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

AI Builder Survey

2 implied HN points • 21 Aug 24
🕹 Technology AI Machine Learning Data science Development Software
  1. OpenAI is very popular among AI builders, but many are experimenting with other models like Claude. A lot of developers are switching models to find better options.
  2. Expect many builders to switch or add new model providers soon. They want better performance, lower costs, and increased security.
  3. Most builders are using techniques like fine-tuning and Retrieval-Augmented Generation to improve their AI models. The focus is shifting more towards fine-tuning as they learn.

Eight Key Lessons for Rocket-Shipping AI Products

1 implied HN point • 10 Apr 24
🕹 Technology AI Products User Experience Product Development Market Strategy
  1. Move quickly and launch your product fast. It’s better to get user feedback sooner than to wait for the perfect version.
  2. Involve your users in the creation process. Let them guide the product's direction so that the final result meets their needs.
  3. Testing your product internally before releasing it to users is key. It helps to ensure quality and makes sure you’re delivering something valuable.

Why Databricks Bought Mosaic and The Rapidly Decreasing Costs of AI

2 HN points • 29 Jun 23
🕹 Technology AI Machine Learning Costs Cloud Infrastructure Business Models
  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.
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OpenAI's Product Leader Discusses Their Strategy & the Future of AI

1 implied HN point • 09 Jun 23
🕹 Technology AI Machine Learning Open Source Safety Research
  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.

Scaling and Orchestrating Large Language Models: In Conversation With Databricks CTO Matei Zaharia

1 implied HN point • 20 Mar 23
🕹 Technology AI Startups Data ML Ops Cloud Computing
  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.

Coming soon...

0 implied HN points • 16 Feb 23
🕹 Technology AI Business World
  1. Subscribe now for updates on AI and business.
  2. Follow Patrick Chase on Substack for insights.
  3. Learn about the rapidly developing AI landscape.