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Researchers propose a method to use language models to elicit human preferences and improve AI understanding of user desires.
NVIDIA develops an AI agent called Yurika that can teach robots complex skills by writing its own reward functions, improving performance by over 50%.
Researchers develop the world's first open-source 8K text embedding model, Lemma, which outperforms open AI in math-related tasks.
A study investigates the existence of a general intelligence factor in language models like GPT-4 and finds a single factor that captures 85% of the intelligence variance.
Research shows that open source models generally perform poorly in agent tasks compared to more advanced models like GPT 3.5 or GPT4. However, a new approach using fine-tuning and a specialized agent instruction dataset has shown promising results in creating open source language models that excel in agent-based tasks.
IBM has developed a brain-inspired computer chip called Northpol that integrates memory and processing to work faster and consume less power. This new chip architecture aims to eliminate the bottleneck caused by the separation of compute and memory storage in traditional chips. Although still in the research and development phase, this chip has the potential to significantly improve the energy efficiency and processing speed of AI systems.
Our 142nd episode with a summary and discussion of last week's big AI new.
Apologies for this one coming out after a pause, episodes will resume being released regularly as of this week.
Read out our text newsletter and comment on the podcast at https://lastweekin.ai/
Email us your questions and feedback at contact@lastweekin.ai
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