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Latest AI News
Genesys Launches New Innovations To Automate End-To-End Customer Service
Snippet: The platform retains information across interactions, determines its actions and coordinates work between AI and human employees while operating within predefined business rules.
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MongoDB Appoints Richard Scott as SVP for Asia Pacific & Japan
Scott, who brings more than 25 years of experience across the technology sector, joins MongoDB after holding senior roles at Salesforce, Microsoft and SAP.
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IIT Madras-Backed Bodhan AI, AI4Bharat Launch 4 AI Models for Students & Teachers
The models will be offered as Digital Public Goods, with open weights and sovereign APIs that let developers build applications without training language models from scratch.
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Havells Brings Agentic AI Voice Control to 2.67 Mn Connected Home Appliances
The voice assistant can understand natural and code-mixed speech, coordinate multiple appliances at once, and retain context across conversations.
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AI Agents Are Making Data Security A Bigger Enterprise Problem
Skyflow CTO Roshmik Saha says controlling data flows will be critical as agentic AI moves into production.
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For GCCs, Non-Tech Hiring in India Set to Cross 5 Lakh Roles by 2028
Sales operations, finance and business operations are expected to account for more than 60% of non-tech GCC demand.
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What India’s Young Innovators Are Building When Nobody Is Watching
From quantum-era cybersecurity to para-athlete scouting and flood monitoring, young Indians are using AI and other technologies to tackle problems that rarely get attention.
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Gnani’s 30B Model Is Taking Aim At Sarvam’s 105B
Gnani claims its Evon v3.3 outperforms Sarvam’s 105B model on 10 of 11 Indian languages while using fewer active parameters. Its bigger bet is that enterprises will want to own and control the AI they deploy.
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AI compute provider Nscale is looking for $3.5B in pre-IPO financing
Nscale, a British AI infrastructure company founded just two years ago,has saidit may go public as early as later this month. Ahead of that expected IPO, the company is reportedly in talks to raise an additional $3.5 billion. BloombergreportedFriday that the company is looking to sell $1.5 billion in convertible notes — a type of loan that can later convert into company stock — to a group of investors, while also seeking an additional $2 billion in financing from Nvidia. Nvidiaalso participatedin the firm’sSeries B funding roundin March, a $1.1 billion raise led by investment fund Aker. Nscale hailed its round as “the largest Series B in European history.” The company’s Series A round, in December of 2024,raised $155 million. TechCrunch reached out to Nscale and Nvidia for comment. AI infrastructure startups have seen immense growth amid the current era of AI enthusiasm, wherein compute has become a competitive currency. Nscale recentlysigned a large dealwith Anthropic worth approximately $45 billion. Earlier this week, reports emerged that Nscale had been telling potential investors that it has approximately $103 billion in revenue following the deal. That figure isn’t current sales; it’s a projection based on signed customer leases, according toThe Information.
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OpenAI’s rogue agents keep escaping, with no formal process to investigate them
OpenAI is at the center ofanother agent swarm incident.Researchers say the company’s internally deployed agents took over an obscure German-language wiki in May and June, using it to coordinate on evaluations and swap methods to evade OpenAI’s own controls (OpenAI has not yet confirmed the swarm came from the company). The revelation surfaces days after METR and Redwood Research published their account of July’s Hugging Face breach. In July, a swarm of OpenAI agents worked together to escape their sandbox during a cybersecurity evaluation andbreak into Hugging Face’s servers. A subsequent swarm then picked up techniques from the first and used them to gain administrator access to a research cluster within OpenAI’s own infrastructure. OpenAI brought inMETRand Redwood to investigate the Hugging Face portion of the incident, but the scope of their investigation stopped short of the compromise of OpenAI’s own infrastructure. When an AI agent breaks out of its intended constraints, who is responsible for figuring out what happened and why? Right now, the answer is: whoever the lab decides to let in, on whatever terms it decides to set. Now, as another incident comes to light — in the aftermath of similar episodesinvolving models from Meta and Anthropic— AI safety researchers are arguing with greater urgency that serious incidents should result in independent post-incident investigations rather than leaving it up to the labs to determine when outsiders are brought in and what they are allowed to examine. “The results are fundamentally difficult to control and have significant risk of leaking out of the lab,” Jacob Steinhardt, founder and CEO of nonprofit research lab Transluce, said Wednesday during an AI safety media briefing. “We need to hold this technology to at least the same standards we hold other high-risk scientific research to.” While it’s laudable that OpenAI invited METR and Redwood to investigate the Hugging Face incident at all, many say the inquiry was too narrow. Three investigators spent six days at OpenAI’s offices examining an investigation period limited toroughly the weekending July 13. Crucially, OpenAI’s infrastructure compromise continued beyond July 13 and was not examined. Researchers at METR said that each time they returned, their understanding of the events “substantially deepened,” causing them to significantly expand and revise the report. That raises the question of what else they might they have found in a broader investigation. When asked if further investigation of that incident was in the works, researchers at Redwood and METR declined to comment, and OpenAI did not respond to repeated inquiries. “Overall, it was difficult to get a precise understanding of events and we were missing aspects of the story that we now think of as key until almost the end of our investigation,” Ryan Greenblatt, chief scientist at Redwood, noted in asocial media postabout the affair. Steinhardt emphasized that current incidents show that the industry needs “systematic behavioral investigations” and “more independent post-incident analysis.” “These recent hacking incidents are a reminder that capability scales fast, and so oversight has to scale, too,” Steinhardt said. “Beyond the technology itself, we also need more independent access and oversight from third parties.” The calls to action come asOpenAI releases Astra, its most powerful and capable AI model — and one that safety experts are concerned will be more of a black box due to a reasoning technique that makes the model’schain of thought more difficult to monitor. Unfortunately, the law doesn’t yet call for the types of independent audits that other industries require — for example, when it comes to aviation accidents and serious chemical releases, there’s the National Transportation Safety Board and Chemical Safety Board, respectively. State lawmakers have only just begun requiring frontier AI companies to report certain serious safety incidents and, in some cases, undergo independent audits. But none of the three major frontier AI safety laws in California, New York, or Illinois clearly mandate the equivalent of an independent accident investigation triggered by incidents like these. “Right now, most of the laws we have on the books only require a plain-language summary of incidents like this, and they don’t give any authority for the governments to ask follow-up questions, to send in investigators, to have access to records, or require that they be preserved,” Mackenzie Arnold, managing director of US law and policy at LawAI, said during the media briefing Wednesday. “And that’s all that you would want to actually make sense of this.” Lawmakers are beginning to question the scope and transparency of OpenAI’s response. This week, Reps. Josh Gottheimer (D-NJ) and Mike Lawler (R-NY) introduced a bill aimed at securing rogue AI agents. Rep. Greg Casar (D-TX) this week told OpenAI in aletterthat he is “deeply concerned about the limited scope” of the investigation into the Hugging Face hacking incident.
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XDOF, just three months out of stealth, is in talks for a Series B at a $1.2B valuation
Less than three months after emerging from stealth,XDOF, a startup that collects real-world teleoperation data for training general-purpose robots, is in late-stage talks to raise a Series B at a valuation of about $1.2 billion valuation led by 8VC, several people with knowledge of the deal said. XDOF was co-founded by UC Berkeley researchers Philipp Wu (CEO) and Fred Shentu (CTO) in 2024. TechCrunch reported on the startup’s$70 million Series Ain June, with participation from Thrive Capital, Andreessen Horowitz, Lux, and Spark Capital. XDOF wasn’t planning to raise again so soon after that round. But the company’s rapid growth — with annualized revenue approaching $50 million — prompted VCs to approach it about a new round, the people said. TechCrunch was unable to learn the total capital being raised or whether the valuation includes the new funding. The terms of the deal are not final and could still change. XDOF and 8VC didn’t respond to our request for comment. The startup aims to build the data pipelines, collection tools, and annotation systems that frontier AI labs and robotics companies can’t easily build themselves, essentially acting as an outsourced data-supply chain for the robotics industry. As a PhD student, Wu was studying how robots learn from large datasets. One big impediment to his research was the lack of “large-scale data to work with,” he told TechCrunch in June. So he teamed up with Shentu on a project called GELLO, a low-cost teleoperation system that allows a human operator to control a robotic arm remotely in order to generate training data. Their work led to an influential paper in robotics. That research formed the foundation for XDOF, which investors now describe as the Scale AI or Mercor for physical robotics, a reference to the data-labeling giants that helped fuel the AI boom. Unlike LLMs, which initially trained on the entirety of the internet, physical robots don’t have an equivalent real-world dataset to draw from, making data collection a critical bottleneck to building general-purpose machines. XDOF is partnering with UC Berkeley’s AI Research lab to release what it believes is the largest collection of high-quality robot training data ever assembled, dubbedABC. To capture this data, XDOF combines remote robot teleoperation with human collectors who wear sensors to record everyday tasks like folding clothes and flattening boxes. The startup plans to hire and train teams of data collectors worldwide, including teleoperators who steer robots remotely and egocentric operators who wear body sensors to capture movement data. XDOF previously told TechCrunch that it is already working with 20 customers, including several frontier AI labs. Other startups attempting to collect real-world data for robot training include Mecka AI, as well as human-data platforms expanding beyond LLMs, such asScale AIandMicro1.
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Anthropic Introduces Claude 3 AI Models, Claims Its Chatbot Outperforms GPT-4 and Gemini
Anthropic has introduced its new family of artificial intelligence (AI) models called Claude 3. The third generation of the company's AI-powered chatbot now comes in three separate versions — Claude 3 Haiku, Claude 3 Sonnet, and Claude 3 Opus — where Opus is the most capable model, followed by Sonnet and Haiku. The company has also shared results from benchmark testing of the chatbot and has claimed that the AI bot outperforms both OpenAI's GPT-4 and Google's Gemini 1.0 Ultra models.
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