{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Leadership for the Physical AI Age","title":"Ep 13: Beyond LLMs: Why Vision-Language-Action Models Are the Future of Physical AI","html":"","width":"100%","height":180,"duration":1698,"description":"If you are an engineer, this is the one to stay for. Prerna Dhareshwar of Voxel51 returns with Nathan Maroney to go a layer deeper than Episode 12, into what actually runs inside a physical AI system. The short version: these models predict the next action the way a language model predicts the next token, and that one architectural fact quietly dissolved a problem the industry spent years solving in hardware.\nPrerna works on the product side at Voxel51, where she led the platform that physical AI teams use to explore, visualize, curate and query their data. She came to it from the other end of the problem: an engineering degree from IIT Madras, a Master's from Stanford, research at India's National Aerospace Laboratories, predictive analytics at Pure Storage, and vision based anomaly detection for manufacturing at Instrumental. She has seen this from the model side, the data side, and the factory floor.\nStart with VLMs. They are the large language models you already know, trained on images and video alongside text, so they carry an understanding of what they are looking at. Then VLA models, Vision-Language-Action. They take every sensor input, take a language prompt describing the goal, and output action tokens continuously, each one conditioned on what the sensors are saying at that instant.\nPrerna walks it through with a robotic arm unloading a dishwasher. At timestamp zero it sees the dishes and decides its next move is to reach for a plate. That changes the inputs. Now the next action is to grasp. And so on. The model was never trained on your dishwasher, or Nathan's, and it does not need to be.\nThe consequence is the most contested claim in the episode. Because the model conditions on whatever it has at each moment, the sensor streams do not need to be time synchronized. In her words, alignment of sensors is not really something people are too worried about anymore.\nThen trust. Titto puts the noise problem to her using his own house. A spotless kitchen is...","thumbnail_url":"https://img.transistorcdn.com/Ihrz8HSnPRaMTVxRNvisLmWsNDNh5xUG7H6qCEdVfDQ/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS81MTgw/ZGIxM2UxOTE2ZGRl/OGFhMGE5Y2JhOTFh/ZGUwOC53ZWJw.webp","thumbnail_width":300,"thumbnail_height":300}