How risky are open-source LLMs really?

Fatih shares video language model providing real-time feedback and coaching on activities #CVPR2024

Do LLMs and RL offer a path to AGI? #machinelearning #ai #podcast

How risky are open-source LLMs really? #machinelearning #ai #podcast

Video generators are world models #machinelearning #ai #podcast

Are GPUs enough to run AGI? #ai #podcast #machinelearning #reinforcementlearning

Nightshade: a poison pill that subtly distorts image composition, causing model confusion & collapse

The main challenge comes from difficulties in discrete optimization. Scaling needs improved strategy

Prediction of shift in LLMs: Embedding desiderata in training to avoid post hoc interventions.

Success lies in listening, engaging, adapting to evolving needs and building sticky, useful products

Retrieval patterns allows the model to act as a ‘problem-solving brain’, yielding smarter solutions.

Turn problems to checklists and restructure from low to high-level judgments to avoid hallucinations

Evaluating AI's general performance & finding models for unrestricted prompting remains a challenge

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

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

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

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

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

Teaching Large Language Models to Stick to the Script

AI Ethics; A practioners perspective

Causal Reasoning in Machine Learning