Artificial Intelligence. Overview
Key components of AI applications
Businesses can collect data from internal and external sources. Success of any AI application depends on its data quality. AI applications generally yield the best results when the underlying data sets are large, valid, current and substantial.
Data Processing and Analytics
Reporting and Data Output
Foundation models of Artificial Intelligence
Foundation models (FMs) are ML models trained on a broad spectrum of generalized and unlabeled data. They’re capable of performing a wide variety of general tasks.
FMs are the result of the latest advancements in a technology that has been evolving for decades. In general, an FM uses learned patterns and relationships to predict the next item in a sequence.
For example, with image generation, the model analyzes the image and creates a sharper, more clearly defined version of the image. Similarly, with text, the model predicts the next word in a string of text based on the previous words and their context. It then selects the next word using probability distribution techniques.
Large language models
Large language models (LLMs) are one class of FMs. For example, OpenAI’s generative pre-trained transformer (GPT) models are LLMs. LLMs are specifically focused on language-based tasks such as such as summarization, text generation, classification, open-ended conversation, and information extraction.
Why are large language models important?
Large language models are incredibly flexible. One model can perform completely different tasks such as answering questions, summarizing documents, translating languages and completing sentences. LLMs have the potential to disrupt content creation and the way people use search engines and virtual assistants.
While not perfect, LLMs are demonstrating a remarkable ability to make predictions based on a relatively small number of prompts or inputs. LLMs can be used for generative AI (artificial intelligence) to produce content based on input prompts in human language.
LLMs are big, very big. They can consider billions of parameters and have many possible uses.
Artificial Intelligence. Examples
- Open AI’s GPT-3 model has 175 billion parameters. Its cousin, ChatGPT, can identify patterns from data and generate natural and readable output. While we don’t know the size of Claude 2, it can take inputs up to 100K tokens in each prompt, which means it can work over hundreds of pages of technical documentation or even an entire book.
- AI21 Labs’ Jurassic-1 model has 178 billion parameters and a token vocabulary of 250,000-word parts and similar conversational capabilities.
- Cohere’s Command model has similar capabilities and can work in more than 100 different languages.
- LightOn’s Paradigm offers foundation models with claimed capabilities that exceed those of GPT-3. All these LLMs come with APIs that allow developers to create unique generative AI applications.