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19 May 2026, 18:15

Google DeepMind Fuses Street View with Genie 3 to Create Interactive AI Worlds

BitcoinWorld Google DeepMind Fuses Street View with Genie 3 to Create Interactive AI Worlds Google DeepMind has taken a significant step in bridging the physical and digital worlds by integrating its Street View imagery directly into Project Genie, the company’s general-purpose world model. Announced at the Google I/O developer conference, the integration allows users to generate interactive, explorable environments anchored to real-world locations captured over two decades of Street View data. From Street View to Simulated Reality For 20 years, Google has collected over 280 billion images across 110 countries using camera-equipped cars and backpack-mounted trackers. Now, that vast dataset is feeding Genie 3, a world model capable of generating diverse, interactive 3D environments from text prompts or images. Jack Parker-Holder, a research scientist on DeepMind’s open-endedness team, explained to Bitcoin World that the combination of real-world data with generative simulation opens up powerful use cases for both robotics and human exploration. “It’s really powerful for both the agent [and robotics] use case and for humans to play with,” Parker-Holder said. He described a scenario where a robot deployed in London — a city that rarely sees sun — could be trained on simulated sunny days generated from Street View data, so the sudden glint of sunlight off Victorian-era housing doesn’t disrupt its sensors. Similarly, a traveler planning a trip to New York City in winter could use the tool to visualize a snowy version of a specific block, adjusting weather conditions on demand. Robotics and Autonomous Driving Training Genie 3 is already being used by Waymo, Google’s self-driving car subsidiary, to simulate exceedingly rare events — such as tornadoes or unexpected animal encounters — for training autonomous vehicles. Parker-Holder noted that while Waymo has its own simulator focused on the car’s point of view, Street View integration allows shifting the perspective to other agents, like pedestrians or delivery robots, enabling more comprehensive training scenarios. The ability to anchor simulations to real geographic locations could accelerate Waymo’s expansion into new cities around the globe, giving its AI driver exposure to diverse road layouts, signage, and environmental conditions without requiring physical fleet deployment. Still an Experiment with Room to Grow Despite the impressive demos — including an underwater simulation of a neighborhood — the technology remains experimental. Diego Rivas, a product manager at DeepMind, cautioned that Street View in Genie is still a work in progress. In samples shown to reporters, the environments were recognizable but video-game quality rather than photorealistic. The models also lack physics awareness: in one simulation, a woman running through a snowy Joshua Tree scene passed straight through cacti and bushes. Parker-Holder acknowledged the gap, comparing Genie’s current accuracy to that of video-generation models from six to twelve months ago. “I think it’s something we will solve,” he said, noting that physics understanding emerges intuitively through passive observation, similar to how living beings learn. Jonathan Herbert, director of Google Maps and a 12-year Street View veteran, emphasized that the real breakthrough is spatial continuity. When a user turns 360 degrees, the AI correctly remembers and simulates the environment behind them, then builds new environments on top of that understanding. “We have long thought about how we can build out the best and richest model of the world on top of Street View data,” Herbert said. Availability and Next Steps Google is launching Street View in Genie to select Ultra users in the United States starting today, with broader U.S. access rolling out over time. Global Ultra users will gain access over the next few weeks. The researchers’ goal, according to Rivas, is to put the capability into as many hands as possible, though he stressed that accuracy improvements remain a priority. Conclusion By connecting two decades of real-world imagery with generative AI, Google DeepMind is laying the groundwork for a new class of interactive simulations. While still in its early stages, the integration of Street View into Genie 3 represents a meaningful step toward AI systems that can understand, simulate, and interact with the physical world — with implications for robotics, autonomous driving, urban planning, and immersive education. FAQs Q1: What is Genie 3? Genie 3 is Google DeepMind’s general-purpose world model that can generate interactive, explorable 3D environments from text prompts or images. It is designed for robotics training, gaming, and educational experiences. Q2: How does Street View integration work? The integration allows Genie 3 to use Google’s massive Street View image dataset — over 280 billion images from 110 countries — as a foundation for generating simulations anchored to real-world locations. Users can explore these environments interactively and adjust conditions like weather. Q3: Is the simulation physically accurate? Not yet. The current version lacks physics awareness, meaning objects may not interact realistically (e.g., a character running through solid objects). Google expects this to improve over the next 6–12 months as the model learns physics intuitively through more data. This post Google DeepMind Fuses Street View with Genie 3 to Create Interactive AI Worlds first appeared on BitcoinWorld .

19 May 2026, 17:24

Japan’s manufacturers see factory floors as AI’s next frontier

Japan is searching for its place in the global AI race. While American and Chinese companies dominate AI models and computing infrastructure, Japanese companies believe their expertise in robotics could help pioneer AI in real world tasks. On May 13 Japanese industrial equipment maker, Fanuc, announced a partnership with Google that aims to create factory robots that can understand spoken and handwritten instructions and carry out factory tasks autonomously. Fanuc, founded in Japan in 1956, is one of the world’s largest industrial machinery manufacturers. It’s developed an AI system with the help of Google Gemini that can be operated without programming skills. It plans to make all its robots compatible with Google software. In December 2025, Fanuc also announced a collaboration with NVIDIA which will see it open its previously closed robot software systems. At a press conference on May 13, Senior Managing Officer Kenishiro Abe said the partnership stems from the limitations of developing an entire AI ecosystem in-house. It plans to incorporate AI systems from a host of different companies. What is physical AI? Factories are set to benefit the most from physical AI. While robots are already used extensively, they remain limited to repetitive tasks. Physical AI is the practical application of AI. These AI systems are trained to perceive the real world, reason with it, act autonomously in real time as well as learn and collaborate from humans. They excel at handling complex and unpredictable tasks. What is the winning formula? For decades, Japanese factories have been shaped by knowledge that was never written down. Now, Japanese companies are trying to teach that knowledge to machines. According to a Nomura Securities report , Japan’s decades-long manufacturing expertise and factory-floor data could power industrial humanoid robots. In the 1990s Japanese manufacturers made up 80 percent of the global industrial robot market, according to the International Federation of Robotics. The figure has since fallen to roughly 40 percent. As of 2024, Chinese companies such as Estun Automation and Inovance Technology are gaining ground and account for 40 percent of the global humanoid robot market. But many Chinese companies still rely on Japanese machinery components. Nomura Securities predicts that Japan’s expertise in motion control technologies, industrial datasets, precision manipulators (i.e. robot hands) and semiconductor equipment could drive growth in a post-2030 economy. Japan’s gaping ‘digital deficit’ Fanuc’s decision to open its source robotics software is a significant pivot from the Japanese manufacturing sector’s emphasis on hardware. The country trails behind the U.S. and China in AI digital transformation (DX). Japanese companies rely heavily on software from U.S. tech giants resulting in a massive ‘digital deficit’ in which payments for digital services flow overseas. The Ministry of Economy, Trade and Industry (METI) recorded a $4.9 billion digital services deficit in 2023. The U.S. on the other hand, posted a $173.7 billion surplus while China logged a $40.4 billion digital surplus. As companies integrate AI into manufacturing, the Japanese government anticipates that rising demand for industrial robots will support the growth of Japanese industrial machinery companies. Japanese technology company ARUM Inc has developed a fully automated, AI-enabled production line for metal part manufacturers. Its TTMC system costs approx. $2.3 million each. At Tokyo Sushi Tech Expo 2026, the company said it will install 100 units across Japan and has received enquiries from South Korea and the United States. “We are not simply selling machines. We are connecting them through the cloud and building infrastructure,” said Takayuki Hirayama, CEO of ARUM Inc. ARUM Inc believes that AI-driven manufacturing automation can solve global labor shortages and changing career preferences. “Even in younger countries like India and Southeast Asia, skilled manufacturing workers are disappearing because IT and tourism are seen as more lucrative.” The Japanese government wants to lead the robotics AI race At a New Years press conference, Japanese Prime Minister Sanae Takaichi announced plans to accelerate physical AI innovation and expand the technology globally. She stated that AI-powered robots will learn from high-quality domestic data, in particular, Japan’s long established factory know-how. The initiative builds on remarks made in December 2025 when Takaichi directed the government to support domestically produced general purpose AI models which are an essential component of physical AI . METI is set to launch a one trillion yen funding package (approx. $6.45 billion) over five years to help develop Japanese physical AI. CEO Masato Fujino of Japanese industrial devices company, Fairy Devices Inc, believes that the challenge is no longer using AI within computers but bringing AI into the real world. The company has produced wearable AI devices that prevent technicians from missing important checks. They are built with cameras, microphones, sensors and communications capabilities. The devices have accumulated large volumes of data and have trained the company’s vision language model which aims to replace experts such as air conditioner repair technicians. At Tokyo Sushi Tech Expo 2026, Fujino said specialized data directly from skilled workers is indispensable for industrial AI systems. “Google Gemini is powerful because Google owns Youtube. But when it comes to highly specialized industrial tasks, such as repairing industrial equipment, that data does not exist on Youtube.” What role can Japan play in the physical AI sector? Japan’s answer to AI is not frontier models but industrial data. Despite fierce competition for low-cost, high-quality physical AI, Japanese industry leaders are optimistic about Japan’s trajectory. In their eyes Japan’s reputation for manufacturing excellence and proven track record in factory automation is difficult to replicate anywhere else. If you're reading this, you’re already ahead. Stay there with our newsletter .

19 May 2026, 16:15

OpenAI co-founder Andrej Karpathy joins Anthropic to lead pre-training research team

BitcoinWorld OpenAI co-founder Andrej Karpathy joins Anthropic to lead pre-training research team Andrej Karpathy, the prominent AI researcher who co-founded OpenAI and previously led Tesla’s Autopilot and Full Self-Driving programs, has joined Anthropic to work on pre-training research. Karpathy announced the move on X Tuesday, calling the next few years at the frontier of large language models especially formative. A strategic hire for Anthropic Karpathy started this week at Anthropic, where he is working under team lead Nick Joseph on pre-training — the computationally intensive phase responsible for giving Claude its core knowledge and capabilities. An Anthropic spokesperson confirmed to Bitcoin World that Karpathy will establish a new team focused on using Claude itself to accelerate pre-training research. This hire signals Anthropic’s belief that AI-assisted research, rather than simply scaling compute, is the key to staying competitive with rivals like OpenAI and Google. Karpathy is one of the few researchers who bridges the gap between theoretical understanding of large language models and the practical realities of large-scale training runs. Karpathy’s career arc Karpathy’s journey through the AI industry has been closely watched. He left OpenAI in 2017 to join Tesla, where he led the company’s Full Self-Driving and Autopilot programs until 2022. He returned to OpenAI for a year before departing again in 2024 to launch Eureka Labs, a startup focused on applying AI assistants to education. Since then, he has shared few updates on Eureka Labs, and it remains unclear whether he will continue that venture alongside his new role at Anthropic. He has also maintained an active presence in AI education through his online course Neural Networks: Zero to Hero and his YouTube channel, where he posts lectures on LLMs and AI. In his announcement, Karpathy said he remains deeply passionate about education and plans to resume that work in time. Anthropic strengthens security team Separately, Anthropic has brought on Chris Rohlf to its frontier red team, which stress-tests advanced AI models against severe threats. Rohlf, a cybersecurity veteran with over 20 years of experience, previously worked at Yahoo’s well-known security team known as The Paranoids and spent six years at Meta. He was also a fellow at Georgetown’s Center for Security and Emerging Technology, where he contributed to the CyberAI project. In a post on X, Rohlf said there is a real opportunity to dramatically improve cybersecurity with AI and that he could not think of a better company or team to join at this critical moment. What this means for the AI landscape Karpathy’s move to Anthropic, combined with the addition of a seasoned cybersecurity expert, suggests the company is investing heavily in both frontier model development and safety research. Pre-training remains one of the most expensive and compute-intensive phases of building advanced AI systems, and Anthropic’s decision to focus on using AI to accelerate that work could give it a unique advantage. For readers following the AI industry, this development underscores a broader trend: leading AI labs are increasingly competing not just on raw compute power, but on the quality of their research teams and their ability to innovate in how models are built and trained. Conclusion Andrej Karpathy’s return to frontier AI research at Anthropic, alongside the company’s parallel investment in cybersecurity expertise, reflects a dual focus on capability and safety. As the race to build more advanced language models intensifies, the composition of research teams and the methods they use to accelerate progress will likely become as important as the scale of the hardware they deploy. FAQs Q1: What will Andrej Karpathy do at Anthropic? He will lead a new team focused on using Claude to accelerate pre-training research, working under team lead Nick Joseph. Pre-training is the phase that gives AI models their core knowledge and capabilities. Q2: Why is this hire significant? Karpathy is one of the few researchers with deep experience in both the theory and large-scale practice of training LLMs. His move signals Anthropic’s strategy of prioritizing AI-assisted research over simply scaling compute. Q3: Will Karpathy continue his education work? He has said he remains deeply passionate about education and plans to resume that work in time, but has not provided specific details. His startup Eureka Labs has not shared recent updates. This post OpenAI co-founder Andrej Karpathy joins Anthropic to lead pre-training research team first appeared on BitcoinWorld .

19 May 2026, 13:21

BSC Post-Quantum Upgrade Clears Major Test as Network Throughput Drops 40%

The move to quantum-resistance of blockchain infrastructure is bringing new challenges to the forefront besides cryptography design, namely, heavy data overhead caused by post-quantum algorithms. BNB Chain Developers made the announcement recently on the BSC Post-Quantum Cryptography Migration Report which describes successful implementation of post-quantum upgrades to the network. All of these upgrades used ML-DSA-44 for transaction signatures and pqSTARK for consensus vote aggregation. Importantly, the report verifies that the novel cryptographic mechanisms are backwards compatible with existing addresses, wallets, RPC interfaces and SDKs allowing the network to maintain its current architecture while priming itself for upcoming quantum threats. Nevertheless, testing revealed a key scalability issue. By introducing these quantum resistant signatures we saw a downward shift in terms of network performance, this was attributed mainly to the increase in data being passed around the network. A lot of people assume the hardest part of post-quantum cryptography is the cryptography itself. In our testing, that wasn’t really the case. The bigger challenge came from the amount of additional data moving through the network once quantum-resistant signatures were… pic.twitter.com/r5xAc0KKfb — BNB Chain Developers (@BNBChainDevs) May 19, 2026 Signature Sizes Bring New Scaling Pressure As per BNB Chain analysis of the incident, the main challenge was not verifying signature and cryptographic calculations but dealing with large growing transaction data size. The size of a normal transaction signature grew from 65 bytes to ~2.4 KB when integrated ML-DSA-44 It fully propagated through the network stack: transaction sizes rose from approximately 110 bytes to nearly 2.5 KB, and block sizes swelled from around 110 KB to nearly 2 MB. These increased-size payloads limited propagation overhead between regions, consequently significantly reducing the maximum throughput of the network. The cross-region testing quickly revealed the performance hit, where native transfer throughput fell from 4,973 TPS to around 2,997 TPS, almost a minus-40% drop. Developers emphasized that the cryptographic computations themselves were still relatively manageable during tests. The throughput drop came mainly from the additional cost of data transfer and inter-block synchronisation between validators across multiple regions. Contrary to popular belief, the biggest hurdle in post-quantum blockchain upgrades is not algorithm design. However, according to BNB Chain, bandwidth efficiency and data-layer scalability might be the more significant long-term constraints. Compatibility Remains A Major Advantage Despite the throughput decline, BNB Chain stressed that its post-quantum framework was engineered to be compatible with the chain’s current ecosystem. The testing phase showed that wallet integrations, RPC functionality, SDK support and address formats worked as before without many disruptions. Backward compatibility decreases migration friction and could enable adoption ease if post-quantum security begins becoming a sector-wide requirement. Second, most wallets in the space are designed for use with other chains, whether it’s ethereum or bsc. The report further reveals that BNB Chain is taking an incremental route toward post-quantum readiness rather than imposing a rapid transition. This is just a promise, and developers recognize that there remain unresolved points regarding network and data-layer scaling before production-level deployment can be considered practical. Quantum Concerns Continue Growing Across Crypto When the quantum computing area is progressing greatly recently, post-quantum cryptography has raised meaningful attention in the blockchain community. Current blockchains are built on cryptographic schemes which could be broken by a sufficiently powerful quantum adversary. While experts disagree about how imminent this threat is, several major ecosystems are already exploring migration trends to quantum-resilient protocols. It furthermore serves as a harbinger of things to come in blockchain scaling. In addition to optimizing execution speed and gas costs, data efficiency, propagation strategies, and validator communication architectures may move into focus as quantum-resistant signatures reach production environments. However, BNB Chain developers stress that the technology needs more fine-tuning before mass-usage is feasible. Binance Launches HTTP-native x402 Payments Binance announced Binance x402 underlined as a programmable payments framework based on HTTP and built on BNB Chain. Targeting agent-driven commerce and software-native financial interactions, this system empowers applications and autonomous agents to perform programmable payments directly over vanilla HTTP 402 payment flows. The framework includes off-chain authorization and on-chain settlement, as well as pay-per-call and usage-based billing models, according to Binance. This architecture has been created to help machine-to-machine transactions and automated software payments without an overreliance on conventional payment rails. Binance also notes its support for autonomous transactions, part of a growing strategy to create crypto financial infrastructure designed usable by AIs across the BNB Chain. The purpose of the launch coincides with growing curiosity among blockchain networks around payment solutions for AI agents, automated APIs and decentralized software services. Introducing #Binance x402 HTTP-native programmable payments on @BNBCHAIN Built for agent-driven and software-native commerce: Standard HTTP 402 payment flows Off-chain authorization + on-chain settlement Pay-per-call and usage-based billing Autonomous transactions… pic.twitter.com/DKm0ebMrAQ — Binance (@binance) May 19, 2026 BNB Chain Shifts its Focus to Infrastructure BNB Chain works to extend its infrastructure strategy by simultaneously launching the post-quantum migration report as well as Binance x402. Beyond consumer-facing applications, the ecosystem is rapidly prioritizing long-to-to-long network resiliency and how & when architecture can be future-proof. The post-quantum report reveals technical challenges that continue to impede large-scale quantum-resistant deployment, including the ability of validators to synchronize with data throughput in high-pressure conditions. On the other hand, Binance x402 projects BNB Chain into an era where software programs and AI-driven applications will autonomously be performing work as parts of blockchain economies. These announcements collectively shape a larger trend in evolution away from pure transaction execution on the blockchain at scale. Where it gets most critical is less about if post-quantum cryptography actually fits inside the BNB Chain ecosystem, and more in how that data should be stored as a by-product of those systems. Disclosure: This is not trading or investment advice. Always do your research before buying any cryptocurrency or investing in any services. Follow us on Twitter @nulltxnews to stay updated with the latest Crypto, NFT, AI, Cybersecurity, Distributed Computing, and Metaverse news !

19 May 2026, 08:30

Elon Musk Loses OpenAI Trial, Vows Appeal After Jury Dismisses Claims Over Statute of Limitations

A federal jury has thrown out every claim in Elon Musk’s lawsuit against OpenAI and Sam Altman, finding the case to have been filed past its legal deadline. Verdict Reached, But Battle Not Over A federal jury in Oakland, California sided with OpenAI on May 18, unanimously dismissing all claims in Elon Musk’s lawsuit against

19 May 2026, 05:46

Ripple CTO Explains XRPL Hard Forks

Ripple Chief Technology Officer David Schwartz has addressed concerns over the XRP Ledger's (XRPL) frequent "technical hard forks."