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Comment Meanwhile, at Carnegie Mellon... (Score 4, Interesting) 193

Jensen Huang to college grads: "Run. Don't walk" toward AI

https://www.axios.com/2026/05/...

Nvidia founder and CEO Jensen Huang told graduates at Carnegie Mellon University in Pittsburgh yesterday that demand for AI infrastructure is creating a "once-in-a-generation opportunity to reindustrialize America and restore the nation's capacity to build."

Why it matters: With many college grads fearing AI could obliterate their career dreams, Huang pointed to boundless opportunity as a "new industry is being born. A new era of science and discovery is beginning ... I cannot imagine a more exciting time to begin your life's work."

Nvidia, which makes AI chips, is the world's most valuable company. Huang told 5,800 recipients of undergraduate and graduate degrees that the AI buildout will require plumbers, electricians, ironworkers, and builders for chip factories, data centers and advanced manufacturing facilities.

"No generation has entered the world with more powerful tools â" or greater opportunities â" than you," he said. "We are all standing at the same starting line. This is your moment to help shape what comes next. So run. Don't walk."

"Every major technological revolution in history created fear alongside opportunity," Huang added. "When society engages technology openly, responsibly, and optimistically, we expand human potential far more than we diminish it."

Full speech: https://www.youtube.com/watch?...

Comment Really? (Score 2, Funny) 289

Posted by BeauHD on Wednesday November 26, 2025 @11:40AM from the language-doesn't-equal-intelligence dept

Don't you mean "from the well duh! dept"?

Comment Re:Case in point (Score 1) 211

I never said Folding@home was exactly like modern AI algorithms that accomplish the same thing. I used it as an example of datasets being farmed out to many nodes for processing using specific algorithms.

Modern AIs like Alphafold3 are trained using these large datasets, possibly even including results from Folding@home, and applying modern LLM AI algorithms to find results.

The point I was making is that these AIs are trained on a specific set of datasets and targeted towards a specific purpose. Unlike general purpose AIs which are trained on the garbage heap of the Internet and only occasionally find a nugget of gold

Again tldr; AI trained on specific datasets: Useful. AI trained on the Internet: Crap

Comment Re:Case in point (Score 1) 211

But what I am really impressed about is the fact that AI has made huge scientific discoveries.

Even if you don't use AI at all. Are you not impressed that AI solved protein folding (a Nobel prize problem)? Are you not impressed that AI is next used to discover new drugs that work exactly to the problem and because we know all human proteins, we can also let the AI test for side effects. And once the system is up and running, we can just let it solve all known deceases.

I am impressed that algorithms trained on specific datasets have improved to the point whereby they can find solutions to protein folding, or discover new drugs. The principle is not new however, it has just vastly improved in speed and efficiency
You may or may not recall Foldit@home or Seti@home which used a similar principle, but spread it out amongst many nodes.
These are instances of using large datasets from within a specific field where the algorithms of AI are useful and certainly impressive

What is not impressive however, are the LLMs trained on datasets taken from every corner of the Internet and then pretend to have all the answers. There is a large amount of slop/crap/bs on the Internet and these general purpose AIs are not trained to tell the difference between that and useful/factual data.
Sure if you want it to write a piece of fiction (though the output may not actually be worth reading), or to create a piece of artwork it will likely perform well. But if you're using it for serious work/research then much of the time you will get slop. Garbage In/Garbage Out

tldr; AI trained on specific datasets: Useful. AI trained on the Internet: Crap

Comment M.A.S.H. got it right. (Score 2) 290

War is Hell

War isn't hell
War is war and Hell is hell, and of the two, war is a lot worse.
How do you figure that, Hawkeye?
Easy, Father. Tell me, who goes to Hell?
Sinners, I believe.
Exactly. There are no innocent bystanders in Hell.
But war is chock full of them. Little kids, criples, old ladies.
In fact, except for a few of the brass, almost everybody involved is an innocent bystander.

Comment Separate from the rebranding of covid.gov... (Score 5, Insightful) 213

...an article worth considering from Princeton University's Zeynep Tufekci:

We Were Badly Misled About the Event That Changed Our Lives

Since scientists began playing around with dangerous pathogens in laboratories, the world has experienced four or five pandemics, depending on how you count. One of them, the 1977 Russian flu, was almost certainly sparked by a research mishap. Some Western scientists quickly suspected the odd virus had resided in a lab freezer for a couple of decades, but they kept mostly quiet for fear of ruffling feathers.

Yet in 2020, when people started speculating that a laboratory accident might have been the spark that started the Covid-19 pandemic, they were treated like kooks and cranks. Many public health officials and prominent scientists dismissed the idea as a conspiracy theory, insisting that the virus had emerged from animals in a seafood market in Wuhan, China. And when a nonprofit called EcoHealth Alliance lost a grant because it was planning to conduct risky research into bat viruses with the Wuhan Institute of Virology â" research that, if conducted with lax safety standards, could have resulted in a dangerous pathogen leaking out into the world â" no fewer than 77 Nobel laureates and 31 scientific societies lined up to defend the organization.

So the Wuhan research was totally safe, and the pandemic was definitely caused by natural transmission â" it certainly seemed like consensus.

We have since learned, however, that to promote the appearance of consensus, some officials and scientists hid or understated crucial facts, misled at least one reporter, orchestrated campaigns of supposedly independent voices and even compared notes about how to hide their communications in order to keep the public from hearing the whole story. And as for that Wuhan laboratoryâ(TM)s research, the details that have since emerged show that safety precautions might have been terrifyingly lax.

Full article

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