Language models (LLMs) use reasoning to produce answers by breaking down multistep problems into intermediate steps using methods like chain-of-thought prompting. LLMs can accurately solve complex reasoning problems when provided with the problem and solving method. Reasoning also helps LLMs avoid hallucinations by understanding connections in data.
Tokens are linguistic units, vectors are mathematical representations, and embeddings are trained vectors capturing deep semantic relationships. Tokens start as discrete data, vectors offer a processing framework, and embeddings create nuanced, contextually aware semantic spaces in LLMs, allowing for human-like versatility in AI tasks.
Tokens are the basic units of data processed by LLMs. They can represent words, parts of words, or characters, depending on the tokenization process. Tokens are encoded into specialized vectors by a tokenizer, allowing LLMs to understand and process the text. These vectors are then decoded back into text. Tokens are essential in the input and output processes of LLMs, with different models having varying context lengths that determine the number of tokens they can process.
Generative AI has the potential to bring revolutionary new capabilities to robotics. Not only are we able to communicate with robots in natural language, but connecting to internet-scale language and image data is giving robots a much more robust understanding and reasoning about the world. But we are still in the early days; more work is needed to understand how to ground image and language knowledge in the types of physical intelligence required to make robots truly useful.
