OpenAI’s Sneak Peak of GPT-o1

In “Learning to Reason with LLMs,” OpenAI’s Noam Brown presents the o1 modelwhich boosts reasoning in large language models through reinforcement learning that generates a hidden chain of thought. The model consistently outperforms prior state-of-the-art models in reasoning benchmarks, including mathematics and programming contests, with improved performance linked to increased compute resources. Brown discusses the implications of further scaling this approach.

Meta is under fire for labeling its Llama AI models as “open-source,” with the Open Source Initiative (OSI) accusing the corporate of obscuring the term’s true meaning. OSI chief Stefano Maffulli highlighted that while developers can access model weights, crucial components remain proprietary, diverging from open-source principles of full access to software. Critics warn that such terminology dilution may stifle real innovation. Other tech giants like Google and Microsoft have adapted their language in response to those concerns, leaving Meta’s approach under scrutiny.

In a separate development, OpenAI announced a pledge to avoid offensive patent usage, claiming a commitment to “broad access” and “collaboration.” The corporate stated it will utilize patents defensively unless threatened. Nevertheless, experts have called this pledge a little bit greater than ‘virtue signaling,’ suggesting it serves more as a public relations strategy than a real effort to boost competition within the AI sector. Read more.

Open Sora Plan (not affiliated with OpenAI’s Sora) has launched version 1.3.0 of its video generation model, following the August release of v1.2.0, which adopted a 3D full attention architecture to enhance spatial-temporal feature capture. Nevertheless, the substantial computational demands and unclear training strategies hindered progress.

Version 1.3.0 introduces five significant features: a more powerful and cost-efficient Wavelet VAE (WFVAE) that decomposes videos into sub-bands for improved learning; a Prompt Refiner, a big language model that enhances short text inputs; a high-quality data cleansing strategy that retains only 27% of the panda70m dataset; DiT with recent sparse attention for efficient learning; and dynamic resolution and duration capabilities to optimize videos of various lengths.

Open Sora Plan can be open-sourced, allowing the community access to all code, data, and models to advance video generation development. Read more.

Nvidia has quietly launched the Llama-3.1-Nemotron-70B-Instruct AI model, outperforming industry giants like OpenAI and Anthropic. Available on Hugging Face, this model has garnered attention for its impressive benchmark scores, including 85.0 on the Arena Hard test and 57.6 on AlpacaEval 2 LC.

By enhancing Meta’s open-source Llama 3.1 model with advanced training techniques, Nvidia goals to supply businesses with a potent and cost-effective alternative for language processing tasks. The model stands out for its strong alignment capabilities, delivering precise, contextually relevant responses that enhance customer satisfaction.

Nvidia offers free hosted inference through its platform and provides an OpenAI-compatible API, broadening access to advanced AI solutions. Nevertheless, enterprises must exercise caution, because the model shouldn’t be optimized for specialised fields demanding high accuracy. Read more.

Sieve, an AI startup, has launched an API that robotically corrects eye contact in videos, enhancing viewer engagement for 10 cents per minute. The technology uses an AI model to investigate the attention region and head position in three dimensions, adjusting gaze direction in real time to create the looks of direct eye contact.

It processes the attention region through a neural network to estimate the viewing angle and modify eye positioning accordingly. The correction adapts based on head orientation and accounts for natural behaviors like blinking. Read more.

Microsoft and OpenAI’s partnership is under strain as each corporations navigate conflicting priorities. OpenAI, facing a projected $5 billion loss, has pushed for more computing power and reduced costs, while Microsoft has grown cautious about its dependence on the AI firm.

OpenAI negotiated a take care of Oracle for extra resources, signaling a shift from its exclusive reliance on Microsoft. Meanwhile, Microsoft has diversified by investing in rival AI talent, including hiring key staff from Inflection. Disagreements over development timelines and resource allocation have fueled tensions.

Despite the recent renegotiations, the partnership faces ongoing friction as each corporations weigh their options for the long run of AI. Read more.