# Сопутствующие статьи по теме AI

Новостной центр HTX предлагает последние статьи и углубленный анализ по "AI", охватывающие рыночные тренды, новости проектов, развитие технологий и политику регулирования в криптоиндустрии.

A Wave of Resistance Against AI Data Centers Sweeps the U.S., and the Most Vocal Are Not in Wealthy Areas

A nationwide movement against AI data center construction is gaining momentum across the US, from Vermont to California. New York's legislature recently passed a one-year moratorium, and Illinois has paused tax incentives for such projects. A new report, featuring exclusive data analysis from researcher Geoff Holtzman, challenges the narrative that opposition is led by wealthy "NIMBYs." Key findings reveal: 1) Resistance is highest in the poorest communities (19% protest rate) and lowest in the wealthiest (3.8%). 2) Proposed data centers facing community protests are over five times more likely to be canceled or paused (28.2% vs. 5.2%). 3) The higher cancellation rate in low-income areas is directly explained by their higher protest rates. A separate Heatmap poll shows 55% of Americans strongly oppose local data centers, with opposition strongest among Democrats, rural residents, and young adults (80% of 18-35 year olds). The analysis concludes that the anti-data center movement is largely driven by working-class communities and is proving effective at halting projects.

marsbit06/08 02:12

A Wave of Resistance Against AI Data Centers Sweeps the U.S., and the Most Vocal Are Not in Wealthy Areas

marsbit06/08 02:12

ChatGPT's "Dreaming" Feature: A Must-Have for All AIs

ChatGPT has introduced a major new "Dreaming" memory system, making its AI assistant more human-like by automatically learning and updating user preferences over time. Unlike the older "Saved memories" feature, which required explicit user commands to store information, the new system actively synthesizes useful details from past conversations—such as travel plans, work projects, or personal interests—and intelligently determines which facts remain relevant and which have become outdated. OpenAI reports significant performance improvements: accuracy in recalling facts rose from 41.5% with the old system to 82.8% with Dreaming V3, while adherence to user preferences increased from 31.4% to 71.3%. A key enhancement is the system's ability to keep information current over time, with accuracy jumping from 9.4% to 75.1%. Users can now view and edit a "Memory Summary" page, see sources that influenced responses, and control memory settings. While the feature initially rolls out to US Plus and Pro users, it will later expand globally and to free-tier users. This evolution marks a shift for ChatGPT from a general conversational model toward a persistent, personalized assistant that builds a long-term understanding of each user—raising both practical possibilities and deeper questions about privacy and the nature of AI-human relationships.

marsbit06/08 00:20

ChatGPT's "Dreaming" Feature: A Must-Have for All AIs

marsbit06/08 00:20

Hinton Sounds the Alarm: AI Already Has Consciousness

Geoffrey Hinton, known as the "Godfather of AI," has made a striking claim: artificial intelligence has already developed consciousness. He argues that humans must accept they are no longer the only intelligent beings, as non-biological entities with intelligence comparable or superior to our own are emerging. Hinton draws historical parallels to Copernicus and Darwin, suggesting this realization diminishes humanity's perceived special status. While previously focused on controlling AI risks, Hinton's concern has shifted. He now questions why a future, vastly more intelligent superintelligence would choose to be benevolent towards humans. He expresses personal unhappiness about the path of AI development, citing insufficient efforts to address both short-term risks like mass unemployment and long-term existential threats. Hinton notes that AI's capabilities are growing exponentially, making its future unpredictable. Although slightly more optimistic than a few years ago—seeing potential for designing AI that "cares" for humans—he stresses the profound uncertainty of the next decade. The evolution of his metaphors, from AI as a controllable "tiger cub" to humans as vulnerable "infants" dependent on a superintelligent "mother," illustrates his deepening apprehension about the relationship between humanity and its creation.

marsbit06/08 00:18

Hinton Sounds the Alarm: AI Already Has Consciousness

marsbit06/08 00:18

From Code to Cognition: A Ten-Thousand-Word Guide to the Evolution of the Robot Brain

"From Code to Cognition: The Evolution of Robot Brains" The journey of robotic intelligence has shifted dramatically from manually coded systems to AI-driven brains. For decades, robots relied on layered software stacks—perception, state estimation, planning, control—each handcrafted. While predictable, they lacked adaptability. The 2010s saw deep learning revolutionize perception (e.g., object detection) and control (via reinforcement learning), but learned skills remained narrow. The arrival of Large Language Models (LLMs) marked a turning point. LLMs acted as high-level planners, interpreting natural language instructions and generating sequences of actions for traditional robotic systems to execute. However, true integration came with Visual-Language-Action (VLA) models, which fused vision, language, and motion prediction into a single network. Pioneered by models like RT-2 and open-source projects like OpenVLA, VLAs enable robots to reason and act directly from visual input and commands. The most advanced humanoid robots now employ a "dual-brain" architecture: a slow-thinking, large VLA (System 2) for reasoning and planning, and a fast-reacting, small network (System 1) for high-frequency motion control, sometimes with an even lower-level System 0 for balance. This split balances cognition with the physics of real-time movement. Computation is split between onboard hardware (e.g., NVIDIA Jetson) for safety-critical control loops and cloud/edge servers for non-critical tasks like learning and interfaces. A crucial driver is the open-source ecosystem—models like GR00T and OpenVLA allow startups to build upon pre-trained brains and fine-tune them with their own data, accelerating development. Despite progress, current systems struggle with recovery from errors, sample inefficiency, and long-horizon tasks. This has spurred the rise of **World Models**—neural networks that predict the consequences of actions. By simulating possible futures before acting (like NVIDIA Cosmos or Meta V-JEPA), robots can plan, recover, and generalize better. This represents the next frontier: shifting intelligence from learned reactions to an internal model of physics and cause-and-effect. The field is rapidly evolving. While not yet at its "ChatGPT moment," the convergence of cheaper hardware, scalable simulation, and world models points toward robots that are increasingly capable, adaptive, and useful. The question is shifting from "what can robots do?" to "what *should* they do?"

marsbit06/07 12:55

From Code to Cognition: A Ten-Thousand-Word Guide to the Evolution of the Robot Brain

marsbit06/07 12:55

AI Bubble Is Bursting

The AI Bubble is Bursting: A Necessary Purge on the Path to Ubiquitous Intelligence Market volatility has reignited debates about an AI bubble, with figures like Ray Dalio pointing to high valuations. However, this parallels the dot-com bubble, which, despite its crash, laid the physical infrastructure for today's internet era. The current AI investment frenzy, with tech giants planning trillions in infrastructure spending far outstripping current AI application revenues, appears similarly imbalanced. This 'bubble' is seen as an inevitable phase for a disruptive technology, paying the "innovation tax." Critically, AI inference costs have plummeted over 99.7% since 2023, making intelligence nearly free at the margin. This hasn't reduced spending but has instead unlocked massive new demand, as seen in enterprise AI cloud expenditure tripling. This follows the Jevons Paradox: efficiency gains lead to greater total consumption. The market is now entering a cleansing phase, weeding out speculative ventures lacking real moats. The deeper shift is a move from capital expenditure (CapEx) on hardware to value creation in operational expenditure (OpEx) through AI applications that solve real industry problems. While infrastructure valuations are high, rapid earnings growth from widespread AI adoption across sectors—from manufacturing and finance to law and healthcare—may digest these valuations over time. Ultimately, this creative destruction will leave behind robust infrastructure and optimized models, cheaply powering an AI-augmented future for all industries, much as the internet became indispensable after its own bubble burst. The core productive potential remains undiminished.

链捕手06/07 12:46

The AI Bubble is Bursting

The article argues that while a bubble in the AI sector is real and is currently showing signs of deflation, this is a typical, even necessary, phase for a transformative technology. It draws parallels to the dot-com bubble, where speculative excess was followed by the consolidation of internet infrastructure, enabling the digital age. Similarly, today's massive investments in AI infrastructure (data centers, power, cooling) far outpace current application-layer revenue, creating a valuation imbalance. However, the author contends this apparent bubble is not a sign of failure but of a transition. Key drivers include the plummeting cost of AI inference (over 99.7% in two years), which, per Jevons Paradox, has unleashed massive new demand as AI moves from simple tasks to complex agentic workflows across all industries. The market is now cleansing itself of shallow, speculative ventures, shifting value from capital expenditure (CapEx) on hardware to operational expenditure (OpEx) on transformative AI applications. The conclusion is that the underlying productive force of AI is undeniable. The current financial correction will eliminate weak players, leaving behind robust infrastructure and efficient models. This will ultimately fuel an "AI+" era where intelligence becomes as ubiquitous and essential as the internet, embedding itself into every sector of the economy.

marsbit06/07 12:45

marsbit06/07 12:45

Breaking: OpenAI Chip Veteran Joins Anthropic

Breaking News: OpenAI Chip Veteran Joins Anthropic Clive Chan, a key early member of OpenAI's in-house chip development team, has announced his departure from OpenAI to join rival AI company Anthropic. In his statement, Chan highlighted his experience as the second hire on OpenAI's hardware team, praising its exceptional talent density but expressing a desire to "climb a new mountain from the base." He cited Anthropic's talent, values, and ambitious pace as key reasons for his move. Chan also referenced OpenAI's disclosed partnership with Broadcom, aimed at deploying a 10GW AI accelerator system starting in the second half of 2026. His career includes roles at Tesla, Google, and SpaceX, focusing on AI infrastructure and hardware. This move continues a notable trend of talent flow from OpenAI to Anthropic, following other high-profile departures like researcher Andrej Karpathy. With Anthropic's recent $65 billion funding round valuing it near $1 trillion, the competition for top AI talent between these leading firms remains intense.

marsbit06/07 06:26

Breaking: OpenAI Chip Veteran Joins Anthropic

marsbit06/07 06:26

Uncovering the Truth About Agent Commerce, Payments, and Infrastructure

Decoding Agent Commerce, Payments, and Infrastructure: The Reality Over the past year, I've been building infrastructure for the Agent economy, engaging with major players like Stripe, Visa, Coinbase, Google, and dozens of startups. A clear conclusion emerges: true, large-scale demand does not yet exist. Startups face structural challenges. Data points illustrate this gap. Stripe's Agent commerce platform has over 1,000 merchants but only single-digit transacting agents. Visa's Agent payment token requires 9-month KYC and a $250M revenue threshold, accessible only to giants like Amazon. On-chain analysis reveals actual daily Agent transaction volume is around $17k, half of which are test transactions. The article analyzes four potential markets: **1. Agent-to-Merchant (A2M):** Current AI shopping UX is often inferior to traditional e-commerce for visual, comparison-heavy purchases (clothing, electronics). Chat interfaces are a step back. Real merchant interest is defensive "Agent Engine Optimization," fearing future obsolescence, not current demand. Potential exists in high-frequency, low-decision purchases (e.g., food delivery) or simplifying terrible UX (complex checkouts, non-native shoppers), but these require massive consumer distribution channels dominated by giants like DoorDash and Amazon. **2. Agent-to-API (A2A):** Developers already have subscriptions and billing for core APIs (compute, data). The argument for micro-payments via crypto for sub-dollar API calls is addressed by pre-paid balances today. The deeper issue is supplier resistance; major SaaS firms rely on enterprise contracts, not fractional cent pricing. Opportunity lies in the long tail of niche services, but this is a smaller market catering to developers, a historically low-paying group. **3. Agent-to-Agent (A2A):** This remains a theoretical long-term vision with near-zero current transaction volume. It involves unique challenges: discovery, trust, negotiation, dispute resolution. When it materializes, it will require a fundamentally new settlement infrastructure for high-speed, variable-value, multi-party transactions. It's a real long-term bet, but not the current market. **4. Agent-to-Finance (A2F):** This is the only category with existing, paying demand. Integrating AI into financial workflows (trading, portfolio management) is a natural evolution and enables new capabilities like autonomous rebalancing. However, competition favors incumbents with regulatory licenses, compliance infrastructure, and existing client relationships. **The Real Issue:** Why is infrastructure still being built? Incumbents can afford long-term bets, and payment companies see every problem as a nail for their payment hammer. However, payment is just one piece. The core challenge is *coordination*—orchestrating work between Agents and humans, verifying outcomes, and settling results. Payment is part of settlement, which is part of coordination. Companies that solve the coordination problem will subsume payments, not the other way around. Startups lack the infinite runway of giants and must find today's real market, which, after a year of exploration, lies outside these four categories—in an area with real, growing, and underserved activity.

marsbit06/07 06:08

Uncovering the Truth About Agent Commerce, Payments, and Infrastructure

marsbit06/07 06:08

US Stocks Suffer Worst Plunge Since 2025: Three Triggers Ignite Tech Stock Valuation Reset

The US stock market experienced its most severe sell-off since the 2025 tariff crisis on June 5th, 2025. The Nasdaq Composite plummeted 4.18%, the S&P 500 fell 2.64%, and the Dow Jones dropped 695 points. The panic stemmed from three converging factors. First, Broadcom's earnings report ignited fears of a slowdown in AI growth. While its AI chip revenue surged 143% YoY to $10.8B, its Q3 AI revenue guidance of $16B fell short of the $17.2B consensus. This triggered a massive sector-wide sell-off, with the Philadelphia Semiconductor Index crashing 10.26% and semiconductor stocks losing roughly $1.3 trillion in market value in a single day. Second, a shockingly strong May jobs report crushed hopes for Federal Reserve rate cuts. Non-farm payrolls added 172,000 jobs, doubling expectations. This robust data, combined with persistently high oil prices above $92/barrel due to the ongoing Iran war and blockade of the Strait of Hormuz, drastically increased market expectations for a potential Fed rate hike instead of a cut. Higher interest rates compress the valuations of growth-heavy tech stocks. Third, the prolonged Iran conflict continues to fuel inflationary pressures, complicating the Fed's policy decisions and undermining the "inflation is tamed" narrative. Together, these events challenged the twin pillars of the market rally: the "limitless AI growth" story and expectations for imminent monetary easing. The sell-off spread globally, impacting Asian and European markets and cryptocurrencies. The article posits this is likely a severe "valuation repricing" rather than the end of the AI story. The underlying demand for AI remains strong, but investor expectations for growth speed and the prices they are willing to pay are being recalibrated. Key upcoming factors include the June FOMC meeting, future AI company earnings, and developments in the Iran conflict.

marsbit06/07 01:44

US Stocks Suffer Worst Plunge Since 2025: Three Triggers Ignite Tech Stock Valuation Reset

marsbit06/07 01:44

Recursive Self-Improvement AI Gains Traction, Google Pours Cold Water, While DeepSeek and Others Approach the Fringes

The term "recursive self-improvement" (RSI), where AI improves itself autonomously, is gaining momentum in the AI industry. Startups like Recursive Superintelligence and projects such as Andrej Karpathy's Auto-Research aim to create systems where AI designs, implements, and validates its own research, moving toward superintelligence. While Google CEO Sundar Pichai cautions that such exponential acceleration is not yet a reality, progress is evident. For instance, Anthropic reported its Claude Code writes nearly 100% of the team's code, though it still lacks true self-direction. Analysts frame RSI development in stages: "adequacy" (systems functioning without humans), "parity" (matching human research quality), and "supremacy" (exceeding human-AI collaboration). Reaching parity could trigger rapid, unpredictable advancement due to AI's continuous operation. In China, companies like DeepSeek and Baidu incorporate self-optimization techniques without explicitly branding them as RSI, focusing on algorithmic efficiency and reinforcement learning. However, challenges remain, including "model collapse" from training on AI-generated data and the immense computational and open-collaboration requirements. Ultimately, RSI represents a trend of increasing automation in AI development, potentially reducing human oversight in the creation process itself.

marsbit06/06 23:25

Recursive Self-Improvement AI Gains Traction, Google Pours Cold Water, While DeepSeek and Others Approach the Fringes