# Пов'язані статті щодо Foundation Model

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

Fei-Fei Li's Latest Interview: Humans Don't Rely Solely on Real Data for Learning

Fei-Fei Li's latest interview on a16z explores the vision for **spatial intelligence** as AI's next frontier, with robotics as its primary testing ground. She discusses World Labs' acquisition of SceniX, a robotics startup co-founded by her former postdoc Yunzhu Li, aiming to tackle the core challenge of scarce and expensive real-world training and evaluation data for robots. The key strategy involves creating scalable, realistic **simulation environments** via a "real-to-sim-to-real" pipeline. This allows for safe, efficient, and controllable data generation, training, and robust evaluation of robotic systems, complementing physical data collection. The combined strengths of World Labs' generative world models (like Marble) and SceniX's simulation and robotics expertise aim to build foundational **infrastructure**—a digital proving ground agnostic to specific robot hardware or AI models. The discussion highlights the necessity of simulation for **counterfactual reasoning**, reliability, and accelerating development, drawing parallels to human learning and industries like autonomous vehicles. The near-term focus is on practical deployment in **semi-structured environments** (e.g., warehouses) rather than fully unstructured ones like homes, emphasizing a calibrated, stepwise approach to achieving robust and commercially viable robotic automation.

marsbit19 год тому

Fei-Fei Li's Latest Interview: Humans Don't Rely Solely on Real Data for Learning

marsbit19 год тому

Embodied Intelligence 'Gaokao' is Insanely Hard, Humans Score 100, Best Model Only 12.8

Embodied AI Faces a Daunting "Everest": New Benchmark Reveals Huge Gap Between Models and Humans A comprehensive new benchmark for robotic manipulation, RoboDojo, has been released, painting a stark picture of the current state of embodied AI. It serves as a unified evaluation platform covering both simulation and real-world robot tasks. The benchmark assesses five core capabilities: Generalization (adapting to new scenes/objects), Memory, Precision manipulation, Long-Horizon multi-step tasks, and Open semantic understanding. It includes 42 simulation tasks and 18 standardized real-world tasks across three dual-arm robot platforms. The results are sobering. In simulation, the best-performing generalist robot policy achieved an average success rate of only 8.80%. Performance in the real world was slightly higher but still low, with the top model succeeding 12.8% of the time on average. In stark contrast, human experts scored 76.03% in simulation and 100% in real-world tests. The benchmark highlights significant, uneven gaps in current models' abilities. While some excel in specific areas like visual recognition or simple actions, they struggle with reliability, especially in long-horizon tasks where errors accumulate and in open-ended semantic instructions. The low scores, particularly in real-world deployment with physical uncertainties like camera noise and contact dynamics, underscore that today's models are far from being robust, general-purpose operational robots. RoboDojo is more than just a ranking; it's an infrastructure designed for fair, reproducible comparison. Its companion system, XPolicyLab, standardizes the interface for different models to be evaluated. Maintained by an academic consortium without commercial ties, it aims to provide a community-wide "altitude meter" to track genuine progress toward reliable and generalizable robot manipulation.

marsbit07/08 11:49

Embodied Intelligence 'Gaokao' is Insanely Hard, Humans Score 100, Best Model Only 12.8

marsbit07/08 11:49

Li Fei-Fei's Latest Long-Form Article: When Video Generation, Robotics, and NVIDIA All Call Themselves World Models, We Need a Taxonomy

In a new article, Dr. Fei-Fei Li addresses the widespread and often inconsistent use of the term "world model" in AI. She proposes a clear, functional taxonomy rooted in the classic Partially Observable Markov Decision Process (POMDP) loop (agent → action → state → observation → agent). According to this framework, current systems called "world models" are different projections of this loop, categorized by their primary output: 1. **Renderers**: Output observations (pixels). Their goal is visual fidelity for human consumption (e.g., video generation models like Sora). They are the most commercially mature but are limited by a focus on appearance over physical accuracy. 2. **Simulators**: Output states (geometric, physical, dynamic representations). They provide a structurally accurate world for both human professionals (e.g., architects) and computational agents (e.g., robots for training). Li argues simulators are the crucial, underappreciated bridge, as they can underpin both rendering and planning. 3. **Planners**: Output actions. Given an observation and a goal, they decide what an agent should do next (e.g., robotic action models). This area is highly promising but remains the least mature for real-world deployment. Li highlights a key trend: the boundaries between these three categories are beginning to blur, as they all rely on a shared underlying understanding of geometry, physics, and dynamics. The logical endpoint is a unified world foundation model capable of switching between rendering, simulation, and planning based on downstream needs. This convergence, she concludes, is central to advancing spatial intelligence—enabling machines not just to talk about the world, but to truly understand, imagine, and interact with it.

marsbit07/05 09:24

Li Fei-Fei's Latest Long-Form Article: When Video Generation, Robotics, and NVIDIA All Call Themselves World Models, We Need a Taxonomy

marsbit07/05 09:24

Li Feifei's Latest Article: When Video Generation, Robotics, and NVIDIA All Claim to Have 'World Models,' We Need a Taxonomy

"World Model" has become a widely used yet ambiguous term in AI. Drawing from the classic POMDP framework (agent → action → state → observation), this article proposes a functional taxonomy to clarify the concept. It identifies three distinct types, categorized by their output in the perception-action loop: 1. **Renderers**: Output visual observations (pixels). These models, like advanced video generators, prioritize visual fidelity but often lack underlying physical accuracy. 2. **Simulators**: Output the state of the world (geometry, physics, dynamics). They provide a structurally accurate representation for professionals (e.g., architects) and serve as training environments for robots and AI agents. 3. **Planners**: Output actions. Given an observation and a goal, they determine what an agent should do next, closing the perception-action loop (e.g., vision-language-action models). While renderers are currently the most commercially mature and planners are the most aspirational, the article argues that **simulators are the crucial, underappreciated hub**. By working at the level of geometry and physics, a simulator can project upwards to create visuals for humans and downwards to predict action consequences for agents. The future lies in the convergence of these three functions. Emerging research and products, like World Labs' Marble model which outputs both visual splats and physical collision meshes, are beginning to blur these boundaries. The logical endpoint is a unified world foundation model capable of rendering, simulating, and planning based on a shared understanding of spatial and temporal structures—ultimately enabling machines to understand, imagine, and interact with the physical world.

链捕手07/05 09:12

Li Feifei's Latest Article: When Video Generation, Robotics, and NVIDIA All Claim to Have 'World Models,' We Need a Taxonomy

链捕手07/05 09:12

Apple, a Company with a $4 Trillion Market Cap, Why Can't It Create a Smart Siri?

On the eve of Tim Cook's final WWDC as CEO, Apple unveiled its long-awaited AI strategy, centering on a revamped "Siri AI." Facing years of criticism for Siri's stagnation and pressure from competitors like ChatGPT, Apple's answer involves partnering with Google, utilizing Gemini model technology to build its "Apple Foundation Models." The new Siri aims to be an operational hub, understanding on-screen content and coordinating across apps. However, its demonstrated capabilities largely mirror existing AI assistants, leading to a lukewarm market response and a drop in Apple's stock. The article details Apple's historical "AI debt" under Cook's risk-averse management, including missed opportunities with Siri, the cancelled car project, and the slow adoption of Vision Pro. Analysts suggest Apple's primary goal is not having the strongest model but defending the iPhone's AI entry point, keeping user data and system permissions within its ecosystem to potentially unlock new subscription revenue. The challenge for Cook's successor will be adapting Apple's traditionally deliberate product cycle to the faster-paced demands of the AI era.

marsbit06/10 00:04

Apple, a Company with a $4 Trillion Market Cap, Why Can't It Create a Smart Siri?

marsbit06/10 00:04

Alibaba 'Stocks Up', ByteDance 'Trains'

"In late May, two closely timed events in China's AI industry clearly revealed the divergent strategic approaches of two tech giants: Alibaba and ByteDance. Alibaba is aggressively integrating AI into its existing commercial ecosystem, prioritizing immediate monetization. Its Qwen App now fully integrates with Taobao, leveraging the platform's 4-billion-item database for AI-powered shopping features like virtual try-on and price comparison. Internally, Alibaba has reorganized to incentivize AI-driven business growth, notably through the 'Agentic Commerce Trust Protocol' to enable AI-agent transactions. Financially, it emphasizes ROI, with CEO Daniel Wu stating every AI chip purchased is generating revenue. Alibaba's strategy bets that foundational AI model capabilities won't be leapfrogged in the next five years, allowing its 'AI-as-a-utility' approach to succeed. In stark contrast, ByteDance's Seed division focuses on pushing the frontiers of AGI with a long-term, research-oriented mindset. Its video generation model, Seedance 2.0, topped international benchmarks. The division, led by researchers Wu Yonghui and product head Zhu Wenjia, is tasked with 'exploring the upper limits of intelligence,' even considering open-sourcing its models—a rare move among Chinese firms. ByteDance is investing heavily, with reports of its 2026 capital expenditure plan being nearly triple that of 2024, funded by its substantial private profits. This allows it to pursue projects like an 8-month research paper questioning if video models are true 'world models,' devoid of immediate commercial pressure. The core divergence is less about corporate philosophy and more about structural constraints. As a publicly traded company, Alibaba is bound to quarterly financial expectations, forcing a pragmatic, revenue-focused AI integration. As a private entity, ByteDance has the luxury to fund long-term, high-risk foundational research without answering to public markets. The article concludes that the true determinant of a Chinese company's AI path is its IPO status, suggesting that if ByteDance were public, or if Alibaba were private, their strategies might well be reversed."

marsbit06/01 00:08

Alibaba 'Stocks Up', ByteDance 'Trains'

marsbit06/01 00:08

What Does the Top AI Influencer Karpathy Seek by Joining Anthropic?

AI pioneer Andrej Karpathy, a founding member of OpenAI, has joined AI safety-focused company Anthropic to lead a new team focused on pre-training research. His role will specifically involve using Anthropic's Claude model to accelerate exploration in pre-training, the foundational stage of large language model development. Karpathy's move is seen as a significant signal in the AI industry, combining his deep technical credibility with substantial public influence. It follows a period of momentum for Anthropic, which recently surpassed OpenAI in enterprise adoption rates and has expanded its offerings to smaller businesses. The move also highlights a continuing trend of talent migration from OpenAI to Anthropic, often linked to differing priorities on commercialization versus research depth and safety. By placing Karpathy on pre-training, Anthropic is making a long-term bet on fundamental AI advancements, exploring the meta-idea of using current AI to help build the next generation of models.

marsbit05/21 08:02

What Does the Top AI Influencer Karpathy Seek by Joining Anthropic?

marsbit05/21 08:02

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