An emerging LLM application stack could help create $4.4 trillion in annual value, but how can we ensure generative AI is safe and what will the next 10 years bring? Check out our roundup of the top AI and MLOps articles for July 2023!
Emerging Architectures for LLM Applications
The emerging LLM application stack, relying on data embedding and prompt operations, is completely unlike traditional computing resources. Andreessen Horowitz shares what they see developing: https://a16z.com/2023/06/20/emerging-architectures-for-llm-applications
AI Explained: AI Safety in Generative AI
On our last AI Explained webinar, AI icon Peter Norvig explored best practices for AI safety in generative AI. Missed the episode? Check out his key takeaways and listen to the full recording: https://www.fiddler.ai/blog/ai-safety-in-generative-ai
Andrew Ng predicts the next 10 years in AI
What will the next 10 years in AI bring? Andrew Ng offers his predictions: https://venturebeat.com/ai/andrew-ng-predicts-the-next-10-years-in-ai
The economic potential of generative AI: The next productivity frontier
McKinsey & Company's latest research estimates that generative AI could add up to $4.4 trillion in value annually to the global economy - more than the UK's entire GDP! Check out their report for more interesting findings: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-AI-the-next-productivity-frontier
LangChain tutorial: Build an LLM-powered app in 18 lines of code
Interested in using LangChain to build an LLM-powered app? Here is a great tutorial in just 18 lines of code: https://blog.streamlit.io/langchain-tutorial-1-build-an-llm-powered-app-in-18-lines-of-code
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Google DeepMind’s CEO Says Its Next Algorithm Will Eclipse ChatGPT
Google DeepMind is working on a system called Gemini that uses techniques from AlphaGo to create an AI that is more capable than GPT-4: https://www.wired.com/story/google-deepmind-demis-hassabis-chatgpt
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