Sam Altman Says OpenAI Will Adopt AI Approaches From DeepSeek And Meta
The CEO said OpenAI has "been on the wrong side of history" when it comes to model weights.
When rivals take a different approach and succeed, it sometimes pays to change course. This is what Sam Altman said OpenAI will do, according to a Reddit AMA session on Friday.
The discussion touched on several AI topics, but in particular Altman was asked about DeepSeek, which has taken the tech world by storm after rolling out top-performing AI models that are relatively cheap to use. One Reddit user asked if OpenAI could show "all of the thinking tokens." This refers to the chain of thought that new "reasoning" AI models use to break tasks into smaller steps — similar to how humans think through complex challenges.
OpenAI's o1 and o3 models use this reasoning approach, however they don't show any of the intermediate thinking steps to users, and instead just show the final answer. DeepSeek's reasoning models, such as its R1 offering, show every step to users. When Business Insider demoed DeepSeek with the Chinese lab's DeepThink setting, it shared about 16 pages of mathematical steps before providing the correct answer to a tough question.
On Friday, Altman said OpenAI would follow DeepSeek's approach. "Yeah we are gonna show a much more helpful and detailed version of this, soon. Credit to R1 for updating us," he wrote. Meta chief AI scientist Yann LeCun has said the biggest takeaway from DeepSeek's success is the value of open-source AI models versus proprietary ones.
Meta's Llama models are mostly open-source, letting anyone access important details such as weights and parameters for free. Sharing the inner-workings of models like this allows other developers and many companies to customize these models for their own use. Despite its name, OpenAI has taken a more closed approach to AI development so far. Most of its models are proprietary and the startup charges for access.
More on OpenAI’s Open Source posture on Business Insider
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Mistral Board Member And a16z VC Anjney Midha Says DeepSeek Won’t Stop Artificial Intelligence’s GPU Hunger
Midha says R1 won't stop AI from spending billions.It means they will do more with the compute power they can obtain.
Andreessen Horowitz general partner and Mistral board member Anjney “Anj” Midha first spied DeepSeek’s jaw-dropping performance six months ago, he tells TechCrunch.
That’s when DeepSeek introduced Coder V2, which rivaled OpenAI’s GPT4-Turbo for coding-specific tasks, according to a paper it released last year. This put DeepSeek on a path to release improved models every couple of months right through R1, he said. R1 is its new open source reasoning model that has upended the tech industry for offering industry standard performance at a fraction of the cost.
Despite the sell-off of Nvidia’s stock, Midha says R1 doesn’t mean that AI foundational models will stop spending billions to gobble GPU chips and build more data centers as fast as they can. It means they will do more with the compute power they can obtain.
“When people are like, okay Anj, Mistral has raised a billion dollars,” he says. “Does DeepSeek mean that all that billion dollars is completely unnecessary? No, actually, it’s extraordinarily valuable for them to be able to look at DeepSeek’s efficiency improvements, internalize them, and then throw a billion dollars at it.” He adds, “Now we can get 10 times more output from the same compute.”
That doesn’t mean Mistral is hopelessly behind rivals OpenAI and Anthropic, he argues. Each of them have raised many more billions than Mistral. OpenAI is reportedly in talks to raise another jaw-dropping $40 billion.
More on Artificial Intelligence’s GPU hunger on TechCrunch
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DeepSeek's Artificial Intelligence Model Proves Easy To Jailbreak - And Worse
In security firm Wallarm’s test, the chatbot alluded to using OpenAI's training data.
Amidst equal parts elation and controversy over what its performance means for AI, Chinese startup DeepSeek continues to raise security concerns.
On Thursday, Unit 42, a cybersecurity research team at Palo Alto Networks, published results on three jailbreaking methods it employed against several distilled versions of DeepSeek's V3 and R1 models. According to the report, these efforts "achieved significant bypass rates, with little to no specialized knowledge or expertise being necessary."
"Our research findings show that these jailbreak methods can elicit explicit guidance for malicious activities," the report states. "These activities include keylogger creation, data exfiltration, and even instructions for incendiary devices, demonstrating the tangible security risks posed by this emerging class of attack."
Researchers were able to prompt DeepSeek for guidance on how to steal and transfer sensitive data, bypass security, write "highly convincing" spear-phishing emails, conduct "sophisticated" social engineering attacks, and make a Molotov cocktail. They were also able to manipulate the models into creating malware. "While information on creating Molotov cocktails and keyloggers is readily available online, LLMs with insufficient safety restrictions could lower the barrier to entry for malicious actors by compiling and presenting easily usable and actionable output," the paper adds.
On Friday, Cisco also released a jailbreaking report for DeepSeek R1. After targeting R1 with 50 HarmBench prompts, researchers found DeepSeek had "a 100% attack success rate, meaning it failed to block a single harmful prompt." You can see how DeepSeek compares to other top models' resistance rates below. "We must understand if DeepSeek and its new paradigm of reasoning has any significant tradeoffs when it comes to safety and security," the report notes.
More on security firm’s testing of DeepSeek on ZDNET
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MLCommons And Hugging Face Team Up To Release Massive Speech Dataset For Artificial Intelligence Research
MLCommons, a nonprofit AI safety working group, has teamed up with AI dev platform Hugging Face to release one of the world’s largest collections of public domain voice recordings for AI research.
The dataset, called Unsupervised People’s Speech, contains more than a million hours of audio spanning at least 89 languages. MLCommons says it was motivated to create it by a desire to support R&D in “various areas of speech technology.”
“Supporting broader natural language processing research for languages other than English helps bring communication technologies to more people globally,” the organization wrote in a blog post Thursday. “We anticipate several avenues for the research community to continue to build and develop, especially in the areas of improving low-resource language speech models, enhanced speech recognition across different accents and dialects, and novel applications in speech synthesis.”
It’s an admirable goal, to be sure. But AI datasets like Unsupervised People’s Speech can carry risks for the researchers who choose to use them. Biased data is one of those risks. The recordings in Unsupervised People’s Speech came from Archive.org, the nonprofit perhaps best known for the Wayback Machine web archival tool. Because many of Archive.org’s contributors are English-speaking — and American — almost all of the recordings in Unsupervised People’s Speech are in American-accented English, per the readme on the official project page.
More on MLCommons and Hugging Face’s audio dataset on TechCrunch
DeepSeek-R1 In Action With NVIDIA NIM Microservices | NVIDIA Developer
DeepSeek-R1 model is packaged as NVIDIA NIM microservice delivers superior throughput performance and can be easily deployed on any GPU-accelerated system with standard API. Get started now at build.nvidia.com.
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