quantum computing 2018½

hi all, its been a very busy year for QM computing and it seems as if an inflection point has been reached. google/ IBM are announcing designs with more than 50 qubits.[b] intel is getting into the game. there is a lot of recent innovation going on with qubits in silicon, a trend spotted here previously.[a2] simulation is a very big topic, there are two types: simulating QC calculations on a conventional computer, and using QC computers to simulate physics problems such as atomic or molecular interactions.[a4] as far as “combine trendy buzzwords” its sometimes AI + ML + QC.[a3] this seems to me to be “jumping the gun” because our QCs are not even that powerful yet, but its reasonable to explore more abstractly.

another hot topic is “quantum supremacy” the idea that QC computing can be demonstrated in some sense to be “faster” than conventional computing on “some/ any problem”.[c] that problem is now being defined a bit circularly by google et al as “quantum calculations” but nevertheless results are that supremacy by that metric is real at around 50 qubits in the sense that these calculations are out-of-reach of conventional machines. (the scientists among us know in complexity theory nothing is described in terms of constant threshholds, in fact they are rejected as meaningless, but theres always been a lot of handwaving in this field!)

another huge event was the launching of stackexchange quantum computing site which seems to be healthy so far and its sponsorship by the new unusual startup strangeworks.[f] another very big deal/ gamechanger/ milestone is that the US congress is discussing some QC related funding/ development legislation/ bills.[g]

almost 2 decades ago some of the 1st popsci books came out on quantum computing. a few of us read those. its energizing/ enthralling/ inspiring/ exciting that a “relatively short time” later today its now a worldwide reality being pursued by top corporations/ even leading govt research programs. the wild/ starry-eyed early promise has not yet materialized; a general purpose machine stills seems off in the distance, and its not yet clear a QM computer will have the same revolutionary economic impact as the integrated circuit/ microprocessor, but the technology is advancing steadily now and looks like it will have a permanent niche somewhere. as the old expr goes its gone from glimmer in the eye to something real and thats really something to celebrate.

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collatz new strategy

hi all. on vac this week & doing some new stuff (happy BTD US). there is a semifamous thousands-year old quote by sun-tzu maybe not yet contained in this blog (its been going thru my mind for quite awhile now, but wasnt able to find it in the blog via google). it is a quite favorite quote of business consultants which might tell you something about modern “leave no prisoners” business attitudes/ culture in our at-times militaristic/ hypercapitalistic modern age. (dramatic alpha-male stuff, but to put it more bluntly, one with a conscience/ empathy/ independent mind might wonder about the “fine print,” ie how many “enemy…” men did sun-tzu kill personally or oversee killing as a general? …or even humans which includes women/ children? oh but ofc its utterly metaphorical right?) 😮 😳 o_O

Strategy without tactics is the slowest route to victory. Tactics without strategy is the noise before defeat. –Sun Tzu.

collatz has been described as the very impenetrable/ unconquerable adversary. strategy/ tactics both play a key role and have commented at length on both. they are like a yin-yang combination. victory will likely not come without some kind of balance between the two.

have been more/ very tactical for quite awhile but have been musing on some overarching strategy/ perspective/ pov lately, thinking it all over at current point. this involves more abstraction.

couldnt find this basic idea pointed out in old blogs. was it? the key question is to prove f(x) < g(x) for all x. here f(x) is collatz stopping distance or some similar metric and g(x) is “any recursive function” (either time/ space bounded). now apparently f(x) in many related forms has extreme entropy, the “needle in haystack” property, and also “fat/ long tails” distribution, and fractal. earlier blogs have outlined the idea that it appears that victory seems to lie on the path of decreasing or minimizing entropy somehow. g(x) can be regarded as an orderly function from analytic mathematics and f(x) is “far from it” in the sense of being extremely disorderly.

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AI + ML trends 2018½

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.

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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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collatz shift

this months title is hopeful and in line/ theme with the last entry title. the disordered climbs were found to be a major wrench in the works of many months of analysis. however there are now some signs that even though seemingly without signal, maybe there are some angles to leverage on the disordered glides. am starting to get some general ideas. have developed/ honed some even stronger tools/ techniques. will they be enough to crack the problem?

this code is a modification of review51 to extract the recent/ latest generated glides from mix25g. it turned out extracting usable climbs from the table/ database was really nontrivial. 1st this code looks for the most common bit width encountered in the glides (climbs). this ends up to be exactly 200 the same as the bin count. this is interesting and maybe worth exploring further: all the lower glides are tending to cross (repeatedly) through that section. there are ~10K total iterations. then the climbs containing at least 1 iterate with that width are selected.

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