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AI Human Augmentation using LLM’s: A Look into the Future

Human augmentation is a topic that has been gaining traction in recent years. It is the process of using technology to enhance human capabilities. One of the most promising technologies in this field is AI. Large Language Models (LLMs) are a type of AI that are currently trained on a massive trove of articles, Wikipedia entries, books, internet-based resources and other input to produce human-like responses to natural language queries1.

LLMs are machine-learning neuro networks trained through data input/output sets; frequently, the text is unlabeled or uncategorized, and the model is using self-supervised or semi-supervised learning methodology. Information is ingested, or content entered, into the LLM, and the output is what that algorithm predicts the next word will be1.

Augmented Language Models (ALMs) are another type of LLMs that have been developed to improve reasoning skills and the ability to use tools. The former is defined as decomposing a potentially complex task into simpler subtasks while the latter consists of calling external modules such as a code interpreter. LMs can leverage these augmentations separately or in combination via heuristics or learn to do so from data3.

The use of LLMs in human augmentation has been gaining popularity due to their ability to understand natural language queries and provide human-like responses. They have been used in various applications such as chatbots, virtual assistants, and customer service1.

In conclusion, LLMs have shown great potential in human augmentation due to their ability to understand natural language queries and provide human-like responses. They have been used in various applications such as chatbots, virtual assistants, and customer service1.

Here are some references for further reading:

  • What are LLMs, and how are they used in generative AI?

  • AI21 Labs’ mission to make large language models get their facts right

  • Human augmentation: Past, present and future - ScienceDirect

  • Augmented Language Models: a Survey - arXiv.org

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