The influence of foundation models in the world of artificial intelligence (AI) has been nothing short of transformative. Large language models such as ChatGPT, LLaMA and Bard are making significant strides in language-related tasks. As AI grows, the next big thing is definitely robotics.
Since ChatGPT became super popular, people are starting to see that AI can do a lot more, especially in the world of robotics. The aim is to make robots that use AI to move around and do things in the real world. This may revolutionize repetitive work across industries from logistics to healthcare.
The success of models like GPT lies in their foundational approach. Instead of making special AIs for each job, now we have one model that can handle lots of different things, just like how it works with language-related AI. The building blocks for the “GPT for robotics” are rooted in the same principles that fueled the rise of large language models.
A big reason why GPT is so good is that it learned a ton from a huge, special dataset on the internet. The model works even better because people picked out important tasks, making a dataset that helps the AI get smarter.
Reinforcement learning (RL) plays a crucial role in aligning the model’s responses with human preferences, allowing for nuanced problem-solving. In situations where there is no clear rule, the AI has to figure things out by trying different things – like trial and error.
The tech that makes ChatGPT understand and talk with text is now being used for robots and this is opening up a new era in AI. Robots equipped with foundation models can understand their surroundings, make informed decisions and adapt to dynamic situations.
