arXiv:2404.04267v1 Announce Type: new
Abstract: What applications is AI ready for? Advances in deep learning and generative approaches have produced AI that learn from massive online data and outperform manually built AIs. Some AIs outperform people. It is easy (but misleading) to conclude that today’s AI technologies can learn to do everything. Conversely, it is striking that big data, deep learning, and generative AI have had so little impact on robotics. For example, today’s autonomous robots do not learn to provide home care or to be nursing assistants. Instead, current projects rely on mathematical models, planning frameworks, and reinforcement learning. These methods have not lead to the leaps in performance and generality seen with deep learning. Today’s AIs do not learn to do such applications because they do not collect, use, and effectively generalize the necessary experiential data by interacting with the world including people. Aspirationally, robotic AIs would learn experientially, learn from people, serve people broadly, and collaborate with them. Getting to such a future requires understanding the opportunity and creating a path to get there. A path forward would combine multimodal sensing and motor control technology from robotics with deep learning technology adapted for embodied systems. Analogous to foundation classes in deep learning, it would create experiential foundation classes. Success would greatly increase the broad utility of AI robots and grow the market for them. This would lead to lower costs and democratize AI.
Advancements in AI and its Impact on Robotics
Artificial Intelligence (AI) has witnessed tremendous growth in recent years, particularly in the domains of deep learning and generative approaches. These advancements have enabled AI systems to learn from vast amounts of online data, surpassing the performance of manually built AI models. It is tempting to conclude that current AI technologies are capable of mastering any task. However, the lack of significant impact on the field of robotics raises questions about the multi-disciplinary nature of these concepts.
While AI has excelled in various domains, such as image and speech recognition, natural language processing, and computer vision, its utilization in robotics has been limited. For instance, autonomous robots today do not possess the ability to provide home care or serve as nursing assistants and rely heavily on mathematical models, planning frameworks, and reinforcement learning. These traditional approaches have not led to significant advancements and improvements like those witnessed in deep learning.
The primary reason for the limited progress of robotics is the inability of current AI systems to collect, use, and effectively generalize experiential data from real-world interactions with humans. To achieve a future where robotic AI serves and collaborates with humans while learning from them, it is crucial to understand the potential and establish a pathway towards achieving this goal.
One potential way forward would be to amalgamate multimodal sensing and motor control technologies from robotics with deep learning techniques specifically adapted for embodied systems. Similar to foundation classes in deep learning, this approach would create experiential foundation classes, enabling robots to learn experientially, learn from humans, and serve a broad range of tasks.
Achieving success in this endeavor would result in the widespread utility of AI robots and a significant expansion of their market. This, in turn, would drive down costs and democratize AI, making it more accessible to various industries and individuals.
The Multi-Disciplinary Nature of Advancements
The concepts discussed in this article highlight the multi-disciplinary nature of advancements in AI. Integrating robotics, deep learning, and generative AI requires expertise in computer science, engineering, and cognitive science. In effect, it necessitates collaboration among experts from various fields to create intelligent systems capable of learning from and interacting with the physical world.
The synergy between robotics and deep learning is particularly fascinating. By combining the sensory capabilities and physical manipulation skills of robots with the learning power of deep neural networks, we can envision a future where AI robots augment human abilities and contribute significantly to various industries such as healthcare, manufacturing, and transportation.
Furthermore, the democratization of AI through the integration of experiential foundation classes holds great potential for societal advancements. Affordable and accessible AI robots can revolutionize industries, empower individuals, and address crucial challenges in fields like healthcare, eldercare, and education.
“The integration of robotics, deep learning, and generative AI is a transformative step towards creating AI systems capable of learning from and interacting with the world, including humans. This interdisciplinary collaboration holds the key to unlocking the full potential of AI and revolutionizing how we perceive and utilize intelligent machines.”