|
Claude Mythos Preview discovered new attacks in testing against weakened cryptographic algorithms, which protect online financial transactions, private communications and more.
|
|
Anthropic CEO Dario Amodei has argued that policymakers should keep lower-risk open-weight AI accessible while placing stricter safeguards around frontier systems, including mandatory testing and limits on China's access to advanced computing and model capabilities.
In a post outlining Anthropic's position, Amodei said broad restrictions, including bans on Chinese open-weight models used by US businesses, would not address his main national security concerns. Instead, he pointed to the possibility of authoritarian governments surpassing the US in advanced AI, as well as cyber, biological, and alignment risks posed by increasingly capable systems.
Amodei also called for action against industrial-scale model distillation, which he said allows Chinese developers to improve their models with less computing power than would be needed to train comparable systems from scratch.
The statement followed criticism of Anthropic for not signing an industry letter backed by Nvidia, Microsoft, Meta, IBM, Mistral, Hugging Face and other technology companies urging policymakers to avoid premature restrictions on open-weight models.
The letter said that open wei
|
|
Nvidia is investing a reported $5 billion in Ilya Sutskever's SSI and providing Vera Rubin systems for a tenfold increase in AI compute.
|
|
The 19th-century French novelist Honore de Balzac is believed to have said that behind every great fortune lies a great crime.
That's even more true today than it was 200 years ago — just look at how Big AI, including Anthropic, OpenAI, Google, and others have built their trillion-dollar fortunes.
They all use vast amounts of copyrighted material to train their large language models (LLMs) without paying the copyright holders. In other words, they steal it. They don't call it stealing, though. They call it "fair use," which in this case amounts to the same thing.
Generative AI (genAI) training requires massive amounts of text. The better-written and more information-dense that text is, the more it helps. AI gets a lot smarter a lot faster when it's trained on well-written books and magazine and newspaper articles than when it's trained on social media banter (or most everything else you find on the internet).
Since the dawn of AI, companies have been hoovering up copyrighted material wherever they find it — on the open web, behind paywalls, even in manually scanned books — and then used the scanned text. And they do it all without asking authors' or publishers' permissions, and without paying them.
It's the greatest intellectual property theft in history by a long shot — billions and b
|
|
But as some reviewers have noted, the boosts from DLSS aren't always as impressive in real life as they are in marketing materials. And it's often less powerful systems—like gaming laptops—that see the most modest benefits. So, I decided to test it out myself.
Here are my Nvidia DLSS 4 results on a gaming laptop. Keep reading for the exact laptop I used and the various gains (or lack thereof) I saw in a handful of different high-profile games.
The laptop I used for these tests
When Nvidia boasted about the awe-inspiring eight-fold improvement in Cyberpunk 2077 performance, those tests were done under ideal conditions. According to the fine print, it was achieved on a PC with an RTX 5090 at 4K resolutions with all the graphical bells and whistles turned on, and in DLSS performance mode.
But that's far from your typical gaming PC. Most modern gaming PCs are budget laptops—like the Lenovo LO
|
|
