We use cookies to help you navigate efficiently and perform certain functions. You will find detailed information about all cookies under each consent category below.
The cookies that are categorized as "Necessary" are stored on your browser as they are essential for enabling the basic functionalities of the site. ...
Necessary cookies are required to enable the basic features of this site, such as providing secure log-in or adjusting your consent preferences. These cookies do not store any personally identifiable data.
Functional cookies help perform certain functionalities like sharing the content of the website on social media platforms, collecting feedback, and other third-party features.
Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics such as the number of visitors, bounce rate, traffic source, etc.
Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience for the visitors.
Advertisement cookies are used to provide visitors with customized advertisements based on the pages you visited previously and to analyze the effectiveness of the ad campaigns.
In a conversation with John Furrier of SiliconANGLE & theCUBE at NYSE Wired – Robotics & AI Media Week, our co-founder and CEO Akash Gupta shared his perspective on how AI is transforming physical automation in ways we couldn’t imagine just a few years ago.
Akash explained how the application of robotics can accelerate AI 10x because of two aspects: “being able to sense the environment really well and being able to train the neural networks to make sure that the learning is much faster, but then also being able to orchestrate hundreds and thousands of robotic agents to collaborate with each other, to take a mission and execute.”
One of the most fascinating parts of the discussion centered around how reinforcement learning is changing the game. While supervised learning has dominated AI conversations, the real breakthrough comes when systems continuously learn from real-world interactions. As Akash noted, the more the world can move toward a balance of supervised learning and reinforcement learning, the easier training will become.
The interview also explored the critical role of digital twins in physical orchestration – creating virtual replicas of real physical warehouses and stores that evolve in real-time as conditions change. This approach enables dynamic decision-making that adapts to constantly shifting retail store and warehouse environments.
Watch the full interview for more insights on the evolving landscape of physical AI and automation.