AI BS Detectors & the Origins of Life (by Silly Rabbit)

Confidence levels for the Social and Behavioral Sciences

DARPA recently put out an RFI:

…requesting information on new ideas and approaches for creating (semi)automated capabilities to assign ‘Confidence Levels’ to specific studies, claims, hypotheses, conclusions, models, and/or theories found in social and behavioral science research (and) help experts and non-experts separate scientific wheat from wrongheaded chaff using machine reading, natural language processing, automated meta-analyses, statistics-checking algorithms, sentiment analytics, crowdsourcing tools, data sharing and archiving platforms, network analytics, etc.

A visionary and high value RFI. Wired article on the same, enticingly titled, DARPA Wants to Build a BS Detector for Science.

Claude Berrou on turbo codes and informational neuroscience

Fascinating short interview with Claude Berrou, a French computer and electronics engineer who has done important work on turbo codes for telecom transmissions and is now working on informational neuroscience. Berrou describes his work through the lens of information and graph theory:

My starting point is still information, but this time in the brain. The human cerebral cortex can be compared to a graph, with billions of nodes and thousands of billions of edges. There are specific modules, and between the modules are lines of communication. I am convinced that the mental information, carried by the cortex, is binary. Conventional theories hypothesize that information is stored by the synaptic weights, the weights on the edges of the graph. I propose a different hypothesis. In my opinion, there is too much noise in the brain; it is too fragile, inconsistent, and unstable; pieces of information cannot be carried by weights, but rather by assemblies of nodes. These nodes form a clique, in the geometric sense of the word, meaning they are all connected two by two. This becomes digital information…

Thermodynamics in far-from-equilibrium systems

I’m a sucker for methods to try to understand and explain complex systems such as this story by Quanta (the publishing arm of the Simons Foundation — as in Jim Simons or Renaissance Technologies fame) of Jeremy England, a young MIT associate professor, using non-equilibrium statistical mechanics to poke at the origins of life.

Game theory

And finally, check out this neat little game theory simulator which explores how trust develops in society. It’s a really sweet little application with fun interactive graphics framed around the historical 1914 No Man’s Land Ceasefire. Check out more fascinating and deeply educational games from creator Nicky Case here.

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AI & Video Games, Tricky Chatbots and More… (by Silly Rabbit)

AI and video games (again)

Vicarious (a buzzy Silicon Valley company developing AI for robots) say they have a new and crazy-good AI technique called Schema Networks. The Allen Institute for Artificial Intelligence and others seem pretty skeptical and demand a throw-down challenge with AlphaGo (or, failing that, some peer-reviewed papers with commonly used terms and a broader set of tests).

In other AI video game news, Microsoft released a video of their AI winning at Ms. Pacman, with an instructive voiceover of how the system works.

Tricky chatbots

I recently stumbled upon Carl Icahn’s Twitter feed which has the tag line: “Some people get rich studying artificial intelligence. Me, I make money studying natural stupidity.” Me, I think in 2017 this dichotomy is starting to sound pretty quaint. See: Overview of recent FAIR (Facebook Artificial Intelligence Research division) study teaching chatbots how to negotiate, including the bots self-discovery of the strategy of pretending to care about an item to which they actually give little or no value, just so they can later give up that item to seem to have made a compromise. Apparently, while they were at it, the Facebook bots also unexpectedly created their own language.

The quantum age has officially arrived

I’ve been jabbering on and pointing to links about quantum computing and the types of intractable problems it can solve for some time here, here and here, but now Bloomberg has written a long piece on quantum we can officially declare “The quantum age has officially arrived, hurrah!”. Very good overview piece on quantum computing from Bloomberg Markets here.

Your high dimensional brain

We tend to view ourselves (our ‘selfs’) through the lens of the technology of the day: in the Victorian ‘Mechanical age’ we were (and partly are) bellows and pumps, and now we are, by mass imagination, a collection of algorithms and processors, and possibly living in a VR simulation. While this ‘Silicon Age’ view is probably not entirely inaccurate it is also, probably, in the grand scheme of things, nearly as naive and incomplete as the Victorian view was. Blowing up some of the reductions of current models, this new (very interesting, pretty dense, somewhat contested) paper points towards brain structure in 11 dimensions. Shorter and easier explainer here by Wired or even more concisely by the NY Post“If the brain is actually working in 11 dimensions, looking at a 3D functional MRI and saying that it explains brain activity would be like looking at the shadow of a head of a pin and saying that it explains the entire universe, plus a multitude of other dimensions.”

And in other interesting-brain-related news:

Taming the “Black Dog”

And finally, three different but complimentary technology-enabled approaches to diagnosing and fighting depression:

  • basic algorithm with limited data has shown to be 80-90 percent accurate when predicting whether someone will attempt suicide within the next two years, and 92 percent accurate in predicting whether someone will attempt suicide within the next week.
  • In a different predictive approach, researchers fed facial images of three groups of people (those with suicidal ideation, depressed patients, and a medical control group) into a machine-learning algorithm that looked for correlations between different gestures. The results: individuals displaying a non-Duchenne smile (which doesn’t involve the eyes in the smile) were far more likely to possess suicidal ideation.
  • On the treatment-side, researchers have developed a potentially revolutionary treatment that pulses magnetic waves into the brain, treating depression by changing neurological structures, not its chemical balance.

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AI Hedge Funds, Corporate Inequality & Microdosing LSD (by Silly Rabbit)

Machines and suchlike

DARPA has produced a 15 minute AI explainer video. A fair review: “Artificial intelligence is grossly misunderstood. It’s a rare clear-eyed look into the guts of AI that’s also simple enough for most non-technical folks to follow. It’s dry, but IRL computer science is pretty dry.” Well worth watching for orientation on where we are — and where we are not — with AI today.

In case you are interested in ‘AI hedge funds’ and haven’t come across them, Sentient should be on your radar. And Walnut Algorithms, too. They look to be taking quite different AI approaches, but at some point, presumably, AI trading will become a recognized category. Interesting that the Walnut article asserts — via EurekaHedge — that “there are at least 23 ‘AI Hedge Funds’ with 12 actively trading”. Hmm …

[Ed. note — double hmm … present company excepted, there’s a lot less than meets the eye here. IMO.]

On the topic of Big Compute, I’m a big believer in the near-term opportunity of usefully incorporating quantum compute into live systems for certain tasks within the next couple of years and so opening up practical solutions to whole new classes of previously intractable problems. Nice explanation of ‘What Makes Quantum Computers Powerful Problem Solvers’ here.

[Ed. note — for a certain class of problems (network comparisons, for example) which just happen to be core to Narrative and mass sentiment analysis, the power of quantum computing versus non-quantum computing is the power of 2n versus n2. Do the math.]

Quick overview paper on Julia programming language here. Frankly, I’ve never come across Julia (that I know of) in the wild out here on the west coast, but I see the attraction for folks coming from a Matlab-type background and where ‘prototype research’ and ‘production engineering’ are not cleanly split. Julia seems, to some extent, to be targeting trading-type ‘quants’, which makes sense.

Paper overview: “The innovation of Julia is that it addresses the need to easily create new numerical algorithms while still executing fast. Julia’s creators noted that, before Julia, programmers would typically develop their algorithms in MATLAB, R or Python, and then re-code the algorithms into C or FORTRAN for production speed. Obviously, this slows the speed of developing usable new algorithms for numerical applications. In testing of seven basic algorithms, Julia is impressively 20 times faster than Python, 100 times faster than R, 93 times faster than MATLAB, and 1.5 times faster than FORTRAN. Julia puts high-performance computing into the hands of financial quants and scientists, and frees them from having to know the intricacies of high-speed computer science”. Julia Computing website link here.

Humans and suchlike

This HBR article on ‘Corporation in the Age of Inequality” is, in itself, pretty flabby, but the TLDR soundbite version is compelling: “The real engine fueling rising income inequality is “firm inequality”. In an increasingly … winner-take-most economy the … most-skilled employees cluster inside the most successful companies, their incomes rising dramatically compared with those of outsiders.” On a micro-level I think we are seeing an acceleration of this within technology-driven firms (both companies and funds).

[Ed. note — love TLDR. It’s what every other ZeroHedge commentariat writer says about Epsilon Theory!]

A great — if nauseatingly ‘rah rah’ — recent book with cutting-edge thinking on getting your company’s humans to be your moat is: Stealing Fire: How Silicon Valley, the Navy SEALs, and Maverick Scientists Are Revolutionizing the Way We Live and Work. Warning: Microdosing hallucinogens and going to Burning Man are strongly advocated!

Finally, on the human-side, I have been thinking a lot about ‘talent arbitrage’ for advanced machine learning talent (i.e., how to not to slug it out with Google, Facebook et al. in the Bay Area for every hire) and went on a bit of world-tour to various talent markets over the past couple of months. My informal perspective: Finland, parts of Canada and Oxford (UK) are the best markets in the world right now—really good talent that have been way less picked-over. Does bad weather and high taxes give rise to high quality AI talent pools? Kind of, in a way, probably.

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