Forbes' Hessie Jones sat down with Peter Chen to discuss Covariant’s pioneering vision for adaptive intelligence in robotics.
The following is an excerpt of Hessie Jones’ interview with Peter published in Forbes on how AI will redefine work in warehouses and beyond.
In the interview, Peter recounts the Covariant founding team’s early days at OpenAI, and draws parallels between the trajectory and success of foundation models for language and Covariant’s approach to building foundation model for robotics. Discover how the foundation model approach to our universal AI platform, the Covariant Brain, enables our robots to understand their surroundings, make informed decisions, and adapt their actions to changing circumstances.
Leaving OpenAI to Redefine Robotics towards Adaptable Intelligence
When Chen had decided to leave OpenAI, the company’s focus at the time was research. Chen and his cofounders embraced a different perspective. They believed that to advance general AI technology, it was crucial to create tangible products and deploy systems within real production environments. This would enable them to gather valuable data and insights for refining their AI models. Eventually, OpenAI transitioned towards developing a product, a public API and user interface, while leveraging real-world data and user feedback to enhance the technology. By then, Chen was already thinking about redefining robotics towards adaptable intelligence.
"Robotics has long served as a foundational element in modern manufacturing, historically confined to executing repetitive movements based on programmed instructions," Chen explains. In the earlier iterations, robots were confined to specific tasks, operating within a rigidly structured environment. The limitations were significant; any deviation from their set tasks or alterations in the environment could render them ineffective.
However, the landscape has evolved, giving rise to what Chen refers to as "AI Robotics" or modern robots. These robots integrate the precision and reliability of industrialized hardware with a crucial addition—a cognitive AI component. This AI "brain" — aptly named at Covariant as the Covariant Brain — equips robots with the capability to comprehend their surroundings, make informed decisions, and adapt their actions according to changing circumstances. This pivotal advancement is poised to unlock a multitude of potential use cases that were previously inaccessible.
In the current scenario, a vast array of tasks requires a level of adaptability that traditional robotics could not offer. Consider the simple act of picking up a phone—depending on its placement, the required motion varies. Such nuanced tasks demand a robot to possess cognitive abilities and the capability to perceive its environment dynamically. The amalgamation of AI and robotics addresses this requirement, enabling robots to perceive, understand, and react differently based on the situation at hand.
Chen envisions a future where AI acts as the catalyst for an explosion in robotics applications. "You want robots to have a brain to see the world and understand it and make a different movement every time," he emphasizes. This paradigm shift hinges on the realization that a single foundational model, a general AI platform, can underpin robots across various locations and tasks, enabling them to navigate the world intelligently and autonomously. Unlike narrow AI and the search for patterns in a defined manner, developing a generalized AI means being able to handle anomalies within the environment.
Jordan Jacobs, managing partner of Toronto-based Radical Ventures and investor in Covariant, was impressed by the company’s tech’s ability to receive instructions and pick items out of a bin and prepare them for shipping. This is a “valuable task to automate”, however he alluded to the challenges:
Chen explained the intricate challenges posed by AI: "The heart of this challenge lies in the ever-present variability of scenarios that must be addressed." This inherent diversity, as Chen outlined, prompted the genesis of Covariant Brain with a focus on warehouses and logistics—an environment characterized by constant flux. Warehouses, by nature, encounter a barrage of changes—ranging from the introduction of new products daily to alterations in packaging and potential damage to existing packaging. This dynamic environment has posed an array of novel problems that robots must grapple with. The sheer multitude of scenarios that demand adept handling requires the capacity to navigate each one with a pronounced level of autonomy. "High throughput and pinpoint accuracy," Chen emphasized, are the dual pillars that underscore this undertaking, presenting potential formidable AI obstacles.
Chen explained the rationale behind the Covariant Brain’s foundation model's efficacy, "Covering various industries—from pharmaceuticals and cosmetics to fashion apparel and groceries—enables our AI to garner a comprehensive grasp of the world." This breadth of exposure empowers AI with a generalized understanding of diverse contexts. When confronted with new and unfamiliar situations, the AI leverages its rich reservoir of past experiences to discern parallels and patterns. "Even though this exact circumstance has not been seen before," Chen elaborated, "the AI has encountered countless similar instances, equipping it to tackle the new scenario adeptly and with a high degree of proficiency."
This is the core of technology: an adaptable AI that thrives in an environment characterized by unpredictability. Chen’s vision is to imbue robots with the capacity to conquer novel challenges with precision and autonomy. He clarified, "The ability to learn across diverse tasks gives our AI a generalized understanding of the world, enabling it to handle novel situations effectively."