Podcast Summary
Unlocking the full potential of robotics and AI: Covariant's general-purpose robots use AI to learn and adapt to new tasks, revolutionizing industries and creating new opportunities.
When building a company focused on robotics and artificial intelligence, it's essential to find the balance between short-term value and long-term opportunity. Traditional robots in factories have been programmed for specific tasks but lack the ability to learn and adapt. However, the integration of AI into robotics can enable robots to learn and perform new tasks in various industries, such as warehouses and logistics. Robotics is not a new technology, but it has been limited to repeatable motions in costly automation lines. However, there are numerous tasks in the world that cannot be reduced to repeatable motions. These include strawberry picking and manipulation tasks that require human hands. By focusing on these areas, we can unlock the full potential of robotics and AI in the real world. Peter Chen, the CEO and co-founder of Covariant, is at the forefront of this innovation. With a background in robotics and AI, he founded Covariant in 2017 and raised significant funding to develop general-purpose robots that can learn and adapt to new tasks using AI. By addressing the limitations of traditional robots and unlocking their potential, we can revolutionize industries and create new opportunities for businesses and consumers alike.
AI enhances robot capabilities in semi-controlled environments: AI enables robots to handle dynamic and diverse circumstances in warehouses and factories, expanding their capabilities beyond repetitive tasks in structured environments.
AI plays a significant role in expanding the capabilities of robots beyond repetitive tasks in structured environments. While traditional programming is sufficient for mechanical movements, AI enables robots to handle dynamic and diverse circumstances in semi-controlled environments like warehouses and factories. Companies like Covariant are pioneering this approach, starting with warehouses and logistics but aiming for broader applications. This evolution of robot autonomy from perfectly structured to semi-structured environments exposes robots to real-world complexities without encountering fully open-world scenarios. For startups looking to hire engineers to develop AI-driven robotics projects, platforms like Lemon.io offer access to on-demand developers with the necessary expertise. By partnering with such resources, startups can reduce their burn rate and quickly find experienced developers to help bring their ideas to life. In the context of AI in robotics, we've come a long way since finite games like chess and Go, but we're still not at the level of handling the massive human variability found in games like poker. The current state of 2023 AI in robotics reinforcement learning can be compared to the stage between finite and nearly finite games, with a larger base of possible outcomes.
The lack of diverse and abundant data is a major challenge in robotics and AI: Despite advancements in AI technology, algorithms, and models, the field of robotics is held back by the lack of diverse and abundant data for training and improving systems.
While we have made significant strides in AI technology, algorithms, and models, particularly in areas like reinforcement learning, the field of robotics is still facing a major challenge: the lack of diverse and abundant data. This was illustrated in the discussion of how AlphaGo, an AI program that mastered the complex game of Go, was able to do so through the vast amount of data it was trained on. The complexity of games like Go and Starcraft, which have more possibilities than the number of particles in the observable universe, was once thought to be insurmountable for AI. However, the key factor that enabled AlphaGo's success was the massive amount of data it was trained on. Similarly, in robotics, while we have the technology and algorithms to build advanced AI systems, the lack of diverse and abundant data is holding us back. To explain reinforcement learning on a basic level, it involves an agent learning from doing different actions and receiving rewards or penalties based on the outcomes. The agent then uses this information to improve its future actions. In short, the lack of data is a major challenge in the field of robotics and AI, and overcoming this hurdle will be crucial for further advancements.
Learning from actions and outcomes in AI: Reinforcement learning involves agents making decisions based on outcomes, optimizing for delayed rewards, and learning most effectively from their own actions.
Reinforcement learning is a method in artificial intelligence (AI) where an agent learns to make decisions by taking different actions and determining which one results in the best outcome. This process involves having a behavior, making a choice, defining what winning means, and setting up an incremental reward function. For instance, in chess, a piece move could be a behavior choice, capturing an opponent's piece could be a good incremental reward, and winning the game is the ultimate reward. However, a challenge in reinforcement learning is optimizing for delayed rewards with long-term dependencies. Magnus Carlsen, a top chess player, demonstrates this concept well by making sacrifices that seem detrimental in the short term but lead to a checkmate in the long term. In robotics, the lack of diverse data is a significant limitation, and while observing humans packing boxes (third-person imitation learning) could provide some learning, it's never as effective as learning from direct experience. Bezos's factories with cameras watching humans could potentially serve as a data source, but it's essential to note that learning from one's own actions is the most effective way to optimize in reinforcement learning.
Collecting high-quality data for autonomous robot operation: To operate autonomously, robots need high-quality data tailored to their unique abilities. Compliance with data security regulations like SOC 2 is essential for businesses dealing with sensitive data.
Collecting high-quality data in a purposeful way is crucial for teaching robots to operate autonomously. The data must account for the robot's unique abilities, such as its flexibility and speed, which differ from human capabilities. Industrial robots, which are robust and cost-effective, are a mature technology that can work 24/7 and have a long lifespan. However, collecting the necessary data for autonomous robot operation requires a deep understanding of the specific challenges and needs. For businesses, ensuring compliance with data security regulations like SOC 2 is essential for securing major deals. Solutions like Vanta can help businesses achieve and maintain SOC 2 compliance faster and more cost-effectively.
Robots with vacuum-based hands adapt to diverse objects: Vacuum-based hands enable robots to pick up a wide range of items, but AI must adapt to various hardware designs for universally applicable robotics.
Robotics technology is advancing to enable robots to understand and manipulate diverse objects in complex environments. The use of vacuum-based hands, a simple yet versatile technology, is a step towards making robots more adaptable to various items and settings. However, it's essential to note that different use cases require different hardware designs. AI for robots must adapt to these varying physical embodiments, making the development of universally applicable robotics a challenging task. The video showcased a robot from ABB, a leading robot manufacturer, equipped with vacuum-based hands, successfully picking up a wide range of items, including food, pharmaceuticals, and CPG products. This demonstrates the potential of vacuum-based hands in the warehouse setting, although they may not be as dexterous as human hands. Ultimately, the ability to adapt to different physical hardware is crucial for AI in robotics, ensuring the technology remains versatile and effective for various applications.
Building a foundation model for AI robotics: Partnering with companies to build a foundation model for AI robotics allows for the development of a single AI that can adapt to various robot hardware platforms and customer scenarios, addressing the data problem in AI and robotics.
Building an AI that can learn across multiple physical bodies and scenarios for robotics requires a foundation model that learns from diverse datasets and tasks. This approach addresses the data problem in AI and robotics, allowing for the development of a single AI that can adapt to various robot hardware platforms and customer scenarios. The go-to market strategy for such a foundation model involves partnering with innovative companies that recognize the importance of AI robotics but cannot build the necessary competencies internally. These companies contribute their data to the platform, benefiting from the increased performance of the AI on their specific use cases even before fine-tuning. When selling to B2B buyers, it's essential to target the decision-makers who have the authority to sign the purchase. By offering a powerful foundation model that can learn from a vast dataset and adapt to various scenarios, companies can bring advanced AI robotics capabilities to their operations more effectively and efficiently.
LinkedIn's Importance for B2B Marketing and Robotics Technology Advancements: LinkedIn's vast user base of decision-makers and executives makes it a prime platform for B2B marketing. Robotics technology advancements, like humanoid robots, expand automation's reach and flexibility.
LinkedIn is an essential platform for B2B marketing due to its large user base of senior decision-makers and executives. With over 930 million members, LinkedIn offers unparalleled targeting capabilities based on location, organization size, vertical, and title. Business equals LinkedIn, and the platform's B2B marketing solutions provide opportunities to reach potential clients in a professional setting. Meanwhile, advancements in robotics technology, such as humanoid robots, are expanding the scope of automation beyond heavy industrial environments. These robots can find work in various settings and don't need to be fixed in one place, making them a crucial development for the future of robotics and automation.
Balancing Immediate Value and Long-Term Opportunities in Robotics: To succeed in robotics, focus on providing immediate value through specific applications while pursuing long-term goals of building general AI and humanoid robots.
Developing a successful robotics company involves finding the right balance between providing immediate value to customers and pursuing long-term opportunities. The cost and general applicability of hardware platforms are significant considerations, and high-value, general-purpose humanoid robots may not be feasible for most consumers in the short term. Instead, robots can be used in specific applications where the high cost is justified, such as military or hazardous environments. However, the ultimate goal should be to build a general AI that can transcend specific use cases and hardware platforms. From a robotics perspective, the biggest challenge is the speed of customization required for each new problem. For classical robotic automation, the key is the ability to quickly customize hardware designs for specific physical problems. For humanoid robots, the challenges are both product and hardware-related, as we cannot build a humanoid robot as capable as a human today. The first humanoid product to build could focus on specific use cases, such as bomb removal, where the high cost is justified. The ultimate goal should be to build a general-purpose humanoid robot, but the challenges in hardware, particularly actuators and pulley systems, need to be addressed first. The key to success in robotics is finding the right balance between immediate value and long-term opportunities while continuously improving hardware and AI technology.
Building AI for complex markets like e-commerce logistics: Focusing on high-value, complex markets provides ample real-world data for creating advanced AI and robotics technology, essential for understanding and navigating the physical world effectively. Specific industries with high transaction volumes can serve as a starting point for developing AI technology applicable to various use cases and hardware platforms.
Identifying a high-value, complex, and variable market like e-commerce logistics is crucial for building advanced AI and robotics technology. This approach provides a vast amount of real-world data and interactions, which is essential for creating an AI that can understand and navigate the physical world effectively. Moreover, focusing on specific industries with high transaction volumes can serve as a starting point for developing AI technology that can be applied to various use cases and hardware platforms in the future. As the speaker mentioned, the success of companies like OpenAI and FACEBOOK in this field has validated the importance of building general AI capabilities rather than task-specific ones. This shift in perspective has attracted renewed interest from investors who previously passed on the opportunity to invest in the company.
Validation of general AI approach by industry and market: Covariant.ai's shift towards general AI has gained significant interest and demand from customers and investors, leading to a surge in hiring top engineering talent to innovate in the field.
The shift towards general AI as a more effective approach compared to specific AI has been a game-changer for Covariant.ai. Peter Chen, the founder, shared that they faced numerous rejections from investors and customers earlier, but the recent trend validated their approach. Now, customers prefer a more general AI platform over training their own specific models, leading to a significant increase in interest and demand. Covariant.ai is constantly hiring top engineering talent to tackle unsolved problems and innovate in this exciting field. To apply, visit their careers page at covariant.ai/careers. The validation from the industry and market has been a tremendous boost for Covariant.ai, and they look forward to continuing their journey in the world of AI technology.
