Unsupervised Learning

Unsupervised Learning provides detailed analysis of the AI landscape, focusing on implications for business, evolving AI technology, cost dynamics, open source versus closed source debates, and specific applications in fields such as drug design and content creation. It features insights from industry leaders and future outlooks.

Large Language Models AI for Business Open Source AI AI Costs and Economics AI in Drug Discovery Generative AI in Content Creation AI Model Scaling and Training AI Safety and Ethics

The hottest Substack posts of Unsupervised Learning

And their main takeaways
2 HN points 29 Jun 23
  1. Training costs for AI models have decreased significantly, making it more cost-effective for companies to build their own models.
  2. Inference costs for AI models have also decreased, creating more affordable options for companies utilizing AI features.
  3. The decreasing costs of AI models are leading to increased competition and more attractive business models for startups building on foundation models.
1 implied HN point 09 Jun 23
  1. Value accrual in AI is likely to happen at the application layer, enabling more builders to create products on top of AI technology.
  2. The debate between open source and closed source Language Model Models (LLMs) continues, with closed source models currently dominating.
  3. One of the biggest risks in AI is the lack of understanding of what goes on inside AI models, including interpretability and goal specification.
1 implied HN point 20 Mar 23
  1. Decoupling semantic understanding and facts in large language models is challenging and using external indexes for knowledge retrieval can be powerful.
  2. Pulling work out of large language models and into code can give engineers more control and help with complex workflows.
  3. The need for scale in training large language models poses challenges as few can reproduce the largest models, impacting research and innovation.
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0 implied HN points 16 Feb 23
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