The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence‪)‬ Sam Charrington

Today we’re joined by Joon Sung Park, a PhD Student at Stanford University. Joon shares his passion for creating AI systems that can solve human problems and his work on the recent paper Generative Agents: Interactive Simulacra of Human Behavior, which showcases generative agents that exhibit believable human behavior. We discuss using empirical methods to study these systems and the conflicting papers on whether AI models have a worldview and common sense. Joon talks about the importance of context and environment in creating believable agent behavior and shares his team's work on scaling emerging community behaviors. He also dives into the importance of a long-term memory module in agents and the use of knowledge graphs in retrieving associative information. The goal, Joon explains, is to create something that people can enjoy and empower people, solving existing problems and challenges in the traditional HCI and AI field.

Today we’re joined by Hugo Larochelle, a research scientist at Google Deepmind. In our conversation with Hugo, we discuss his work on transfer learning, understanding the capabilities of deep learning models, and creating the Transactions on Machine Learning Research journal. We explore the use of large language models in NLP, prompting, and zero-shot learning. Hugo also shares insights from his research on neural knowledge mobilization for code completion and discusses the adaptive prompts used in their system. 

The complete show notes for this episode can be found at twimlai.com/go/631.

Today we’re joined by Dan Fu, a PhD student at Stanford University. In our conversation with Dan, we discuss the limitations of state space models in language modeling and the search for alternative building blocks that can help increase context length without being computationally infeasible. Dan walks us through the H3 architecture and Flash Attention technique, which can reduce the memory footprint of a model and make it feasible to fine-tune. We also explore his work on improving language models using synthetic languages, the issue of long sequence length affecting both training and inference in models, and the hope for finding something sub-quadratic that can perform language processing more effectively than the brute force approach of attention.
The complete show notes for this episode can be found at https://twimlai.com/go/630

Today we continue our coverage of ICLR 2023 joined by Dhruv Batra, an associate professor at Georgia Tech and research director of the Fundamental AI Research (FAIR) team at META. In our conversation, we discuss Dhruv’s work on the paper Emergence of Maps in the Memories of Blind Navigation Agents, which won an Outstanding Paper Award at the event. We explore navigation with multilayer LSTM and the question of whether embodiment is necessary for intelligence. We delve into the Embodiment Hypothesis and the progress being made in language models and caution on the responsible use of these models. We also discuss the history of AI and the importance of using the right data sets in training. The conversation explores the different meanings of "maps" across AI and cognitive science fields, Dhruv’s experience in navigating mapless systems, and the early discovery stages of memory representation and neural mechanisms.
The complete show notes for this episode can be found at https://twimlai.com/go/629

Today we’re joined by Jerry Liu, co-founder and CEO of Llama Index. In our conversation with Jerry, we explore the creation of Llama Index, a centralized interface to connect your external data with the latest large language models. We discuss the challenges of adding private data to language models and how Llama Index connects the two for better decision-making. We discuss the role of agents in automation, the evolution of the agent abstraction space, and the difficulties of optimizing queries over large amounts of complex data. We also discuss a range of topics from combining summarization and semantic search, to automating reasoning, to improving language model results by exploiting relationships between nodes in data. 
The complete show notes for this episode can be found at twimlai.com/go/628.

Today we kick off our coverage of the 2023 ICLR conference joined by Christos Louizos, an ML researcher at Qualcomm Technologies. In our conversation with Christos, we explore his paper Hyperparameter Optimization through Neural Network Partitioning and a few of his colleague's works from the conference. We discuss methods for speeding up attention mechanisms in transformers, scheduling operations for computation graphs, estimating channels in indoor environments, and adapting to distribution shifts in test time with neural network modules. We also talk through the benefits and limitations of federated learning, exploring sparse models, optimizing communication between servers and devices, and much more. 
The complete show notes for this episode can be found at https://twimlai.com/go/627.