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Large Language Models (LLMs) have emerged as highly successful and widely adopted AI technologies in recent times. Major players in the industry, including OpenAI, Google, Nvidia, Meta, and Microsoft, leverage these models either for their own products or offer access to them. Among the current LLMs, GPT-3 (short for Generative Pretrained Transformer) and its successor GPT-4 reign supreme in terms of popularity.
Normally, computers only understand their own programming languages, like Python or C++. If you want to teach them human languages, like English, Ukrainian, or Japanese, it is possible, but it takes effort. The respective branch of science is called Natural Language Processing (NLP), which combines computer science (including machine learning and deep learning) with traditional linguistics.
Natural language is the most natural (pun intended) way to store and share information for humans. Software solutions that can understand, analyze and even use it for communication are becoming the key to success in many industries, with the recent rise of Large Language Models (LLMs) and AI chatbots being yet another proof of this.
At the moment this article appears, generative large language models (LLMs) are discussed a lot in the media. After the release of OpenAI ChatGPT and later GPT-4, GPT became the “word of the day”.
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The development of modern neural networks has brought about a revolution in the field of image generation. One such example is the text-to-image neural network, DALL-E 2, which can generate beautiful art when supplied with good text descriptions (typically referenced as “prompts”). The quality of images generated by DALL-E 2 heavily depends on the proper structure of inputs.