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782 Solving The Need For AI To Access The Present

In this episode, your host, Chris Kalaboukis, delves into a significant challenge in the world of AI – its inability to understand and adapt to real-time changes. As AI models are primarily trained on past data, they often fall short in handling the fluidity of the present moment.

Chris discusses the implications of this gap in AI, highlighting instances where real-time decisions are paramount – from autonomous driving systems that need to navigate through ever-changing traffic conditions, to financial market predictions that require immediate responses to new information.

Exploring potential solutions, he argues that advancements in AI technology, driven by ambitious startups and innovative founders, can play a pivotal role in bridging this gap. He underscores the significance of a dynamic approach to AI training, emphasizing the need for AI systems that can learn and adapt continuously, absorbing new data as it becomes available, thereby becoming more responsive to present conditions.

Join Chris as he navigates through the complexities of AI and its relationship with the present moment, offering invaluable insights to those interested in the intersection of AI and real-time decision making, especially prospective startup founders looking to make a mark in this exciting and challenging field. Tune in to gain a new perspective on the challenges and opportunities in AI’s journey towards understanding the now.



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