As more organizations adopt AI, we emerge from the wild west of training black-box machine learning models and placing AI into production to "see what happens"! With the burgeoning of several specifically designed AI solutions, such as Watchful (insert shameless plug), we begin to demystify the deployment of AI. The Grounded Truth Podcast, hosted by John Singleton (co-founder of Watchful), gathers some of the world's most influential data scientists, machine learning practitioners, and innovation leaders for discussions on how we can accelerate our understanding, development, and implementation of cutting-edge machine learning applications in both academia and enterprise.
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As more organizations adopt AI, we emerge from the wild west of training black-box machine learning models and placing AI into production to "see what happens"! With the burgeoning of several specifically designed AI solutions, such as Watchful (insert shameless plug), we begin to demystify the deployment of AI. The Grounded Truth Podcast, hosted by John Singleton (co-founder of Watchful), gathers some of the world's most influential data scientists, machine learning practitioners, and innovation leaders for discussions on how we can accelerate our understanding, development, and implementation of cutting-edge machine learning applications in both academia and enterprise.
Leveraging Machine Teaching to Build Autonomous Agents
Grounded Truth
54 minutes
2 years ago
Leveraging Machine Teaching to Build Autonomous Agents
Welcome to "Grounded Truth," the podcast where we bring together influential data scientists, machine learning practitioners, and innovation leaders to discuss the most relevant topics in AI. I'm John Singleton, your host, and I'm thrilled to be joined by Kence Anderson, CEO and co-founder of Composabl (https://composabl.ai/), a platform for building autonomous intelligent agents. Kence is a seasoned entrepreneur and has a wealth of experience in the AI field, including his work at Microsoft as a principal program manager for AI and research machine teaching innovation.
In this episode, we delve into the fascinating world of autonomous agents and their impact on various industries. From autonomous driving to industrial automation, we explore the challenges and advancements in human-like decision-making. Composabl's mission is to empower individuals without deep AI expertise to create intelligent agents using modular building blocks and their own subject matter expertise. Through the concept of machine teaching, users can train agents to make real-time decisions, whether it's controlling a drone, a bulldozer or even optimizing virtual processes in factories or logistics.
Joining us as well is Shayan Mohanty, co-founder and CEO of Watchful, the machine teaching platform for data-centric AI. Together, we discuss the distinct roles of perception and action in AI and how they differ. While perception involves perceiving and understanding the environment, action focuses on making decisions and taking appropriate steps. We explore the significance of supervised learning in perception tasks like computer vision and prediction, as well as its limitations in driving actionable outcomes.
To learn more from Kence Anderson, be sure to check out his latest publication, "Designing Autonomous AI: A Guide for Machine Teaching," available on O'Reilly (https://www.oreilly.com/library/view/designing-autonomous-ai/9781098110741/) Don't forget to like, subscribe, and follow us on Apple Podcasts, Spotify, YouTube, and other podcast platforms.
Reference: "Dreyfus model of skill acquisition" - https://en.wikipedia.org/wiki/Dreyfus_model_of_skill_acquisition
Grounded Truth
As more organizations adopt AI, we emerge from the wild west of training black-box machine learning models and placing AI into production to "see what happens"! With the burgeoning of several specifically designed AI solutions, such as Watchful (insert shameless plug), we begin to demystify the deployment of AI. The Grounded Truth Podcast, hosted by John Singleton (co-founder of Watchful), gathers some of the world's most influential data scientists, machine learning practitioners, and innovation leaders for discussions on how we can accelerate our understanding, development, and implementation of cutting-edge machine learning applications in both academia and enterprise.
