LLMs have evolved, but what is ChatGPT actually thinking and how will this affect U.S. AI policy? Check out our roundup of the top AI and MLOps articles for June 2023!
Understanding Large Language Models
Want to deep dive into LLMs? Sebastian Raschka, PhD has compiled a great list of research papers covering different architectures and various techniques to improve transformer efficiency and fine-tune performance: https://magazine.sebastianraschka.com/p/understanding-large-language-models
Unlocking Real-time Predictions with Shopify's Machine Learning Platform
Shopify customers require real-time predictions. Their ML team needed to upgrade their platform Merlin to be robust enough to handle this requirement and every function's use cases, while allowing low-latency and serving models at scale. Here's how they did it: https://shopify.engineering/shopifys-machine-learning-platform-real-time-predictions
What is ChatGPT thinking?
What is ChatGPT's reasoning for its responses? Understanding how LLMs think is central to develop responsible AI applications built on those models. We used a 'time travel' game to uncover some critical clues: https://www.fiddler.ai/blog/what-is-chatgpt-thinking
A Turning Point for U.S. AI Policy: Senate Explores Solutions
The US Senate met with AI leaders to understand how to shape AI policy. Marc Rotenberg, founder of the Center for AI and Digital Policy, describes the hearing's highlights and risks: https://cacm.acm.org/blogs/blog-cacm/273011-a-turning-point-for-us-ai-policy-senate-explores-solutions/fulltext
Learning from deep learning: a case study of feature discovery and validation in pathology
Human learning from deep learning moves traditional ML from distilling existing knowledge to a tool for knowledge discovery. Google Health demonstrates how this can be used to improve cancer diagnosis and prognosis: https://ai.googleblog.com/2023/03/learning-from-deep-learning-case-study.html
Reinforcement Learning from Human Feedback
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