Browse Lessons

AI shifts from engineering to complex system science, struggles with understanding emergent behavior

Shift to multimodal models: Visual grounding, embodiment, & more data unlock exciting possibilities

Scaling Multi-Modal Generative AI with Luke Zettlemoyer - 650

Data transparency fosters accountability. Awareness & change in scientific practices drive fairness

Reimagining AI: A tool for empowerment, not inevitability

Turning researchers into fighters and fighters into researchers and building technology for people

Pushing Back on AI Hype with Alex Hanna - 649

Altering the weights is vital in adding concepts. Hypernetwork personalize weights for faster 🖼️ gen

Through fine-tuning and specialization, you resurface the prior knowledge leading to better results.

The core idea behind diffusion models: Clean ➡️ Noise ➡️ Train ➡️ Denoise ➡️ Generate

Personalization for Text-to-Image Generative AI with Nataniel Ruiz - 648

The ML metrics are behind where the tech is. LLMs score lower on metrics but generate better results

"LLMs are narcissistic in that they find their own work better than a human-generated text” - Shreya

Is RAG an essential component of the LLM stack? #llm #aisafety #artificialintelligence

Hallucination is a top concern in LLM safety but broader AI safety issues lie beyond hallucinations

Ensuring LLM Safety for Production Applications with Shreya Rajpal - 647

Language 🗣️ is the key ingredient to achieving human-like AI 🧠

Specific and meaningful labels instead of generic labels leads to a richer language representation

The transition to attention models and the lack of true recurrence yields better model training

What’s Next in LLM Reasoning? with Roland Memisevic - 646

Consistency & robustness in #generativeai models are crucial for software stability and performance

Just how different were the results between GPT-3.5 and GPT-4?

Efficiency: A fundamental principle in neuroscience

Artificial neural networks find solutions similar to the brain's mathematical transformations

How necessary are new tools for monitoring the behavior of models like #GPT4?

Is ChatGPT Getting Worse? with James Zou - 645

Why Deep Networks and Brains Learn Similar Features with Sophia Sanborn - 644

Inverse Reinforcement Learning Without RL with Gokul Swamy - 643

Advancing Biology and Medicine with Computer Science and Machine Learning

Explainable AI for Biology and Medicine with Su-In Lee - 642

Accelerating Deep Learning in Traditional Models

From Sparsity to Density: The Power of Embeddings

Combining Deep Learning and Traditional Models

Transformers On Large-Scale Graphs with Bayan Bruss - 641

Teaching Large Language Models to Stick to the Script

Reinventing Tooling in the age of Generative AI

What is the Right Approach for Enterprise LLM Users? The Jury is Still Out

Enterprise Machine Learning Trends; What is your experience?

The Enterprise LLM Landscape with Atul Deo - 640

Hallucination in Large Language Models (LLMs)

The Future of Bloomberg's GPT-Style Large Language Model

How to Train a Large Language Model

Harnessing the Power of Smaller Models: Unraveling the Secrets of LLaMA-style Training

BloombergGPT - an LLM for Finance with David Rosenberg - 639

AI Ethics; A practioners perspective

Data Ingestion for Surveillance?

The Pitfalls of Using Language Models for Decision Making

The Realism Gap in Data Collection