Is CUDA still a moat for NVIDIA? #aihardware #aichips #podcast
What if AI hardware innovation isn’t about the future but focusing on what won’t change?
The convergence of two market forces is driving more efficient and intelligent frontier models
Hallucinations are common in LLMs, but omission can be just as crucial in generating harmful content
What are the two key risk areas posed by generative AI and LLMs? #machinelearning #ai #llm #podcast
Sustainable practices are now table stakes. Andres shares opportunities & how Microsoft leads change
What is ESG? Andres breaks it down & how MS Sustainability Manager help achieve sustainability goals
Food waste impacts the environment and economy. Learn how ML models help cut waste from 15-40% to 1%
Fatih shares video language model providing real-time feedback and coaching on activities #CVPR2024
The worlds largest computer chip is being used in AI #machinelearningbasics #ai #podcast
Terra Praxis is using AI to help make new nuclear reactors #machinelearningbasics #ai #podcast
Smaller models will reduce AI's load on the grid. #machinelearningbasics #ai #podcast
Can AI help us manage the grid? #machinelearningbasics #ai #podcast
Can AI control plasma instability in fusion reactors? #machinelearning #ai #podcast
Do LLMs and RL offer a path to AGI? #machinelearning #ai #podcast
Why do LLMs sometimes fail to solve simple math problems? #machinelearning #ai #podcast
What are LLMs really doing internally? #machinelearning #ai #podcast
The Model Editing Problem #machinelearning #ai #podcast
Will it ever be possible to stop jailbreaks? #machinelearning #ai #podcast
Are LLMs too vulnerable to be used as agents? #machinelearning #ai #podcast
Video is better than language at controlling AI robots #machinelearning #ai #podcast
Open-source AI makes deepfakes more accessible. #machinelearning #ai #podcast
Is OpenAI changing its model behind the scenes? #machinelearning #ai #podcast
Akshita finds an issue in PyTorch #machinelearning #ai #podcast
Emergent behaviors in LLMs depend on your evaluation metric #machinelearning #ai #podcast
How risky are open-source LLMs really? #machinelearning #ai #podcast
Joint Embedding Architectures simplify AI reasoning #machinelearning #ai #podcast
V-JEPA might be the future of AI reasoning #machinelearning #ai #podcast
Video generators are world models #machinelearning #ai #podcast
What's in your Big Data? #artificialintelligence #ai #opensource
Teaching AI to think step by step #artificialintelligence #ai #reasoning
Fine Tuning is key to building practical AI tools
Are GPUs enough to run AGI? #ai #podcast #machinelearning #reinforcementlearning
Reinforcement learning is replacing control algorithms. #ai #reinforcementlearning
Generative AI is making RL agents practical
The challenge of keeping vector databases up to date
Vector databases are the evolution of search engines
Vector databases allow startups to compete with SOTA
RAG makes LLMs better, even on data they have already been trained on. #ai #ML #Pinecone #RAG
Rapid AI advancements amplify social gaps like underrepresentation and bias, magnifying disparities
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
Writing the expressive transformer program solves tasks despite discrete optimization limitations
Mechanistic interpretability's goal is to reverse engineer the model to human-readable algorithms
Better tools & open-source models enable the academic community to understand model's complex system
Build robust system for an open world that can manage novelty & classify LLMs to avoid hallucination
Vision community faces evaluation challenges and should lean on cost-effective automatic evaluation
LangChain vs LlamaIndex. Beyond data, constructing context & post-filtering enhance accuracy from VB
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Machine learning and artificial intelligence are dramatically changing the way businesses operate and people live. By sharing and amplifying the voices of a broad and diverse spectrum of machine learning and AI researchers, practitioners, and innovators, we hope to help make ML and AI more accessible and enhance the lives of our audience and their communities.
Through the podcast, we bring the top minds and ideas from the world of ML and AI to a broad and influential community of ML/AI researchers, data scientists, engineers, and tech-savvy business and IT leaders.
The TWIML AI Podcast is hosted by Sam Charrington, a sought-after industry analyst, speaker, commentator, and thought leader. Sam’s research is focused on the business and consumer application of machine learning and AI, bringing AI-powered products to market, and AI-enabled and -enabling technology platforms
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