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  • ...AI: Apple Integrates ChatGPT, Unbabel's SOTA Translation LLM, and a Mixture of Agents Paper (6.11.24)

...AI: Apple Integrates ChatGPT, Unbabel's SOTA Translation LLM, and a Mixture of Agents Paper (6.11.24)

OpenAI, Unbabel, Apple, Hugging Face

This week we had WWDC, and Apple and OpenAI announced the integration of ChatGPT with Siri on Apple devices. This move didn't surprise many, as OpenAI and Apple's talks have been an open secret for months. We see a great summary by Andrej Karpathy outlining what many are thinking and seeing on this. We also see new state-of-the-art (SOTA) models in translation, an open-source robotics offering, and a research paper on MoA (Mixture of Agents) and how the framework is pushing the frontier of AI capabilities yet again.

— Sasha Krecinic

First impacted: Apple device users, AI technology enthusiasts

Apple announced at WWDC 2024 that ChatGPT will be integrated into Siri and available for free in iOS 18 and macOS Sequoia later this year. This partnership with OpenAI aims to enhance Apple's AI features, making advanced AI accessible while maintaining a commitment to safety and innovation. Sam Altman also says he is excited about partnering with Apple to integrate ChatGPT into their devices later this year, which he believes users will greatly appreciate. The highlight of the show and the conference was the new Siri demo. [via @sama] Share this story by email

First impacted: Apple device users, developers

Andrej Karpathy praised Apple's Intelligence announcement, highlighting the integration of AI across the entire OS. He outlined key themes: enabling multimodal I/O, seamless inter-operation of OS and apps, and a frictionless user experience. He also emphasized the potential for proactive AI features, on-device intelligence, and modular support for various function calling while maintaining privacy with on-device computing. Zooming out, we agree that this is possibly the first step in a fully autonomous and highly personalized AI strategy and capable agent model, where computer vision takes on-screen data as visual context and potentially moves in the direction of Microsoft's Copilot/Recall feature by 'seeing' or recording on-screen activity. [via @karpathy] Share this story by email

First impacted: multilingual content managers, software developers

Unbabel says it has launched TowerLLM, a new translation LLM that outperforms competitors like GPT-4, GPT-3.5, Google, and DeepL in accuracy and cost-efficiency. The company highlights that TowerLLM, built on billions of words of high-quality translation data, offers features such as source correction, and named entity recognition, and supports 18 language pairs across various domains. [Introducing TowerLLM, Multilingual by design Unbabel’s Generative AI model is the best performing machine translation on the market, enabling our customers to scale globally with lower costs and higher accuracy.] Share this story by email

First impacted: AI developers, tech-savvy consumers

Hugging Face has launched a second batch of local Generative AI apps, now available on compatible model pages. The company welcomes new additions to its community, a sentiment echoed by retweets from its CEO, Clement Delangue. [via @julien_c] Share this story by email

First impacted: robotics engineers, AI researchers

LeRobot, developed on PyTorch, has been launched on the Hugging Face community page to enhance accessibility in robotics using advanced AI tools and models. According to their press release, LeRobot provides pre-trained models, datasets, and simulation environments to facilitate learning complex tasks in robotics without the need for physical robot assembly. [via @RemiCadene] Share this story by email

First impacted: AI researchers, software developers

In a recent research paper, scientists introduced the Mixture-of-Agents methodology, which combines multiple LLMs to enhance language model performance, achieving a 65.1% score on AlpacaEval 2.0 and surpassing GPT-4 Omni's 57.5%. This method utilizes a layered architecture where each layer's LLM agents refine responses based on the previous layer's outputs, demonstrating improved performance using only open-source LLMs. [Mixture-of-Agents Enhances Large Language Model Capabilities] Share this story by email