Post by yamanhosen8564 on Feb 14, 2024 3:28:14 GMT -5
orking behind the scenes: Microsoft products. Bing's AI search and Microsoft 365 Copilot (all the AI features Microsoft is adding to Word, Excel, and its other Office apps) have GPT working in the background. Sudowrite is a GPT-powered app designed to help people write short stories, novels, and other works of fiction. In fact, many (if not most) AI writing generators use GPT as at least one of the models powering the app. GitHub Copilot uses a version of GPT-4 called Codex, designed to generate computer code, to help developers work faster and automate repetitive tasks. Zapier uses GPT in the background for many of its AI capabilities, including its AI chatbot builder. It also offers an OpenAI integration, which connects to GPT and other OpenAI models like DALL·E. Duolingo is a language learning app that lets you have a conversation with a GPT-powered chatbot in your target language.
In short, if you can think of a situation where generating high quality, human-like text could help, GPT can probably be employed to do it—and likely already is. How did we get to GPT? In the mid-2010s, the best performing AI models relied on manually-labeled data, like a database with photos of different Faroe Islands Email List animals paired with a text description of each animal written by humans. It was a process called "supervised learning," and it was used to develop the underlying algorithms for the models. While supervised learning can be effective in some circumstances, the training datasets are incredibly expensive to produce.
Even now, there just isn't that much data suitably labeled and categorized to be used to train LLMs. Things changed with BERT, Google's LLM introduced in 2018. It used the transformer model—first proposed in a 2017 research paper—which fundamentally simplified how AI algorithms were designed. It allows for the computations to be parallelized (done at the same time), which means significantly reduced training times, and it makes it easier for models to be trained on unstructured data. Not only did it make AI models better; it also made them quicker and cheaper to produce.
In short, if you can think of a situation where generating high quality, human-like text could help, GPT can probably be employed to do it—and likely already is. How did we get to GPT? In the mid-2010s, the best performing AI models relied on manually-labeled data, like a database with photos of different Faroe Islands Email List animals paired with a text description of each animal written by humans. It was a process called "supervised learning," and it was used to develop the underlying algorithms for the models. While supervised learning can be effective in some circumstances, the training datasets are incredibly expensive to produce.
Even now, there just isn't that much data suitably labeled and categorized to be used to train LLMs. Things changed with BERT, Google's LLM introduced in 2018. It used the transformer model—first proposed in a 2017 research paper—which fundamentally simplified how AI algorithms were designed. It allows for the computations to be parallelized (done at the same time), which means significantly reduced training times, and it makes it easier for models to be trained on unstructured data. Not only did it make AI models better; it also made them quicker and cheaper to produce.