Category Archives: r&d homegrown

CURIOSITY PARADIGM OF INTELLIGENCE gains traction! via open AI + deepmind + google brain

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!

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EMERGENT QM BREAKTHRU: ADLER+VINANTE MEASURE SUBQUANTUM EFFECTS

⭐ ⭐ ⭐ 💡 ❗ 😮 ❤ 😀 😎

SO EXCITING/ “JAZZED”!

hi all it was only about 2 mos ago a big ambitious update on quantum mechanics shifts was posted on the “fluid paradigm shift”. if you ask me this is all at least as epochal as the invention/ discovery of quantum mechanics itself about a century ago. and (unbeknownst to me at the time) theres been a striking new development.

around here updates to QM are measured in years and not months. have been blogging now a solid ½ decade on the topic and thats just the recorded history, my inquiries go back decades. but as mentioned in that last blog “my neurons are really buzzing”. something is in the air, the zeitgeist is electric right now. lightning is striking. all my spider senses are tingling. normally would not write very quickly on all this but heres a “flash update” based on the sheer significance of the announcement/ finding.

all the research is paying off. a breakthru has arrived. there is now SOLID EXPERIMENTAL EVIDENCE FOR SUBQUANTUM EFFECTS.[a] thats right they have now been MEASURED and published in a reputable physics journal (physical review letters… uh hey preemptively addressing skeptics obsessing about peer review etc, didnt einstein + bohr + other founders have papers in there?).

that breakthru credit goes to the VINANTE-ADLER team. they have been working on this line of inquiry maybe about 2 decades judging by their papers.

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top AGI leads 2018½

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.

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FLUID PARADIGM SHIFT 2018½

hi all. what if research into the interpretation of QM leads to a QM + GR path/ direction for unification? that is exactly the ambitious, overarching, but not inconceivable promise of the fluid paradigm of physics.

its been ~¾ a year since a last bold/ ambitious blog on copenhagen interpretation and new fluid ideas in quantum mechanics and physics.

at this point have blogged about ½ decade on some of these subjs and my neurons are really buzzing, crackling and snapping lately at full volume, the field is going thru an identifiable paradigm shift predicted years ago in this blog.

have collected a copious collection of new info/ leads, my links really runneth over. it really looks like nearly critical mass in some ways.

have been waiting to blog on this a few months and looking for an opportune moment. am expecting some massive signs to show up. many have already shown up. am finally deciding to write all this up at the near ½ year point.

one of the biggest signposts/ BREAKTHRUS is the new Becker book, What is Real, the Unfinished Quest for the Meaning of Quantum Physics.[a] havent bought it yet but its on the top of my to-read pile. this is causing big, maybe even massive waves in the mass/ popular media eg NYT but also the scientific journals such as Nature. its being reviewed by top experts positively. major response on social media such as reddit also. maybe a gamechanger.

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secret/ blueprint/ path to AGI: novelty detection/ seeking

hi all. kurzweil wrote in 2006 “the singularity is near”. foreboding words! but today, still maybe more of a feeling than a fact. definitely, the AI field has started to mature into a new steady advance period/ era in the last few years, also with a burst of energy/ enthusiasm/ innovation heralded by the Google Deepmind acquisition in 2014, and other massive shifts toward increased investment by large corporations and govts. the Musk Open AI initiative was announced in 2015.

the other massive milestone is the ready conquering of Go by Google in 2016 by AlphaGo. in late 2017, a new version AlphaZero was announced that plays superior to AlphaGo (at “beyond human grandmaster level”) after learning merely from the rules and reinforcement learning, ie no example human-level play presented as training whatsoever. AlphaZero also plays grandmaster level chess after learning “from scratch”. this breakthrough is not fully/ widely appreciated in some ways. it is the first case of a potentially more general algorithm for AI emerging from “previously relatively narrow” study of AI in games.

AI has the terminology “weak AI” and “strong AI” for different levels/ sophistication/ “ability”. more recently the term AGI, Artificial General Intelligence has been coined.

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