The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Sam Charrington
Machine learning and artificial intelligence are dramatically changing the way businesses operate and people live. The TWIML AI Podcast brings 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. Hosted by Sam Charrington, a sought after industry analyst, speaker, commentator and thought leader. Technologies covered include machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, computer science, data science and more.
Episodes
Mentioned books
21 snips
Aug 7, 2023 • 39min
Transformers On Large-Scale Graphs with Bayan Bruss - #641
Bayan Bruss, Vice President of Applied ML Research at Capital One, dives into groundbreaking research on applying machine learning in finance. He discusses two key papers presented at ICML, focusing on interpretability in image representations and the innovative global graph transformer model. Listeners will learn about tackling computational challenges, the balance between model sparsity and performance, and the significance of embedding dimensions. With insights into advancing deep learning techniques, this conversation opens new avenues for efficiency in machine learning.
39 snips
Jul 31, 2023 • 37min
The Enterprise LLM Landscape with Atul Deo - #640
Atul Deo, General Manager of Amazon Bedrock, brings a wealth of experience in software development and product engineering. He dives into the intricacies of training large language models in enterprises, discussing the challenges and advantages of pre-trained models. The conversation highlights retrieval augmented generation (RAG) for improved query responses, as well as the complexities of implementing LLMs at scale. Atul also unveils insights into Bedrock, a managed service designed to streamline generative AI app development for businesses.
13 snips
Jul 24, 2023 • 37min
BloombergGPT - an LLM for Finance with David Rosenberg - #639
David Rosenberg, head of the machine learning strategy team at Bloomberg, discusses the fascinating development of BloombergGPT, a tailored large language model for finance. He dives into its unique architecture, validation methods, and performance benchmarks, revealing how it successfully integrates finance-specific data. David also addresses the challenges of processing financial information and the importance of ethical considerations in AI deployment, especially regarding bias and the necessity for human oversight.
31 snips
Jul 17, 2023 • 48min
Are LLMs Good at Causal Reasoning? with Robert Osazuwa Ness - #638
In this discussion, Robert Osazuwa Ness, a senior researcher at Microsoft Research, delves into the intriguing world of causal reasoning in large language models like GPT-3.5 and GPT-4. He examines their strengths and limitations, emphasizing the need for proper benchmarks and the importance of domain knowledge in causal analysis. Robert also highlights innovative methods for improving model performance through tailored reinforcement learning techniques and discusses the role of prompt engineering in enhancing causal inference tasks.
8 snips
Jul 10, 2023 • 38min
Privacy vs Fairness in Computer Vision with Alice Xiang - #637
Alice Xiang, a Lead Research Scientist at Sony AI and Global Head of AI Ethics at Sony Group Corporation, shares her expertise on the critical issues of privacy and fairness in computer vision. She discusses the impact of data privacy laws and the dangers of unauthorized data use, emphasizing the importance of ethical practices in AI. Alice highlights the history of unethical data collection and the challenges posed by generative technologies. Solutions such as community engagement and interdisciplinary collaboration are also explored, alongside the need for robust AI regulation.
59 snips
Jul 3, 2023 • 48min
Unifying Vision and Language Models with Mohit Bansal - #636
In this engaging discussion, Mohit Bansal, a Parker Professor and Director of the MURGe-Lab at UNC, dives into the unification of vision and language models. He highlights the benefits of shared knowledge in AI, introducing innovative models like UDOP and VL-T5 that achieve top results with fewer parameters. The conversation also tackles the challenges of evaluating generative AI, addressing biases and the importance of data efficiency. Mohit shares insights on balancing advancements in multimodal models with responsible usage and the future of explainability in AI.
Jun 26, 2023 • 53min
Data Augmentation and Optimized Architectures for Computer Vision with Fatih Porikli - #635
Fatih Porikli, Senior Director of Technology at Qualcomm AI Research, shares insights from over 30 years in computer vision. He explores cutting-edge topics such as data augmentation techniques, optimized architectures, and advances in optical flow for video analysis. The conversation delves into the use of language models for fine-grained labeling, enhancing 3D object detection, and the role of generative AI in model efficiency. Fatih also discusses training neural networks and innovative approaches to integrating various data sources for improved accuracy.
38 snips
Jun 19, 2023 • 57min
Mojo: A Supercharged Python for AI with Chris Lattner - #634
In a captivating discussion, Chris Lattner, co-founder and CEO of Modular AI and creator of the Swift programming language, dives into Mojo, a groundbreaking programming language designed for AI developers. He explains how Mojo bridges the gap between Python's ease of use and C++'s performance, tackling the limitations posed by Python, particularly the global interpreter lock. Lattner emphasizes Mojo's compatibility with existing Python libraries, its potential to enhance AI workflows, and the need for a unified approach in AI model deployment.
10 snips
Jun 12, 2023 • 40min
Stable Diffusion and LLMs at the Edge with Jilei Hou - #633
Jilei Hou, VP of Engineering at Qualcomm Technologies, specializes in information theory and signal processing. He discusses the rise of generative AI and the advancement of deploying these models on edge devices. Challenges like model size and inference latency are highlighted, alongside solutions like quantization for optimizing performance. The conversation also dives into local optimization techniques that drastically reduce computation times for diffusion models. Jilei emphasizes the need for multimodal models, reshaping AI interactions and future innovations.
22 snips
Jun 5, 2023 • 47min
Modeling Human Behavior with Generative Agents with Joon Sung Park - #632
Joon Sung Park, a PhD student at Stanford University, is passionate about creating AI systems that address human challenges. He discusses his groundbreaking work on generative agents that mimic believable human behavior, emphasizing the role of context in AI interactions. The conversation delves into the complexities of long-term memory in agents and the significance of knowledge graphs for information retrieval. Joon also challenges traditional views on AI's worldview, exploring how emergent behaviors can reshape human-computer interaction.
