In this episode of CLM Rx Podcast, Randy Friedman is joining me, an actual mathematician that then translated his skill into technology particularly in the AI space. He will be explaining the technical aspects of generative AI, particularly the transformer architecture, encoder and decoder parts, and how they work together to process and generate content like text, images, and sound. He will share his knowledge about contract intelligence, the rise of AI and the disruptive impact of AI in the industry. The application of artificial intelligence and neural networks particularly in the context of contract analysis and data processing can present both opportunities and challenges but when applied properly it can do things that we, as people, cannot do.   “So there's an unsupervised you just letting it read or see or the supervised is that you're now providing it with some context. So you have to and that's done through labeling, so that you could think of labeling as saying in the context of a contract? Oh, here's the start of an indemnification clause. Here's the ending of it, right. So that it knows Oh, when I, I have seen this, I vectorized it now let me go look for other vectors that are like this, now I can recognize that. And it's doing that through a math algorithm. And that's what's important, is to be able to find that pattern. So there is a part of this technology that you know, of course, is just able to absorb like a baby does. You don't, you know, yeah, you tell a baby, this is a banana, and this is an apple, but for the most part, babies are just absorbing the world and learn, right? And then you can give them annotated language lessons, and they can learn a language and then they can create and generate for you something very, very similar, very, very similar. And so that's a good paradigm is first, absorb. Second, instruct, third, interpret or create. And so that's the continuum you know, in plain old English. It's obviously a lot more complex in data science terms, and math but in plain old English, that's what's going on.”   The key takeaways from this episode: The importance of the transformer architecture in modern generative AI models. Transformers have revolutionized natural language processing and can be adapted for various tasks beyond text generation. Generative AI models often consist of encoder and decoder parts. The encoder processes input data, while the decoder generates output. These components work together to create meaningful content. To perform effectively, AI models like GPT-3 or DALL·E rely on a combination of technologies, including transformers, retrievers, and other components.  It's important to recognize that AI is not magic but rather a mathematical and technological system. It requires careful engineering and training to produce meaningful results. The use of AI models also raises ethical concerns, such as bias in training data and potential misuse. It's important to use AI responsibly and address these ethical challenges. The field of AI is continually evolving, with researchers working on improving models, reducing biases, and making AI more accessible and useful for a wider range of applications.