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AI in the Enterprise: Lessons from seven frontier companies
It outlines seven lessons for effective AI adoption in enterprises, emphasizing evaluation processes, embedding AI in products, early investment, customization, expert involvement, streamlining development, and setting ambitious automation goals.
cdn.openai.com
Remember reinforcement fine-tuning? We’ve been working away at it since last December, and it’s available today with OpenAI o4-mini! RFT uses chain-of-thought reasoning and task-specific grading to improve model performance—especially useful for complex domains. Take https://t.co/7V8Oxlfa2L
OpenAI Developersx.comToday, every Nomic-Embed-Text embedding becomes multimodal. Introducing Nomic-Embed-Vision:
- a high quality, unified embedding space for image, text, and multimodal tasks
- outperforms both OpenAI CLIP and text-embedding-3-small
- open weights and code to enable indie hacking, research, ... See more
CalCox.com
EVA-CLIP: Improved Training Techniques for CLIP at Scale
Proposes EVA-CLIP, a series of models that significantly improve the efficiency and effectiveness of CLIP training.
proj: https://t.co/LNOE9rKSdq
abs:... See more
LLMs can now self-optimize.
A new method allows an AI to rewrite its own prompts to achieve up to 35x greater efficiency, outperforming both Reinforcement Learning and Fine-Tuning for complex reasoning.
UC Berkeley, Stanford, and Databricks introduce a new method called GEPA (Genetic-Pareto... See more
Improved baselines for vision-language pre-training
Finds that a simple CLIP baseline can be improved up to a 25% relative improvement on downstream zero-shot tasks, by using well-known training techniques that are popular in other subfields.
https://t.co/gfDb2AT2At... See more