"There’s no sense in being precise when you don’t even know what you’re talking about."
Bostrom is one of those medieval cartographers drawing fantastical beasts in the blank spots of continents which he has never visited.
While Gwern has already mentioned Reinforcement Learning, UAI is a less known (but even more rigorous and well received) mathematical theory of general AI that arose from Marcus Hutter work [1].
My point here is how can one say that there is no definition of AI when there are several precise mathematical definitions available with many theorems proven about them?
That said it is also not computable with finite time or resources, so it is unclear what relevance it has to practical applications.
> That said it is also not computable with finite time or resources, so it is unclear what relevance it has to practical applications.
You can define it as space or time-bound and then it's finite but still intractable.
Also there is a turing-complete implementation of OOPS, a search procedure related to AIXI that can solve toy problems, programmed by none other than Jurgen Schmidthuber 10 years ago [2]
Even more important: there is a breadth of RL theory built around MDPs and POMDPs. There are asymptotical, convergence, bounded regret, on-policy/off-policy results, etc. Modern practical Deep RL agents (the ones DeepMind is researching) are developed on the same RL theory and inherit many of these results.
From my POV it looks unfavorable to researchers that produced these results over decades of work when the comment's grandfather (and grand-grandfather) write that there is no definition and theory about AI, and that AI is like alchemy.
1. https://www.jair.org/media/3125/live-3125-5397-jair.pdf 2. http://people.idsia.ch/~juergen/oops.html
The analogy I like to use for our understanding of AI is alchemy. We threw Sir Isaac Goddamned Newton at chemistry and he couldn't make forward progress, because the tools were not precise enough. Similarly, we just don't understand minds enough yet to formulate sensible questions about AI.
This doesn't bother Bostrom. He builds castles of thought in the air, and then climbs up into them.
We most certainly do understand enough to formulate questions, and even answer some of them. The problem is that the people making the most noise (Bostrom et al) are not trained in neuroscience or computer science, nor do they have practical experience in deploying working systems. They have about as much training and expertise as science fiction writers, and the end result is similar.
There is also the Advances in Cognitive Systems journal and associated conference, which is AGI even if they prefer to avoid that specific acronym:
And there are always a small but growing number of papers related to AGI in each AAAI conference.
2. Setting aside cogsys (CogSci is a whole different beast than AI/ML in computer science), the only impactful journal/conference you've listed is AAAI.
3. Papers are also typically incremental and all of the AGI papers I've seen in AAAI (and there have been very few) are no different, tackling some small theoretical subproblem.
4. I'm not saying the research is useless. It's very valuable. But it's is pure theory right now, and to claim it has insights for us about what AGI would actually look like is very premature.
This is incorrect. Among his degrees, Bostrom has a master's in computational neuroscience. His arguments have also convinced PhD neuroscientists (such as Sam Harris) and computer scientists (such as Stuart Russell) about the potential dangers of AI.
It would be a different story if he published in the meantime, but he did not. Nor did he work on practical projects in industry or anything. He shifted gears to philosophical speculation which he has done since.
There are good arguments to be made against Bostrom's Superintelligence, but malignments and surface analogies aren't appropriate. Please engage the ideas, not the man.
Read what I wrote again please. I think you misinterpreted.