It goes without saying that foundation models have revolutionized AI in the online environment. AI for language was revolutionized by large language models (LLMs) such as Bard, LLaMA, and ChatGPT. Although there are other large language models available, OpenAI’s GPT models have gained the most widespread recognition for their ability to process text and image inputs and provide responses that are similar to those of a human, even for tasks that call for sophisticated reasoning and complex problem-solving.
The way that society views this new era of artificial intelligence has been significantly influenced by ChatGPT’s extensive and viral adoption.
Robotics will be the next major development that defines AI for future generations. All repetitive jobs in industries like manufacturing, logistics, transportation, retail, agriculture, and even healthcare will be improved by the development of AI-powered robots that can learn how to interact with the real world. Additionally, it will unleash as many efficiencies in the physical world as the previous few decades have shown us in the digital realm.
Though the problems in robotics are different from those in language, there are commonalities among the fundamental ideas. Furthermore, considerable progress has been made in developing the “GPT for robotics” by some of the most brilliant minds in AI.
What makes GPT successful?
Examine the fundamental principles that have contributed to the success of LLMs like GPT in order to comprehend how to develop the “GPT for robotics.”
The foundation model approach
An enormous, varied dataset was used to train the AI model known as GPT. To address a particular issue, engineers have previously gathered data and trained particular AI. Next, in order to solve another, they would have to gather fresh data. An additional issue? Once again, new data. By using a foundation model approach, the situation is now completely reversed.
An all-purpose AI can be created rather than creating specialized ones for each use case. Furthermore, all the specialized models are not as successful as that one extremely general model. For a given task, the AI in a foundation model performs better. Because it has acquired new abilities from having to function well across a wide range of tasks, it can better apply lessons from other tasks and generalize to new ones.
Training on a large, proprietary, and high-quality dataset
You must first have access to a huge amount of data in order to develop a generalized AI. The real-world data required for the GPT models to be reasonably efficiently trained was acquired by OpenAI. With a sizable and varied dataset that includes books, news articles, social media posts, code, and more, GPT has been trained using data gathered from the entire internet.
The process of selecting high-quality, high-value data also has a significant impact, so it’s not just about dataset size. Since their high-quality datasets are primarily based on the tasks that users care about and the most helpful answers, the GPT models have performed at a level never before seen.
Role of reinforcement learning (RL)
To match the model’s response with human preference—that is, what is deemed advantageous to a user—OpenAI uses reinforcement learning from human feedback, or RLHF. Pure supervised learning (SL) is insufficient because SL can only handle problems that have a clear pattern or set of examples. LLMs demand the AI to accomplish a task in the absence of a singular, accurate solution. Put RLHF in.
With RLHF, a human can accept or reject right answers (high reward) or incorrect answers (low reward), allowing the algorithm to progress towards a goal through trial and error. AI uses reinforcement learning (RL) to figure out how to arrive at the reward function that best explains human preference. Learning from human feedback, ChatGPT can produce responses that are comparable to or better than those of a human.
Robotics is the next frontier of foundation models
A machine’s ability to see, think, and act is made possible by the same core technology that makes GPT able to speak and see. Foundation model-driven robots are capable of comprehending their physical environment, making defensible decisions, and changing course when necessary.
In the same way that GPT was constructed, the “GPT for robotics” is being developed to lay the foundation for a revolution that will once again completely reconfigure artificial intelligence.
Foundation model approach
You can also create a single artificial intelligence (AI) that can perform a variety of tasks in the real world by using a foundation model approach. Experts recommended creating a specific artificial intelligence for grocery item picking and packing robots a few years ago. Furthermore, that model differs from the one that unloads pallets from a truck, as does the model that can sort different electrical parts.
With this paradigm change to a foundation model, the AI can now respond more effectively to edge-case scenarios, which are common in unstructured real-world settings and could otherwise confuse models with more limited training. It is more successful to build a single generalized AI for all of these cases. The human-level autonomy that has been lacking from earlier robot generations is achieved through comprehensive training.
Training on a large, proprietary, and high-quality dataset
It is very hard to teach a robot what behaviours result in success and what behaviours result in failure. A vast amount of high-quality data derived from actual physical interactions is needed. A single lab setting or a single video example is not a trustworthy or robust enough source (e.g., academic datasets tend to be small in scope, and YouTube videos do not accurately translate the details of the physical interaction).
No existing dataset outlines how robots should interact with the real world, in contrast to AI for language or image processing. Because of this, creating a diverse dataset in robotics becomes more difficult, and the only way to do so is by putting a fleet of robots in production.
Role of reinforcement learning
Robotic control and manipulation require an agent to seek progress towards a goal for which there is no one, exclusive, correct answer (e.g., “What’s a successful way to pick up this red onion?”). This is analogous to responding to text questions with human-level capability. Again, it takes more than just supervised learning alone.
To be successful in robotics, a robot must be able to perform deep reinforcement learning, or deep RL. Deep neural networks and reinforcement learning are combined in this autonomous, self-learning method to achieve higher performance levels. As it encounters new situations, the AI will automatically modify its learning strategies and keep improving its abilities.
Challenging, explosive growth is coming
Some of the brightest minds in robotics and artificial intelligence have been working together for a few years to lay the technological and business foundations for a revolution in robotic foundation models that will change the face of artificial intelligence in the future.
Even though these artificial intelligence models are constructed similarly to GPT, two factors make reaching human-level autonomy in the physical world a distinct scientific question:
- A remarkable set of complex physical requirements must be met in order to build an AI-based product that can serve a variety of real-world settings. Since it is unlikely that a single piece of hardware will be suitable for use in a wide range of industries and activities (such as logistics, transportation, manufacturing, retail, agriculture, healthcare, etc.), artificial intelligence must be adapted to different hardware applications.
- For AI models to learn in the real world, warehouses and distribution centers are perfect. Any facility will frequently have hundreds of thousands or even millions of distinct stock-keeping units (SKUs) flowing through it at any given time, providing the sizable, confidential, and superior dataset required to train the “GPT for robotics.”
AI robotics “GPT moment” is near
The trajectory of robotic foundation models is expanding at an extremely quick speed. In real-world production settings, robotic applications are already being used, especially for tasks requiring precise object manipulation. By 2024, there will be an exponential increase in the number of commercially viable robotic applications implemented at scale.