Artificial Intelligence #227

Hey, in this issue: AI is making economists rethink the story of automation; scientists use generative AI to answer complex questions in physics; training and finetuning embedding models with Sentence Transformers v3; let's talk about LLM evaluation; PaliGemma: a lightweight vision-language model; and more.

The sponsors of this issue are Snorkel and Deep Atlas.

• [HBR] AI is making economists rethink the story of automation
• [SciAm] An alternative to conventional neural networks could help reveal what AI is doing behind the scenes
• [MIT] Scientists use generative AI to answer complex questions in physics
• [Sponsored] Live Demo: Accelerate Data Labeling with Snorkel Flow

Join Snorkel Applied ML Engineer Nazanin Makkinejad on June 6 for a live demo of information extraction and classification in Snorkel Flow. Learn to capture SME knowledge with programmatic labeling to accelerate AI development. And get your questions answered during a live Q&A session.

• [Tutorial] Training and finetuning embedding models with Sentence Transformers v3
• Let's talk about LLM evaluation
• [Google] PaliGemma: a lightweight vision-language model
• [Sponsored] Future-proof your career with AI/ML skills by joining Deep Atlas, an immersive course designed for software engineers.
• How to organize continuous delivery of ML/AI systems: a 10-stage maturity model
• [Tutorial] Large scale transformer model training with tensor parallel

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