Enterprises are integrating AI into their operations and develop agent pilots. As we deploy more capable agent-based intelligent applications, including multi-agent systems, we will utilize agent-centric models for planning, reasoning, coordinating, and learning. Such agents will incorporate neurosymbolic components and agent-centric models (LRM, LAM). They will communicate using specialized languages and appropriate communication protocols.
Most of you know our firm from its investments in early-stage AI software startups and as an AI advisor to corporations. Few know about the AI systems we have been developing and how we use them with our corporate customers or in new startups we spin out. Over the past several years we have been working on a class of AI-based mobility intelligence systems that are used for understanding a population’s mobility behavior within a region, such as a neighborhood, a city, or even an entire state. We found that neurosymbolic systems that incorporate generative AI components can be extremely effective in understanding such behaviors and providing their users with mobility intelligence.
During the last six months, we spent time with our firm’s corporate partners to assess whether the enterprise is ready for generative AI and updated our investment theses accordingly. Our work convinced us that Large Language Models (LLMs)/Foundation Models and applications that incorporate them will open the door to the development of a new class of intelligent enterprise applications.
AI has three roles in new mobility. It is an enabler, a differentiator, or a monetizer. The recent explosion of interest in generative AI begs the question: could generative AI contribute to new mobility and if so, in which of the three roles? This post attempts to answer this question by presenting a few ideas and identifying problems that may inhibit the broad use of generative AI in new mobility.