Cracking the “Covid code” with genomic sequencing

Does genomic sequencing represent a paradigm shift in medical technology? In this deeply reported feature for The New York Times, journalist Jon Gertner investigates the notion that commercial genome sequencing is akin to the invention of the microscope in the 1600s—an innovation so profound that it fundamentally altered the course of medical technologies and capabilities forever.

“Historians of science sometimes talk about new paradigms, or new modes of thought, that change our collective thinking about what is true or possible. But paradigms often evolve not just when new ideas displace existing ones, but when new tools allow us to do things — or to see things — that would have been impossible to consider earlier. The advent of commercial genome sequencing has recently, and credibly, been compared to the invention of the microscope, a claim that led me to wonder whether this new, still relatively obscure technology, humming away in well-equipped labs around the world, would prove to be the most important innovation of the 21st century.”


How Spotify uses machine learning and AI to curate your playlists (podcast)

Over the last few weeks, Spotify has quietly rolled out an excellent podcast series, “Spotify: A Product Story.” The series offers thoughtful interviews from insiders including founder/CEO Daniel Ek, investors like Mary Meeker, and several early employees who speak candidly about the company’s history, early challenges, business model innovations, and ultimate ambitions. The fourth installment of the series, published this week, is perhaps the most ambitious yet: a deep-dive conversation with AI/ML expert Andrew Ng on how Spotify is integrating artificial intelligence onto its platform.

“Will AI truly replace us humans when it comes to music? Listen to machine learning (ML) legend Andrew Ng and Spotify insiders on what it really means to develop products in an AI/ML first world, how we invented the term “Algotorial” and how reinforcement learning (RL) applies to music. Features interviews with Andrew Ng, Oskar Stål, Ajay Kalia, Meg Tarquinio, and Tony Jebara.”


The liquidation of Archegos 

Investors and global markets were stunned last week when a little-known family office based out of New York received what many believe to be one of the largest margin calls of all time. This piece from the Wall Street Journal offers one of the most comprehensive post-mortems on how excessive leverage and lack of diligence may have contributed to one of the biggest meltdowns since LTCM.

“Because of Archegos’s highly concentrated positions, the sharp drop in Viacom hit the fund’s portfolio, and many in the market believe that Archegos started selling other stocks in its portfolio to cushion the blow. Those sales sent other stocks it held tumbling, including Discovery. Suddenly, the collateral Archegos had given the banks was no longer enough to back the loans. Banks hit Archegos with margin calls to back up its trades, which initially were met by the fund, but as ViacomCBS fell further on Wednesday, the firm didn’t have the money to provide its lenders, a person involved in the unwinding said.”


Leveraging neural nets (and a First Principles approach) to create a fully autonomous transportation system

At a fundamental level, there are two paths being pursued right now to achieve a fully-functional autonomous driving network: LiDAR (which uses active photon generation and mapping) vs computer vision (i.e. passive optical) and neural nets. This week, data scientists Ivan Kopas and Dheepan Ramanan published an excellent in-depth analysis on the subject. Coincidentally, shortly before press time of The Nightcrawler, we learned that Waymo CEO John Krafcik was stepping down from his post at Waymo, Google’s LiDAR-based autonomous driving division.

“LiDAR stands for ‘light detection and ranging’ and is essentially sonar using pulse laser waves to map distance between objects and build high-definition maps of the area. A simple analogy can illustrate the conceptual difference between Computer Vision and LiDAR. Imagine two students, where one is just cramming and memorizing the content (LiDAR), while the other one is trying to really understand the material and truly learn it (Tesla FSD). The student that learned the material (Tesla FSD) will be able to answer the exam questions correctly, even if the questions on the exam are swapped, the questions are rephrased, or new components are added to the questions, while the student that memorized the content (LiDAR) will likely fail the exam.”


A few more links I enjoyed: 

“Skill is non-linear: it can be just as hard, or likely harder, to go the last 10% of the way as it is to go the first 90%, since by that point the insights logically have to be harder to uncover and sources of progress have to be more difficult to tap. But it’s that last little bit that makes a difference if you’re in a field where you’re competing against other people who probably also went through a similar progression. If everyone can fairly easily and rapidly get through the first 90%, what will separate the wheat from the chaff will be mostly found in that last, harder-to-get, 10%.”
“There’s now a growing movement to ban dark patterns, and that may well lead to consumer protection laws and action as the Biden administration’s technology policies and initiatives take shape. California is currently tackling dark patterns in its evolving privacy laws, and Washington state’s latest privacy bill includes a provision about dark patterns.”
“Because equity total return swaps are bespoke or “over the counter” contracts between two parties, they are not cleared and reported through an exchange. Nor are investors required to report their synthetic equity exposure to the US Securities and Exchange Commission, as they would if they had the same amount of exposure through a cash holding. The industry is required to report most swaps deals to a data warehouse that provides authorities with information on derivatives, but rules covering equity total return swaps do not come into force until later this year.”

This information should not be considered a recommendation to purchase or sell any particular security. It should not be assumed that any of the investments or strategies referenced were or will be profitable, or that investment recommendations or decisions we make in the future will be profitable. This article contains links to 3rd party websites and is used for informational purposes only. This does not constitute as an endorsement of any kind. While Nightview uses sources it considers to be reliable, no guarantee is made regarding the accuracy of information or data provided by third-party sources. Nightview Capital Management, LLC (Nightview Capital) is an independent investment adviser registered under the Investment Advisers Act of 1940, as amended. Registration does not imply a certain level of skill or training. More information about Nightview Capital including our investment strategies and objectives can be found in our ADV Part 2, which is available upon request.