collatz killer

hi all here we go with the latest installment. trying to come up with new names/ themes. again theres a “pivot” going on at the moment but maybe there are now too many too count. time now for an intermediate retrospective/ pov. my recent physics blog talked about “killing the copenhagen interpretation” and thats my latest idea for this problem. the problem is definitely “killer” in many senses of the word. it kills all great ideas launched against it, its like an impenetrable fortress.

there was a tone of optimism in a lot of prior writing. now looking all that over, it was based on a longtime theme that was yielding fruit(s) of labor(s). the basic idea is that there are locally computable “features” that can, with enough ingenuity, predict longterm glide behavior with high accuracy, and also generally explain other basic trajectory dynamics properties. this clearly ties in with the machine learning approaches. this research theme has been pursued for several years now.

however, last month there was a massive setback on this particular theme/ direction. did you catch it? to summarize, the features being used, mostly based on (binary) density, were leading to a lot of insights and leverage on the problem. but there was a moment a few years ago when the research started to focus on generating density-based seed trajectories instead of more generally. that turned out to be a major detour bordering on a mistake (in 2020 hindsight). 😮 😳 😥 😡 👿 o_O

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cybersec 2018 intl russia infestation/ cyberwar

hi all. what a crazy topic at full possible worldwide volume and really exhausting to track. have been taking a break from misc blogging but sometimes it seems the world is in flames. it took a year to figure out some of the basics of the russian infestation[a] which shows how difficult investigative work can be esp wrt espionage but every day leads to more revelations/ alarm. its clearly an intl full fledged cyberwar at this point larger than any in history. the ejection of russians out of multiple democracies is met with threats of retaliation by russia. there is always the risk, discussed abstractly by military experts, that cyberwar turns into real war. that seems “less unlikely” every day.

  • at this point the outlines of the russian troll factory with a few million dollars a month budget is mostly revealed, a psyop that probably also involved Brexit.[a2]
  • Mueller investigation issued indictments for russian espionage/ psyop operations.
  • looks pretty solid now that russia hacked DNC via Guccifer but also has covered it up. close connections with Guccifer via Roger Stone lead to “high exposure.”
  • to me the murder of seth rich is highly suspect. it made headlines for several months last year but is not any more on the media radar. the family vehemently rejects any “conspiracy theories”.
  • the recent exotic poisoning attempt on a russian double agent in britain and his daughter has raised intl alarm over russian espionage activities. in the publics compartmentalized mind maybe that event has nothing to do with other russian (psy)ops, but anyone in the “biz” knows its all interconnected aka intersectionality.
  • facebook/ zuckerberg are currently caught in a buzzsaw of harsh public opinion related to the cambridge analytica data leak and realization of how deep the russian psyops were without prior detection, a public relations crisis and costing tens of billions in stock price drop.[b] historically facebook goes thru periodic “privacy crises/ scandals” but this looks like the largest ever in its history. looks like that whole decade-long business model of doing journalism for cheap has what might be called hidden costs/ unintended consequences. zuckerberg now admits mistake(s) and being unduly “dismissive” in full page newspaper apologies. 😮 o_O
  • there is serious question about how democracy can persist amidst these serious threats (misinformation, disinforation, propaganda, etc).[d]
  • net neutrality is grabbing lots of headlines lately also.[f]
  • other hostile foreign actors NKorea & China still grab headlines. note there is some affiliation between Russia/ Nkorea/ China.[k]
  • social media[e] is a whitehot topic and theres strong congressional scrutiny at this point.

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collatz trap(s)

the last installment ended with some idea of possible “traps” in certain metrics. this idea occurred to me quite awhile ago and didnt work out under some examination previously but theres some new ways of looking at this. in the past, it is clear that eg there is no strict density trap in the sense of one iterates density bounding the next ones eg wrt to the density core. but the last experiments led to a different idea. what if a metric is bounded over some count of iterations, does that limit future glide potential? its a simple variation, seems to be quite related, but is maybe the key twist that looks more plausible as a measurable/ consistent property.

this new experiment simplifies the code a lot and bounds distance-from-core labelled ‘dc’ and a entropy metric. the entropy is counting total # of 0-to-1 and 1-to-0 transitions in the binary form, scaled by the bit width. the (scaled) inverse entropy formula (aka “order”) ends up as one minus sum of count of 0/ 1 runs/ groups divided by iterate bit width, labelled ‘e’. this upper bound on the inverse entropy is equivalent to a lower bound on the entropy (because as mentioned entropy increases as glides progresses, and the potential trap is at the end; also note a “low upper bound (on order)” is a “high lower bound (on entropy)”). it finds a sharp transition point ‘e’ ≈ 0.40. (as mentioned, suspect that both low and high entropy may be tending to bound glide length, therefore maybe glide bounding wrt density-distance-from-core is inversely related to entropy-distance-from-core?)

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collatz pivot/ new ideas

hi all, have been working on some other ideas re A(G)I, heavily promoting them all over cyberspace + analyzing/ collecting copious references, and havent been banging on collatz quite as much last few weeks. honestly its a bit of a (well deserved) break or respite. however, its always at the back of my mind. feel that am getting close to a solution but theres a lot of trickiness/ subtlety in the current stage.

here is a new analogy/ pov. the linear regression is finding a “global/ local gradient”. for the theoretical trajectory it is both, for the actual trajectory there are local perturbations/ disturbances/ noise fluctuations in the global trend. the picture is something like the wind blowing a leaf. the leaf has a very definite position but does a sort of multi-dimensional (3d) random walk in the wind. the wind is a general trend. now the basic idea/ question is whether the leaf will land at a given location/ circumscribed area given a predictable/ consistent wind dynamic.

further thought, another way of looking at it is that the leaf has a very dynamic/ even sharp response to the wind depending on what its current orientation is, and it also has an internal momentum. actually since the (real) wind is typically so dynamic whereas the linear regression is fixed (although arriving at the final regressions was dynamic, cf earlier saga of that), one might instead use the similar analogy of an irregularly shaped object in a (more consistent/ uniform) fluid flow, maybe even a field.

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AI 2017 part 2 highlights/ trends

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.

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