It's a surprise-free document. It could have read roughly the same in 1985, but different technologies would have been mentioned.
The big change in AI is that it now makes money. AI used to be about five academic groups with 10-20 people each. The early startups all failed. Now it's an industry, maybe three orders of magnitude bigger. This accelerates progress.
Technically, the big change in AI is that digesting raw data from cameras and microphones now works well. The front end of perception is much better than it used to be. Much of this is brute-force computation applied to old algorithms. "Deep learning" is a few simple tricks on old neural nets powered by vast compute resources.
You may be right about the selling power of the "AI" brand, but it seems that AI technology routinely becomes thought of as just technology.
Boole called his algebra "The Laws of Thought"; OOP; lisp was an AI technology (much of which has made its way into other languages); formal languages; etc.
The traditional goalpost rule is that once computers can do it, it's no longer "intelligent" (e.g, chess). A change today is "AI" success as a marketing term.
Great point. What people today would think of as "intelligent machines", children of future will think of same things as mere "technological tools".
Once this is widely established, things like "laws of robotics", "moral dilemma of autopilot" and "AI and ethics" will be just bizarre ideas of the past. Asimov's laws are already viewed as one of "misguided ideas of the past" by many, although there still are some rusty minds out there believing in things like that.
I've always been fascinated by the concept of software agents that become self-sufficient (mining/stealing bitcoin to pay for their own hosting) and auto-generate desires.
I was curious to see what the Distributed Autonomous Organization would do. But when push came to shove, it turned out that one guy was really in charge, and it was really just a way to fund his programmable door lock.
The big change in AI is that it now makes money. AI used to be about five academic groups with 10-20 people each. The early startups all failed. Now it's an industry, maybe three orders of magnitude bigger. This accelerates progress.
Technically, the big change in AI is that digesting raw data from cameras and microphones now works well. The front end of perception is much better than it used to be. Much of this is brute-force computation applied to old algorithms. "Deep learning" is a few simple tricks on old neural nets powered by vast compute resources.