Complex Systems, Multiscale Information and Strange Loops (by Silly Rabbit)

Complex systems

Neat and accessible primer on complex systems, multiscale information theory and universality by Yaneer Bar-Yan, and a related paper on the conceptual applications of the same topic: From Big Data To Important Information (suggest start reading from section VII if you read the primer, and from sub-section D on page 13 if you just want the markets application).

Machine learning software creates machine learning software

Lots of buzz about Google’s AutoML announcement at the Google Annual developer conference I/O 2017 last week. AutoML is machine learning software which takes over some of the work of creating machine learning software and, in some cases, came up with designs that rivals or beats the best work of human machine learning experts. MIT Technology Review article on AutoML.

One-shot imitation

Also lots of buzz around one-shot imitation using two neural nets, as demonstrated by OpenAI. Personally, one-shot imitation is the one AI-type concept which gives me the fear. But if Elon’s supporting it then it must be OK… right? One-shot imitation paper here but, more to the point, watch this video and tell me you are not at least a little bit afraid.

The power of the platform

And to the practical applications of technology, I really like the language of this recent press release by Two Sigma CEO, Nobel Gulati, and particularly the paragraph:

Moving forward, durable advantages will to accrue to those building a substantial platform based on massive amounts of data, along with the technology and institutional expertise to use it. Building such a platform requires significant and ongoing investment in R&D, and a fundamentally different culture and mindset to apply a scientific approach to the data-rich world of today.

Personally, I believe that the 2020s will be more defined by big compute than big data but this is, nonetheless, a powerful statement and language, and there’s a key implicit point buried in here on the cultural balance of ‘researchers’ (math and physics natural genii) and ‘production engineers’ (coders who, by nurture, have seen and solved many practical problems). Specifically, how the majority of quant funds have to-date been culturally focused too heavily on the math genius research folks to the detriment of hiring and rewarding the more workmanlike practical folks who can build and maintain a substantial platform which, I agree, is the new durable advantage.

去吧

I was reminded last week by China’s censorship of Google’s latest AlphaGo win against Ke Jie just how substantial a stance it was when Google shut down its Mainland search engine in 2010 and why these kind of bold moves (bets) are essential to developing a truly winning technology company (and also why I don’t live in China anymore!). As Rusty Guinn has written about: A man must have code.

Strange loops

Finally, to bring us back up to the level of self and consciousness, I finally got ‘round to reading Douglas R. Hofstadter’s 2007 book I am a Strange Loop. A long, winding and compelling book summarized by the quote “In the end, we are self-perceiving, self-inventing, locked-in mirages that are little miracles of self-reference.” If you dip in and only read one section, read the section on simmballs in Chapter 3, which loops us back to where we started this column on multiscale information.

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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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