AMD on Thursday unveiled new details about its next-generation AI chips, the Instinct MI400 series, that will ship next year. CEO Lisa Su unveiled the chips at a launch event in San Jose, California.
The chips will be able to be used as part of a “rack-scale” system, AMD said. That’s important for customers that want “hyperscale” clusters of AI computers that can span entire data centers.
OpenAI CEO Sam Altman appeared on stage on with Su and said his company would use the AMD chips. “It’s gonna be an amazing thing,” Altman said. — Read More
Monthly Archives: June 2025
Meta’s Llama 3.1 can recall 42 percent of the first Harry Potter book
In recent years, numerous plaintiffs—including publishers of books, newspapers, computer code, and photographs—have sued AI companies for training models using copyrighted material. A key question in all of these lawsuits has been how easily AI models produce verbatim excerpts from the plaintiffs’ copyrighted content.
For example, in its December 2023 lawsuit against OpenAI, the New York Times Company produced dozens of examples where GPT-4 exactly reproduced significant passages from Times stories. In its response, OpenAI described this as a “fringe behavior” and a “problem that researchers at OpenAI and elsewhere work hard to address.”
But is it actually a fringe behavior? And have leading AI companies addressed it? New research—focusing on books rather than newspaper articles and on different companies—provides surprising insights into this question. Some of the findings should bolster plaintiffs’ arguments, while others may be more helpful to defendants. — Read More
The Browser Company launches its AI-first browser, Dia, in beta
raditional web tools are facing an existential crisis as AI products and tools increasingly eat up attention — and therefore market share and money — from a wide swathe of products that people have used for years to interact with the internet. At least, that’s what The Browser Company seems to think is happening.
The company last year decided to stop developing its popular web browser Arc, acknowledging that while Arc was popular among enthusiasts, it never hit scale, as it presented too steep a learning curve to reach mass adoption. The startup has since been heads-down on developing a browser that bakes in AI at the heart of the browser. That browser, called Dia, is now available for use in beta, though you’ll need an invite to try it out. — Read More
The history and future of the data ecosystem (w/ Lonne Jaffe)
In this decades-spanning episode, Tristan talks with Lonne Jaffe, Managing Director at Insight Partners and former CEO of Syncsort (now Precisely), to trace the history of the data ecosystem—from its mainframe origins to its AI-infused future.
Lonne reflects on the evolution of ETL, the unexpected staying power of legacy tech, and why AI may finally erode the switching costs that have long protected incumbents. The future of the AI and standards era is bright. — Read More
Apple Intelligence gets even more powerful with new capabilities across Apple devices
Apple today announced new Apple Intelligence features that elevate the user experience across iPhone, iPad, Mac, Apple Watch, and Apple Vision Pro. Apple Intelligence unlocks new ways for users to communicate with features like Live Translation; do more with what’s on their screen with updates to visual intelligence; and express themselves with enhancements to Image Playground and Genmoji.1 Additionally, Shortcuts can now tap into Apple Intelligence directly, and developers will be able to access the on-device large language model at the core of Apple Intelligence, giving them direct access to intelligence that is powerful, fast, built with privacy, and available even when users are offline. These Apple Intelligence features are available for testing starting today, and will be available to users with supported devices set to a supported language this fall. — Read More
IBM now describing its first error-resistant quantum compute system
On Tuesday, IBM released its plans for building a system that should push quantum computing into entirely new territory: a system that can both perform useful calculations while catching and fixing errors and be utterly impossible to model using classical computing methods. The hardware, which will be called Starling, is expected to be able to perform 100 million operations without error on a collection of 200 logical qubits. And the company expects to have it available for use in 2029.
Perhaps just as significant, IBM is also committing to a detailed description of the intermediate steps to Starling. These include a number of processors that will be configured to host a collection of error-corrected qubits, essentially forming a functional compute unit. This marks a major transition for the company, as it involves moving away from talking about collections of individual hardware qubits and focusing instead on units of functional computational hardware. If all goes well, it should be possible to build Starling by chaining a sufficient number of these compute units together.
“We’re updating [our roadmap] now with a series of deliverables that are very precise,” IBM VP Jay Gambetta told Ars, “because we feel that we’ve now answered basically all the science questions associated with error correction and it’s becoming more of a path towards an engineering problem.” — Read More
Duolingo’s CEO outlined his plan to become an ‘AI-first’ company. He didn’t expect the human backlash that followed
On April 28, Duolingo cofounder and CEO Luis von Ahn posted an email on LinkedIn that he had just sent to all employees at his company. In it, he outlined his vision for the language-learning app to become an “AI-first” organization, including phasing out contractors if AI could do their work, and giving a team the ability to hire a new person only if they were not able to automate their work through AI.
The response was swift and scathing. “This is a disaster. I will cancel my subscription,” wrote one commenter. “AI first means people last,” wrote another. And a third summed up the general feeling of critics when they wrote: “I can’t support a company that replaces humans with AI.” — Read More
How You Can Use Few-Shot Learning In LLM Prompting To Improve Its Performance
You must’ve noticed that large language models can sometimes generate information that seems plausible but isn’t factually accurate. Providing more explicit instructions and context is one of the key ways to reduce such LLM hallucinations.
That said, have you ever struggled to get an AI model to understand precisely what you want to achieve? Perhaps you’ve provided detailed instructions only to receive outputs that fall short of the mark?
Here is where we’ll use the few-shot prompting technique to guide LLMs toward producing accurate, relevant, and properly formatted responses. In it, you’ll teach the LLM by example rather than through complex explanations. Excited?! Let’s begin! — Read More
How to Use Banned US Models in China
In China, U.S.-based large language models like ChatGPT, Claude, or Gemini are technically banned, blocked, or buried under layers of censorship. The Chinese government has only explicitly banned ChatGPT, citing concerns over political content, while other U.S. models like Claude and Gemini are not formally banned but remain inaccessible due to the Great Firewall. U.S. LLM providers also restrict access from China but leave some loopholes: OpenAI blocks API use but Azure continues to serve enterprise clients via offshore data centers; Anthropic blocks access to Claude within China but permits use by Chinese subsidiaries based in supported regions abroad; and Google does not offer the Gemini API in China, but access seems to be still possible via third-parties like Cloudflare (we reached out to Google for a comment but didn’t hear back).
But on Taobao, the country’s largest e-commerce platform, consumers and companies can buy access to these models with just a few clicks. This piece explains how Western models are priced, advertised, bought, and sold in China, and what their popularity reveals about state censorship, platform enforcement, and consumer demand. — Read More
The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity
Recent generations of frontier language models have introduced Large Reasoning Models (LRMs) that generate detailed thinking processes before providing answers. While these models demonstrate improved performance on reasoning benchmarks, their fundamental capabilities, scaling properties, and limitations remain insufficiently understood. Current evaluations primarily focus on established mathematical and coding benchmarks, emphasizing final answer accuracy. However, this evaluation paradigm often suffers from data contamination and does not provide insights into the reasoning traces’ structure and quality. In this work, we systematically investigate these gaps with the help of controllable puzzle environments that allow precise manipulation of compositional complexity while maintaining consistent logical structures. This setup enables the analysis of not only final answers but also the internal reasoning traces, offering insights into how LRMs “think”. Through extensive experimentation across diverse puzzles, we show that frontier LRMs face a complete accuracy collapse beyond certain complexities. Moreover, they exhibit a counterintuitive scaling limit: their reasoning effort increases with problem complexity up to a point, then declines despite having an adequate token budget. By comparing LRMs with their standard LLM counterparts under equivalent inference compute, we identify three performance regimes: (1) low complexity tasks where standard models surprisingly outperform LRMs, (2) medium-complexity tasks where additional thinking in LRMs demonstrates advantage, and (3) high-complexity tasks where both models experience complete collapse. We found that LRMs have limitations in exact computation: they fail to use explicit algorithms and reason inconsistently across puzzles. We also investigate the reasoning traces in more depth, studying the patterns of explored solutions and analyzing the models’ computational behavior, shedding light on their strengths, limitations, and ultimately raising crucial questions about their true reasoning capabilities. — Read More
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