It’s not a false dichotomy — it’s pointing out a differing standard in conduct based on race, which is racism.
It’s also pointing out that the fundamental choice in what’s acceptable or not is dumb: someone who made a mistake while young and genuinely has tried to build a better life should be welcomed into society, while criminals who continue to promote a criminal and violent lifestyle should not be.
It’s also mocking that people who talk about “social justice” are often deeply racist and unforgiving people — which perhaps could be fairly criticized for tone, but is again not a false dichotomy.
“The thing I have noticed is that when the anecdotes and the data disagree, the anecdotes are usually right. There is something wrong with the way that you are measuring it.”
— Jeff Bezos
Trusting your eyes over people with replication rates in the 20-50% range (ie, social scientists) is probably a rational choice.
There’s plenty of evidence that polls have systemic inaccuracies — question phrasing, people giving “right” answers, etc.
Not just because manager quality can’t be guaranteed, but because when you have 10,000+ employees, the odds that some are fired and subsequently make a false discrimination claim are high — and you need a lot to deal with that.
Look at how Amazon is treated: with nearly a million workers, a few dozen complaining is enough for major media outlets to broadcast that they’re a bad employer.
Can you point to any employer where 1 in 10,000 workers doesn’t have a bad experience?
I only really know about the company-sci touching parts, but —
Mathematics didn’t have a strong notion of proof until the late 1800s/early 1900s. Contradictory “proofs” in calculus created a need for a more rigorous system of reasoning. (It took until the 1950s to formalize what an ‘infinitesimal’ is — but those were used in early calculus prior to limits.)
That gave us electronic computers, as the effort to reduce proofs to things that could be mechanized bore fruit. Every program is a proof!
Since then, the idea of proof and structuring mathematics has been radically redefined — introducing things like category theory and type theory as alternatives to set theory, while studying the impact of certain axioms and developing tools around “reverse mathematics”, which is fitting a set of axioms to the theorems you want to be true.
Modern mathematics is working on the study of logic topology, extending our reasoning tools to deal with complexities around proving equality in the hopes we can automatically verify mathematics. These tools overlap heavily with AI research.
The applications here are what you might guess: new AI methods, data analysis methods, verification of software, etc. The DOD has paid a fair bit to support that research into software verification, for instance. The NSF and others fund “big data” analysis.
I’m less familiar with other parts of mathematics, but —
The study of knot theory has impacts for physical sciences, from particle physics (anyons) to fluid dynamics. In particular, there’s some work to be done in higher order knot theory and computational knot theory (ie, how to be efficient). MSFT is building a quantum computer based on this — and has suggested that higher order knots might not require as expensive of hardware.
In number theory, we still don’t know much about almost all real numbers. Things like Chaitins constant exist — and such uncomputable, normal numbers form the bulk of the reals — but we don’t really know how to get our hands on them. In less exotic research, elliptic curves are used in cryptography. There’s some work I don’t quite understand in building out a homomorphic encryption — where we can operate on encrypted data.
In many areas, we’re still working through the algebra-geometry correspondence, which we got hints of 400 years ago but only formalized once topology and category theory were invented — and still are building tooling around.
And there’s lots of areas I have no idea about — but I assume are being similarly productive.
Ones that I know of, but can’t comment on: fractals, differential equations, bifurcation theory, and chaos theory.
Someone can reasonably be called an artist if, having listened to thousands of hours of music and training to replicate it, can create a new piece using a request from a patron — “make me a song that reminds me of my youth in Saxony” or whatever.
What’s the difference between that and a GAN transforming the request?
It sounds like worrying if submarines swim.
Not that I believe computers have quite reached the level that an expert artist does — being able to suggest modifications to a request — but saying they don’t produce “art” also seems incorrect.
“@Disney #MayThe4th This tweet enters into Disney’s offered contract, except that Disney agrees to pay $100,000 for the license to optionally publish this Tweet and to include it in Tweet analysis corpuses. If Disney doesn’t explicitly object within 48 hours or collects this tweet as part of a database, they accept this modification.”
If you butcher an animal, you have X lbs of meat now. Maybe you don’t need all of that immediately just to survive.
If you only need Y < X to feed yourself, you can sell the excess and buy a new animal plus food to fatten it up.
This converts a onetime benefit (killing an animal you have now) into a sustainable way to provide for yourself.