The Summer Reading List (by Jeremy Radcliffe)

It’s that time of year, when the kids get out of school and somehow you’re supposed to have more time to spend reading. I’m going to share a few of my current, hopefully off-the-beaten-path favorites with you. These recommendations are going to focus on good old-fashioned free email subscriptions, kind of like Epsilon Theory. If you want to read great literature, please check out the McSweeney’s store, where the books are as beautiful on the outside as the words are on the inside. And if you want the list of finance-related classics, well, Ben’s already done that work for you here (I can’t recommend Fortune’s Formula highly enough!). So, on to my email list recommendations:

Bob Lefsetz

Ostensibly, Bob writes about music and the music business, so this is certainly most applicable for those with an interest in music and the music scene, but Bob’s near-daily communiques are about so much more than music. I’ve been reading Bob for about three years now and his advice for artists is applicable to business leaders as well — primarily to focus on being authentic and not to worry about appearing vulnerable, which is actually humanizing and allows others to bond with you.  http://lefsetz.com/wordpress/

Scott Galloway

I don’t know where I first came across Scott’s blog/newsletter, which is nominally about digital marketing strategy, but it’s now a weekly blessing. He’s a professor at NYU Stern and just sold his consulting business L2, but he’s continued to publish notes that are very much in the Lefsetz vein. Scott’s an expert in his field, and he also understands that transparency and authenticity drive the connection with the reader. His tagline or motto is “life is so rich,” and it is, especially when you’re reading his smart, beautiful, and brutally honest stuff.  https://www.l2inc.com/

Scott Belsky

When it comes to technology and the VC world, my go-to used to be Bill Gurley of Benchmark Capital and his wonderful Above the Crowd (great name; Bill’s super-tall); however, Bill is down to about a post a year of late, so don’t expect much on a regular basis, but consider signing up because when he does post, it’s a must-read. However, his friend and Benchmark venture partner, Scott Belsky has started doing a monthly-ish collection of his thoughts and links to interesting content in the technology and design arena which he is calling Positive Slope, and I highly recommend it.  http://digest.scottbelsky.com/

 Tim Urban

Tim’s WaitButWhy blog is tech-focused also, but his specialty seems to be explaining Elon Musk’s ambitions in relatively plain but plentiful (like 40,000 words at a time) English for those of us who aren’t engineers, using low-tech stick figure diagrams and clip art.  http://waitbutwhy.com/

Lacy Hunt & Van Hoisington

OK, so this is a more straightforward investment management letter, but if you want to understand why interest rates are so stubbornly low in the face of unprecedented “money printing” by central banks around the world (spoiler alert: velocity of money!), you should be reading whatever Lacy and his partner Van Hoisington of Hoisington Asset Management in Austin, Texas are writing. Yes, they run a long-dated Treasury fund and are “talking their book,” but they’ve been so right for so long while almost everybody else in our business has used every 20-basis-point backup in rates as an excuse to call for the Death of the Bond Bull Market.  http://www.hoisingtonmgt.com/newsletter

Eknath Easwaran

I learned to meditate a few years ago using a simple technique called passage meditation pioneered (or documented!) by Blue Mountain Center of Meditation founder, Eknath Easwaran. You can sign up for a daily dose of wisdom, taken from his book Words to Live By and delivered via email.  https://www.bmcm.org/subscribe/

PDF Download (Paid Subscription Required): http://www.epsilontheory.com/download/16016/

1999 v2.0

On episode 21 of the Epsilon Theory podcast, Dr. Ben Hunt is joined by Brad McMillan, CFA, CAIA, the chief investment officer at Commonwealth Financial Network®. Brad graciously hosts us at Commonwealth’s headquarters in Waltham, Massachusetts. Ben and Brad talk about their mutual love for Terry Pratchett, narrative causality, the French elections, and how technology is changing the financial advisory business.

2016-07-et-podcast-itunes 2016-07-et-podcast-gplay 2016-07-et-podcast-stitcher

Future Flash Crashes, Digital Darwinism & the Resurgence of Hardware (by Silly Rabbit)

Future flash crashes

Remember a few years back when a bogus AP tweet instantly wiped $100bn off the US markets? In April 2013 the Associated Press’ Twitter account was compromised by hackers who tweeted “Breaking: Two Explosions in the White House and Barack Obama is injured.”

For illustrative purposes only.

Source: The Washington Post, 04/23/13, Bloomberg L.P., 04/23/13.

The tweet was quickly confirmed to be an alternative fact (as we say in 2017), but not before the Dow dropped 145 points (1%) in two minutes.

Well, my view is that we are heading into a far more ‘interesting’ era of flash crashes of confused, or deliberately misled, algorithms. In this concise paper titled “Deceiving Google’s Cloud Video Intelligence API Built for Summarizing Videos”, researchers from the University of Washington demonstrate that by inserting still images of a plate of noodles (amongst other things) into an unrelated video, they could trick a Google image-recognition algorithm into thinking the video was about a completely different topic.

Digital Darwinism

I’m not sure I totally buy the asserted causality on this one, but the headline story is just irresistible: “Music Streaming Is Making Songs Faster as Artists Compete for Attention.” Paper abstract:

Technological changes in the last 30 years have influenced the way we consume music, not only granting immediate access to a much larger collection of songs than ever before, but also allowing us to instantly skip songs. This new reality can be explained in terms of attention economy, which posits that attention is the currency of the information age, since it is both scarce and valuable. The purpose of these two studies is to examine whether popular music compositional practices have changed in the last 30 years in a way that is consistent with attention economy principles. In the first study, 303 U.S. top-10 singles from 1986 to 2015 were analyzed according to five parameters: number of words in title, main tempo, time before the voice enters, time before the title is mentioned, and self-focus in lyrical content. The results revealed that popular music has been changing in a way that favors attention grabbing, consistent with attention economy principles. In the second study, 60 popular songs from 2015 were paired with 60 less popular songs from the same artists. The same parameters were evaluated. The data were not consistent with any of the hypotheses regarding the relationship between attention economy principles within a comparison of popular and less popular music.

Meanwhile, in other evolutionary news, apparently robots have been ‘mating’ and evolving in an evo-devo stylee. DTR? More formal translation: Researchers have added complexity to the field of evolutionary robotics by demonstrating for the first time that, just like in biological evolution, embodied robot evolution is impacted by epigenetic factors. Original Frontiers in Robotics and AI (dense!) paper here. Helpful explainer article here.

The resurgence of hardware

As we move from a Big Data paradigm of commoditized and cheap AWS storage to a Big Compute ­­paradigm of high performance chips (and other non-silicon compute methods), we are discovering step-change innovation in applied processing power driven by the Darwinian force of specialization, or, as Chris Dixon recently succinctly tweeted: “Next stage of Moore’s Law: less about transistor density, more about specialized chips.”

We are seeing the big guys like Google develop their specialized chips custom-made for their specific big compute needs, with a very significant increase of speed of up to 30 times faster than today’s conventional processors and using much less power, too.

Also, we are seeing increased real-world applications being developed for truly evolutionary-leap technologies like quantum computing. MIT Technology Review article on implementing the powerful Grover’s quantum search algorithm here.

And, finally, because it just wouldn’t be a week in big compute-land without a machine beating a talented group of humans at one game of another: Poker-Playing Engineers Take on AI Machine – And Get Thrashed.

Key points:

  1. People have a misunderstanding of what computers and people are each good at. People think that bluffing is very human, but it turns out that’s not true. A computer can learn from experience that if it has a weak hand and it bluffs, it can make more money.
  2. The AI didn’t learn to bluff from mimicking successful human poker players, but from game theory. Its strategies were computed from just the rules of the game, not from analyzing historical data.
  3. Also evident was the relentless decline in price and increase in performance of running advanced ‘big compute’ applications; the computing power used for this poker win can be had for under $20k.

PDF Download (Paid Subscription Required): http://www.epsilontheory.com/download/16079/

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.

PDF Download (Paid Subscription Required): http://www.epsilontheory.com/download/16098/