Perhaps for mathematicians like Terry Tao AI will be a tool they will rely on, but for mathematicians like the aforementioned, a pen, paper and the published research literature will continue to be their main tools!

Anyone of the super computers listed here https://en.wikipedia.org/wiki/TOP500 suffers from the same issues.

Think about it. While the national labs use these systems to model serious stuff -such as climate or nuclear weapons- Meta uses them to train LLMs. What a joke, honestly!

- California continues to be a necessary place for America and the world. By this I mean not just the geography, but a place that welcomes people from all walks of life seeking new beginnings.

- Never give up. Seriously. She was in her late 30s, early 40s -an age many would have considered "old" in the 1970s- when she made the breakthrough that made her famous.

I stopped counting the number of times I have watched https://www.youtube.com/watch?v=Tp4t_5v_y_0 that features him prominently as the scientific director of the Voyager missions.

I can't remember well but I was either in my second or third year of college when I saw this documentary for the first time.

What a sad day!

We should all be thankful for her contributions and for those who preceded her who were similarly "abnormal" by the standards of their time.

Thank you in advance!

When it was released in 1985, the Amiga was at least 5 years ahead of its time when it came to the graphics and sound technology. I don't care about angering Apple bots, but the capabilities of the Mac paled in comparison.

The mismanagement at Commodore that led to the fall of the company is well documented.

For me personally, as a tech professional, the lesson of the episode is clear: superior technology alone is not a guarantee of business success.

The technology companies are high performing in both technology and business. The most clear example of what I mean is NVIDIA that was launched when Commodore was still alive.

Python is a great language, but just as the generation of kids who got out of computer science programs in the 2000s were clueless about anything that wasn't Java, it seems this generation is clueless about anything that isn't Python.

There was life before Python and there will be life after Python!

Citizens having a say in deciding who sits at the top of the government is a revolutionary idea. Differentiating government from people is another revolutionary idea. These two ideas triggered America's founding in contraposition to the form of totalitarian governments that had been the norm in Europe until the 1700s.

I fully blame Google for fostering this environment. In fact, Google's two co-founders, particularly Sergey Brin, were very proud of this being in Google's DNA.

Here is the upside. Given Google's power -although its influence to set norms in tech has diminished in recent years- I hope incidents like this set a new normal in which when you go to work for a tech company, you are measured exclusively for your contributions on the technical domain -whether they are technical, sales, or what have you.

I always found the idea of "bringing your whole self to work" complete BS. This example illustrates why.

There is a long history of embezzlement and snake oil salesmanship in the United States.

The hardest form of embezzlement to put a check on is is things such as when you promise that GPUs, if they are powerful enough, will one day produce AGI by running code because intelligence is a "property of matter". Kind of "if you compute it, it will show up".

The best example to illustrate this principle is the unholy alliance between Big Pharma and the government, particularly the FDA.

Big Pharma lobbyist write the rules that the FDA enforces and the most ambitious people working at the FDA, after a few years, leave to work at Big Pharma.

The covid19 pandemic exposed the public at large to the way this unholy alliance works, but there have been numerous cases of abuse by the Big Pharma companies who consider paying fines like these https://en.wikipedia.org/wiki/List_of_largest_pharmaceutical... a cost of doing business.

In the context of AI, we are assisting to another of these abusive practices in the making particularly if an FDA-styled agency is created to regulate AI.

I was given the advice early in my career of being mindful of very nice people. I misunderstood it as "be a disgusting person".

You need to be nice and be a honest person of integrity. That's the magic. Not "OR" but "AND".

Thank you in advance!

Equating intelligence to being good as these games is as silly as equating intelligence to being good at solving differential equations. Computers have bested humans at solving differential equations for many decades now. Nobody said "gee humans are now stupid".

AI, as a knowledge field, is biased in this notion that all it matters when it comes to intelligence is that computers beat humans at Go or Chess.

If you have watched his other talks, Jeff Dean generally does a very good job explaining things from a high level point of view.

Here is a controversial view: I think that the current neural networks driven approach coupled with massively distributed computing has plateaued.

For the machine learning field to move forward, it will need a different, less data/compute hungry paradigm.

My own solution to this problem has been to work in startups for 2-3 years and then move on rather than try to seek a well paid job at a large, prestigious company.

Why? I started my career at one of these brand name, "everybody wants to get in" kind of companies that hadn't made institutional layoffs ever. When time came to do their first mass layoff, I saw people who had spent their entire careers at the company lose their jobs unemployable because thy had essentially become bureaucrats.

Startups offer you the possibility of "on the job training" for the most recent technical stack. And precisely because there are too many people with golden handcuffs at the large companies, good startups are a bit easier to get into (not "easy", but "easier").

The downside is that 90% of startups fail, and you need to live with that. At the same time, if you happen to work at one that succeeds spectacularly, you won't have to worry about making a living until you die.

Obviously, the First Amendment case law only applies to public entities -originally only to federal entities and since the passage of the 14th amendment due to https://en.wikipedia.org/wiki/Incorporation_of_the_Bill_of_R... also to states and municipalities- and certain private entities that receive public money such as universities.

There are also limitations to broadcasters that use properties, such as the allocation of frequencies, regulated by government https://en.wikipedia.org/wiki/Fairness_doctrine .

While how these latter policies apply to social media companies such as X/Twitter is part of ongoing debate and litigation, private social media companies, irrespective of the law cases, can decide what kind of censorship regimes they like to have.

Under Parag Agrawal and team, Twitter was a far left platform.

Under Elon Musk and team, Twitter/X is a centrist platform, meaning that neither the far right nor the far left have a free reign and the big middle is pretty much free to say anything they want as long as the First Amendment is not violated.