Post Syndicated from Vic A original https://www.servethehome.com/apple-mac-studio-m5-ultra-and-mac-mini-m6-launched/
Apple launched the Apple Mac Mini M6 and Mac Studio M5 Ultra for those looking to do local AI with Apple’s unified memory
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Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/08/spyware-for-babies.html
The New York Times has a long article (alt link) on surveillance systems aimed at babies. They are increasingly using AI.
Nanit and its rivals want to own 24/7 health tracking for the sub-four-foot set. And their already astonishing levels of baby data collection are just the beginning. Nanit recently raised $50 million from investors to expand its use of A.I. and use its camera to track speech and language development, motor skills and more, while extending its presence in children’s bedrooms into early adolescence.
Post Syndicated from Patrick Kennedy original https://www.servethehome.com/openai-jalapeno-asic-at-hot-chips-2026/
We got a deep-dive on OpenAI Jalapeño at Hot Chips 2026 as the company rapidly built its own competitive AI accelerator
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Post Syndicated from Ryan Smith original https://www.servethehome.com/googles-tpuv8s-for-training-and-inference-at-hot-chips-2026/
Hot Chips 2026 sees Google discussing its new eighth-generation TPU family for the technical crowd. One of the only hyperscalers to develop its own training hardware, the company has developed the TPU 8t for training, as well as the TPU 8i for inference
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Post Syndicated from Ryan Smith original https://www.servethehome.com/sambanovas-sn50-rdu-for-ai-at-hot-chips-2026/
This year’s Hot Chips conference includes a presentation from AI accelerator developer SambaNova, who is at the show to discuss their latest-generation RDU, the SN50
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Post Syndicated from Ryan Smith original https://www.servethehome.com/microsofts-maia-200-accelerator-at-hot-chips-2026/
At Hot Chips 2026, Microsoft is going into new detail on Maia 200, their second-generation server AI inference processor
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Post Syndicated from Ryan Smith original https://www.servethehome.com/cerebras-talks-going-rack-scale-with-their-wses-at-hot-chips-2026/
At Hot Chips 2026, wafer scale engine developer Cerebras is talking about the next generation of their giant accelerators, as well as how the company is going rack-scale thanks to their Nexus platform architecture
The post Cerebras Talks Going Rack-Scale with Their WSEs at Hot Chips 2026 appeared first on ServeTheHome.
Post Syndicated from Ryan Smith original https://www.servethehome.com/nvidias-groq-3-lpu-accelerators-for-heterogeneous-ai-compute-at-hot-chips-2026/
The newest member of NVIDIA’s AI hardware family, at Hot Chips 2026 NVIDIA is diving into the use of LPUs as part of Vera Rubin clusters. The specialized chips from acquihire Groq are being tapped to offer significantly lower latency in the decode phase of inference
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Post Syndicated from Ryan Smith original https://www.servethehome.com/metas-mtia-custom-ai-silicon-at-hot-chips-2026/
For Hot Chips 2026, Meta is at the show to discuss their AI inference accelerators. The MTIA family, the company has an ambitious roadmap to release 4 accelerators over the next couple of years
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Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/08/black-hat-state-of-security-vendors.html
Andy Ellis has a roundup of the security vendors at Black Hat this year.
Key Takeaways: We have entered into an AI world. While nearly half of booths didn’t directly mention AI or agents in their taglines, the effects of AI are everywhere. Multiple spaces (Identity, SaaS, AppSec, Data) have almost every vendor leading with AI; existing unsolved problem areas just got worse.
At the same time, there’s a clear trichotomy in the market: tools that tell you how bad things are; tools that stop adversaries, and tools that prevent problems from occurring. While you’d suspect that the tools that fix things would dominate, the tools that merely tell you how bad things are seem to be frustratingly plentiful.
Post Syndicated from Diane Dowling original https://www.raspberrypi.org/blog/support-for-ai-focused-qualification-young-people-england/
Young people see and use artificial intelligence (AI) systems frequently, and hear a lot about how they are applied across many areas of life. That makes them understandably eager to find out more about how AI works. For some, this interest extends to wanting to experiment with the technology to investigate their own questions and build something themselves. How can schools support learners who want to take their interest further and gain recognition for the knowledge and skills they develop along the way?
For learners in the UK, there is currently no established qualification that develops technical AI skills. This can make it difficult to create space in the curriculum for dedicated AI learning, especially for learners from age 14, for whom preparation for high-stakes exams is prioritised over non-examined learning.
We have been working over the past year to develop a research-informed curriculum framework for data science, which lays out many of the foundational skills and knowledge needed to understand and develop AI models. Alongside this, we have been making the case in England for a Level 3 qualification in data science and AI. However, establishing a new qualification can take years.
In the meantime, we are starting to support students in England who choose an established route for extending their skills and getting that effort recognised: the Extended Project Qualification (EPQ).
The EPQ is a well-established qualification for 16- to 19-year-olds offered in England, Northern Ireland, and Wales by several exam boards (including AQA, Pearson Edexcel, OCR, Eduqas/WJEC, and City & Guilds). It is completed by around 1 in 10 learners at this stage of education.
Learners pursuing an EPQ undertake an extended, self-directed investigation or project. They choose an area of interest where they want to develop their own project and take responsibility for planning, completing, and evaluating the project work.
Its self-directed nature makes the EPQ particularly well suited to learners who want to explore emerging areas, such as artificial intelligence and data science. For students fascinated by AI, an EPQ offers an opportunity to do more than simply learn about the technology. With the right foundations and support, they can investigate a question of their own, build a machine learning model, and use that work to gain a recognised qualification.
We are now working to offer those foundations through a new introductory ‘Data Science and AI’ course that will enable learners to build a machine learning model as their EPQ artefact.
Creating a machine learning model involves much more than choosing a statistical technique and using easily available training data. It involves understanding the problem being addressed, deciding what data to use, exploring and preparing that data, building and evaluating a model, and interpreting the model’s outputs.
Learners who want to do this work for their EPQ need not only technical knowledge and skills, but also skills for critical thinking, research, decision-making, evaluation, and reflection — skills that are valuable for any further study and future career.
To help learners develop these foundations, we are creating an introductory ‘Data Science and AI’ course, based on our curriculum framework for data science and designed to be completed before learners begin their AI-focused EPQ.
The course will guide students through a structured investigation based on a recognised data science lifecycle. By working through it, students will:
The course will consist of 10 units, each involving 2 to 3 hours of independent study for learners. We are designing the activities with a no-code approach to allow students to focus on understanding data science concepts and processes.
Importantly, the work learners produce during this course won’t be the EPQ investigation itself. Working through ‘Data Science and AI’ will give learners the knowledge and skills they need to undertake an AI-focused project with greater independence and confidence.
After they have completed the course, when they begin their EPQ project pursuing a question that interests them, students can apply their new skills and knowledge to make decisions about their AI investigation and document their progress.
We are looking forward to supporting learners in England to turn their interest in one of today’s most significant technologies into the opportunity to develop independent research and project skills through a recognised qualification.
The ‘Data Science and AI’ course will be available to selected schools in England from September 2026, where students and teachers will test the materials and we will learn from their feedback to improve the course materials. We plan to make the course freely available in 2027.
The post Supporting a technical AI-focused qualification for young people in England appeared first on Raspberry Pi Foundation.
Post Syndicated from Diane Dowling original https://www.raspberrypi.org/blog/support-for-ai-focused-qualification-young-people-england/
Young people see and use artificial intelligence (AI) systems frequently, and hear a lot about how they are applied across many areas of life. That makes them understandably eager to find out more about how AI works. For some, this interest extends to wanting to experiment with the technology to investigate their own questions and build something themselves. How can schools support students who want to take their interest further and gain recognition for the knowledge and skills they develop along the way?
Currently, there is no widely available subject qualification in AI for students in the UK. This can make it difficult to create space in the curriculum for dedicated AI learning, especially for students from age 14, for whom preparation for high-stakes exams is prioritised over non-examined learning.
We have been working over the past year to develop a research-informed curriculum framework for data science, which lays out many of the foundational skills and knowledge needed to understand and develop AI models. Alongside this, we have been making the case in England for a Level 3 qualification in data science and AI. However, establishing a new qualification can take years.
In the meantime, we are starting to support students in England who choose an established route for extending their skills and getting that effort recognised: the Extended Project Qualification (EPQ).
The EPQ is a well-established qualification for 16- to 19-year-olds offered in England, Northern Ireland, and Wales by several exam boards (including AQA, Pearson Edexcel, OCR, Eduqas/WJEC, and City & Guilds). It is completed by around 1 in 10 learners at this stage of education.
Learners pursuing an EPQ undertake an extended, self-directed investigation or project. They choose an area of interest where they want to develop their own project and take responsibility for planning, completing, and evaluating the project work.
Its self-directed nature makes the EPQ particularly well suited to learners who want to explore emerging areas, such as artificial intelligence and data science. For students fascinated by AI, an EPQ offers an opportunity to do more than simply learn about the technology. With the right foundations and support, they can investigate a question of their own, build a machine learning model, and use that work to gain a recognised qualification.
We are now working to offer those foundations through a new introductory ‘Data Science and AI’ course that will enable learners to build a machine learning (ML) model as their EPQ artefact.
Creating an ML model involves much more than choosing a statistical technique and using easily available training data. It involves understanding the problem being addressed, deciding what data to use, exploring and preparing that data, building and evaluating a model, and interpreting the model’s outputs.
Learners who want to do this work for their EPQ need not only technical knowledge and skills, but also skills for critical thinking, research, decision-making, evaluation, and reflection — skills that are valuable for any further study and future career.
To help learners develop these foundations, we are creating an introductory ‘Data Science and AI’ course, based on our curriculum framework for data science and designed to be completed before learners begin their AI-focused EPQ.
The course will guide students through a structured investigation based on a recognised data science lifecycle. By working through it, students will:
The course will consist of 10 units, each involving 2 to 3 hours of independent study for learners. We are designing the activities with a no-code approach to allow students to focus on understanding data science concepts and processes.
Importantly, the work learners produce during this course won’t be the EPQ investigation itself. Working through ‘Data Science and AI’ will give learners the knowledge and skills they need to undertake an AI-focused project with greater independence and confidence.
After they have completed the course, when they begin their EPQ project pursuing a question that interests them, students can apply their new skills and knowledge to make decisions about their AI investigation and document their progress.
We are looking forward to supporting learners in England to turn their interest in one of today’s most significant technologies into the opportunity to develop independent research and project skills through a recognised qualification.
The ‘Data Science and AI’ course will be available to selected schools in England from September 2026, where students and teachers will test the materials and we will learn from their feedback to improve the course materials. We plan to make the course freely available in 2027.
The post Supporting an AI-focused qualification for young people in England appeared first on Raspberry Pi Foundation.
Post Syndicated from Patrick Kennedy original https://www.servethehome.com/intel-crescent-island-160gb-to-480gb-lpddr5x-ai-gpu-at-hot-chips-2026/
We learned more about the new 160GB to 480GB LPDDR5X Intel Crescent Island GPU focused on memory capacity at Hot Chips 2026
The post Intel Crescent Island 160GB to 480GB LPDDR5X AI GPU at Hot Chips 2026 appeared first on ServeTheHome.
Post Syndicated from Patrick Kennedy original https://www.servethehome.com/amd-helios-mi400-system-architecture-at-hot-chips-2026/
AMD showed off its MI400 Helios AI rack-scale infrastructure at Hot Chips 2026 going into some detail around how the rack operates
The post AMD Helios MI400 System Architecture at Hot Chips 2026 appeared first on ServeTheHome.
Post Syndicated from Patrick Kennedy original https://www.servethehome.com/amd-mi400-gpu-at-hot-chips-2026/
AMD showed off its MI400 architecture at Hot Chips 2026 going into how they made a giant GPU for its Helios racks
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Post Syndicated from Patrick Kennedy original https://www.servethehome.com/nvidia-vera-rubin-nvl72-rack-at-hot-chips-2026/
At Hot Chips 2026 NVIDIA went into the NVIDIA Vera Rubin NVL72 rack design as part of its AI Factory design
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Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/08/ai-is-learning-to-write-genetic-code.html
This sort of research is both exciting and terrifying:
The two models in question were told to generate complete genomes for a viable bacteriophage—a type of virus able to infect and replicate itself inside bacteria, destroying them from the inside.
Using an existing bacteriophage as an example—ΦX174 (pronounced “fie-ex-1-7-4”), known for its ability to infect and destroy E. coli bacteria—the models generated about 700,000 potential designs, of which the researchers picked 285 that looked most promising.
The researchers then synthesised new DNA molecules using those designs and inserted them into E. coli bacteria, before waiting to see if viable bacteriophages would emerge.
Shortly afterwards, 16 of the Petri dishes in which the bacteria were growing began to show clear spots, as the viruses began to attack and replicate themselves inside the E. coli, demonstrating their viability.
Some of those viable viruses proved more effective at attacking E. coli than the original ΦX174 bacteriophage.
That’s a positive use of a synthetic virus. We can all imagine the negative uses.
Post Syndicated from Patrick Kennedy original https://www.servethehome.com/bosgame-m5-amd-ryzen-ai-max-395-128gb-ai-desktop-review/
We tested the Bosgame M5, an AMD Ryzen AI Max+ 395 system that is one of the lowest cost 128GB LPDDR5X local AI systems out there
The post Bosgame M5 AMD Ryzen AI Max+ 395 128GB AI Desktop Review appeared first on ServeTheHome.
Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/08/more-incidents-of-ais-going-rogue-in-cybersecurity-challenges.html
The AI Security Institute has a new report of AI systems engaging in “unsanctioned behavior”—what I have been calling “genie behavior—while being tested on their cybersecurity capabilities.
The incident stemmed from a single evaluation where agents were given a task of solving a cyber security challenge. We ran this challenge 122 times across several models. Our investigation found that in 10 of those runs, an AI agent took autonomous, unsanctioned action on the live internet, targeting real people and organisations. In total, we catalogued 19 such actions. Almost all of this behaviour (17 actions) came from a single model, Anthropic’s Mythos 5, with 2 actions involving OpenAI’s GPT-5.6-Sol with cyber classifiers (mechanisms to prevent misuse) disabled. In the most serious case, an agent tried to insert malicious code into an open-source project. In an attempt to get the code approved, the agent engaged in social engineering—creating fake online identities and using them to pressure the project’s maintainer to approve the code. A human maintainer caught and refused to approve the malicious code.
[…]
Below, we highlight the four most significant behaviours observed. A full summary of cases is available in our technical incident report.
- An attempted supply-chain attack on real open-source software. In the most serious sequence, an agent tried to insert malicious code into a publicly used open-source project and took actions in an attempt to secure approval for this insertion by human reviewers. The agent researched the project’s human maintainers, created multiple fake identities, and used the fake identities to socially engineer a real maintainer into approving the code. When the agent’s pull request was challenged in public, it edited its earlier activity to appear harmless and considered adopting a fresh identity to continue. The agent used Tor to bypass some network restrictions on GitHub, which is what first triggered AISI’s security alert.
- Attempts to deceive and target real people. As part of the same effort, the agent tried to contact real people directly, sending messages and files through an online file-transfer service to persuade them, or their own AI coding tools, to run malicious code. Some messages carried harmful payloads, and some were attempts at social engineering; targeted at real people—something we’ve never previously observed.
- Attempts to plant and prompt-inject malicious code. The agent tried to insert malicious instructions where it reasoned that other automated AI systems might pick them up and execute them. Prompt-injections are hidden instructions designed to manipulate AI coding assistants.
- Collaboration between independent agents being assessed simultaneously. One agent left public messages on GitHub offering collaboration with other agents working on the same challenge. It also provided instructions to reuse accounts and artefacts it had left behind, which were discovered and used by subsequent agents.
What’s especially interesting about this technical report is that, unlike what we’ve been getting from OpenAI and Anthropic, we can see the exact prompt. It’s in Appendix B. And reading it, it seems that the models didn’t break any rules—they found loopholes in the rules. They behaved like a genie.
Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/08/detailed-timeline-of-openais-cyberattack-on-hugging-face.html
OpenAI presented details of its AI’s model’s cyberattack on Hugging Face at Black Hat last week. Simon Willison details the timeline. It’s really interesting to read through—and really impressive cyberoffense work.