It’s widely acknowledged that foundation models have revolutionized artificial intelligence (AI) in the digital realm. Large language models (LLMs) such as ChatGPT, LLaMA, and Bard have played a pivotal role in transforming AI for language tasks. While GPT models from OpenAI are not the sole LLMs available, they have gained the most mainstream recognition for their ability to handle text and image inputs, delivering human-like responses even in tasks requiring complex problem-solving and advanced reasoning.
The widespread adoption and virality of ChatGPT have significantly influenced societal perceptions of this new era in artificial intelligence.
The forthcoming frontier that is poised to shape AI for generations is robotics. Developing AI-powered robots with the capability to learn interaction with the physical world will optimize various repetitive tasks across sectors, including logistics, transportation, manufacturing, retail, agriculture, and healthcare. This advancement is anticipated to bring about efficiencies in the physical world similar to those witnessed in the digital realm over the past few decades.
Despite the unique challenges within robotics compared to language-related tasks, there are shared foundational concepts. Some of the most brilliant minds in AI are making substantial progress in constructing the equivalent of a “GPT for robotics.”
To comprehend what makes GPT successful, it’s essential to examine the core pillars that underpin the triumph of LLMs like GPT:
Foundation Model Approach: GPT follows an approach where the AI model is trained on an extensive and diverse dataset. Unlike the traditional method of creating specific AIs for distinct problems, the foundation model approach allows for a universal model applicable across various use cases. This general model excels in specific tasks by leveraging insights gained from diverse tasks, exhibiting superior performance.
Training on a Large, Proprietary, and High-Quality Dataset: GPT achieves its generalization by being trained on a vast and diverse dataset collected from the entire internet, including books, news articles, social media posts, and code. The quality of the dataset is crucial, as it is informed by the tasks users find valuable and the most helpful answers.
Role of Reinforcement Learning (RL): OpenAI utilizes reinforcement learning from human feedback (RLHF) to align the model’s responses with human preferences. Unlike pure supervised learning, RLHF allows the model to learn through trial and error, adapting its strategies based on correct and incorrect feedback from humans. This approach contributes to ChatGPT’s ability to deliver responses that approach or exceed human-level capabilities.
The next phase in foundation models is in the realm of robotics. The same core technology enabling GPT’s language understanding also empowers machines to perceive, think, and take actions in the physical world. Robots driven by a foundation model can comprehend their physical surroundings, make informed decisions, and adapt their actions to changing conditions.
Building the “GPT for robotics” follows the same principles that made GPT successful, setting the stage for a revolution in AI that will redefine its capabilities once again.
Foundation Model Approach: Adopting a foundation model approach in robotics allows for the creation of a single AI applicable across multiple tasks in the physical world. This paradigm shift enables the AI to handle edge-case scenarios prevalent in unstructured real-world environments, where specialized models might struggle.
Training on a Large, Proprietary, and High-Quality Dataset: Teaching robots to learn successful actions requires extensive high-quality data derived from real-world physical interactions. Unlike language or image processing AI, no preexisting dataset represents how robots should interact with the physical world, making the challenge more complex. Deploying a fleet of robots in production is essential to building a diverse dataset.
Role of Reinforcement Learning: Similar to language tasks, robotic control and manipulation demand an approach beyond pure supervised learning. Deep reinforcement learning (deep RL) is essential for success in robotics, combining RL with deep neural networks to enable the AI to adapt and fine-tune its skills autonomously.
Anticipate challenging yet explosive growth in the field of robotic foundation models. Over the past few years, experts have laid the groundwork for a revolution that will redefine the future of artificial intelligence, specifically in the physical world.
While the models share similarities with GPT, achieving human-level autonomy in the physical world poses distinct scientific challenges due to complex physical requirements and the need to adapt to diverse hardware applications across various industries.
The warehouse and distribution center environment serves as an ideal learning ground for AI models in the physical world, providing the diverse dataset required to train the “GPT for robotics.” The growth trajectory indicates a rapid acceleration, with commercially viable robotic applications deployed at scale expected in 2024.
In conclusion, the “GPT moment” for AI in robotics is on the horizon, with robotic foundation models poised to redefine the landscape of artificial intelligence in the physical world.
