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AI

'AI Can't Think' (theverge.com) 289

In an essay published in The Verge, Benjamin Riley argues that today's AI boom is built on a fundamental misunderstanding: language modeling is not the same as intelligence. "The problem is that according to current neuroscience, human thinking is largely independent of human language -- and we have little reason to believe ever more sophisticated modeling of language will create a form of intelligence that meets or surpasses our own," writes Riley. A user shares: The article goes on to point out that we use language to communicate. We use it to create metaphors to describe our reasoning. That people who have lost their language ability can still show reasoning. That human beings create knowledge when they become dissatisfied with the current metaphor. Einstein's theory of relativity was not based on scientific research. He developed it as thought experiment because he was dissatisfied with the existing metaphor. It quotes someone who said, "common sense is a collection of dead metaphors." And that AI, at best, can rearrange those dead metaphors in interesting ways. But it will never be dissatisfied with the data it has or an existing metaphor.

A different critique (PDF) has pointed out that even as a language model AI is flawed by its reliance on the internet. The languages used on the internet are unrepresentative of the languages in the world. And other languages contain unique descriptions/metaphors that are not found on the internet. My metaphor for what was discussed was the descriptions of the kinds of snow that exist in Inuit languages that describe qualities nowhere found in European languages. If those metaphors aren't found on the internet, AI will never be able create them.

This does not mean that AI isn't useful. But it is not remotely human intelligence. That is just a poor metaphor. We need a better one.
Benjamin Riley is the founder of Cognitive Resonance, a new venture to improve understanding of human cognition and generative AI.
AI

Mira Murati's Stealth AI Lab Launches Its First Product (wired.com) 33

An anonymous reader quotes a report from Wired: Thinking Machines Lab,a heavily funded startup cofounded by prominent researchers from OpenAI, has revealed its first product -- a tool called Tinker that automates the creation of custom frontier AI models. "We believe [Tinker] will help empower researchers and developers to experiment with models and will make frontier capabilities much more accessible to all people," said Mira Murati, cofounder and CEO of Thinking Machines, in an interview with WIRED ahead of the announcement.

Big companies and academic labs already fine-tune open source AI models to create new variants that are optimized for specific tasks, like solving math problems, drafting legal agreements, or answering medical questions. Typically, this work involves acquiring and managing clusters of GPUs and using various software tools to ensure that large-scale training runs are stable and efficient. Tinker promises to allow more businesses, researchers, and even hobbyists to fine-tune their own AI models by automating much of this work.

Essentially, the team is betting that helping people fine-tune frontier models will be the next big thing in AI. And there's reason to believe they might be right. Thinking Machines Lab is helmed by researchers who played a core role in the creation of ChatGPT. And, compared to similar tools on the market, Tinker is more powerful and user friendly, according to beta testers I spoke with. Murati says that Thinking Machines Lab hopes to demystify the work involved in tuning the world's most powerful AI models and make it possible for more people to explore the outer limits of AI. "We're making what is otherwise a frontier capability accessible to all, and that is completely game-changing," she says. "There are a ton of smart people out there, and we need as many smart people as possible to do frontier AI research."
"There's a bunch of secret magic, but we give people full control over the training loop," OpenAI veteran John Schulman says. "We abstract away the distributed training details, but we still give people full control over the data and the algorithms."
News

VP.NET Publishes SGX Enclave Code: Zero-Trust Privacy You Can Actually Verify 12

VP.NET has released the source code for its Intel SGX enclave on GitHub, allowing anyone to build the enclave and verify its mrenclave hash matches what's running on the servers. This takes "don't trust, verify" from marketing to reality, making privacy claims testable all the way down to hardware-enforced execution.

A move like this could set a new benchmark for transparency in privacy tech.

Comment Re:If... (Score 0) 43

Well,
I know about a (good no idea?) developer who had a lot of money.
He paid "online gamers" to harvest items for him in an online game.

Because he thought (and told so in public): "I love that game, and when I play it the 5h a week while I have time, I want to play it by the most potential".

Using an LLM for coding is more or less the same.

If you have to write 100 lines of code that you have clearly in your mind, and takes 3h to do right, but an LLM can spit it out in 30 seconds ... does that make you a bad coder? Using an LLM I mean? I would say your boss calls you a good coder, haha!

Comment Re:No emergency plan (Score 1) 125

Because they did not run on pay check to pay check limits and/or still where able to do business without computers, or we simply do not know about them.

Again: a million dollar profit bakery which has 10 or 15 places to sell bread and 2 or 3 bakeries, with a computer crash still has the supply chain of incoming flour and other raw materials: as that was an agreed delivery contract months/years ago. So: no immediate harm. They might have some big long term customers (daily delivered) , and they just continue to deliver. Then the remaining customers are day to day passing customers.

While it seriously might be a pain ... they can continue.

And then look at the ransomware gangs: they are probably small teams ... they do not have the time to attack an absurd amount of companies, but have to do the research to find some which might pay.

Comment Re:Three times? (Score 1) 81

You learn about Kelvin in school.
In Physics, in 5th grade.

So I assume "99.9999% of the human population cann^Ho^Ht^H do that"

And outside of the US: no one is using F ... so your idea how many people can convert easy from C/K to F: is absurd :P

Obviously my assumption above might be wrong. Gosh, we should google now what the temperature scale is in Japan, Kenya or ...

Perhaps one of those uses F?

Comment Re:Three times? (Score 1) 81

Of course in Physics we use Kelvin.

Having a random zero point makes no sense.

About the unit one could argue, as the "degree" (aka one step from one point to the other) is Celsius.

Sitting here after a beer, I have no immediate idea if there is a more plausible "stepping" rate.

For example one could have picked absolute zero and then use Fahrenheit steps. Would make sense or not? Question is: would there be a human conceivable step size that makes more sense in physics? Everything much bigger than Celsius, or much smaller than Fahrenheit would not make so much sense in daily life.

Comment Re:imperial units for scientific experiments... (Score 1) 81

Perhaps you have a different idea what a significant digit is? (I assume you are American)

First of all: before the decimal point, all digits are significant. Ooops.

Then lets look at this:
19000K converted to Fahrenheit: 33740 F

So, you want to adjust this for "your significant digits", and cut it to 33000F.

Now, we "lost" 740F ....

Which translates back to: roughly 200K ... so let me check my calculator. It seems to be 190K. (Nice coincident, see below)

So: because of "your idea of significant digits" you want to lose 190K, in a unit conversion. Well, that explains Mars lander crashes.

Looking at the coincidental loss of 190K by "your conversion" to F, we talk about 1 percent

You want to lose 1% precision/information by converting to another unit? Because of: what exactly?

The measurements in the original article averaged/settled around "19000K plus/minus 4000K"
That is not a margin of error, but the range of measurements.

This is the logarithm of 7/3 (picked it as 7 and 3 are primes): 0.3679_7666
0.3679 I calculated by hand. The _ is the marker for "significant digits".
The 7666 after the _ marker: is a guestimate. Which is close enough for any real world applications, like finding your "exact" position on the globe by triangulating 4 stars in the sky. So the significant digits are: "0.3679" because they are a real confirmed part of log(7/3) and because I indicated with the "_" (and explained so) that the digits behind are not "significant" as I guessed them.

It is perhaps not good enough to land a probe on Mars, though.

Point is: Converting 19000K plus/minus 4000K translates to Fahrenheit as 33740 plus/minus 6740.33 (yes, for nitpicking purpose I did not round it) has absolutely nothing to do with significant figures.

Rot learning for your exam, is not the same as understanding.

Sorry: but do you really have "significant digits" before a decimal point in your country? Then I feel sad for your education ... as that makes no sense at all.

You lose 1% of accuracy by converting to another unit, and you find that: okay! ... as an engineer? You are working for Boing, right?

Of course, if you do not like my logarithm above, we could follow your "zero schema" and write it as " 0.36790000" into your log table. Then: my guestimate about the other 4 digits, is lost. And that increases your problem of landing a probe on Mars.

So, no damn idea why you insist a conversion from a scientific unit into another one has to lose 1% "precision" or "information" because of "significant digits".

That is simply not what "significant" means. I owe you $4444? No problem, I just transfer $4000. Or I just transfer $5000 ... hm, I have to think about that. Keeping your "error margins" transferring $4888 is cheaper for me and makes you hopefully more happy? Strange: 4444 was significant. And if it was 4400 and I convert it to the current currency exchange from Thai Bath to dollar, it would be: 144,121.14 THB versus 142,694.20 THB. That is a one month rent difference in SIGNIFICANT, for a simple apartment in Bangkok.

Anyway, if I have to rely on a payment then I need to pay my rent, I rather have it either paid "exactly" or rounded up, and not cut into: significant digits :P

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