies that link more and more with our senses. Stephen Wolfram
From the point of view of some democratizing knowledge, it's pretty exciting and it's already clear that it's leading to some very interesting things. Inside though, it's definitely a strange kind of object. It starts from all sorts of sources of data that are first just raw data, but the real work is in making that data computable. Making it so that one's not just looking things up but instead someone is able to figure things out from the data.
In that process, a big piece is that one has to implement all of it as methods and models and algorithms that have been developed across science and all those other areas. One has to capture the expertise of actual human experts that are always needed. So then the result is that one can compute all kinds of things. Then the challenge is to be able to say what to compute. The only realistic way to do that is to be able to understand actual human language or actually the strange utterances that people enter in various way to Wolfram Alpha.
Differences Between Human & Artificial Intelligence
But it is interesting to the extent that it's not like a human intelligence. Think for example, of how it solved some specific physics problem. It could do it like a human kind of reasoning through to an answer, kind of in the sort of in the medieval philosopher kind of style. But instead, what it does is make use of the last three hundred or so years of science. From an AI kind of point of view it just cheats. It just sets up the equations, blasts through to the answer. It's really not trying to emulate some human like intelligence. Rather it's trying to be the structure that all the like human intelligence can build to do what it does as efficiently as possible. It's not trying to be bird. It's trying to be an airplane.
Predictions for the Future of AI
I guess there are some things that are progressing in straight lines in that way. Nothing is terribly surprising. I must say personally, I always prefer to build to what I refer to as "alien artifacts." Stuff that people didn't even imagine was possible until it arrives. What can we see in kind of straight lines in where we are?
Data & Computation Will Be Ubiquitous
How Will We Be One With Our Technology?
But here's a critical point. We can have all this amazing computation, effectively with all this amazing intelligence, but the question is what is it suppose to do? What is its purpose? You look at all these systems in the computational universe and you see them doing all this amazing stuff that they're doing. But, what is their purpose?
We will continue with the rest of Wolfram's talk in our next installment of this series.