China’s decision to use domestic AI chips instead of buying from Nvidia signals progress — and newfound confidence — in its own semiconductor industry.
st week, China barred its major tech companies from buying Nvidia chips. This move received only modest attention in the media, but has implications far beyond what’s widely appreciated. Specifically, it signals that China has progressed sufficiently in semiconductors to break away from dependence on advanced chips designed in the U.S., the vast majority of which are manufactured in Taiwan. It also highlights the U.S. vulnerability to possible disruptions in Taiwan at a moment when China is becoming less vulnerable.
After the U.S. started restricting AI chip sales to China, China dramatically ramped up its semiconductor research and investment to move toward self-sufficiency. These efforts are starting to bear fruit, and China’s willingness to cut off Nvidia is a strong sign of its faith in its domestic capabilities. — Read More
Recent Updates Page 21
Flooding the AI Frontier
Why is China giving away AI?
Chinese models are DOMINATING the open-weight LLM space.
Open-weight models are freely available to download, run, and fine-tune, often released with highly permissive licenses. Some open-weight models are also open-source, meaning the code and training data to reproduce those models are openly available as well.
These models are incredible, and compete with or even outperform leading proprietary US models on common benchmarks while costing a small fraction of the price.
One might be surprised to learn that not only are Chinese tech companies making AI models freely available, but the Chinese government has also promoted open models as part of its AI strategy. In July, China released its Global AI Governance Action Plan, heavy on “international public good,” “collaboration,” and “openness,” which sounds lovely until you remember that China maintains one of the most restrictive and censorious regions of the internet.
So what gives? Why is the Chinese government suddenly a champion of openness in AI? — Read More
AI Focus: Interception
This is a very quick post. I had an idea as I was walking the dog this evening, and I wanted to build a functioning demo and write about it within a couple of hours.
While the post and idea started this evening, the genesis of the idea has been brewing for a while and goes back over a year to August 2024, when I wrote about being sucked into a virtual internet. WebSim has been on my mind for a while, because I loved the idea of being able to simulate my own version of the web using the browser directly and not via another web page. And a couple of weeks ago, I managed to work out how to get Puppeteer to intercept requests and respond with content generated via an LLM. — Read More
Why Building Superintelligence Means Human Extinction (with Nate Soares)
The Voice Lives On: Moises Powers Whitney Houston’s Return to the Stage
Moises’ AI stem separation technology extracts Whitney Houston’s vocals from original recordings, enabling live orchestral performances across a seven-city tour
Whitney Houston’s voice moved generations, and through a collaboration between The Estate of Whitney E. Houston, Primary Wave Music, and Park Avenue Artists, it has now returned to the stage. The Voice of Whitney: A Symphonic Celebration, which debuted in August 2024, brings Houston’s legendary vocals to concert halls across US cities. The concert transports fans into Houston’s musical world, as live orchestras perform alongside Houston’s vocals and rare footage. Audiences experience the power of Houston’s voice in a live setting, with a breathtaking fusion of technology and artistry that celebrates her enduring legacy. — Read More
Mind the Gap: Time-of-Check to Time-of-Use Vulnerabilities in LLM-Enabled Agents
Large Language Model (LLM)-enabled agents are rapidly emerging across a wide range of applications, but their deployment introduces vulnerabilities with security implications. While prior work has examined prompt-based attacks (e.g., prompt injection) and data-oriented threats (e.g., data exfiltration), time-of-check to time-of-use (TOCTOU) remain largely unexplored in this context. TOCTOU arises when an agent validates external state (e.g., a file or API response) that is later modified before use, enabling practical attacks such as malicious configuration swaps or payload injection. In this work, we present the first study of TOCTOU vulnerabilities in LLM-enabled agents. We introduce TOCTOU-Bench, a benchmark with 66 realistic user tasks designed to evaluate this class of vulnerabilities. As countermeasures, we adapt detection and mitigation techniques from systems security to this setting and propose prompt rewriting, state integrity monitoring, and tool-fusing. Our study highlights challenges unique to agentic workflows, where we achieve up to 25% detection accuracy using automated detection methods, a 3% decrease in vulnerable plan generation, and a 95% reduction in the attack window. When combining all three approaches, we reduce the TOCTOU vulnerabilities from an executed trajectory from 12% to 8%. Our findings open a new research direction at the intersection of AI safety and systems security. — Read More
Jack Ma Returns With a Vengeance to ‘Make Alibaba Great Again’
During China’s yearslong crackdown on the tech sector, Alibaba Group Holding Ltd.’s internal messaging boards lit up with dreams to “MAGA” – Make Alibaba Great Again. Now, the company is deploying one of its most potent weapons to accomplish that mission: Jack Ma.
After vanishing from the public eye at the outset of an antitrust investigation in late 2020, China’s most recognizable entrepreneur is back on Alibaba’s campuses – and he’s more directly involved than he’s been in half a decade, according to people familiar with the company. Signs of his unseen hand are coming into sharper focus, perhaps no more so than in the company’s pivot to artificial intelligence and its declaration of war on e-commerce foes JD.com Inc. and Meituan. Ma was instrumental in Alibaba’s decision to spend as much as 50 billion yuan ($7 billion) on subsidies to beat back JD’s surprise entry to the market, said one of the people, requesting not to be named because the matter is private. — Read More
New Ultrasound Helmet Reaches Deep Inside The Brain Without Surgery
Deep-brain structures like the basal ganglia or the thalamus wield major influence on our behavior. If something goes awry, dysregulation in the deep brain may trigger neurological conditions like Parkinson’s disease or depression.
Despite the clear importance of these structures, our knowledge about them remains limited by their location, making them difficult to study and treat.
In a new study, researchers unveil a device that might offer an alternative to invasive procedures. Featuring a novel ultrasound helmet, it not only modulates deep-brain circuits without surgery, but reportedly can do so with unrivaled precision. — Read More
Read the Study
VaultGemma: The world’s most capable differentially private LLM
As AI becomes more integrated into our lives, building it with privacy at its core is a critical frontier for the field. Differential privacy (DP) offers a mathematically robust solution by adding calibrated noise to prevent memorization. However, applying DP to LLMs introduces trade-offs. Understanding these trade-offs is crucial. Applying DP noise alters traditional scaling laws — rules describing performance dynamics — by reducing training stability (the model’s ability to learn consistently without experiencing catastrophic events like loss spikes or divergence) and significantly increasing batch size (a collection of input prompts sent to the model simultaneously for processing) and computation costs.
Our new research, “Scaling Laws for Differentially Private Language Models”, conducted in partnership with Google DeepMind, establishes laws that accurately model these intricacies, providing a complete picture of the compute-privacy-utility trade-offs. Guided by this research, we’re excited to introduce VaultGemma, the largest (1B-parameters), open model trained from scratch with differential privacy. We are releasing the weights on Hugging Face and Kaggle, alongside a technical report, to advance the development of the next generation of private AI. — Read More
AI Will Not Make You Rich
Fortunes are made by entrepreneurs and investors when revolutionary technologies enable waves of innovative, investable companies. Think of the railroad, the Bessemer process, electric power, the internal combustion engine, or the microprocessor—each of which, like a stray spark in a fireworks factory, set off decades of follow-on innovations, permeated every part of society, and catapulted a new set of inventors and investors into power, influence, and wealth.
Yet some technological innovations, though societally transformative, generate little in the way of new wealth; instead, they reinforce the status quo. Fifteen years before the microprocessor, another revolutionary idea, shipping containerization, arrived at a less propitious time, when technological advancement was a Red Queen’s race, and inventors and investors were left no better off for non-stop running.
Anyone who invests in the new new thing must answer two questions: First, how much value will this innovation create? And second, who will capture it? Information and communication technology (ICT) was a revolution whose value was captured by startups and led to thousands of newly rich founders, employees, and investors. In contrast, shipping containerization was a revolution whose value was spread so thin that in the end, it made only a single founder temporarily rich and only a single investor a little bit richer.
Is generative AI more like the former or the latter? Will it be the basis of many future industrial fortunes, or a net loser for the investment community as a whole, with a few zero-sum winners here and there? — Read More
Rick's Cafe AI 