Matt Levine for Bloomberg:
There are two massive areas of job opportunity for data scientists: They can build models that help hedge funds trade stocks and bonds, or they can build models that help internet companies sell advertisements on web pages. Oh or they can build models that help cure cancer or whatever, but compared to financial trading and internet advertising that is a small and unprofitable niche. One of the most incredible feats of marketing of our century is that the internet companies have convinced a lot of people that selling advertisements on web pages is basically the same as curing cancer, while buying stocks and bonds is evil:
“At tech companies, the permeating value is that they’re about trying to make the world a better place, whereas at hedge funds it’s about making more money,” Mr. Epstein said.
As a former scientist, I cannot stop thinking about what would happen if we take all this human computing power and apply it to solve fundamental problems that impact society and the human species.
However, I do not buy the argument that selling advertisement on webpages is not perceived as “bad” so the same should happen with hedge funds. Selling advertisement on webpages has generated enormous value in terms of by-products that are quite tangible, e.g. ML translation. What has gave us hedge funds?
Maciej Cegłowski’s talk at Republica Berlin 2017 really makes you reflect on the status quo of the tech industry.
Little did I know when I started my career as a research physicist at CERN that I was a member of the Bayesianist “tribe”. In fact, I was not even aware back then that what we called
data analysis “another day working with data” was even a branch of the Machine Learning religion.
The content below is from the The Master Algorithm by Pedro Domingos. Formatting all mine.
||All intelligence can be reduced to manipulating symbols
||Inverse Deduction: It figures out what knowledge is missing in order to make a deduction go through, and then makes it as general as possible
||Learning is what the brain does, and we need to reverse engineer it
||Back Propagation: It compares a system’s output with the desired one and then successively changes the connections in layer after layer of neurons so as to bring the output closer to what it should be
||The mother of all learning is natural selection
||Genetic programming: It mates and evolves computer programs in the same way that nature mates and evolves organisms
||All learned knowledge is uncertain, and learning itself is a form of uncertain inference
||Bayes’ theorem: It tells us how to incorporate new evidence into our beliefs, and probabilistic inference algorithms do that as efficiently as possible
||The key to learning is recognizing similarities between situations and thereby inferring other similarities.
||Support vector machine: It figures out which experiences to remember and how to combine them to make new predictions
I found this fascinating: Cade Metz for Wired:
As detailed in a research paper published by OpenAI this week, Mordatch and his collaborators created a world where bots are charged with completing certain tasks, like moving themselves to a particular landmark. The world is simple, just a big white square—all of two dimensions—and the bots are colored shapes: a green, red, or blue circle. But the point of this universe is more complex. The world allows the bots to create their own language as a way collaborating, helping each other complete those tasks.
You could thing about this as a new level of Cryptophasia, i.e. language created by twins that only the two children can understand. Some might say that it is scary, some might say that it is amazing that we are getting to this level of reinforced learning.
Johana Bhuiyan for Recode:
The Chinese company’s new U.S. lab, which will focus on intelligent driving systems and AI-based security for transportation, also formalizes what many already knew: Didi is working on self-driving cars.
The company has already partnered with Udacity — a college-level nanodegree startup — on its self-driving program, at the end of which Didi and a number of other partnering companies get first pick of the graduates the companies want to hire.
Didi did not only acquired Uber China assets last year but it is also actively poaching AV talent from
Google Waymo and Uber itself.
I can just but imagine the potential of AVs deployed at a Didi scale in China in a near future.
Julia Love and Heather Somerville for Reuters:
Now, if the Waymo suit damages Uber, GV’s investment in the ride-hailing company stands to go down as a Silicon Valley rarity: a large funding deal undermined by the firm’s own investors.
“Whatever Waymo gains, Google Ventures loses,” said Stephen Diamond, associate professor of law at Santa Clara University.
An interesting dichotomy indeed.
Spacetime was not just a computer, it was a trusted quantum computer. To run anything on it, you needed a key, to open Planck-scale locks.
The Causal Angel by Hannu Rajaniemi.
Bloomberg Technology’s Brad Stone has started a new podcast called Decrypted where he takes a look into the global technology industry. On his latest episode, he takes a look in to the rise of Didi and the demise of Uber in China:
It is really worth the listening!
Wired UK has created a beautiful documentary about Shenzen and the present and future of hardware manufacturing.
It offers also a terrific view on some of the challenges that China faces with regards to innovation:
Leslie Hook for Financial Times:
Uber is preparing to pour $500m into an ambitious global mapping project as it seeks to wean itself off dependence on Google Maps and pave the way for driverless cars.
It is interesting to watch how car manufacturers (namely VAG, BMW, Daimler) and the bigger mobility companies are trying to cut their dependencies with Google Maps.
It is not surprising though: Google is very vocal about its Autonomous Driving ambitions and sooner than later will have to clarify how Google Maps fits in its overall strategy. Mapping will be strategic for the AV future and I do not think that Google will provide all the needed mapping features (e.g. accuracy) to direct competitors.
By developing its own maps Uber could eventually reduce its reliance on Google Maps, which currently power the Uber app in most of the world.
Although Google was an earlier investor in Uber, the two companies have avoided working closely together and are now developing rival technologies for driverless cars.
Last year Uber hired one of the world’s leading digital mapping experts, Brian McClendon, who previously ran Google Maps and helped create Google Earth.
“Accurate maps are at the heart of our service and backbone of our business,” Mr McClendon said in a statement. “The ongoing need for maps tailored to the Uber experience is why we’re doubling down on our investment in mapping.”
Competition is always good and brings nothing but better products!