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The billionaires are preventing 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 supply, OpenAI CEO Sam Altman earlier Monday authored a cheeky submit on X, writing, “No thank you, but we will buy Twitter for $9.74 billion if you want.” (Musk and buyers 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 must reveal 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 might make the case that Musk’s bid is a hostile takeover try on condition that Musk and Altman aren’t the most effective of associates. It might additionally argue that Musk’s supply isn’t credible as a result of OpenAI is already within the midst of a restructuring course of. Or OpenAI might problem Musk on whether or not he has the funds.
In a assertion Tuesday, Andy Nussbaum, exterior counsel representing OpenAI’s board, stated that Musk’s bid “doesn’t set a value for [OpenAI’s] nonprofit” and that the nonprofit is “not for sale.” Nussbaum added, “Respectfully, it is not up to a competitor to decide what is in the best interests of OpenAI’s mission.”
My colleague Maxwell Zeff and I wrote a extra detailed piece on what to anticipate within the coming weeks. However assured, Musk’s supply — to not point out his ongoing lawsuit in opposition to OpenAI over what he claims is fraudulent conduct — guarantees to make for fierce courtroom brawls.
Information
Apple’s new robotic: Apple created a analysis robotic that takes a web page from Pixar’s playbook. The corporate’s robotic lamp operates as a extra kinetic model of a HomePod or different good speaker. The particular person going through the lamp asks a question, and the robotic responds in Siri’s voice.
Is AI making us dumb?: Researchers lately revealed a research how utilizing generative AI at work impacts crucial pondering abilities. It discovered that once 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 broadly distributed — and stated that OpenAI is open to “strange-sounding” concepts like a “compute budget” to “enable everyone on Earth to use a lot of AI.”
Christie’s controversy: Wonderful artwork public sale home Christie’s has offered AI-generated artwork earlier than. However quickly it plans to carry its first present devoted solely to works created with AI, an announcement that has been met with combined critiques — and a petition calling for the public sale’s cancellation.
Higher than gold: An AI system developed by Google DeepMind, Google’s main AI analysis lab, seems to have surpassed the common gold medalist in fixing geometry issues in a global arithmetic competitors.
Analysis paper of the week
We all know that the majority AI fashions can’t carry out fundamental duties reliably, like fixing grade-school-level math issues. What we don’t at all times know is the rationale behind their failures. Based on a staff of researchers at MIT CSAIL, faulty benchmarks could also be partly guilty.
In a brand new research, the MIT CSAIL researchers discovered that whereas immediately’s top-performing fashions nonetheless make real errors on standard AI benchmarks, over 50% of “model errors” are literally brought on by mislabeled and ambiguous questions in these benchmarks.
“If we want to properly quantify model reliability, we need to rethink how we construct benchmarks to minimize label errors,” stated one of many researchers, MIT college member and OpenAI staffer Aleksander Madry, in a submit on X. “This is just a first step.”
Mannequin of the week
You’ve heard of deepfakes earlier than. However what about deepfakes of boring on a regular basis scenes? That’s the thought behind Boring Actuality Hunyuan LoRA (Boreal-HL), a fine-tuned AI video generator that excels at creating movies of … nicely, fairly banal stuff.
Boreal-HL can generate clips of vacationers consuming ice cream, individuals barbecuing meat, individuals 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 at the least 5 minutes to generate a single clip.
Seize bag
Due to latest breakthroughs in AI effectivity, it’s getting cheaper — and simpler — to coach extremely subtle fashions.
In a brand new paper, researchers at Shanghai Jiao Tong College and an AI firm referred to as SII reveal {that a} mannequin skilled on simply 817 “curated training samples” can outperform fashions skilled on 100x extra information. The staff claims that their mannequin was even in a position to reply sure questions it hadn’t seen throughout the coaching course of, exhibiting what they name “out of domain” capabilities.
The research follows on the heels of a Stanford-led venture that discovered it’s attainable to create an “open” mannequin rivaling OpenAI’s o1 “reasoning” mannequin for underneath $50.
