hi all. 2018 has been another banner year for AI+ML and its not easy to summarize or keep up with developments but heres one of the most comprehensive/ top content lists youll find anywhere. heres a big pile of ~200 top links on the 2nd half.
My view is throw it all away and start again. —Hinton[x]
⭐ 😮 ❗ 💡 😎 ❤ hi all. blogged about a curiosity-based AGI theory in early 2018 after being inspired by recent Deepmind Go advances. the title was “novelty detection/ seeking”. the short word for that is curiosity. “curiously” the word “curious” didnt appear in the essay a single time. thinking back, suspect my thinking was that maybe the term was too advanced/ bold at the time. its a relatively abstract concept not even fully understood in neurobiology or psychology. it also crosses the species boundaries to other animals besides humans ie a general biological consideration also.
but now its time to use that word. the machine learning field has discovered curiosity in a big way… both locally and more globally.
the futures already here, its just not evenly distributed. —Gibson
the field of ML is very vast, grown rapidly in the last few years (esp wrt deep learning), and its not easy to keep track of these days. its something of a minor obsession for me and track it daily and over several years in this blog, almost since the beginning, via hundreds of links a year. did not run into some key references myself, and that shows how broad the field is and how hard it is even for dedicated/ committed individual researchers to keep up. but theres another element, curiosity was, and to some degree still is, “flying below the radar”.
this blog is timed based on some renewed attention and traction. the researchers in the field are definitely starting to notice something. they are still scattered but theyre the leaders, the pioneers, and suspect a mass herd shift is in the near future/ horizon (say within a few years) just like what happened with ML/ deep learning explosion/ wave itself in the last few years.
so in brief, thats exciting! the Curiosity Paradigm of Intelligence is gaining unmistakable signs of traction. this blog tracks some of those new milestones. yes its been studied for decades and by others, but the core/ nearly radical new theory/ proposal here (not entirely espoused by the following researchers, but aligned/ close) is that curiosity is necessary and sufficient for intelligence.
have been advocating/ promoting/ proseletyzing it myself in cyberspace heavily and got a lot of views on my blog. can be sure it is influencing some. also promote it on reddit and for that, earned some serious resistance there, some battle scars in cyberspace (oops/ yikes “promote” is nearly a 4-letter word in certain quarters of cyberspace that supposedly uphold/ glorify/ exist/ have entire business models based on user-generated-content!). but also a lot of excellent/ positive/ pointed feedback from redditors. thx for that guys!
hi all, ~250 links for 2nd half of 2018 AI + ML trends. very broad! AI/ML is now understood to be penetrating many fields/ areas. there are major (new) international govt initiatives, a new one announced in France by Macron.[c23]
this year looks a little more incremental than last year when Deepmind announced the Go game advancement. close to my own interests there is a lot of continued research into more advanced games such as starcraft and dota.[a4] there is bigger alignment with robotics.[a5] there are regular/ frequent announcements of major shifts/ innovations in methods of increasing sophistication/ scope.[a6]
closely tied into ethics analysis (covered heavily last blog) there is more serious/ heightened prognostication/ consternation of the future implications.[b] the tools/ chips hardware are advancing noticeably.[d][d2] there is top competition for/ jockeying among leaders in the field.[f] the sophia robot made a lot of headlines earlier in the year “for better or worse”.[i]
as noted in prior blog it looks to me like AI/ ML is in a sensitive/ vulnerable/ difficult/ at times awkward transition period of inching closer to AGI capabilities.
hi all, earlier this year released a new theory of AGI on this blog. it got substantial/ gratifying hits and am still pursuing it. aligned with this work, did a massive survey of existing state-of-the-art AGI leads. my initial idea was to try to summarize/ survey the different approaches. still have that in mind but its a almost herculean task and too much to bite off at this moment. this blog gets respectable hits but the audience is very spread out and not vocal/ participatory, dampening some of my energy for that high effort currently (but also not ruling it out).
however, this is a massive step in that direction, just painstakingly collecting this large ~180 link set/ collections of top leads.
much of this was found via the MIT AGI slack channel. its like trying to keep up with a firehose, but its very lively and cutting edge and also with tons of noise. as an expr goes used in this blog on various occasions, not for the fainthearted!
in compiling this its striking to me how both/ simultaneously brilliant and obscure some of these approaches are. some seem to me to be very much getting at the heart of AGI (and realized they are closely aligned with my own) but like my own audience, there is a lot of scattering. so far there is fairly low coalescing/ coalescence of groups around common themes/ consensus. my feeling is this disconnection may fall dramatically in the coming years esp with widely known/ publicized breakthrough(s) that drive the currently somewhat meandering herd down much more specific directions. it will be challenging-to-difficult but not inconceivable, exactly that happened on a substantial scale with deep learning within the last few years.
while it may seem overwhelming/ insurmountable at times, in some ways the AGI problem purely reduces to an architecture/ coordination problem, aka engineering. and notice some groups are arriving at the same answer from different directions (mainly psychology, (neuro)biology, machine learning, statistics/ data science/ big data, education/ learning theory, robotics, game AI, etc), with different languages/ vocabularies/ terminologies/ paradigms that are showing some/ early signs of converging/ convergence.
with new technologies, its all about “traction + momentum”. within the next few years, am expecting some major strategy/ consensus to emerge that builds on deep learning that gives rise to a plausible path/ route to AGI. have already outlined it myself, and think my ideas are close to the “secret sauce”, but my influence is low. fully expect nearly the exact same ideas to gain major traction but when espoused by some other leading light/ monolithic authority in the field, either an organization or individual or some combination of the two. it will likely be in the form of some step from the following ideas toward the more specific/ “laser-focused” direction.
odds are if there is some major AGI theory circulating at the present time, its pointed to in these refs, the well-known and not-so-well-known. and boldly both going out on a limb with a crystal ball, furthermore, think odds are strong that a “correct/ viable” AGI theory is in the not-too-distant future/ intermediate horizon and that the seeds will be contained (“holographic like”) in at least some refs cited here, maybe even many.
hi all. 2nd half AI in 2017 was very fast paced and as usual its hard to keep up. many headlines on the subj. this is a 2nd half review.
seems like some of the biggest news is the increasing dominance of China in the area after the govt announced major initiatives, funding, and resolve/ determination.[b]
google/ deepmind continues to dominate headlines.[a] the other big news is googles alphago and alphazero that no longer required human training games![a2] officially its under the name of “reinforcement learning” but my feeling is that this technology is something like “directed learning” where the AI is manipulating its environment to “detect/ seek/ digest novelty,” and that this will be a key, paradigm shifting trend in the near future. the same algorithm also works in chess/ go!
starcraft[a3] and Dota are starting to show up in AI engines/ research and OpenAI just announced breakthru championship level play vs humans in Dota, and Deepmind is attacking starcraft. despite all this major domination, it looks like games will not move out of cutting edge research anytime soon.[c4]
another notable shift/ trend is that AI/ machine learning is starting to couple stronger with physics and robotics. eg Deepmind humanoid walking simulations etc.