Search “Google DeepMind”

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.

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.

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.

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%.

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.

Hugging Face Releases Open-Source DNA Model Carbon-X, Runs Locally on a Laptop

Hugging Face on Thursday released the open-source DNA understanding model Carbon-X, which can run locally on a laptop, letting users ask in natural language how DNA changes affect gene activity and health. The model was developed in collaboration with researchers at Zhongguancun Academy and the University of Naples Federico II; it extends the vocabulary of an existing language model to read DNA in six-letter units and was further trained on 300 billion tokens of DNA sequences, English text, and samples pairing DNA with biological descriptions. The model has 14B parameters and can process sequences of about 50,000 base pairs at a time; it cannot upload a complete human genome. The developers also note that the prediction accuracy has not yet been validated through wet-lab experiments.

Idealab founder Bill Gross opens free access to 1.2 million AI-generated business ideas

Idealab founder and CEO Bill Gross has launched idealab.ai, offering free access to 1.2 million AI-generated business ideas. These ideas were generated by combining 31 ideation methodologies with roughly 17 million source documents, including news articles, research reports, expired patents, public company filings, and community discussions. Users can search by industry, skill, interest, and region, and can also upload a résumé or LinkedIn profile to receive personalized ideas. Gross believes the surge in entrepreneurship brought about by AI can partially offset its impact on employment.

Eka Robotics raises $150 million at a $14.5 billion valuation to build robotic hands with a sense of touch

Eka Robotics has completed a $150 million Series A funding round at a $14.5 billion valuation, led by Andreessen Horowitz and Fidelity's F-Prime. Its vision-force-action model uses force sensors in custom grippers to sense grip strength, allowing it to adjust its hold mid-grasp when an object slips. The robot can pick up about 1,500 objects per hour that are outside its training data, compared with a human baseline of about 600. The gripper costs less than $1,000 and is currently rated for 5 million work cycles; the company aims to raise that to 20 million, the standard for industrial robotic arms.

a16z Leads Eka Robotics Series A Funding

a16z announced it has led the Series A funding round of Eka Robotics, betting on its approach to solving the challenge of robotic dexterous manipulation. Eka builds a Vision-Force-Action model that combines visual perception with robotic hand force feedback, and trains manipulation policies with reinforcement learning in physical simulation, enabling robots to obtain information from contact itself and correct in real time when slipping or insertion is obstructed. The founding team includes Pulkit Agrawal of MIT CSAIL and Tuomas Haarnoja, co-proposer of Soft Actor-Critic.

English translation is being prepared.

English translation is being prepared.

English translation is being prepared.

Elorian Releases Its First Model Glimpse, Using a Visual Draft Space for Image Reasoning

Elorian has released its first model, Glimpse, opening the API as a research preview, with a waitlist for the chat interface. During reasoning, the model can crop image regions, extract video frames, or run code to measure and compare what it sees, which Elorian calls a visual draft space. Glimpse is priced at $0.60 per million input tokens and $3 per million output tokens, and according to Elorian's evaluations it surpasses GPT-6 Astra on scientific chart reasoning and short video description. The company says the model was trained on several hundred GPUs, a scale notably smaller than the frontier systems it benchmarks against, and it initially targets enterprises with large amounts of video, such as robotics, research laboratories, and media and creative firms.

Imperial College London paper: search-augmented LLMs often cite web pages they never opened

Three authors from Imperial College London have published the paper "Retrieved but Not Read: Understanding Why Search-Augmented LLMs Fail to Use Available Evidence," examining why search-augmented LLMs frequently cite web pages they never actually open. The study tested GPT-5.6 Luna and six open-weight configurations including GLM-5.3-Flash, gpt-oss-120b, gpt-oss-20b, gemma-4-31b, and qwen3-8b using 225 facts that changed after February 16, 2026, finding that models often answer based solely on search snippets without opening the source pages; under the same set of search results, the proportion of cases where no page was opened ranged from 2.6% to 76%, and when Luna used its built-in search, 53.6% of turns relied only on snippets. When answering from snippets alone, outdated answers accounted for 6.4% and misleading citations for 6.4%, whereas when pages were opened and cited, these fell to 1.3% and 2.3% respectively; placing the correct page directly into the prompt significantly reduced errors, but some models still favored old information even when shown the new facts.

English translation is being prepared.

English translation is being prepared.

English translation is being prepared.

English translation is being prepared.

English translation is being prepared.

English translation is being prepared.