Artificial intelligence (AI) is the simulation of human intelligence by a machine. AI isn’t one technology but rather a general term that encompasses a broad range of still-emerging technologies, including machine learning (ML), deep learning, neural networks, natural language processing (NLP), speech recognition, and machine vision. New kinds of AI are continuously being developed and improved.
AI can be categorized in a number of different ways. For example, AI can either be “narrow” or “general.” Narrow AI is designed for a specific purpose, like answering customer service questions or driving a car. General AI can adapt and learn to perform any cognitive task asked of it. The latter AI is still an aspirational goal at this point in time.
AI systems can also be typed based on the techniques they use to arrive at their responses. These are some of the most common techniques employed:
Retrieval AI — Retrieval-based AI extracts information from pre-existing sources and summarizes the results. Such models cannot produce original or unique responses.
Generative AI — Generative AI can produce original responses that are within the context of what is being asked but aren’t always accurate.
Retrieval augmented generative (RAG) AI — RAG AI combines the strengths of both generative AI and retrieval AI and produces more accurate and context-aware results from users’ prompts than either of the two supplementing types can achieve on their own.
Cognitive AI — Cognitive AI mimics how the human brain works to get results.
What are the different types of AI?
There are several different types of AI currently being discussed or referenced. We’ll examine several of them here, including:
Conversational AI and generative AI
Retrieval augmented generation
What’s the difference between conversational AI and generative AI?
Conversational AI simulates human conversations through natural language processing (NLP) and other related techniques such as transformers. Conversational AI can interpret user input and generate appropriate responses based on its understanding of the user’s intent. The responses can be in the form of text, voice, or other modalities, depending on the application, such as virtual assistants, chatbots, and voice assistants, among others. This is most likely what most people are familiar with regarding conversational AI thanks to the swift popularity of OpenAI’s ChatGPT.
Conversational AI systems can be trained on large amounts of data to improve their ability to understand and respond to user input. They can also use context to generate more personalized responses that are tailored to the user’s specific needs and preferences. Conversational AI is a rapidly evolving field, and researchers and developers are constantly exploring new ways to improve the technology’s capabilities and expand its applications.
Conversational AI has the potential to dramatically change how organizations interact with data, systems, and workflows, making it possible to create more personalized and intuitive experiences with deeper insights and value. With further advancements in NLP, machine learning (ML), and other related fields, conversational AI is poised to become even more integral to organizations in the years to come.
Generative AI, on the other hand, uses algorithms to generate new content such as images, text, and music. Unlike traditional ML algorithms, designed to classify or predict existing data, generative AI models are trained to generate new data that resembles a specific dataset. Generative AI uses special algorithms to learn from existing examples and then generates new content that looks and sounds similar to what it learned.
For example, generative AI can be used to create new artwork that is inspired by existing artwork. It can also generate realistic images of things that don’t exist, or it can write stories or articles that sound like they were written by a person.
What is cognitive AI?
Cognitive AI aims to replicate human cognitive abilities such as perception, reasoning, problem-solving, and decision-making. It uses ML, NLP, and other related techniques to create intelligent systems that can learn and improve based on their interactions with users and the environment.
The goal of cognitive AI is to not only perform tasks but also understand the context and the meaning behind those tasks. Cognitive AI can analyze vast amounts of data, identify patterns, and make predictions based on that data, as well as learn from mistakes and adapt behavior to better meet objectives.
Cognitive AI has many potential applications in various fields, including healthcare, finance, transportation, and education. For example, it could be used to develop personalized healthcare plans based on a patient’s medical history and current condition, or to optimize financial investment strategies based on market trends and risk assessments.
What is retrieval AI?
Retrieval AI involves searching and retrieving information from large datasets. It uses techniques such as NLP and ML algorithms to understand the intent of a user’s query and retrieve the most relevant results from a database (similar to search engines.)
Retrieval AI is used in various applications, such as chatbots, customer support systems, and search engines. For example, when you ask a chatbot a question, it uses retrieval AI to search for the most relevant response from a database of pre-existing responses. In search engines, retrieval AI can help users find the most relevant search results based on their search query.
Retrieval AI is a powerful technology that can help organizations improve their customer service, increase efficiency, and enhance user experience by providing accurate and relevant information. It is a crucial component of many AI applications that rely on retrieving and processing large amounts of data.
What is retrieval augmented generation (RAG)
Retrieval augmented generation (RAG) is a NLP technique that combines the benefits of retrieval-based and generative-based approaches to improve the quality of text generation tasks, such as question-answering, summarization, and conversational AI.
Traditionally, generative-based models have been used for text generation tasks, which involve generating new text from scratch based on a given input or prompt. However, generative models can sometimes produce low-quality output, especially when dealing with complex or specialized topics. Retrieval-based models, on the other hand, rely on pre-existing text data to generate responses but are limited by the quality and availability of the pre-existing data.
RAG combines the strengths of both approaches by using retrieval-based models to select relevant information from a pre-existing collection of text and then using generative-based models to generate new text based on that information. This enables RAG models to generate more accurate and coherent text, even for complex or specialized topics, by leveraging the knowledge contained within a large set of text.
All of these AI types are heavily dependent on one thing: data.