Guest Speakers - Christopher Li and Steve Coy

According to the CDC, suicide is the second leading cause of death for people ages 10–24. There have been too many losses like Adam’s; we need to stop this. The truth we can no longer ignore is this: Gen Z are turning to artificial intelligence as if it were a confidant, counselor, or therapist.

Christopher Li is solving this problem two fold. First, by providing a place for high school and college students to find the help they need as well as creating a database of emotional intelligence that AI can train on, a new and necessary focus. Christopher is Founder & Executive  Director of SAY!T , a Gen Z Mental‑Health Prevention Nonprofit  which is a Peer‑Powered Mobile & Campus Program.  He is a former Strategy Consultant & Investor Bridging Tech, Data & Social Impact. He is an instructor UCLA Extension.

This is a timely topic and not talked about enough and Christopher is a crusader in this space.

TimeLike™: a very different kind of artificial intelligence is designed to help human beings find near-optimal solutions to complex and consequential real-world problems. It doesn’t generate text, or images, or videos, it doesn’t hallucinate, and it doesn’t generate disinformation. Instead, it helps its users make sound decisions even in highly complex, rapidly changing, and unfamiliar real-world decision-making situations, in many cases enabling them to obtain much better outcomes than would otherwise be possible.

To do this, TimeLike™ implements a very general problem-solving approach we call SECHI, for Simulation-Enabled Cooperative Human Intelligence. In SECHI we use computer modeling and simulation to predict the probable consequences of different possible courses of action open to our users, and then use model-based optimization to help them zero in on the course of action with the most desirable (or least undesirable) expected consequences.
Unlike conventional AI, TimeLike™ intrinsically “understands” time, space, causality, and the laws of physics. And TimeLike™ users can readily extend the set of things it “understands” to include many other kinds of practically useful human knowledge, simply by encoding that knowledge in the form of composable causal models, which can be coupled together to form computer models of larger and more complex systems, ready for use in modeling and simulation and model-based optimization.

TimeLike™ is the first and only AI platform that has been expressly designed to support cooperative model-based decision-making at scale, thus enabling its users to readily find and take advantage of omni-win-win opportunities while deftly avoiding multipolar lose-lose traps.
Visit TimeLike Systems for more.

Steve is based in Albuquerque.