How a reasoning engine works

- A reasoning engine is a system that uses logic and inference methods to draw conclusions, make decisions, summarize information, or solve problems based on available data and knowledge. - So a reasoning engine, it's in the title, right? You assume that it's making some rational thinking. It's reasoning from the inquiry that you're giving it as compared to a search engine, which is more likely just to give you back a response to the direct information that you queried. - There are many different types of reasoning engines, but here I'd like to talk about generative AI models like ChatGPT and Bing Chat. These systems process and understand human language. So when a user enters a query, the reasoning engine can provide a relevant and informative response using human-like speech. It's kind of like having a digital friend who can answer your questions, engage in discussions, and help you with various tasks. Let's talk just a bit about how this works. The GPT large language model that powers these reasoning engines was pre-trained on a massive amount of training data. This includes text and code from the internet, from books, articles, webpages, Wikipedia, and more. During this pre-training phase, the model learns language patterns, grammar, syntax, and factual knowledge. The model then uses probability to predict the next words in response to a given prompt. In the initial training phases, human supervisors oversee the process, guiding the model toward accurate responses and contributing to its knowledge development. During later training stages, the AI model is asked to generate multiple responses to a prompt, and then human supervisors evaluate and rank these responses from best to worst. Gradually, the model improves its understanding of prompts and increases the probability of generating accurate, comprehensive, human-like responses. Once all of that happens, you have your smart digital friend that you can chat with. - In most cases, these reasoning engines are pre-trained on a bunch of other similar-like reasoning, where you'll see the difference in the kind of response you get from a reasoning engine as opposed to a traditional search engine because it'll be much more conversational, but also much more elaborate in its connection to what you're asking for and the different types of responses that it can create. - Take a look at a few different types of results I can get from a reasoning engine. Instead of asking what the various parts of a grant proposal are, I can ask Bing Chat to actually help me ideate and research and write various parts of a grant proposal in a very customized way. Reasoning engines aren't only used to generate text. They can generate other media types as well, including images. Text-to-image generative AI programs, where neural networks learn the relationship between words and a massive database of images, let you enter a text-based query to produce an image-based result. These models can create complex imagery while also responding to a wide variety of concepts, tones, and styles. Take a look at these images that I can create using the Bing Image Creator when I enter the prompt, a young boy walking a dog in a forest under a full moon, digital art style, bold colors. - You can use these tools to create incredibly beautiful or realistic imagery or imagery that is, you know, perfectly aligned with what you had envisioned. I think that that's a skill that artists can use to say, okay, if you want to use these tools, I have deep skills in being able to write prompts in a way that will actually get to that end product. So I actually think it might be a new tool that artists can use to express themselves. - So whether it's text, image, or other media, reasoning engines have burst onto the scene and attracted a lot of attention and excitement with the possibilities of what users can create. Let's continue to talk about how you can use reasoning engines both independently and in conjunction with search engines.