最新 AI 资讯
Meta Pulls Muse AI Image Feature Less Than a Week After User Backlash Highlights Privacy Risks
Meta introduced a new Muse Image feature last week, based on the firm's first AI model from the tech firm's Superintelligence Labs. The feature lets users reuse “publicly available” content, which has been posted by Instagram users. This AI-generated content can then be shared with others on various Meta-owned platforms, including WhatsApp and Facebook. This drew criticism from Instagram users, highlighting the privacy risks the newly added functionality poses. Now, the US-based company has updated the original blog post to announce that the feature has been removed. However, it did not address the highlighted privacy concerns.
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Superhuman’s new auto-draft feature almost makes me like AI replies
Since the large language model (LLM) explosion started, companies have been trying to solve the problem of overflowing inboxes by using AI tocategorize emailsanddraft repliesthat sound like you. Email clientSuperhumanis launching a new version of its auto-draft feature that identifies important emails and creates draft replies that sound less robotic. Superhuman has attempted this in the past with features like instant replies and follow-up auto-drafts. However, a lot of those emails sounded like an overly enthusiastic AI salesperson, and I didn’t use them much. The new version of the auto-draft feature feels different. In the last few days, after gaining access to the beta, I have sent emails with little to no editing for some generated drafts. The app understands which emails might need replies and drafts a response based on your tone from previous conversations. It also generates two other variations that you might want to send instead. In my experience using the feature, I saw drafts agreeing to embargoes on a pitch to get more details, or confirming timing for a meeting that I could send with minimal edits. The feature also generated responses to emails asking for an authored post on TechCrunch, saying that I don’t handle that work. (TechCrunch does not accept authored posts.) The feature is far from perfect, though. By default, it often generated a positive response to a pitch, or agreed to a meeting at a post-midnight time. Thankfully, I could select another response from the other variations quickly and send it away. The feature learns from your usage and improves responses. For instance, after the midnight meeting debacle, when someone suggested a similar time, the feature generated a draft saying that the timing doesn’t work for me. I receive thousands of emails every month, partially thanks to AI making first drafts easier for others, like comms and PR professionals. I don’t have the confidence to hand over the reins to AI to handle my inbox completely, but this feature could help me respond to more people when I don’t need to type out long messages. Users can personalize emails by heading to Settings> Personalization and adding details about themselves and their role, along with adding files or links for more context. Superhuman’s co-founder, Rahul Vohra, said during the testing phase that 40% of auto-generated drafts were sent within one day, and 60% of those were sent without any manual editing. Vohra said that earlier features like Instant replies were built from older models like GPT-3.5, which were less intelligent or had a smaller context window. With this new implementation, the company is using an array of models. “Today, we are using a mixture of models to make this work. The actual writing is done by frontier models from both Anthropic and OpenAI. So we’re applying the maximum amount of intelligence and context to this that we possibly can to make the feature work,” Vohra said. Last year,Grammarly acquired Superhumanand then rebrandedthe company as Superhuman. Now, the company is building an assistant called Superhuman Go that spans platforms while carrying context over from one app to another.
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Spotify expands its AI push with a ChatGPT-like music assistant
Spotify is taking another step to infuse AI technology into its listening experience, withTuesday’s newsthat Premium users will now be able to have interactive conversations with the app to choose what music or other audio they want to hear. The feature is initially available in the U.S., Ireland, and Sweden across iOS and Android devices for users 18 years old and above in English. It’s considered a beta release, meaning that things may not always work perfectly, Spotify says, but user feedback will help to improve the product. The company didn’t explicitly share more details about the AI technology under the hood in its announcement, but Spotify confirmed to TechCrunch that it uses a mix of its own AI technology and models from multiple providers, based on whatever is best for the task. The addition is the latest example of how Spotify has put AI technology to use to help people interact with the app’s extensive catalog of music, podcasts, and audiobooks. The company also offers tools like an AI DJ, which speaks in an AI voice that you can engage with directly, plus AI features for building playlists with prompts and those for connecting Spotify with third-party AI chatbots, like ChatGPT. Loading the player… The new feature extends the ability to chat with Spotify beyond the AI DJ experience, allowing users to talk to Spotify across the app’s Home and Now Playing views on mobile devices. Users can either type or speak to the app and have back-and-forth conversations to help them choose what to play next. Beyond that, Spotify says the app will also be able to chat with users about their listening history and can help them learn more about their favorite music or go deeper into podcasts or audiobooks. That means you could get into questions like what inspired a certain song, or dates of album releases, or even get suggestions of other artists you might like, based on what you’re playing. You can also ask about your own listening history, like when was the first time you played a certain track, or you could explore more into what sort of genres you’ve been streaming lately. In the announcement about the new feature, Spotify also offers a few suggestions as to how to use this interactive technology. For instance, you could ask Spotify to “play some artists I haven’t heard before,” then continue to shape that selection with follow-ups, like asking it to add a specific artist by name, or narrow the selection to just more recent tracks. You could also shape the request further by asking it to be “more upbeat,” or give it other directions. Plus, you can ask Spotify to save songs, add songs to your queue, or follow the artist via the new feature. The feature is rolling out now to the markets on mobile devices.
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The real AI race may no longer be at the frontier
For several weeks this summer, the AI industry was fixated onAnthropic’s latest frontier modelsandWashington’s fight to controlwho was granted access to them. But while everyone was watching the frontier, developers kept building — and they weren’t waiting around for permission from the Anthropics and OpenAIs of the world. Chinese open-weight models accounted for41% of downloadson Hugging Face this spring, surpassing U.S. models. OnOpenRouter, the top six most popular models are all open models from Chinese firms including Tencent, Xiaomi, DeepSeek, MiniMax, and Z.ai. Anthropic’s Claude Opus 4.7 trails in seventh place, at the time of this writing. Anddata from Vercelshows thatopen weight modelsare absorbing much of the volume-heavy infrastructure of AI apps, while closed models operate as the higher-cost, premium layer. Open models handled nearly a third of AI requests on the platform in June. Those platforms only capture one slice of the AI ecosystem; in particular, they leave out sessions hosted by major labs, which likely account for the bulk of OpenAI and Anthropic’s usage. But open-source models’ large and growing share of the market raises a difficult question: How much do frontier models still matter if most production AI ends up running on cheaper, customizable alternatives? Some see the growth of open-source models as a sign that the most intelligent models may end up being used for only the most specialized use cases. “Maybe in a few years, the frontier models will be for experimenting and [for] some really high value tasks, and most of the production workloads will actually be powered either by private models within companies or by open source models,” Hugging Face CEO Clem Delangue said on a recentepisode of Equity. Loading the player… Hugging Face is a platform and developer community best known for hosting, sharing, and helping companies deploy open models. Delangue says Hugging Face’s customers and community members are increasingly touting the benefits of owning their own AI models rather than renting them, a trend that’s picked up steam in the cold light of day after getting the bill associated with thecost of scaling closed frontier models. “If you’re an AI company or a technology company, you don’t want to outsource your core capabilities to another company, to a black box API that you don’t control, don’t have any visibility on, and don’t really have any sort of ownership,” Delangue said. That shift, Delangue argues, is reflected in the activity happening on Hugging Face. A new repository is created every seven seconds on the platform, which hosts almost three million public models and one million public datasets, per Delangue. That points to a different picture than the “one model to rule them all,” he says. In reality, it looks more like companies using many different models, many of which are customized for their specific use case. Half of all Fortune 500 firms are using Hugging Face to deploy their own private models and open source models, he says. The growing popularity of open models coincides with a steady stream of increasingly capable releases from Chinese AI labs. Every few months, another Chinese AI company releases a powerful open-weight model that is cheaper to deploy and easier to customize than closed competitors, undercutting the economics of proprietary AI that U.S. firms have poured billions into. Most recently, Beijing-based AI company Z.ai released an open weight model called GLM-5.2 that excels at agentic coding and competes with Anthropic’s latest models on identifying security vulnerabilities. Delangue isn’t the only executive arguing that enterprises should avoid tying themselves to a single model provider. Microsoft CEO Satya Nadellarecently warnedagainst single provider lock-in, arguing that control of data should be a primary concern for enterprises using AI. “While the great innovation that comes from model providers having fair use rights to train models on public data is needed, I find it ironic that the status quo is to then turn around and impose restrictive terms on distillation, and to reserve the right to learn from customer usage and interaction data,” Nadella said. “If learning flows in only one direction, economic value converges toward the owners of the learning infrastructure rather than the creators of the knowledge itself. Therefore, it’s imperative that we distribute the learning infrastructure to every firm so that they can control their own learning loop.” The rise of open models has also intensified a debate over whether increasingly capable models should be broadly available at all. Anthropic CEO Dario Amodei hasarguedthat scaling powerful open model weights could become dangerous because once they are released, they become difficult to control.Othershave argued that open models are easier to access by bad actors who could use them to spread disinformation or enact cyber or biological warfare. Delangue sees the tradeoff differently. “The biggest risk in AI is concentration of power,” Delangue said. “The way you make the world safer, in my opinion, is by leveling up the playing fields and creating transparency on these models.” Transparency means defenders can more easily “patch the cybersecurity risks that they already know open source models can exploit,” he said. The Hugging Face executive argues that keeping powerful models closed doesn’t eliminate the risks associated with advanced AI systems, in part because it’s easy to get past frontier model API guardrails and tosteal the weightsand disseminate them openly. Restricting powerful models, Delangue argues, simply concentrates the technology in the hands of a few companies while reducing transparency into how systems work. “You don’t really make it safe by keeping it behind closed doors for just a few players,” Delangue said. “You make it more dangerous because you create asymmetry of power and asymmetry of capabilities.”
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Reflection inks $1B compute deal with Nebius
Reflection AI, a U.S. startup vying to developopen models, has signed a $1 billion compute deal with European AI infrastructure company, Nebius. Nebius, formerly the international arm of Russian tech giant Yandex, will provide Reflection access to Nvidia’s latest chips. The deal comes just a few weeks after the startup signed asimilar deal to access SpaceX’s computing resources, and mirrors several partnerships by AI firms as they race to secure compute for training and deploying their models. Along with its increasingly capable Chinese counterparts, Reflection is one of several open-weight AI model developers that have received ample attention lately as debate rages over the value of top-shelf, closed-source AI models — especially withdata retention concernssurging up, as well as government intervention. Just last month, the Trump administration pressuredAnthropicandOpenAIto restrict their most powerful new models, raising concerns that access toAI models could be taken away overnight. That, plus the release of more capable open models from China, has led to an increase in mainstream interest in open source AI. Reflection, currently valued at $8 billion, was founded in 2024 by two former Google DeepMind researchers. It has already raised close to $2.6 billion in funding from backers including Nvidia, Sequoia Capital, and Lightspeed Venture Partners. Shortly after securing a$2 billion investment from Nvidia, Nebius signed a five-year infrastructure dealwith Metaworth up to $27 billion. Last year, Nebius signed a multi-year deal with Microsoft worth up to $19.4 billion. TechCrunch has reached out to Reflection and Nebius for more information.
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HCLTech's AI Infrastructure Play Can Redefine the Limits of Indian IT
HCLTech's AI strategy shows Indian IT is moving beyond software services, betting on compute infrastructure, sovereign AI, and specialised talent.
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Demis Hassabis Says AGI Could Arrive Within ‘A Few Short Years’, Calls for US Frontier AI Standards Body
Hassabis argues that the pace of AI development is exceeding the current understanding of the technology.
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AI Could Drive Mass Job Displacement and Transform Economies, Economists and 16 Nobel Laureates Warn
Tech firms globally are either in the process of integrating AI technologies and automation into their workflows. This, added with the use of AI chatbots by individuals for various daily tasks, has led to an unprecedented rise in AI adoption. The proponents of AI argue that the agents improve efficiency. However, the accelerated adoption of AI by firms comes as companies have laid off several workers and cut white-collar jobs, including managerial positions. Warning about the potential dangers of AI, various economists, Nobel laureates, and industry leaders have signed a letter, claiming that AI could lead to large-scale job displacements and transform economies.
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Soket AI is Building India’s Answer to Anthropic Fable
Early benchmarks of Soket AI’s upcoming model suggest inference efficiency is roughly 3x better than DeepSeek's and training costs are 30–40% lower.
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Father of Reinforcement Learning Richard Sutton Launches New AI Startup
The Turing Award winner has left Keen Technologies with a former colleague, Khurram Javed, to build AI models that benefit from continual learning.
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Karnataka to Build AI University & Innovation Hub, Announces CM Shivakumar at Google I/O Connect India
Google also introduced on-premises Gemini, AI education initiatives, cybersecurity tools, and expanded language support for Indian enterprises and developers.
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The AI Smoke and Mirrors Fuelling India’s Data Centre IPOs
As Indian data centre operators line up for IPOs, investors are backing tomorrow's AI demand, even as today's utilisation, governance, and policy remain unresolved. As Indian data centre operators line up for IPOs, investors are backing tomorrow's AI demand, even as today's utilisation, governance, and policy remain unresolved.
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