- PlayHD 2.0 Turbo and YouTube Music's generative AI
- AMD's success in cloud services and AI workloads
- Investment in Jipu and the importance of funding
- Open sourcing of Mini GPT-V2 and Habitat 3.0
- Cruz's troubles and ethical concerns
- AI lawsuits and cyber warfare
- AI filters and the release of a final Beatles song
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Quick takeaways
NVIDIA's AI agent Yurika improves robot performance by over 50% through writing its own reward functions.
Lemma, an open-source 8K text embedding model, outperforms open AI in math-related tasks.
IBM's brain-inspired computer chip, Northpol, shows potential to significantly improve energy efficiency and processing speed of AI systems.
Deep dives
Eliciting Human Preferences with Language Models
Researchers propose a method to use language models to elicit human preferences and improve AI understanding of user desires.
NVIDIA Agent Trains Robots Using GP4
NVIDIA develops an AI agent called Yurika that can teach robots complex skills by writing its own reward functions, improving performance by over 50%.
Language Models Explore Math with Lemma
Researchers develop the world's first open-source 8K text embedding model, Lemma, which outperforms open AI in math-related tasks.
Unveiling the General Intelligence Factor in Language Models
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.
Open source models struggle in agent tasks compared to GPT 3.5/4
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 develops brain-inspired computer chip for faster AI processing
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.