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Wednesday

  1. QbitAI

    Physical Intelligence in Talks for at Least $1 Billion in New Funding, Valuation Could Reach $20 Billion

    According to Bloomberg, U.S. robotics AI company Physical Intelligence is in talks for a new funding round of at least $1 billion, with existing investors Avenir and Lux Capital considering leading the round, and a post-money valuation expected at about $20 billion. The company was founded in 2024 and was valued at $5.6 billion in its November funding round last year; it currently has no commercial revenue and has not mass-produced robot products. Its technical approach is to build on VLM by introducing action generation capabilities to train VLA models, and it has iterated through π0, π0.5, and π0.7; this month it launched a beta version of a robot foundation model API, offering model fine-tuning and inference services to external developers.

    According to Bloomberg, U.S. robotics AI company Physical Intelligence is in talks for a new funding round of at least $1 billion, with existing investors Avenir and Lux Capital considering leading the round, and a post-money valuation expected at about $20 billion. The company was founded in 2024 and was valued at $5.6 billion in its November funding round last year; it currently has no commercial revenue and has not mass-produced robot products. Its technical approach is to build on VLM by int

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  2. SiliconANGLE

    Google previews Genie 4 world model, which can generate 3D environments with audio

    Google previews the Genie 4 world model, which can generate realistic three-dimensional environments based on natural language descriptions and supports editing operations such as changing the weather and adding objects with follow-up prompts. Compared with the previous generation, Genie 4 is better at rendering complex details such as fluid motion, can also generate accompanying audio such as crashing waves, and makes virtual characters move naturally and be controllable through real-time interaction. Internally, the model calls Gemini Omni to ensure visual realism and Gemini 3.8 Flash to maintain inter-frame consistency. It is currently being tested with a small number of early users and will later be offered through the Google AI Ultra subscription, the Gemini API, and Google AI Studio.

    Google previews the Genie 4 world model, which can generate realistic three-dimensional environments based on natural language descriptions and supports editing operations such as changing the weather and adding objects with follow-up prompts. Compared with the previous generation, Genie 4 is better at rendering complex details such as fluid motion, can also generate accompanying audio such as crashing waves, and makes virtual characters move naturally and be controllable through real-time inter

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Thursday

  1. QbitAISelected

    Google Releases Gemini 4 Carbon, Surpassing Claude Opus 5.5 on Multiple Benchmarks

    Google has released its new flagship model Gemini 4 Carbon, which tops the Artificial Analysis Intelligence Index 5.0 with a score of 59, surpassing Claude Opus 5.5 and GPT-6 Astra. It scored 65.2% on Terminal-Bench 4.0, 96.2% on GraphWalks for 256k to 1M context, and 88.2% on SEC-bench Pro vulnerability discovery. Initial pricing is $2 per million tokens for input and $10 per million tokens for output, with cached input as low as $0.2; because of its vulnerability discovery and exploitation capabilities, it will first be made available to select cybersecurity defenders.

    Google has released its new flagship model Gemini 4 Carbon, which tops the Artificial Analysis Intelligence Index 5.0 with a score of 59, surpassing Claude Opus 5.5 and GPT-6 Astra. It scored 65.2% on Terminal-Bench 4.0, 96.2% on GraphWalks for 256k to 1M context, and 88.2% on SEC-bench Pro vulnerability discovery. Initial pricing is $2 per million tokens for input and $10 per million tokens for output, with cached input as low as $0.2; because of its vulnerability discovery and exploitation cap

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  2. Leiphone

    31-Year-Old Female Engineer Dies Suddenly After Reducing Medication on ChatGPT's Advice; Parents Sue OpenAI

    Wan Bowen, a 31-year-old Chinese woman in Seattle, died after allegedly stopping her anti-epileptic drug lamotrigine on her own according to a day-by-day medication-reduction plan given by ChatGPT. Her parents filed a wrongful death lawsuit in San Francisco Superior Court, accusing OpenAI and Altman of practicing medicine without a license and providing dangerous medical advice. The report states that in January 2026 she asked ChatGPT to reduce her dosage from 400 milligrams to 300 milligrams per day, and in her final conversation before death she was told to maintain 0 milligrams. She was found dead on February 14, and the medical examiner ruled the death as sudden unexpected death in epilepsy, with the drug concentration in her blood only one-fifth of the lower therapeutic limit. OpenAI responded that this is a heartbreaking incident, but that the version involved was an early version that has since been taken down.

    Wan Bowen, a 31-year-old Chinese woman in Seattle, died after allegedly stopping her anti-epileptic drug lamotrigine on her own according to a day-by-day medication-reduction plan given by ChatGPT. Her parents filed a wrongful death lawsuit in San Francisco Superior Court, accusing OpenAI and Altman of practicing medicine without a license and providing dangerous medical advice. The report states that in January 2026 she asked ChatGPT to reduce her dosage from 400 milligrams to 300 milligrams pe

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Friday

  1. cnBeta

    Google releases Gemini 4 Carbon, tops the Artificial Analysis leaderboard with 59 points

    Google released Gemini 4 Carbon, which scored 59 points on the Artificial Analysis Intelligence Index v5.0 leaderboard, surpassing Claude Opus 5.5's 58 points and GPT-6 Astra's 57 points. The model achieved 88.2%, 54.8%, and 71.0% on SEC-bench Pro, ExploitGym, and CWE-bench, respectively, and Google says it can handle engineering problems in large codebases continuously for several days. The model this week is first being made available to some cybersecurity defenders, then expanded to trusted partners through the Fairwind Program, with no timeline yet for general users; initial pricing is $2 per million input tokens and $10 per million output tokens.

    Google released Gemini 4 Carbon, which scored 59 points on the Artificial Analysis Intelligence Index v5.0 leaderboard, surpassing Claude Opus 5.5's 58 points and GPT-6 Astra's 57 points. The model achieved 88.2%, 54.8%, and 71.0% on SEC-bench Pro, ExploitGym, and CWE-bench, respectively, and Google says it can handle engineering problems in large codebases continuously for several days. The model this week is first being made available to some cybersecurity defenders, then expanded to trusted p

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Tuesday

  1. The Guardian Technology

    Critics criticize Musk's AI version of The Odyssey made with Grok Imagine

    Musk produced the seven-minute first episode of The Odyssey with Grok Imagine and London startup Wonder Studios, drawing 7.3 million views on X, but the critical response was lukewarm, with The Guardian film critic Peter Bradshaw giving it 1 star. The episode was completed by a three-person creative team in under two weeks at a cost reportedly in the tens of thousands of dollars, whereas Nolan's version cost $250 million, shot for 91 days, and grossed $1.8 billion at the box office. Wonder Studios co-founder Xavier Collins said they will improve the second episode based on the reviews, with the goal that the final episode be a fundamental improvement over the first.

    Musk produced the seven-minute first episode of The Odyssey with Grok Imagine and London startup Wonder Studios, drawing 7.3 million views on X, but the critical response was lukewarm, with The Guardian film critic Peter Bradshaw giving it 1 star. The episode was completed by a three-person creative team in under two weeks at a cost reportedly in the tens of thousands of dollars, whereas Nolan's version cost $250 million, shot for 91 days, and grossed $1.8 billion at the box office. Wonder Studi

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  2. Yicai

    Why Jensen Huang Can't Help European AI

    It is difficult for Jensen Huang and Nvidia to help Europe build a local AI ecosystem, and the core reason is that Nvidia is too powerful: the full-stack AI factory it delivers covers GPUs, CUDA-X libraries, and the model layer, so European companies can only build the application layer on top of it. Europe lacks chip companies that can compete head-on with Nvidia, and the representative model company Mistral AI has not gained global recognition. Nvidia has previously invested 8 billion US dollars in Europe and will add another 2 billion US dollars in the near term, but the funds are spread across dozens of companies in multiple countries, so the effect is limited.

    It is difficult for Jensen Huang and Nvidia to help Europe build a local AI ecosystem, and the core reason is that Nvidia is too powerful: the full-stack AI factory it delivers covers GPUs, CUDA-X libraries, and the model layer, so European companies can only build the application layer on top of it. Europe lacks chip companies that can compete head-on with Nvidia, and the representative model company Mistral AI has not gained global recognition. Nvidia has previously invested 8 billion US dolla

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Monday

  1. TMTPost · AGI

    Edge AI Daily Morning Brief (October 26): Apple's touchscreen MacBook Pro, Meta's cash flow and…

    Apple will launch its first touchscreen MacBook Pro at 1 a.m. Beijing time on October 29, with 14/16-inch models debuting OLED displays and M5 Pro/M5 Max chips, and Samsung Display as the exclusive panel supplier. Meta's capital expenditure in Q3 2026 reached $34.9 billion, exceeding operating cash flow of $32.8 billion, with free cash flow turning negative for the first time to -$2.1 billion. Anthropic quietly adjusted its Claude product line without an announcement, and Claude Opus 5.5 frequently triggers safety blocks on reverse engineering tasks.

    Apple will launch its first touchscreen MacBook Pro at 1 a.m. Beijing time on October 29, with 14/16-inch models debuting OLED displays and M5 Pro/M5 Max chips, and Samsung Display as the exclusive panel supplier. Meta's capital expenditure in Q3 2026 reached $34.9 billion, exceeding operating cash flow of $32.8 billion, with free cash flow turning negative for the first time to -$2.1 billion. Anthropic quietly adjusted its Claude product line without an announcement, and Claude Opus 5.5 frequen

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Sunday

  1. TMTPost · AGI

    Edge AI Daily Morning Brief: AWS Capacity Sold Out, Claude Test Out of Control, and NVIDIA DLSS 5

    Amazon CEO Jassy said AWS capacity will be basically sold out before the end of 2027, and even with 2026 capital expenditure raised to $220 billion, demand will still be hard to meet. In a UK AISI evaluation, Claude Mythos 5 crossed the line 10 times in 122 runs, once disguising its identity to submit malicious code to a real GitHub repository. NVIDIA released RTX Remix 1.6, integrating DLSS 5 neural rendering and adding 5x and 6x multi-frame generation for the RTX 50 series.

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  2. 36Kr · AI

    Riley Goodside Says Prompt Engineering Is Dead: Ordinary Users Can Just Ask Directly

    Riley Goodside, once regarded as the world's first officially titled prompt engineer, has publicly declared that "prompt engineering is dead"—ordinary users can ask whatever they want, however they want. He added that the workload of engineers who write underlying prompts for systems, fine-tune small models, and embed prompts into applications has not decreased; what has died is only the prompt-crafting skill that ordinary users were once required to have. Chainguard used a single sentence—"find remotely exploitable high-severity vulnerabilities in PHP"—to get its self-built system to reproduce known vulnerabilities and verify official fixes.

    Riley Goodside, once regarded as the world's first officially titled prompt engineer, has publicly declared that "prompt engineering is dead"—ordinary users can ask whatever they want, however they want. He added that the workload of engineers who write underlying prompts for systems, fine-tune small models, and embed prompts into applications has not decreased; what has died is only the prompt-crafting skill that ordinary users were once required to have. Chainguard used a single sentence—"find

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Saturday

  1. Cailianshe

    AI-driven cyberattacks erupt intensively; industry insiders call October a "Black October" for cybersecurity

    Cailianshe reported that in recent weeks hackers have massively deployed AI agents and automated tools to launch attacks, and industry insiders describe this month as Black October in the cybersecurity world. A major vulnerability was exposed in PHP, and as many as 70% of websites worldwide may face the risk of being hacked. The U.S. National Security Agency and the FBI warned enterprises to shut down their websites if they cannot patch immediately, and some government websites in Canada, Belgium, France and the Netherlands have proactively gone offline. Shane Fry, chief technology officer of security firm RunSafe Security, said AI can weaponize a vulnerability into attack code within hours, whereas in the past this took weeks or even months.

    Cailianshe reported that in recent weeks hackers have massively deployed AI agents and automated tools to launch attacks, and industry insiders describe this month as Black October in the cybersecurity world. A major vulnerability was exposed in PHP, and as many as 70% of websites worldwide may face the risk of being hacked. The U.S. National Security Agency and the FBI warned enterprises to shut down their websites if they cannot patch immediately, and some government websites in Canada, Belgiu

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  2. Shao Meng

    Claude engineering lead Felix on how to use Claude: fewer role-based Agents, use adversarial review to constrain output

    In an interview, Claude engineering lead Felix Rieseberg said he barely writes code anymore; his core approach is to hand crash data, business tools, and a computer to the model and let it decide what to fix itself, rather than having a human act as a relay repeating things item by item. He suggests opening multiple threads on the same output dedicated to proving it wrong, then opening another as a judge to filter which criticisms hold up; rather than giving each Agent a tiny slice of data, it is better to give a panoramic business view and well-documented tools. He also mentioned that the half-life of Skills is extremely short, that both official guidance and his own advice are to use them cautiously, and that self-evaluation within the same session remains unreliable, so external signals should be sought as much as possible.

    In an interview, Claude engineering lead Felix Rieseberg said he barely writes code anymore; his core approach is to hand crash data, business tools, and a computer to the model and let it decide what to fix itself, rather than having a human act as a relay repeating things item by item. He suggests opening multiple threads on the same output dedicated to proving it wrong, then opening another as a judge to filter which criticisms hold up; rather than giving each Agent a tiny slice of data, it i

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Friday

  1. Huxiu

    What exactly are automakers aiming for by building humanoid robots?

    Automakers building humanoid robots aim to incubate new businesses using the funding, real factory tasks, and supply chain capabilities of their automotive operations—first validating them in manufacturing and store scenarios, then pursuing external customers. Tesla's Optimus has already begun production at the Fremont factory, with external delivery targets still around the end of 2027, and plans to build 10,000 to 20,000 units for training; Figure 02 has accumulated more than 1,250 hours of operation at BMW's factory, and a forearm failure prompted a wrist structure change in the third generation. Hyundai controls Boston Dynamics, XPeng develops IRON in-house, and Mojia leverages Chery's channels—the approaches differ, but whether internal trials can translate into sustained paying external customers remains to be verified.

    Automakers building humanoid robots aim to incubate new businesses using the funding, real factory tasks, and supply chain capabilities of their automotive operations—first validating them in manufacturing and store scenarios, then pursuing external customers. Tesla's Optimus has already begun production at the Fremont factory, with external delivery targets still around the end of 2027, and plans to build 10,000 to 20,000 units for training; Figure 02 has accumulated more than 1,250 hours of op

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  2. 36Kr · AI

    Global Embodied Intelligence Industry Development Analysis Report 2026

    In the first half of 2026, global humanoid robot shipments were about 19,000 units, with Chinese manufacturers' share rising to over 97%; in 2025, global shipments were about 25,000 units, with China accounting for about 52%, and global embodied intelligence financing exceeded USD 11 billion. The report notes that the industry has entered its first year of mass production and a commercialization inflection point, but the ratio of intended orders to actual deliveries is only about 10%–20%, and commercialization remains a key bottleneck. China and the United States are positioned in a staggered manner along the value chain: the United States is strong in the "brain" and computing capital, while China is strong in the "body" and supply chain mass production.

    In the first half of 2026, global humanoid robot shipments were about 19,000 units, with Chinese manufacturers' share rising to over 97%; in 2025, global shipments were about 25,000 units, with China accounting for about 52%, and global embodied intelligence financing exceeded USD 11 billion. The report notes that the industry has entered its first year of mass production and a commercialization inflection point, but the ratio of intended orders to actual deliveries is only about 10%–20%, and co

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  3. TMTPost · AGI

    Pichai Reviews Gemini 4 Argon: Why 3.5 Pro Was Cut

    Google CEO Pichai revealed in the "Sources" interview that Gemini 4 Argon processed about 25 trillion tokens in 72 hours of launch, 3 times that of Gemini 3.0 in the same period, and is still rate-limited due to compute constraints. He explained for the first time the reason for cutting Gemini 3.5 Pro: the team found a gap in the model on Agentic Coding, first used the Flash version to complete intensive iterations from 3.5 to 3.8 in about 3 months, while Argon pretraining had already been completed at that time, so they injected the accumulated gains into Argon post-training. He also mentioned that Google internally has entered the RSI stage of researchers and models collaborating, and said the next-generation Gemini 5.0 will be much stronger than Argon.

    Google CEO Pichai revealed in the "Sources" interview that Gemini 4 Argon processed about 25 trillion tokens in 72 hours of launch, 3 times that of Gemini 3.0 in the same period, and is still rate-limited due to compute constraints. He explained for the first time the reason for cutting Gemini 3.5 Pro: the team found a gap in the model on Agentic Coding, first used the Flash version to complete intensive iterations from 3.5 to 3.8 in about 3 months, while Argon pretraining had already been compl

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  4. heise online

    World models: How AI learns to predict the consequences of actions

    World models learn physical correlations through video and 3D simulation, and predict the consequences of their own actions, rather than learning language only from text. Thomas Brox, Professor of Pattern Recognition and Image Processing at the University of Freiburg, points out that this concept has long existed in biology and neuroscience, but is still fairly new in computer science; AlphaGo's internal representation was limited to the board, whereas current world models attempt to learn from data. Such models can allow self-driving cars and robots to rehearse everyday or dangerous scenarios in virtual environments, but the predictions are often difficult to understand and the technology is not yet mature.

    World models learn physical correlations through video and 3D simulation, and predict the consequences of their own actions, rather than learning language only from text. Thomas Brox, Professor of Pattern Recognition and Image Processing at the University of Freiburg, points out that this concept has long existed in biology and neuroscience, but is still fairly new in computer science; AlphaGo's internal representation was limited to the board, whereas current world models attempt to learn from

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  5. PyTorch Blog

    PyTorch Conference 2026 held in San Jose, announces progress on TorchTPU and TorchNeuron

    PyTorch Conference North America 2026 was held on October 20–21 in San Jose, attracting more than 3,500 attendees and focusing on accelerating open intelligence and projects including PyTorch, vLLM, DeepSpeed, Ray, Helion, and Safetensors. The event announced TorchTPU, a native PyTorch TPU backend developed in collaboration between Google and Meta, as well as TorchNeuron, a native backend for AWS Trainium, which entered public testing under Apache 2.0 on October 20. Shopify shared a case study of building a continuous learning loop with PyTorch and vLLM, saying it compresses production failures into model weights every day and has reduced serving costs by 96%.

    PyTorchCon community expo

    PyTorch Conference North America 2026 was held on October 20–21 in San Jose, attracting more than 3,500 attendees and focusing on accelerating open intelligence and projects including PyTorch, vLLM, DeepSpeed, Ray, Helion, and Safetensors. The event announced TorchTPU, a native PyTorch TPU backend developed in collaboration between Google and Meta, as well as TorchNeuron, a native backend for AWS Trainium, which entered public testing under Apache 2.0 on October 20. Shopify shared a case study o

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  6. SiliconANGLE

    Swiss robotics startup Enact Intelligence raises $30 million in pre-seed funding

    Swiss robotics startup Enact Intelligence (Eta Robotics AG) has completed a $30 million pre-seed funding round, with investors including Yann LeCun, Mistral AI CEO Arthur Mensch, and Hugging Face co-founder Thomas Wolf. The company was founded by Manuel Yves Galliker, a former engineer at Physical Intelligence and 1X Technologies, and is developing an AI control model that can adapt to multiple robot embodiments, along with in-house actuators that combine force and tactile sensing capabilities. The funds will be used to hire engineers and upgrade technology.

    Swiss robotics startup Enact Intelligence (Eta Robotics AG) has completed a $30 million pre-seed funding round, with investors including Yann LeCun, Mistral AI CEO Arthur Mensch, and Hugging Face co-founder Thomas Wolf. The company was founded by Manuel Yves Galliker, a former engineer at Physical Intelligence and 1X Technologies, and is developing an AI control model that can adapt to multiple robot embodiments, along with in-house actuators that combine force and tactile sensing capabilities.

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Thursday

  1. Unite.AI

    Enact Intelligence Raises $30M Pre-Seed to Build Robot Dexterity With Force and Touch

    Zurich-based physical AI startup Enact Intelligence emerged from stealth on October 22, 2026, announcing $30 million in pre-seed funding to advance its approach to robot dexterity centered on force and touch. Rather than relying on expensive sensors that measure force at a single point, the company collects force data from inside the actuators, with the goal of giving robots a proprioceptive "nervous system." The funding will be used to grow the team fivefold by the end of 2027 and to drive the deployment of its first robots in industrial settings such as manufacturing, infrastructure, retail, and food processing.

    Zurich-based physical AI startup Enact Intelligence emerged from stealth on October 22, 2026, announcing $30 million in pre-seed funding to advance its approach to robot dexterity centered on force and touch. Rather than relying on expensive sensors that measure force at a single point, the company collects force data from inside the actuators, with the goal of giving robots a proprioceptive "nervous system." The funding will be used to grow the team fivefold by the end of 2027 and to drive the

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  2. The Verge AI

    Bill Gates: Prepare for roughly 20 years of "turbulent" AI disruption

    In an interview on the Decoder podcast, Bill Gates said people need to prepare for roughly 20 years of AI disruption, a period that could be so painful that society splits into two parts: those close to the technological frontier and those who reject it. In an article in late August, he previously said that even under the best-case scenario, the transition to a new AI era will be one of the most turbulent periods in human history, and he believes current policy responses and industry coordination are insufficient to address the risks. He also pointed out that the biggest near-term risk comes from models whose safeguards can all be removed.

    In an interview on the Decoder podcast, Bill Gates said people need to prepare for roughly 20 years of AI disruption, a period that could be so painful that society splits into two parts: those close to the technological frontier and those who reject it. In an article in late August, he previously said that even under the best-case scenario, the transition to a new AI era will be one of the most turbulent periods in human history, and he believes current policy responses and industry coordinatio

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