The Undisputed Champion of Open Source Generative AI
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📝 Editorial: The Undisputed Champion of Open Source Generative AIStability AI is synonymous with open-source generative AI. The release of Stable Diffusion was a sort of Sputnik moment in the evolution of open-source generative AI models. For the first time, a company was trusting the benefits of open-source distribution ahead of the ethical concerns associated with generative AI models. Since the release of Stable Diffusion, we have seen a tremendous wave of innovation in the open-source foundation models space, and Stability AI has been at the forefront of it. Little by little, Stability AI has been assembling one of the most impressive suites of open-source generative AI capabilities in the market. In addition to its hallmark text-to-image model, Stability AI DeepFloyd IF, there is another text-to-image model called Stable Vicuna, which can integrate text into images. Stable Vicuna is an open-source RLHF chatbot based on the Vicuna and LLaMA models. StableLM is a very comprehensive suite of small and efficient open-source LLMs. Just this week, Stability AI released StableStudio, an open-source version of its DreamStudio platform. This release should accelerate the work on user interfaces for generative AI workloads. Additionally, Stability AI also released the Animation SDK, a very robust text-to-animation stack for developers. Stability AI’s frantic pace of innovation is certainly raising the bar for open-source generative AI solutions. At least for now, the open-source generative AI movement has an undisputed champion. 🔎 ML ResearchRL for Open Ended LLM ConversationsGoogle Research published a paper detailing dynamic planning, a reinforcement learning(RL) based technique to guide open ended conversations. The core idea is to use RL to extract information from reputable sources and that can be used with the output of LLMs to generate answers in multi-turn conversations —> Read more. In-Context Learning in LLMsGoogle Research published a paper analyzing the different factors that influence in-context learning in LLMs. Specifically, the paper focuses on semantic priors and input-level mappings as two of the core elements that can influence LLMs ability to learn from different input-label pairs before performing a task —> Read more. Self-Aligned LLMIBM Research published a paper introducing Dromedary, a self-aligned LLM trained with minimum user supervision. The model was also open sourced —> Read more. Interpretability in AlpacaStanford researchers published a paper describing interpretability techniques used in Alpaca. Specifically, the paper focuses on the methods used to match actions with the causal dynamics of the environment —> Read more. 📢 Event: apply(risk), the ML Engineering Community Conference for Building Risk & Fraud Detection SystemsWant to connect with the ML engineering community and learn best practices from ML practitioners at Affirm, Remitly, Block, Tide, and more, on how to build risk and fraud detection systems? 🤖 Cool AI Tech ReleasesChatGPT IOS AppOpenAI launched the first version of its mobile app for IOS —> Read more. GuidanceMicrosoft open sourced Guidance, a framework for controlling LLMs is a more effective way than simply prompting —> Read more. ColabGoogle integrated generative AI capabilities into Colab —> Read more. StableStudioStability AI announced StableStudio, an open source version of its DreamStudio application —> Read more. Animation SDKStability AI released Stable Animation SDK, a text-to-animation open source toolkit —> Read more. MTIAMeta announced the Meta Training and Inference Accelerator(MTIA) which improve over traditional GPT for large foundation model workloads —> Read more. 🛠 Real World MLLLMs for Cloud Management Microsoft details the architecture and technique used to leverage LLMs as part of the Azure incident management infrastructure —> Read more. 📡AI Radar
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