Connexe : "rosbif" pour "roast beef", à la même source, avec parmi les exemples un superbe "rosbif de cheval"
A checklist of things owners of the Tesla Model Y recommend new buyers personally inspect when accepting delivery, because apparently Tesla quality control can’t be bothered to. It warns that you’ll need around 90 minutes to complete the whole list.
A Grab competitor on "web3". Yeah, it's as you would have expected, the relevant quote:
But because they are constantly tweaking features and functionality, Sheikh admits that most of the time operations are actually running on a back-end server that mimics the processing the blockchain is supposed to do.
“But Baldur, pretty much every startup grinds and so to many tech companies?”
Now you know why software project outcomes are nearly universally abysmal and why most software is garbage.
Grind culture is bad in every way for everybody involved.
“Does Bionic Reading actually work? We timed over 2,000 readers and the results might surprise you”
“In fact, participants read 2.6 words per minute slower on average with Bionic Reading than without”
Not surprised. https://blog.readwise.io/bionic-reading-results/
Problems cryptocurrency has created or significantly worsened:
Problems cryptocurrency has solved:
no but seriously the danger of the continued 9€-Ticket is that it is a slippery slope:
- people would buy less cars
- if we can make it 9€, we could also make it free, getting rid of the ticketing infrastructure and ticket inspection, which would save cost
- if it turns out that can provide public transportation for free, people might think what else could be done that benefits everyone and financed by everyone
- next up people will want a UBI??
- people will be harder to exploit
- less profit
Btw, I believe that #AI has hit a philosophical wall that technology alone cannot move.
When I started working on AI in the mid 2000s, it was still all about expert systems, decision trees that modeled first-order logic, onthologies, and graph exploration to come up with a best strategy to solve a problem.
That generation of AI could already solve impressive problems, but it was limited by the amount of knowledge that humans could put into it to describe all the possible combinations of a complex problem, or all the possible logically valid propositions, or all the grammar rules of a language.
It was a purely reasoning-based AI. Deterministic, reliable, but its utility was constrained by the amount of logical and algorithmic rules that humans could put into it.
Then computing became cheaper and more scalable, data for big corporations became cheaper and scalable as well, and neural networks, largely forgotten for nearly 25 years, got their moment. We suddenly got statistical systems that could figure out patterns and rules from labeled data, without a human explicitly encoding them into a graph. And we really thought that we had solved the problem of AI. But then you get systems that can recognize a human and a stop sign individually, but don't know what to do when they are together - because it was never trained to deal with such an unusual combination, or even told what the real meaning of a stop sign is.
Deep learning trashed away decades of reasoning-based expert systems to focus on empirical models trained through statistical pattern matching, but in doing so it created parrots that can talk about anything without even understanding what they're talking about.
It's again the long-lived clash between rationalism and empirism. In spite of the technology, these problems have been around at least since the times of Plato. Do we through deduction (we learn the basic building blocks of reality, and then we learn how to logically connect them together in increasingly complex structures), or do we learn through experience (by observing and replicating things again and again, measuring the feedback, and gradually converging towards a local optimum that statistically minimizes the odds of error)?
Well, it turns out that we may need both, but we can't make such a big theoretical leap in understanding how machines (and even humans) learn while the whole field is in the hands of a handful of companies mostly interested in doing small iterative improvements over their existing imperfect models, with little to no incentives to take big risks required to really push the industry forward.
I just discovered the coin pushers corner of YouTube, and I feel like I found the final boss of instagram make-believe culture
How sad must one be to record almost daily videos of themselves pretending to win thousands upon thousands at different "casinos" that are never shown and all happen to use the exact same machine and tokens? How gullible are their subscribers? When will "coaching" be sold to these gullible fucks?
Dutch person: We never drink a bottle of wine that has been given to us in front of the person who gave it; what if it sucks and they see our faces?
Me: Truly you are an enlightened people
Meanwhile in the "global warming is a chinese hoax" category: Brussels may experience drinking water shortages in the coming weeks, if the Meuse river's flow keeps dropping
I write bugs for a living, pretty cool eh?
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