One of our simplest “screening” questions for DS roles at my company is: “your model is 100% accurate. How do you feel?”
If the answer is anything other than deep skepticism (Data leakage, trivial dataset etc), it’s a big red flag
These guys are saying something like 17M thefts in 2017, which to me is a bit concerning and almost incredible, even if they’re off by a factor of 2. I mean, it’s not the end of the world if someone assumes my identity but it certainly seems like a monumental hassle to deal with and almost certainly bound to cost several thousand dollars.
The UK's NHS recently opened up a large amount of its data to Google [0]. In parallel efforts, a company called Nuna is gathering and unifying data from state level Medicaid programs so it can be analyzed similarly [1].
Does anyone know whether this is a degenerative disease or a condition one is born with? It's possible the whole brain is necessary for learning, (including learning how to learn more abstract things), but then can be pruned heavily to move information into denser networks.
Whenever automated transportation becomes feasible, which is on the order of 5-10 years, a good chunk of a multi-trillion dollar market will go to the few players ready to capture it. At this point, Uber is arguably the most promising bet: tons of data, name recognition, long-term vision, talent, and on-point execution. Not to say they can't miss the mark still, but the market size for transportation of people and goods is gigantic, and Uber is dead set on basically being the One to take it all. I can see why it'd be an appealing investment.
Basically how the "Data Science Machine" works:
1) "Feature synthesis": features related to the target are discovered by following foreign keyed relationships in a relational database, automatically generating "deep" queries involving many joins. Aggregates like MIN, MAX, AVG, and STD are automatically calculated to be used as additional features.
2) "Dimensionality Reduction": Truncated SVD reduces feature length
3) "Modeling": Remaining features are clustered and then modeled by Random Forest decision trees with learned hyper parameters.
There's a ton more optimizations, but that's the gist.
The machine can't be evaluated as a stand-alone product, though, because the researchers also mention manually generating features on the problems they tested, without reporting the effect of their own tampering.
They do conclude the system is more of a time-saving device than the general pattern-recognition one that our journalists seem to think it is.
I think of it as an arms race between traders and regulators. The more complex the security, the longer it will take regulators to pass rules against its misuse, figure out taxation, etc. In the meantime, the inventor stands to make an enormous amount of money.
Also, there's an inherent information asymmetry favoring the inventor.
A) 20% growth in revenue per year is not "flattening out". It's massively exponential growth.
B) Google is at the cutting edge of robotics and computer vision. I'd say they are in an excellent position to cash in on the next big market.
The words "Something All Our Own" come to mind (the name of Grant Hill's collection of African American art).
The motivation to defy expectations has been there for a long time, and makes sense on several levels. It's the same drive, I suspect, that led to utterly unique movements like Jazz, hip-hop, and distinctive forms of dance, which really insisted on creating their own modalities and not borrowing from the pre-existing culture.
Some motivations I can think of:
One, asserting independence from a dominant culture that in turns delegitimizes and exploits your own. Why would one choose to blend in with this culture?
Two, demanding legitimacy / highlighting the fact that there are still dire punishments for choosing to embrace one's ethnicity. Hell, name choice is small beans... consider the racism implicit in having your dialect, spoken by millions, deemed unacceptable at work and school by white authority figures everywhere. Having to deal with the fact that no one will take you seriously if you speak the way you do at home, is a big part of the African American experience and must be a constant reminder that you are not accepted by those in charge unless you toe the line.
Three, an attempt at disproving racist forces that wish to deny your intelligence, creative spark, and potential. Self expression was brutally squashed since the beginning of slavery days, and only allowed insofar as it entertained those in power. The desire to actively disregard whether your action pleases the supremacist (who still doles out reward and punishment), and instead celebrate the differences that have normally only meant trouble, is a fully understandable reaction to all this.