Data Access Battles, Creative Thinking & Full Script AI (by Silly Rabbit)

Data access battles

A couple of weeks back I shared a link to the story of ImageNet and the importance of data to developing algorithms. Ars Technica reports on two ‘at the coalface’ battles over data access with HiQ and Power Ventures fighting with LinkedIn and Facebook over data access. I’m not advocating a position on this but, to be sure, small — and currently obscure — court cases like these will, cumulatively, end up setting the precedents which will have a significant impact on the evolution and ownership of powerful algorithms that are increasingly driving behavior and economics.

Creative thinking

This speech from Claude Shannon at Bell Labs in 1952 has been circulating online for the past couple of weeks. It is a timeless, pragmatic speech on creative thinking which remains, 65 years later, fully relevant for developing novel computational strategies:

Sometimes I have had the experience of designing computing machines of various sorts in which I wanted to compute certain numbers out of certain given quantities. This happened to be a machine that played the game of nim and it turned out that it seemed to be quite difficult. It took quite a number of relays to do this particular calculation although it could be done. But then I got the idea that if I inverted the problem, it would have been very easy to do — if the given and required results had been interchanged; and that idea led to a way of doing it which was far simpler than the first design. The way of doing it was doing it by feedback; that is, you start with the required result and run it back until — run it through its value until it matches the given input. So the machine itself was worked backward putting range S over the numbers until it had the number that you actually had and, at that point, until it reached the number such that P shows you the correct way.

Facebook shuts down robots after they invent their own language

Facebook shuts down robots after they invent their own language has become a widely reported and wildly commentated story over the past month, referencing a story on ’Tricky chatbots’ linked here a couple of months back. For melodramatic illustrative effect, I like switching a couple of words in the Facebook headline so that it reads ‘Lehman (doesn’t) shuts down traders after they invent their own language’ as it illustrates that, in general, if you: put a bunch of agents (human or machine) together and set up a narrowly defined, adversarial, multi-player game with a strong reward function then the agents will develop their own task-specific language and protocols, keep adding complexity, lie to each other (yes, the FB bots also learnt to do that), be tempted to obfuscate behavior in order to reduce interference and maximize the reward function, and develop models which are positive for near-term reward maximization but do not necessarily deal with longer-term consequence or long tail events, and so become very hard for human overseers to truly assess…

DICK FULD (2008): 
I wake up every single night wondering what I could have done differently — this is a pain that will stay with me the rest of my life

FACEBOOK (2017):
Hold my beer

AI: From partial to full script

Thinking more broadly about the longer-term evolution of AI (and the nature of money and contracts, per Ethereum link last week), it has been interesting to re-read Sapiens: A Brief History of Humankind by Yuval Noah Harari which charts the rise to dominance of us Sapiens with especially interesting chapters on the development of written language and money. A concept which particularly grabbed me was that written language was initially developed as ‘partial script’ technology for narrow tasks such as tax accounting, and then evolved to be full script and so capable of much more than it was originally conceived for.

The history of writing is almost certainly a wonderful historical premonition of the trajectory of AI, except with the evolution being much faster and the warning that likely “the AI is more powerful than pen.”

Relevant excerpt from Sapiens:

Full script is a system of material signs that can represent spoken language more or less completely. It can therefore express everything people can say, including poetry. Partial script, on the other hand, is a system of material signs that can represent only particular types of information, belonging to a limited field of activity … It didn’t disturb the Sumerians (who invented the script) that their script was ill-suited for writing poetry. They didn’t invent it in order to copy spoken language, but rather to do things that spoken language failed at … Between 3000 BC and 2500 BC more and more signs were added to the Sumerian system, gradually transforming it into a full script that we today call cuneiform. By 2500 BC, kings were using cuneiform to issue decrees, priests were using it to record oracles, and less-exalted citizens were using it to write personal letters.

The beautiful mathematical explorations of Maryam Mirzakhani

And finally, at the risk of turning into The Economist, we conclude this week’s Rabbit Hole with a touching obituary of the Tehran-born, Fields Medal-winning mathematician Maryam Mirzakhani:

A bit more than a decade ago when the mathematical world started hearing about Maryam Mirzakhani, it was hard not to mispronounce her then-unfamiliar name. The strength and beauty of her work made us learn it. It is heartbreaking not to have Maryam among us any longer. It is also hard to believe: The intensity of her mind made me feel that she would be shielded from death.

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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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Algorithmic Complexes, Alpha Male Brains, and Winnie the Pooh (by Silly Rabbit)

Massively complex complexes of algorithms

Let me come straight out with it and state, for the record, that I believe the best current truth we have is that we humans, along with all other living beings, are simply massively complex complexes of algorithms. What do I mean by that? Well, let’s take a passage from the terrific Homo Deus by Yuval Noah Harari, which describes this concept at length and in detail:

In recent decades life scientists have demonstrated that emotions are not some mysterious spiritual phenomenon that is useful just for writing poetry and composing symphonies. Rather, emotions are biochemical algorithms that are vital for the survival and reproduction of all mammals. What does this mean? Well, let’s begin by explaining what an algorithm is, because the 21st Century will be dominated by algorithms. ‘Algorithm’ is arguably the single most important concept in our world. If we want to understand our life and our future, we should make every effort to understand what an algorithm is and how algorithms are connected with emotions. An algorithm is a methodical set of steps that can be used to make calculations, resolve problems and reach decisions. An algorithm isn’t a particular calculation but the method followed when making the calculation.

Consider, for example, the following survival problem: a baboon needs to take into account a lot of data. How far am I from the bananas? How far away is the lion? How fast can I run? How fast can the lion run? Is the lion awake or asleep? Does the lion seem to be hungry or satiated? How many bananas are there? Are they big or small? Green or ripe? In addition to these external data, the baboon must also consider information about conditions within his own body. If he is starving, it makes sense to risk everything for those bananas, no matter the odds. In contrast, if he has just eaten, and the bananas are mere greed, why take any risks at all? In order to weigh and balance all these variables and probabilities, the baboon requires far more complicated algorithms than the ones controlling automatic vending machines. The prize for making correct calculations is correspondingly greater. The prize is the very survival of the baboon. A timid baboon — one whose algorithms overestimate dangers — will starve to death, and the genes that shaped these cowardly algorithms will perish with him. A rash baboon —one whose algorithms underestimate dangers — will fall prey to the lion, and his reckless genes will also fail to make it to the next generation. These algorithms undergo constant quality control by natural selection. Only animals that calculate probabilities correctly leave offspring behind. Yet this is all very abstract. How exactly does a baboon calculate probabilities? He certainly doesn’t draw a pencil from behind his ear, a notebook from a back pocket, and start computing running speeds and energy levels with a calculator. Rather, the baboon’s entire body is the calculator. What we call sensations and emotions are in fact algorithms. The baboon feels hunger, he feels fear and trembling at the sight of the lion, and he feels his mouth watering at the sight of the bananas. Within a split second, he experiences a storm of sensations, emotions and desires, which is nothing but the process of calculation. The result will appear as a feeling: the baboon will suddenly feel his spirit rising, his hairs standing on end, his muscles tensing, his chest expanding, and he will inhale a big breath, and ‘Forward! I can do it! To the bananas!’ Alternatively, he may be overcome by fear, his shoulders will droop, his stomach will turn, his legs will give way, and ‘Mama! A lion! Help!’ Sometimes the probabilities match so evenly that it is hard to decide. This too will manifest itself as a feeling. The baboon will feel confused and indecisive. ‘Yes . . . No . . . Yes . . . No . . . Damn! I don’t know what to do!’

Why does this matter? I think understanding and accepting this point is absolutely critical to being able to construct certain classes of novel and interesting algorithms. “But what about consciousness?” you may ask, “Does this not distinguish humans and raise us above all other animals, or at least machines?”

There is likely no better explanation, or succinct quote, to deal with the question of consciousness than Douglas Hofstadter’s in I Am a Strange Loop:

“In the end, we are self-perceiving, self-inventing, locked-in mirages that are little miracles of self-reference.”

Let’s accept Hofstadter’s explanation (which is — to paraphrase and oversimplify terribly — that, at a certain point of algorithmic complexity, consciousness emerges due to self-referencing feedback loops) and now hand the mic back to Harari to finish his practical thought:

“This raises a novel question: which of the two is really important, intelligence or consciousness? As long as they went hand in hand, debating their relative value was just an amusing pastime for philosophers, but now humans are in danger of losing their economic value because intelligence is decoupling from consciousness.”

Or, to put it another way: if what I need is an intelligent algorithm to read, parse and tag language in certain reports based on whether humans with a certain background would perceive the report as more ‘growth-y’ vs ‘value-y’ in its tone and tenor, why do I need to discriminate whether the algorithm performing this action has consciousness or not, or which parts of the algorithms have consciousness (assuming that the action can be equally parallelized either way)?

AI vs. human performance

Electronic Frontier Foundation have done magnificent work pulling together problems and metrics/datasets from the AI research literature in order to see how things are progressing in specific subfields or AI/machine learning as a whole. Very interesting charts on AI versus human performance in image recognition, chess, book comprehension, and speech recognition (keep scrolling down; it’s a very long page with lots of charts).

Alpha male brain switch

Researchers led by Prof Hailan Hu, a neuroscientist at Zhejiang University in Hangzhou, China have demonstrated activating the dorsal medial prefrontal cortex (dmPFC) brain circuit in mice to flip the neural switch for becoming an alpha male. This turned the timid mice bold after their ‘alpha’ circuit was stimulated.  Results also show that the ‘winner effect’ lingers on and that the mechanism may be similar in humans. Profound and fascinating work.

Explaining vs. understanding

And finally, generally I find @nntaleb’s tweets pretty obnoxious and low value (unlike his books, which I find pretty obnoxious and tremendously high value), but this tweet really captured me: “Society is increasingly run by those who are better at explaining than understanding.” I pondered last week on how allocators and Funds of Funds are going to allocate to ‘AI’ (or ‘ALIS’). This quote succinctly sums up and generalizes that concern.

And finally, finally, this has nothing to do with Big Compute, AI, or investment strategies, but it is just irresistible: Winnie the Pooh blacklisted by China’s online censors: “Social media ban for fictional bear follows comparisons with Xi Jinping.” Original FT article here (possibly pay-walled) and lower resolution derivative article (not pay-walled) by inUth here. As Pooh says “Sometimes I sits and thinks, and sometimes I just sits…”

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Where’s the Punch Bowl?

On episode 23 of the Epsilon Theory podcast, we’ve assembled the all-star team — Jeremy Radcliffe (Salient’s President), Rusty Guinn (Salient’s EVP of Asset Management), Neville Crawley (Founder & CEO of Engram Labs) and of course, Dr. Ben Hunt — to discuss whether we are at the inflection point when the proverbial punch bowl is taken away, and, as investors, what we do now.

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Alibaba’s AI, JP Morgan’s Risky Language & the Nurture of Reality (by Silly Rabbit)

Video game-playing AI

AI has moved one step closer to mastering the classic video game StarCraft. Google, Facebook and now Alibaba have been working on AI StarCraft players, and last week a team from China’s Alibaba published a paper describing a system that learned to execute a number of strategies employed by high-level players without being given any specific instruction on how best to manage combat. Like many deep learning systems, the software improved through trial and error, demonstrating the ability to adapt to changes in the number and type of troops engaged in battle. Non-technical overview via The Verge here. Original and fairly accessible technical paper here.

While an AI video game ace may not be world changing in and of itself, progress on AI intra-agent communication and coordination has potentially profound implications for markets as the approach matures, or, as the Alibaba researchers rather poetically note in their paper:

In the coming era of algorithmic economy, AI agents with a certain rudimentary level of artificial collective intelligence start to emerge from multiple domains…[including] the trading robots gaming on the stock markets [and] ad bidding agents competing with each other over online advertising exchanges.

And how do agents behave when their game playing becomes stressful? Apparently just like their human creators: Aggressively. Summary of Google’s DeepMind finds on this here.

Risky language

For anyone who has ever taken general NLP algorithms, trained them on the information of the broader world and then pointed them at financial markets-type information, you will have noticed that they get kind of sad and messed up. Partly because markets-ese is odd (try telling your doctor that being overweight is a good thing) and partly because finance folks sure do love a risk discussion…and apparently no one more so than JP Morgan Chase CEO Jamie Dimon. In his much re-published letter to shareholders:

It is alarming that approximately 40% of those who receive advanced degrees in STEM at American universities are foreign nationals with no legal way of staying here even when many would choose to do so…Felony convictions for even minor offenses have led, in part, to 20 million American citizens having a criminal record…The inability to reform mortgage markets has dramatically reduced mortgage availability.

Thanks, Jamie, my algorithm just quit and immigrated to Canada.

The more serious question on this is that as natural language algorithms (of various types) become ubiquitous, at what point do business leaders begin to craft their communications primarily to influence the machine, or at least not include detailed socio-political critiques to accidentally trip it?

The nurture of reality

Clearly, our perception of reality, our world view, is substantially informed by our memories and the stories (links) we tell ourselves about these memories. We are now, for the first time, just starting to get an understanding of how memories are physically stored in the brain. Recollections of successive events physically entangle each other when brain cells store them, as Scientific American reports.

The Map of Physics, a joyous 8 minute video by Dominic Walliman (formerly of D-Wave quantum computing), culminates in the map below with The Chasm of Ignorance, The Future and Philosophy. Walliman points to where we must be operating if we are to break truly new ground (i.e., put the regression models down, please). And if you liked that, keep watching to Your Quantum Nose: How Smell Works

And, finally, a classic, epic, challenging, practical, piece of prose/poetry from one of the the world’s greatest philosophers and orators: the late, great, Tibetan Buddhist meditation master Chögyam Trungpa. Long treatise on Zen vs. Tantra as a system for nurturing the mind:

…the discovery of shunyata [emptiness of determinate intrinsic nature] is no doubt the highest cardinal truth and the highest realization that has ever been known…

Coming next week: The next generation of flash crashes; digital Darwinism and the resurgence of hardware.

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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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