Voxi H. Amavilah

[Voxi H. Amavilah @vhstwit1]

@Simi_2210_ 14+3

[Yuval Noah Harari @harari_yuval]

With all the talk of protecting jobs from AI, it's easy to forget that what we must actually protect isn't jobs – it's humans. If we protect humans, if we can be adaptable and resilient and flexible, it becomes less important what jobs will exist.

[Voxi H. Amavilah @vhstwit1]

@harari_yuval YES — AI can take all jobs “IFF” it improves *all* humanity or leave it as horrible as it is!

[Voxi H. Amavilah @vhstwit1]

@Elifbenad 40

[Mario Nawfal @MarioNawfal]

Japan's national football team vs 100 kids

That goal line is insane 😂

Writer: Ian

[Barack Obama @BarackObama]

I would read letters like Emily’s every night in the White House. They were powerful reminders of why the work we did mattered, and who we were fighting for.

When folks visit the Obama Presidential Center, my hope is that they see their stories reflected in the exhibits, and leave inspired to change their own neighborhoods.

[Voxi H. Amavilah @vhstwit1]

@ChristopherHale One great man in the 7 generations of papacy (1878-todate). Here is one perspective: https://t.co/2TOEtvjd9f .

[Fr Joseph Hudson @acloudofsaints]

The perfect Father's Day message.

Pope Leo:

You’ve heard your whole life that God loves you.

But do you actually believe it?

You are precious in God’s eyes. You are unconditionally loved by Him.

[Voxi H. Amavilah @vhstwit1]

@acloudofsaints Appreciably fascinating statements of human value! Here is one perspective they motivated: https://t.co/jcWbjqJsUA .

[Barack Obama @BarackObama]

To George and Laura, Bill and Hillary — we're grateful for your friendship, counsel, and devotion to this country. And to Joe and Jill, thank you for being on this journey with us.

[Rohan Paul @rohanpaul_ai]

Yann LeCun (@ylecun) explains why LLMs are limited in terms of real-world intelligence during a Bloomberg interview.

"Language is a very approximate, reduced, quantized, and simplified description of the world, and LLMs can only deal with discrete sequences of symbols. The world is much more complicated than language.

The biggest LLMs are pre-trained on the totality of all the publicly available text on the internet. That’s about 20 trillion words, or 30 trillion tokens.

A token is about 3 bytes. So total 10¹⁴ bytes of text.

This is the amount of data a four-year-old has seen through vision during four years. Now, the text, though, would take 400,000 years to read?

So, there is enormously more data from sensory input, like vision, touch, and everything else, than there could ever be through language."

A child does not need 400,000 years of reading to understand cups, doors, balance, faces, falls, or heat, because the body is already collecting dense feedback from vision, touch, motion, and consequence.

Text strips most of that away.

It turns a living scene into symbols, then asks the model to infer the missing world from traces left by people describing it.

That is why an LLM can sound fluent about physics and still have no native sense of how fragile glass feels in a hand.

Moravec’s paradox names this reversal: the things humans find intellectual can be easier for machines than the things toddlers do without applause.

The hard part is not producing an answer, but building a model of the world that survives contact with weight, friction, surprise, and failure.

----

Link to the full video on Bloomberg's site. Link in comment.

[Voxi H. Amavilah @vhstwit1]

@rohanpaul_ai @ylecun Yes, yes! Language is only an aspect of human natural/integral intelligence; LLMs are superb at the lower level of overall human intelligence,especially as human wellbeing. Sometimes people get more satisfaction from inefficiency — eg, not speaking, texting, reasoning, etc. lol

[Cristian @cristiannmillo]

Esto es espectacular como se movió Marruecos defensivamente. Esto se entrena. No es aleatorio. Tremendo bloque corto defensivo. Imposible de entrar sin alguna magia o pase filtrado con extrema exactitud. Por eso Brasil se la pasó lateralizando.

[Voxi H. Amavilah @vhstwit1]

@cesifoti Haha — “watch the game” if you are lucky. Buffering was so bad for me that a France-Senegal simulated game was better watching than the real thing. This WC may generate record revenue and attendance due to high prices and 3-hosts, but relative viewship likely low.

[Voxi H. Amavilah @vhstwit1]

@alyareckles 2208

[Nav Toor @heynavtoor]

You have noticed it. ChatGPT feels dumber than it used to. Your prompts that worked six months ago produce worse results now. The writing sounds flatter. The ideas sound safer. The internet itself feels like it is shrinking. Every article reads the same. Every email sounds the same. Every answer sounds like it was written by the same voice.

You thought it was you. It is not you.

Researchers at Oxford and Cambridge published a paper in Nature proving what is happening. They call it Model Collapse.

Here is the mechanism in one sentence. AI trained on AI-generated data gets dumber every generation until it forgets what real human data looked like.

The internet is filling with AI-generated content. Blog posts. Articles. Reviews. Comments. Social media. AI companies scrape the internet to train the next generation of models. Which means the next generation of AI is being trained on the output of the current generation.

Each cycle loses information. Not randomly. It loses the rarest, most unusual, most creative parts first. The researchers call these the "tails of the distribution." The weird ideas. The unexpected perspectives. The things that made the internet feel human. Those disappear first.

What remains is the average. The safe. The expected. The bland.

Then the next generation trains on that. And loses more. And the next generation trains on that. And loses more. The researchers proved this is not a slow decline. Major degradation happens within just a few iterations. Even when some of the original human data is preserved.

They tested it on large language models. On image generators. On statistical models. The pattern was the same every time. The output converges toward a narrow, flattened version of reality that looks nothing like the original data.

The lead researcher put it plainly. "Large language models are like fire. A useful tool. But one that pollutes the environment."

The pollution is invisible. You cannot see which sentence on the internet was written by a human and which was written by AI. Neither can the AI that is about to train on it. And once the tails are gone, they do not come back. The damage is irreversible.

This is not a prediction anymore. It is a diagnosis.

The internet you grew up on was built by humans writing things no algorithm would have written. Strange, personal, imperfect, alive. That internet is being diluted. One generation of AI at a time. And the models trained on what remains are learning a smaller and smaller version of the world.

Model Collapse is not a technical problem. It is a cultural one. The thing that made the internet worth reading is the thing that disappears first.

[Voxi H. Amavilah @vhstwit1]

@PhilosophyOfPhy His influence outside physics was huge. Here for Economics and economists: https://t.co/mPFGyixmDU and https://t.co/CJaflsBoPS

[Voxi H. Amavilah @vhstwit1]

@larayapmaaq Pin for Swiss wrist watch?

[Voxi H. Amavilah @vhstwit1]

@Airaasayss 90311

[Philosophy Of Physics @PhilosophyOfPhy]

In 1880, a reclusive, self-taught telegraph operator with no university degree went to war with the greatest scientific minds in the British Empire.

He won, changed the mathematics of physics forever, and quietly built the foundation for the entire modern electrical grid.

Yet today, almost no one outside of electrical engineering and applied mathematics even knows his name.

His name was Oliver Heaviside.

The story of how he solved one of the hardest engineering problems in human history is a masterclass in why book smarts fail where deep, messy intuition succeeds.

In the late 19th century, the world was trying to lay massive underwater telegraph cables across the Atlantic Ocean. But they had a crippling problem: the signals kept distorting. You would type a message in London, and by the time it reached New York, it was a smeared, unreadable mess of electricity.

The top physicists of the day, using traditional university math, said the solution was simple: make the cables purer and reduce resistance. They spent millions of dollars trying to make the lines perfect.

It didn't work. The signals still broke.

Heaviside looked at the exact same problem from his messy, self-taught perspective and realized the elite academic establishment was blind.

They were treating an electrical wire like a water pipe. They thought the electricity was inside the copper.

Heaviside figured out that electricity doesn’t flow inside the wire; it flows in the electromagnetic field around the wire.

Then, he did something that made mainstream mathematicians furious. He invented a bizarre shortcut called operational calculus. Instead of spending weeks solving complex, multi-page differential equations to map these fields, he treated calculus like basic algebra.

To the professors at Cambridge, this was a sin. They called his math clumsy, unrigorous, and nonsense.

Heaviside didn't care. His famous response to them was: "Should I refuse my dinner because I do not fully understand the process of digestion?"

He used his illegal math to propose a mind-bending solution: to fix the distorted signal, engineers didn't need to make the cable cleaner. They needed to deliberately add more corruption to it. He suggested wrapping the cables in iron wire to introduce "inductance", intentionally fighting one distortion with another.

The establishment ignored him for years. But when AT&T finally tried his method, the results were instant. Long-distance communication was solved.
Heaviside wasn't trying to pass a math exam or impress a peer-review board. He wanted to solve a real-world problem.

In the process, he took James Clerk Maxwell’s famously complex 20 equations of electromagnetism and condensed them into the 4 beautiful formulas that every single physics student is forced to memorize today. Heaviside did the heavy lifting, but Maxwell got the name.

The lesson Heaviside left behind is a philosophical blueprint for navigating a complex world:
The people who memorize the proper formulas are excellent at solving textbook problems. But they are entirely dependent on the rules staying the same.

The people who understand the underlying system don't care about the rules. They break them to find what actually works.

Most of us approach our life's problems like the 19th-century British establishment. When something goes wrong in our career or relationships, we try to make our existing wire purer. We try harder at a broken method.

But sometimes, the problem isn't that you aren't trying hard enough. The problem is that you are looking inside the wire instead of looking at the field around it.

What is a distortion in your life right now that you keep trying to fix with the standard advice? What happens if you stop trying to follow the textbook formula and start looking at the hidden forces causing the noise?