Facebook says fanning the flames of hate gets you more engagement, and it's ok to do it because it happened before, in the 1930s, with nothing bad coming from it
To quote @Grady_Booch: Facebook is a profoundly unethical company, and it starts at the top.
Fully aware of its own immense influence power, FB deliberately decides to use it in service of far-right radicalization, in order to create "engagement".
Honestly the take "the fact that it happened in the 1930s shows that it's part of human nature and therefore it's fine to encourage it" blows my mind.
Of course it's part of human nature. This realization is at the core of what "never again" means.
Humans have that potential. You can choose to either encourage people's worst instincts, exploit their fears and their ignorance and cultivate their hate, or you can choose to fight against it.
If you are in a position of immense influence (psychological control even) over hundreds of millions of people and you deliberately choose to do the former, for money, then you are absolutely evil. Period.
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Fact check: my 3-year old builds Lego sets (age 5+ ones) on his own by following the instruction booklet. He started doing it before he turned 3 -- initially he needed externally provided error correction and guidance, but now he's just fully autonomous. Can't handle sets for ages 8+ yet though. We'll see what he does at 5.
He also builds his own ideas, which feature minor original inventions. Like this "jeep" which has a spare tire on the back -- not something he saw in any official set. Lego is the best toy ever by the way
Or this Lego garden (fresh from today). It has a hut with a cool door. It looks chaotic, but everything on here has a purpose. Everything is intended to be something (the tire on a stick is a tree, the tiny cone on the ground is a water sprinkler...)
I'm partnering with @mikeknoop to launch ARC Prize: a $1,000,000 competition to create an AI that can adapt to novelty and solve simple reasoning problems.
I published the ARC benchmark over 4 years ago. It was intended to be a measure of how close we are to creating AI that can reason on its own – not just apply memorized patterns.
ARC tasks are easy for humans. They aren't complex. They don't require specialized knowledge – a child can solve them. But modern AI struggles with them.
Because they have one very important property: they're designed to be resistant to memorization.
It's amazing to me that the year is 2024 and some people still equate task-specific skill and intelligence. There is *no* specific task that cannot be solved *without* intelligence -- all you need a sufficiently complete description of the task (removing all test-time novelty and uncertainty), and you can achieve arbitrary levels of skills while entirely by-passing the problem of intelligence. In the limit, even a simple hashtable can be superhuman at anything.
The "AI" of today still has near-zero (though not exactly zero) intelligence, despite achieving superhuman skill at many tasks.
Here's one thing that AI won't be able to do within five years (if you extrapolate from the excruciatingly slow progress of the past 15 years): acquiring new skills as efficiently as humans, using the same data. The ARC benchmark is an attempt at measuring roughly that.
The point of general intelligence is to make it possible to deal with novelty and uncertainty, which is what our lives are made of. Intelligence is the ability to improvise and adapt in the face of situations you weren't prepared for (either by your evolutionary history or by your past experience) -- to efficiently acquire skills at novel tasks, on the fly.
Many of the people who are concerned with falling birthrates aren't willing to consider the set policies that would address the problem -- aggressive tax breaks for families, free daycare, free education, free healthcare, and building more/denser housing to slash the price of homes.
Most people want children, but can't afford them.
I always found it striking how very rich couples (50M+ net worth) all tend to have over 3 children (and often many more). And how young women always say they want children -- yet in practice they delay family building because they are forced to focus on financial stability and therefore career. When money is not an object, families have 3+ children.
For middle incomes (below 1M/year) fertility goes down as income goes up, because *the cost of raising children increases with income* due to *opportunity cost*. If you make $150k and stand to eventually grow to $300k, you are losing a lot of money by quitting your job to raise children (on top of the prohibitive cost of raising children -- which also goes up as your incomes and thus standards go up). You are thus *more* likely to postpone having children.
Starting at 1M/year, fertility rates rise again. And couples that make 5+M/year get to have the number of children they actually want -- which is almost always more than 3, and quite often 5+.
That memorization (which ML has solely focused on) is not intelligence. And because any task that does not involve significant novelty and uncertainty can be solved via memorization, *skill* is never a sign of intelligence, no matter the task.
Intelligence is found in the ability to pick up new skills quickly & efficiently -- at tasks you weren't prepared for. To improvise, adapt and learn.
Here's a paper you can read about it.
It introduced a formal definition of intelligence, as well as benchmark to capture that definition in practical terms. Although it was developed before the rise of LLMs, current state-of-the-art LLMs such as Gemini Ultra, Claude 3, or GPT-4 are not able to score higher than a few percents on that benchmark.arxiv.org/abs/1911.01547
Finding 1: the fastest backend for a given model typically alternates between XLA-compiled JAX and XLA-compiled TF. Plus, you might want to debug/prototype in PT before training/inferencing with JAX or TF.
The ability to write framework-agnostic models and pick your backend later is a game-changer.
Finding 2: Keras 3 with the best-performing backend outperforms reference native PT implementations (compiled) for all models we tried.
Notably, 5 out of 10 tasks demonstrate speedups exceeding 100%, with a maximum speedup of 340%.
If you're not leveraging this advantage for any large model training run, you're wasting GPU time -- and thus throwing away money.