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The billionaires are combating once more.
On Monday, Elon Musk, the world’s richest man, supplied to purchase the nonprofit that successfully governs OpenAI for $97.4 billion. In response to Musk’s provide, OpenAI CEO Sam Altman earlier Monday authored a cheeky submit on X, writing, “No thanks, however we’ll purchase Twitter for $9.74 billion if you’d like.” (Musk and traders famously bought Twitter for $44 billion in 2022.)
Musk’s bid, severe or not, might complicate OpenAI’s effort to transform to a for-profit public profit company inside two years. Now OpenAI’s board should exhibit it’s not underselling OpenAI’s nonprofit by handing the nonprofit’s property, together with IP from OpenAI’s analysis, to an insider (e.g., Altman) for a reduction.
OpenAI may make the case that Musk’s bid is a hostile takeover try provided that Musk and Altman aren’t the most effective of buddies. It may additionally argue that Musk’s provide isn’t credible as a result of OpenAI is already within the midst of a restructuring course of. Or OpenAI may problem Musk on whether or not he has the funds.
In a press release Tuesday, Andy Nussbaum, exterior counsel representing OpenAI’s board, mentioned that Musk’s bid “doesn’t set a worth for [OpenAI’s] nonprofit” and that the nonprofit is “not on the market.” Nussbaum added, “Respectfully, it’s not as much as a competitor to resolve what’s in the most effective pursuits of OpenAI’s mission.”
My colleague Maxwell Zeff and I wrote a extra detailed piece on what to anticipate within the coming weeks. But assured, Musk’s provide — to not point out his ongoing lawsuit towards OpenAI over what he claims is fraudulent conduct — guarantees to make for fierce courtroom brawls.
News
Apple’s new robotic: Apple created a analysis robotic that takes a web page from Pixar’s playbook. The firm’s robotic lamp operates as a extra kinetic model of a HomePod or different good speaker. The particular person dealing with the lamp asks a question, and the robotic responds in Siri’s voice.
Is AI making us dumb?: Researchers lately revealed a examine taking a look at how utilizing generative AI at work impacts vital pondering expertise. It discovered that after we rely an excessive amount of on AI to suppose for us, we worsen at fixing issues ourselves when AI fails.
AI for all, maybe: In a new essay on his private weblog, Altman admitted that AI’s advantages will not be extensively distributed — and mentioned that OpenAI is open to “strange-sounding” concepts like a “compute finances” to “allow everybody on Earth to make use of lots of AI.”
Christie’s controversy: Fine artwork public sale home Christie’s has bought AI-generated artwork earlier than. But quickly it plans to carry its first present devoted solely to works created with AI, an announcement that has been met with blended opinions — and a petition calling for the public sale’s cancellation.
Better than gold: An AI system developed by Google DeepMind, Google’s main AI analysis lab, seems to have surpassed the typical gold medalist in fixing geometry issues in a world arithmetic competitors.
Research paper of the week
We know that the majority AI fashions can’t carry out primary duties reliably, like fixing grade-school-level math issues. What we don’t at all times know is the explanation behind their failures. According to a workforce of researchers at MIT CSAIL, inaccurate benchmarks could also be partially responsible.
In a brand new examine, the MIT CSAIL researchers discovered that whereas right now’s top-performing fashions nonetheless make real errors on standard AI benchmarks, over 50% of “mannequin errors” are literally attributable to mislabeled and ambiguous questions in these benchmarks.
“If we need to correctly quantify mannequin reliability, we have to rethink how we assemble benchmarks to attenuate label errors,” mentioned one of many researchers, MIT college member and OpenAI staffer Aleksander Madry, in a submit on X. “This is only a first step.”
Model of the week
You’ve heard of deepfakes earlier than. But what about deepfakes of boring on a regular basis scenes? That’s the concept behind Boring Reality Hunyuan LoRA (Boreal-HL), a fine-tuned AI video generator that excels at creating movies of … effectively, fairly banal stuff.
Boreal-HL can generate clips of vacationers consuming ice cream, folks barbecuing meat, folks in lunch conferences, executives giving speeches at conferences, {couples} at weddings, and different mundane slices of life. This reporter finds the absurdity of the factor hilarious — notably contemplating how impractical it’s to run. It takes Boreal-HL not less than 5 minutes to generate a single clip.
Grab bag
Thanks to current breakthroughs in AI effectivity, it’s getting cheaper — and simpler — to coach extremely refined fashions.
In a brand new paper, researchers at Shanghai Jiao Tong University and an AI firm known as SII exhibit {that a} mannequin skilled on simply 817 “curated coaching samples” can outperform fashions skilled on 100x extra information. The workforce claims that their mannequin was even capable of reply sure questions it hadn’t seen in the course of the coaching course of, exhibiting what they name “out of area” capabilities.
The examine follows on the heels of a Stanford-led undertaking that discovered it’s potential to create an “open” mannequin rivaling OpenAI’s o1 “reasoning” mannequin for below $50.
