Natural language processing enables machines to understand and respond to text or voice data.

The immense amount of information that can be collected from open sources have become increasingly important to the academic and business communities over the past two decades. Also national and global security is becoming more and more reliant on rapidly making sense of and managing the relevant intelligence data. The enormous size and complexity of the gathered data require advanced and unique data storage, management, analysis, and visualization technologies. The technologies and applications currently adopted typically involve elements like text mining, natural language processing, and stochastic-based algorithms. Natural language processing, in particular, has been a prominent research topic for many years now. We have conceptualized an analysis framework with a strong focus on various techniques of natural language processing to aggregate, manipulate, and analyze intelligence information.

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Learn how to create your own customized model with Azure OpenAI Service by using Python, the REST APIs, or Azure OpenAI Studio.

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OpenAI, the company behind ChatGPT, is exploring making its own artificial intelligence chips and has gone as far as evaluating a potential acquisition target, according to people familiar with the company’s plans.

Le concepteur de ChatGPT étudie plusieurs options, dont celle d’une potentielle acquisition qui lui permettrait de ne pas partir de...-Intelligence artificielle

OpenAI, the company popularly known for its AI chatbot ChatGPT, is reportedly interested in manufacturing custom AI chips.

OpenAI is considering building its own AI chips to power ChatGPT, in the latest sign that the company is diverging from its partner Microsoft.

Explore developer resources, tutorials, API docs, and dynamic examples to get the most out of OpenAI's platform.

OpenAI is a well trodden blog and YouTube path - in this post, I’m going to cover the idea of using it to gather information. Let’s say, for the sake of…

PDF | We investigate the extent to which modern neural language models are susceptible to structural priming, the phenomenon whereby the structure of a... | Find, read and cite all the research you need on ResearchGate

This work shows that finetuning LMs in the few-shot setting can considerably reduce the need for prompt engineering, and recommends finetuned LMs for few- shot learning as it is more accurate, robust to different prompts, and can be made nearly as efficient as using frozen LMs. Prompting language models (LMs) with training examples and task descriptions has been seen as critical to recent successes in few-shot learning. In this work, we show that finetuning LMs in the few-shot setting can considerably reduce the need for prompt engineering. In fact, one can use null prompts, prompts that contain neither task-specific templates nor training examples, and achieve competitive accuracy to manually-tuned prompts across a wide range of tasks. While finetuning LMs does introduce new parameters for each downstream task, we show that this memory overhead can be substantially reduced: finetuning only the bias terms can achieve comparable or better accuracy than standard finetuning while only updating 0.1% of the parameters. All in all, we recommend finetuning LMs for few-shot learning as it is more accurate, robust to different prompts, and can be made nearly as efficient as using frozen LMs.

A startpage with online resources about Seraph, created by start.me.

Machine learning is one way of achieving artificial intelligence. This guide is a comprehensive look at the foundations and applications of machine learning.

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Repository for the LinkScope Client software. Contribute to AccentuSoft/LinkScope_Client development by creating an account on GitHub.