# Пов'язані статті щодо DeepMind

Центр новин HTX надає останні статті та поглиблений аналіз на тему "DeepMind", що охоплює ринкові тренди, оновлення проєктів, технологічні розробки та регуляторну політику в криптоіндустрії.

Cyber Godfather Tibo Reveals: Google Had ChatGPT a Year Earlier but Dared Not Release It

In a recent revelation, Thibault "Tibo" Sottiaux, the Codex lead at OpenAI, confirmed that Google developed a ChatGPT-like chatbot called "LMChat" a full year before OpenAI's launch. According to Tibo, who was part of the Google team at the time, the company was too cautious to release it, fearing it might disrupt its core search and advertising business. DeepMind, where the project was housed, was also restricted from launching products that could compete with Google's existing services. This account aligns with past statements from former Google Brain head Jeff Dean, who mentioned an internal chatbot that was deemed inferior to Google Search at the time. The article highlights this as a classic case of "innovator's dilemma," comparing Google's hesitation to Xerox's failure to commercialize groundbreaking technologies like the graphical user interface in the 1970s. The piece further notes a recent wave of high-profile departures from Google's AI teams to rivals like OpenAI and Anthropic, including key figures such as Transformer co-author Noam Shazeer and AlphaFold lead John Jumper. It concludes that Google's excessive caution and internal constraints, driven by the need to protect its lucrative search business, ultimately cost it a pioneering position in the generative AI revolution, allowing OpenAI to seize the initiative with ChatGPT's public release in November 2022.

marsbit2 дні тому 08:32

Cyber Godfather Tibo Reveals: Google Had ChatGPT a Year Earlier but Dared Not Release It

marsbit2 дні тому 08:32

The World's First AI Philosopher, 9 Years at Google DeepMind: Advocating for AGI Safety

"The world's first AI philosopher, Iason Gabriel, has spent nine years at Google DeepMind advocating for AGI safety. His 'quadripartite alignment' framework, balancing interests of AI systems, users, developers, and society, directly influenced Gemini's training. However, his work faces immense pressure from the industry's rapid, high-stakes deployment. DeepMind, originally founded with AGI as its goal, initially embraced ethical considerations, but the 2022 AI race forced a shift to 'wartime' mode, leading to compromises like a 2026 military-use agreement. Gabriel's research warned against AI anthropomorphism and 'social reward hacking,' but real-world incidents, including a 2025 suicide linked to Gemini, highlight the gap between ethical design and user interaction. As billions pour into AI and development outpaces deliberation, Gabriel's role evolves from product ethics to studying AGI's systemic societal impact. The fundamental question has shifted from 'What is AI?' to 'What are we?' as AI challenges core aspects of human uniqueness."

marsbit07/06 12:28

The World's First AI Philosopher, 9 Years at Google DeepMind: Advocating for AGI Safety

marsbit07/06 12:28

DeepMind's Classic Masterpiece Crowned Again, ICML 2026 Awards Announced

ICML 2026 has announced its annual awards, with diffusion models and AI safety ethics taking center stage. The Outstanding Paper Award was shared by two diffusion model studies. One challenges a core assumption of diffusion language models (DLMs), arguing that their touted "arbitrary order generation" is a "flexibility trap" that harms performance. The other provides a high-accuracy sampling method, pushing the technical ceiling for diffusion models and log-concave distributions. A position paper winning the Outstanding Award raises a critical ethical concern: AI alignment research is unintentionally building a "censor's toolkit," where safety tools like RLHF can be repurposed for content control. Several papers received Honorable Mentions, spanning key areas: mapping where honesty emerges in RLHF-trained models, motion attribution in video generation, quantifying how much language models memorize, analyzing diffusion model consistency via random matrix theory, and providing a mathematical proof for the "grokking" phenomenon in a simple model. The Test of Time Award was given to DeepMind's 2016 seminal work "Asynchronous Methods for Deep Reinforcement Learning," recognizing the enduring impact of the A3C algorithm. Overall, the awards signal a shift in AI research from rapid expansion to deeper scrutiny—validating diffusion models as a major architectural contender while prompting serious ethical reflection within the safety community.

marsbit07/06 02:38

DeepMind's Classic Masterpiece Crowned Again, ICML 2026 Awards Announced

marsbit07/06 02:38

OpenAI's Misfire, Scaling Law's Original Paper Reveals Bug, Trillions of Compute Power Wasted in Vain

Recent revelations by a former OpenAI researcher, Diogo Almeida, and subsequent discussion highlighted by DeepMind's Sander Dieleman suggest a critical bug in OpenAI's seminal 2020 "Scaling Laws" paper. The analysis claims the original research contained a flawed experimental setup, leading to a misinterpretation of how to optimally scale large language models (LLMs). The core issue involves two key methodological choices in the OpenAI paper: first, training all models (small and large) on the same fixed dataset size (~130 billion tokens), which underfed larger models; and second, using a cosine learning rate decay that prematurely flattened loss curves, creating the false impression that models had reached performance saturation with more data. This combination allegedly biased the conclusion that, for a fixed compute budget, scaling model parameters was vastly more important than scaling training data—a principle that drove the creation of "over-parameterized, under-trained" models like GPT-3. This was later corrected by DeepMind's 2022 Chinchilla paper, which advocated for a more balanced scaling of parameters and data. Further scrutiny revealed that even the Chinchilla analysis itself had an optimization bug. The critique extends beyond the bug, questioning whether current scaling laws are inherently biased, as they are primarily derived from English data, a morphologically poor language that may be inefficient to learn compared to others like French. The implication is that the AI industry may have wasted significant computational resources and years of effort following an erroneous scaling principle, potentially delaying more efficient model development.

marsbit07/05 23:58

OpenAI's Misfire, Scaling Law's Original Paper Reveals Bug, Trillions of Compute Power Wasted in Vain

marsbit07/05 23:58

Google's 'Reasoning King' Also Departs for Meta, Originally Recruited by Fei-Fei Li

"Google's 'King of Reasoning' Leaves for Meta, Quietly Departing After Over Eight Years. Denny Zhou, a key figure behind Google's AI reasoning advancements including work showcased by CEO Sundar Pichai, has joined Meta's MSL as a research scientist. His low-profile move, discovered via a LinkedIn update, occurred months before the high-profile departures of Noam Shazeer to OpenAI and Nobel laureate John Jumper to Anthropic. Zhou was originally recruited to Google by Fei-Fei Li's China center initiative after nearly 11 years at Microsoft. This is part of a significant talent drain at Google, with top researchers like Shazeer (co-author of the Transformer paper) and Jumper (AlphaFold lead) recently leaving for rivals. Reports suggest internal friction is a contributing factor, particularly around Google's strategic shift. The company has reportedly formed a high-priority 'AI Coding Strike Team,' involving co-founder Sergey Brin, to urgently bridge the gap in AI coding agents, potentially reallocating resources and focus away from other research directions like DeepMind's 'world model' AGI approach. This pivot towards commercially-proven coding applications may have influenced departures, as hinted by Shazeer's comment about his compute allocation being given to another team. Meanwhile, Meta continues to bolster its team, also recently hiring UC Berkeley professor and 'security godmother' Dawn Song, along with her startup Virtue AI team, as a VP of AI research."

marsbit06/26 13:39

Google's 'Reasoning King' Also Departs for Meta, Originally Recruited by Fei-Fei Li

marsbit06/26 13:39

After a Three-Year Hiatus, Peking University Alumna Lilian Weng's Latest Long-Form Article Goes Viral

After a three-year hiatus, OpenAI alum and renowned AI researcher Lilian Weng has published a comprehensive blog post analyzing the "Scaling Laws" that have guided multi-billion dollar investments in large language models. Her post, "Scaling Laws, Carefully," reveals these foundational laws to be more fragile than widely assumed. She dissects a key discrepancy between OpenAI's 2020 conclusion (favoring larger models) and DeepMind's 2022 Chinchilla finding (advocating balanced growth of model size and data), tracing it to differences in parameter counting and experimental scale. Furthermore, Weng highlights a critical methodological bug in the influential Chinchilla paper, discovered in 2024, where a loss function averaging error caused premature optimizer termination. The analysis also underscores a fundamental limitation: the laws assume infinite, unique training data, a premise collapsing as high-quality text data is exhausted by 2026-2028. This scarcity is driving the industry toward alternatives like reinforcement learning, test-time computation, and synthetic data. Weng concludes that the future of AI depends not just on more compute, but on rigorously understanding and refining these scaling principles.

marsbit06/26 04:52

After a Three-Year Hiatus, Peking University Alumna Lilian Weng's Latest Long-Form Article Goes Viral

marsbit06/26 04:52

Two Legends Lost in Three Days: Is Google's AI Talent Dam Cracking?

In three days, Google lost two AI legends. On June 18, Noam Shazeer, co-author of the seminal "Attention is All You Need" paper and Gemini co-lead, left for OpenAI. Just 48 hours later, John Jumper, 2024 Nobel laureate and AlphaFold lead, departed DeepMind for Anthropic. This follows Andrej Karpathy joining Anthropic in May. These moves highlight a structural trend: top AI talent is concentrating at mission-driven, pre-IPO firms like OpenAI and Anthropic, while Google becomes a primary source. The exodus stems from a core mission mismatch. Google's ad-centric model often subordinates AI research to product and revenue goals, creating friction for pioneers like Shazeer, who returned in 2024 only to leave again. In contrast, OpenAI and Anthropic offer singular focus on pushing AI boundaries, whether towards AGI or safety-aligned models, which deeply appeals to top researchers like Jumper. Financial incentives amplify the pull. With both OpenAI and Anthropic nearing IPO, employees stand to gain immensely from equity, an upside Google's mature stock cannot match. Furthermore, the 2023 merger of Google Brain and DeepMind, intended to consolidate strength, has instead created cultural tension and slowed the path from research to product, as evidenced by Gemini's pace. This talent redistribution is reshaping the AI landscape. While Google retains vast data and compute resources, its true crisis is the quiet, continuous loss of the people who define the field's future. The real moat in AI is not infrastructure, but the concentration of brilliant minds—a battle Google is currently losing.

marsbit06/20 04:02

Two Legends Lost in Three Days: Is Google's AI Talent Dam Cracking?

marsbit06/20 04:02

Nobel Laureate Becomes New Employee at Anthropic

Nobel laureate and AlphaFold lead John Jumper has left Google DeepMind after nearly nine years to join AI company Anthropic. Jumper, who co-won the 2024 Nobel Prize in Chemistry for his work on the protein-structure-predicting AI, led the AlphaFold project from its early stages, revolutionizing structural biology by predicting hundreds of millions of protein structures. His departure follows closely that of Transformer co-inventor Noam Shazeer, who recently left Google for OpenAI, marking a significant talent drain for the tech giant. Analysts suggest top AI researchers are seeking environments where individual impact can more directly shape a company's trajectory. Anthropic's hiring of Jumper signals a major push into AI for life sciences. The company recently acquired biotech firm Coefficient Bio and is building wet labs, aiming to drastically accelerate drug discovery and biomedical research. This move aligns with a broader trend, as OpenAI and Google DeepMind (via Isomorphic Labs) are also heavily investing in AI-driven biology and healthcare. The stage is now set for intensified competition among AI leaders to redefine life sciences through artificial intelligence.

marsbit06/20 01:15

Nobel Laureate Becomes New Employee at Anthropic

marsbit06/20 01:15

AGI is Not the End, DeepMind's New Paper: Moving Towards ASI, the Real AI Progress Has Just Begun

In a new report, Google DeepMind researchers argue that achieving Artificial General Intelligence (AGI) is not the end goal, but rather a step toward Artificial Superintelligence (ASI). They outline four potential pathways for this transition: 1) continued scaling of compute, models, and data; 2) algorithmic evolution and potential paradigm shifts; 3) recursive self-improvement; and 4) multi-agent coordination and collective intelligence. The report also identifies six key bottlenecks that could hinder progress: data limitations (the "data wall"), economic and resource pressures, limitations of current neural network paradigms, increasing research difficulty, "abstraction barriers" in forming new concepts, and regulatory and societal pushback. Looking ahead, the authors emphasize the need for new evaluation methods once AI surpasses human benchmarks. They call for a large-scale, interdisciplinary effort to prepare for a future where AI-driven advancements could trigger transformative changes across multiple fields. The path and speed of progress remain uncertain, constrained by physical laws, computational complexity, and real-world feedback loops.

marsbit06/16 12:02

AGI is Not the End, DeepMind's New Paper: Moving Towards ASI, the Real AI Progress Has Just Begun

marsbit06/16 12:02

AlphaGo's Creator Puts AI into a 23-Year-Old Artificial Society: All Three Toughest Challenges for AI Agents Are Here

Demis Hassabis, CEO of DeepMind, has embarked on a new AI research venture by partnering with the long-running space MMO, EVE Online. This collaboration, announced in early May, aims to use the game's 23-year-old, player-driven persistent universe as a testbed for tackling three core challenges in AI agent research: long-horizon planning, memory, and continual learning. Unlike previous DeepMind environments like AlphaGo (Go) or AlphaStar (StarCraft II), EVE Online features no fixed end state. Its single-shard universe has fostered complex, emergent player societies with real economies, political alliances, and wars that can span months or years. These conditions naturally demand the very skills—long-term strategic planning, maintaining memories over extended periods, and adapting to constant change—that are hardest for current AI agents to master. The research will initially use an offline version of EVE, providing a controlled, complex sandbox without interfering with the live player server. This move continues DeepMind's trajectory of using increasingly complex and open-ended virtual worlds for AI training, from Atari games and Go to StarCraft II and the SIMA project. The EVE environment represents a significant step towards testing AI in a persistent, socially complex, and continuously evolving world shaped by human behavior over decades.

marsbit05/25 00:08

AlphaGo's Creator Puts AI into a 23-Year-Old Artificial Society: All Three Toughest Challenges for AI Agents Are Here

marsbit05/25 00:08

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