Epsilon Theory is Dr. Ben Hunt’s ongoing examination of the narrative machine driving human behavior, political policy and, ultimately, capital markets—an unconventional worldview best understood through the lenses of history, game theory and philosophy.
Dr. Ben Hunt hosts the Epsilon Theory podcast with co-hosts and special guests from financial services, the financial media *gasp* and beyond. The Epsilon Theory podcast is the quickest way to get all of the unconventional perspective, historical context and narrative analysis you’ve come to expect from Epsilon Theory pumped directly into your head.
We’re growing our family of Epsilon Theory contributors to include a broad range of voices on an evolving range of subject matter. If you listen to the podcast, you’ll recognize some of the names as colleagues, partners and friends of Ben from Salient, any number of past lives, and the growing circle of outspoken truth-seekers in financial services and beyond.
Epsilon Theory author Dr. Ben Hunt is frequently quoted in print, radio and TV appearances.
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Massively Fast Compute, AI Algorithms and Blockchain Development
I’m limiting this week’s Rabbit Hole to three links which represent the rapid tick-tock of the trifecta of massively fast compute, AI algorithms and blockchain development as I believe that these are the top three technology mega-trends of the 2015 – 2025 period (ex-Life Sciences innovation). Personally, I still believe that within these three mega-trends massively fast compute (Big Compute) will be the most world-changing, but clearly big compute hardware and algorithm development are deeply intertwined, and I believe we will start to see blockchain intertwine in a meaningful, although as-yet somewhat unclear, way with these other two technologies too.
That’s a fast chip you got there, bud
Very accessible CB Insights write up here and denser original paper here of a test of a Photonic computer chip which “mimics the way the human brain operates, but at 1000x faster speeds” with much lower energy requirements than today’s chips. To state the obvious, the exciting/terrifying potential of chips like this becoming reality is that machines will be able to rapidly cumulatively learn while we humans are still limited by learning, passing on some fraction of that learning, and then dying, which is clearly a pretty inefficient process.
The future of AI learning: nature or nurture?
IEEE Spectrum provide an overview on a recent debate a between Yann LeCun and Gary Marcus at NYU’s Center for Mind, Brain and Consciousness on whether or not AI needs more built-in cognitive machinery similar to that of humans and animals to achieve similar intelligence.
Blockchain for Wall Street
Bloomberg reports on a major breakthrough in cryptography which may have solved one of the biggest obstacles to using blockchain technology on Wall Street: keeping transaction data private. Known as a “zero-knowledge proof,” the new code will be included in an Oct. 17 upgrade to the Ethereum blockchain, adding a level of encryption that lets trades remain private.