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PODCAST

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

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All Episodes

63:39

Inside Nano Banana 🍌 and the Future of...

The TWIML AI Podcast (formerly This Week in Machine... ·

2025/09/23

Today, we’re joined by Oliver Wang, principal scientist at Google DeepMind and tech lead for Gemini 2.5 Flash Image—better known by its code name, “Nano Banana.” We dive into the development and capabilities of this newly released frontier vision-language model, beginning with the broader shift from specialized image generators to general-purpose multimodal agents that can use both visual and textual data for a variety of tasks. Oliver explains how Nano Banana can generate and iteratively edit

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58:26

Is It Time to Rethink LLM Pre-Training? with Aditi...

The TWIML AI Podcast (formerly This Week in Machine... ·

2025/09/16

Today, we're joined by Aditi Raghunathan, assistant professor at Carnegie Mellon University, to discuss the limitations of LLMs and how we can build more adaptable and creative models. We dig into her ICML 2025 Outstanding Paper Award winner, “Roll the dice & look before you leap: Going beyond the creative limits of next-token prediction,” which examines why LLMs struggle with generating truly novel ideas. We dig into the "Roll the dice" approach, which encourages structured exploration by injecting

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65:11

Building an Immune System for AI Generated Software...

The TWIML AI Podcast (formerly This Week in Machine... ·

2025/09/09

Today, we're joined by Animesh Koratana, founder and CEO of PlayerZero to discuss his team’s approach to making agentic and AI-assisted coding tools production-ready at scale. Animesh explains how rapid advances in AI-assisted coding have created an “asymmetry” where the speed of code output outpaces the maturity of processes for maintenance and support. We explore PlayerZero’s debugging and code verification platform, which uses code simulations to build a "memory bank" of past bugs and leverages

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71:48

Autoformalization and Verifiable Superintelligence...

The TWIML AI Podcast (formerly This Week in Machine... ·

2025/09/02

In this episode, Christian Szegedy, Chief Scientist at Morph Labs, joins us to discuss how the application of formal mathematics and reasoning enables the creation of more robust and safer AI systems. A pioneer behind concepts like the Inception architecture and adversarial examples, Christian now focuses on autoformalization—the AI-driven process of translating mathematical concepts from their human-readable form into rigorously formal, machine-verifiable logic. We explore the critical distinction

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70:20

Multimodal AI Models on Apple Silicon with MLX with...

The TWIML AI Podcast (formerly This Week in Machine... ·

2025/08/26

Today, we're joined by Prince Canuma, an ML engineer and open-source developer focused on optimizing AI inference on Apple Silicon devices. Prince shares his journey to becoming one of the most prolific contributors to Apple’s MLX ecosystem, having published over 1,000 models and libraries that make open, multimodal AI accessible and performant on Apple devices. We explore his workflow for adapting new models in MLX, the trade-offs between the GPU and Neural Engine, and how optimization methods like

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61:01

Genie 3: A New Frontier for World Models with Jack...

The TWIML AI Podcast (formerly This Week in Machine... ·

2025/08/19

Today, we're joined by Jack Parker-Holder and Shlomi Fruchter, researchers at Google DeepMind, to discuss the recent release of Genie 3, a model capable of generating “playable” virtual worlds. We dig into the evolution of the Genie project and review the current model’s scaled-up capabilities, including creating real-time, interactive, and high-resolution environments. Jack and Shlomi share their perspectives on what defines a world model, the model's architecture, and key technical challenges and

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61:11

Closing the Loop Between AI Training and Inference...

The TWIML AI Podcast (formerly This Week in Machine... ·

2025/08/12

In this episode, we're joined by Lin Qiao, CEO and co-founder of Fireworks AI. Drawing on key lessons from her time building PyTorch, Lin shares her perspective on the modern generative AI development lifecycle. She explains why aligning training and inference systems is essential for creating a seamless, fast-moving production pipeline, preventing the friction that often stalls deployment. We explore the strategic shift from treating models as commodities to viewing them as core product assets. Lin

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46:01

Context Engineering for Productive AI Agents with...

The TWIML AI Podcast (formerly This Week in Machine... ·

2025/07/29

In this episode, Filip Kozera, founder and CEO of Wordware, explains his approach to building agentic workflows where natural language serves as the new programming interface. Filip breaks down the architecture of these "background agents," explaining how they use a reflection loop and tool-calling to execute complex tasks. He discusses the current limitations of agent protocols like MCPs and how developers can extend them to handle the required context and authority. The conversation challenges the

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73:02

Infrastructure Scaling and Compound AI Systems with...

The TWIML AI Podcast (formerly This Week in Machine... ·

2025/07/22

In this episode, Jared Quincy Davis, founder and CEO at Foundry, introduces the concept of "compound AI systems," which allows users to create powerful, efficient applications by composing multiple, often diverse, AI models and services. We discuss how these "networks of networks" can push the Pareto frontier, delivering results that are simultaneously faster, more accurate, and even cheaper than single-model approaches. Using examples like "laconic decoding," Jared explains the practical techniques

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73:02

Building Voice AI Agents That Don’t Suck with Kwindla...

The TWIML AI Podcast (formerly This Week in Machine... ·

2025/07/15

In this episode, Kwindla Kramer, co-founder and CEO of Daily and creator of the open source Pipecat framework, joins us to discuss the architecture and challenges of building real-time, production-ready conversational voice AI. Kwin breaks down the full stack for voice agents—from the models and APIs to the critical orchestration layer that manages the complexities of multi-turn conversations. We explore why many production systems favor a modular, multi-model approach over the end-to-end models

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60:29

Distilling Transformers and Diffusion Models for...

The TWIML AI Podcast (formerly This Week in Machine... ·

2025/07/09

Today, we're joined by Fatih Porikli, senior director of technology at Qualcomm AI Research for an in-depth look at several of Qualcomm's accepted papers and demos featured at this year’s CVPR conference. We start with “DiMA: Distilling Multi-modal Large Language Models for Autonomous Driving,” an end-to-end autonomous driving system that incorporates distilling large language models for structured scene understanding and safe planning motion in critical "long-tail" scenarios. We explore how DiMA

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56:13

Building the Internet of Agents with Vijoy Pandey - #737

The TWIML AI Podcast (formerly This Week in Machine... ·

2025/06/24

Today, we're joined by Vijoy Pandey, SVP and general manager at Outshift by Cisco to discuss a foundational challenge for the enterprise: how do we make specialized agents from different vendors collaborate effectively? As companies like Salesforce, Workday, and Microsoft all develop their own agentic systems, integrating them creates a complex, probabilistic, and noisy environment, a stark contrast to the deterministic APIs of the past. Vijoy introduces Cisco's vision for an "Internet of Agents," a

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59:31

LLMs for Equities Feature Forecasting at Two Sigma...

The TWIML AI Podcast (formerly This Week in Machine... ·

2025/06/17

Today, we're joined by Ben Wellington, deputy head of feature forecasting at Two Sigma. We dig into the team’s end-to-end approach to leveraging AI in equities feature forecasting, covering how they identify and create features, collect and quantify historical data, and build predictive models to forecast market behavior and asset prices for trading and investment. We explore the firm's platform-centric approach to managing an extensive portfolio of features and models, the impact of multimodal LLMs

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56:45

Zero-Shot Auto-Labeling: The End of Annotation for...

The TWIML AI Podcast (formerly This Week in Machine... ·

2025/06/10

Today, we're joined by Jason Corso, co-founder of Voxel51 and professor at the University of Michigan, to explore automated labeling in computer vision. Jason introduces FiftyOne, an open-source platform for visualizing datasets, analyzing models, and improving data quality. We focus on Voxel51’s recent research report, “Zero-shot auto-labeling rivals human performance,” which demonstrates how zero-shot auto-labeling with foundation models can yield to significant cost and time savings compared to

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85:21

Grokking, Generalization Collapse, and the Dynamics...

The TWIML AI Podcast (formerly This Week in Machine... ·

2025/06/05

Today, we're joined by Charles Martin, founder of Calculation Consulting, to discuss Weight Watcher, an open-source tool for analyzing and improving Deep Neural Networks (DNNs) based on principles from theoretical physics. We explore the foundations of the Heavy-Tailed Self-Regularization (HTSR) theory that underpins it, which combines random matrix theory and renormalization group ideas to uncover deep insights about model training dynamics. Charles walks us through WeightWatcher’s ability to

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26:21

Google I/O 2025 Special Edition - #733

The TWIML AI Podcast (formerly This Week in Machine... ·

2025/05/28

Today, I’m excited to share a special crossover edition of the podcast recorded live from Google I/O 2025! In this episode, I join Shawn Wang aka Swyx from the Latent Space Podcast, to interview Logan Kilpatrick and Shrestha Basu Mallick, PMs at Google DeepMind working on AI Studio and the Gemini API, along with Kwindla Kramer, CEO of Daily and creator of the Pipecat open source project. We cover all the highlights from the event, including enhancements to the Gemini models like thinking budgets and

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57:09

RAG Risks: Why Retrieval-Augmented LLMs are Not Safer...

The TWIML AI Podcast (formerly This Week in Machine... ·

2025/05/21

Today, we're joined by Sebastian Gehrmann, head of responsible AI in the Office of the CTO at Bloomberg, to discuss AI safety in retrieval-augmented generation (RAG) systems and generative AI in high-stakes domains like financial services. We explore how RAG, contrary to some expectations, can inadvertently degrade model safety. We cover examples of unsafe outputs that can emerge from these systems, different approaches to evaluating these safety risks, and the potential reasons behind this

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61:25

From Prompts to Policies: How RL Builds Better AI...

The TWIML AI Podcast (formerly This Week in Machine... ·

2025/05/13

Today, we're joined by Mahesh Sathiamoorthy, co-founder and CEO of Bespoke Labs, to discuss how reinforcement learning (RL) is reshaping the way we build custom agents on top of foundation models. Mahesh highlights the crucial role of data curation, evaluation, and error analysis in model performance, and explains why RL offers a more robust alternative to prompting, and how it can improve multi-step tool use capabilities. We also explore the limitations of supervised fine-tuning (SFT) for

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67:27

How OpenAI Builds AI Agents That Think and Act with...

The TWIML AI Podcast (formerly This Week in Machine... ·

2025/05/06

Today, we're joined by Josh Tobin, member of technical staff at OpenAI, to discuss the company’s approach to building AI agents. We cover OpenAI's three agentic offerings—Deep Research for comprehensive web research, Operator for website navigation, and Codex CLI for local code execution. We explore OpenAI’s shift from simple LLM workflows to reasoning models specifically trained for multi-step tasks through reinforcement learning, and how that enables agents to more easily recover from failures

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56:18

CTIBench: Evaluating LLMs in Cyber Threat...

The TWIML AI Podcast (formerly This Week in Machine... ·

2025/04/29

Today, we're joined by Nidhi Rastogi, assistant professor at Rochester Institute of Technology to discuss Cyber Threat Intelligence (CTI), focusing on her recent project CTIBench—a benchmark for evaluating LLMs on real-world CTI tasks. Nidhi explains the evolution of AI in cybersecurity, from rule-based systems to LLMs that accelerate analysis by providing critical context for threat detection and defense. We dig into the advantages and challenges of using LLMs in CTI, how techniques like

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75 results

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