AI: Innovating or Stagnant?
September 23rd | Superintelligence Newsletter
Hey there!
From the outside, it might seem like AI innovation has hit a plateau, but beneath the surface, the reality is far more dynamic. Groundbreaking research, novel applications, and transformative technologies are quietly reshaping the AI landscape at a pace that's anything but stagnant.
Every day, new advancements push the boundaries of what's possible, fueling the next wave of innovation that's set to redefine industries and everyday life.
This week, we dive into the latest breakthroughs that are driving the future of AI, proving that the field is not just alive—it’s accelerating.
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Amazon Project Amelia: Amazon's AI assistant, Project Amelia, helps sellers with insights and guidance to optimize their operations. Read more.
Alibaba Cloud Open-Source AI Models: Alibaba Cloud launched over 100 AI models to democratize access to advanced technologies and boost innovation. Read more.
Oversight on AI Risks: Former OpenAI staff urged the Senate for oversight on AGI, citing significant security concerns. Read more.
Microsoft Enhances Copilot: Microsoft upgraded Copilot with new AI features for productivity in Excel, PowerPoint, and more. Read more.
LinkedIn AI Data Training: LinkedIn uses user data for AI training, prompting privacy concerns; opting out is complex. Read more.
Learning to Reason with LLMs: The Future of Problem Solving
A new family of LLMs trained with reinforcement learning to reason before it responds to complex tasks; it produces a long internal chain of thought and exceeds in science, code, and math-related tasks; ranked in the 49th percentile in the 2024 International Olympiad in Informatics and exceeds human PhD-level accuracy on science-related benchmarks.
Read MoreChai-1: Multi-Modal Model for Molecular Predictions
A new multi-modal foundation model for molecular structure prediction that can predict proteins, small molecules, DNA, RNA, and more; it achieves state-of-the-art results on a variety of tasks in drug discovery; achieves a 77% success rate on the PoseBusters benchmark (vs. 76% by AlphaFold 3), as well as an Cα LDDT of 0.849 on the CASP15 protein monomer structure prediction set (vs. 0.801 by ESM3-98B).
Read MoreCan LLMs Generate Novel Research Ideas? A Surprising Discovery
Finds that LLM-generated research ideas are judged as more novel (p <0.05) than human expert ideas; however, they were rated slightly weaker in terms of flexibility; they also report that LLM agents lack diversity in the idea generation process and are not reliable evaluators.
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Replit Agent is an AI-powered coding assistant that can autonomously create and deploy applications based on natural language prompts. It's designed to make software development more accessible to users of all skill levels.
Ollama is an open-source framework that allows users to run large language models locally on their own machines. It supports various models like Llama 3.1, Phi 3, Mistral, and Gemma 2, offering customization options.
Mistral's Pixtral 12B is a multimodal AI model capable of processing both text and images. Built on Mistral's Nemo 12B, it can answer questions about images, perform captioning, and count objects in photos.
Google’s AI Course for Beginners in 10 Minutes
Quick-start your AI learning journey with Google’s concise course designed for absolute beginners.
Practical Deep Learning: Free Course by Fast.ai
Learn to apply deep learning to real-world problems with this practical, coding-focused course.
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Stay curious, stay informed, and keep pushing the boundaries of what's possible!