#natural-language-processing

#natural-language-processing

DeepSeek's announcement heralds the arrival of cost-effective AI models with a context length of up to 1 million tokens, enhancing coherence in extended conversations.

A common mistake is trying to build one skill that does everything: audit UI, suggest redesigns, generate documentation. When creating a skill, you should follow a simple logic: One task, One job.

Did you know you can teach ChatGPT how to respond to certain requests? Not only can you give ChatGPT instructions, but they'll stick (mostly) for every session. This feature is called Custom Instructions. It lives in the Personalization tab of ChatGPT's settings. In a minute, I'll show you a set of really powerful directives that can help make you super productive.

By comparing how AI models and humans map these words to numerical percentages, we uncovered significant gaps between humans and large language models. While the models do tend to agree with humans on extremes like 'impossible,' they diverge sharply on hedge words like 'maybe.' For example, a model might use the word 'likely' to represent an 80% probability, while a human reader assumes it means closer to 65%.

A major difference between LLMs and LTMs is the type of data they're able to synthesize and use. LLMs use unstructured data-think text, social media posts, emails, etc. LTMs, on the other hand, can extract information or insights from structured data, which could be contained in tables, for instance. Since many enterprises rely on structured data, often contained in spreadsheets, to run their operations, LTMs could have an immediate use case for many organizations.