Martin Bayton
A – Artificial Intelligence (in the context of search)
Welcome to the inaugural blog in the A-Z of Search series. A series of short blogs exploring all things enterprise search, from Artificial Intelligence to Crawlers to Indexing to Relevance Ranking to Query Processing to something being with ‘Z’ that I have yet to figure out – if you have any suggestions please let me know. A glossary of terms and definitions used in the search industry that will hopefully provide a useful reference. So here we go with the letter ‘A’ for Artificial Intelligence a nice easy one to start with …
There are many different AI technologies used to enhance search the most common being: Natural Language Processing (NLP), Machine Learning (ML), Large Language Models (LLMs) and Generative AI. They are all related to the field of artificial intelligence and have some overlap, but there are also some key differences:
- NLP focuses on enabling machines to understand human language and communication, including syntax, semantics, and context. NLP algorithms are used to analyse and process text, speech, and other forms of natural language, with the goal of improving human-machine interaction and communication. NLP is used in search to understand natural language queries and to return results that are relevant to the user’s intent. For example, if a user searches for “How to make a pizza?”, NLP can understand that the user is looking for a recipe and return results that are relevant to that topic.
- Machine learning is a specialized field of AI that involves training machines to learn from data and improve their performance over time. Machine learning algorithms can be supervised, unsupervised, or semi-supervised. One application of ML in search is to improve the accuracy and relevance of search results. For example, ML can learn which results are clicked on most often and then rank those results higher in the search listings.
- Large Language Models are a type of machine learning model that use deep neural networks to generate human-like text. These models are trained on vast amounts of data and can generate coherent and meaningful responses to user queries. LLMs can be used for a variety of tasks, such as document understanding and question answering. For example, a LLM could be used to extract a portion of text or a snippet from a given document, or set of documents, that directly answers a user’s query. These snippets aim to provide users with concise and relevant information without requiring them to click on a specific search result.
- Generative AI refers to AI algorithms that are capable of generating new content, such as text or images, based on patterns and trends in existing data. Generative AI algorithms are often used in applications such as copy writing and computer programming. Generative AI is still under development, but it has the potential to revolutionize the way we create and consume content.
The main difference between NLP, ML, LLMs, and Generative AI is the level of abstraction. NLP is a broad field that encompasses many different subfields, such as speech recognition, machine translation, and natural language understanding. ML is a more specialized field that focuses on the development of algorithms that can learn from data. LLMs and generative AI are even lower-level fields that deal with specific applications of ML to language processing.
| NLP | ML | LLMs | Generative AI |
|---|---|---|---|
|
Deals with the interaction between computers and human languages. |
Allows computers to learn without being explicitly programmed. |
Type of ML model trained on massive datasets. |
Can create new content such as text and images. |
|
Example: used to understand the intent and meaning behind user queries. NLP techniques enable search engines to interpret search queries accurately, handle synonyms, understand context, recognize named entities and deliver relevant search results. |
Example: search engines can employ ML algorithms to improve the ranking of search results. ML models analyse various factors, including user behaviour, page content and other signals to determine the relevance and quality of documents and deliver more accurate search rankings. Example: recommendation systems. ML algorithms are used to analyze user preferences, behavior and contextual data to provide personalized recommendations for products, content, and services, especially in eCommerce. |
Example: used for extractive answers. Allows search engines to better understand the content of documents and extract concise and relevant information to provide as snippets at the top of search results. |
Example: Generative Answers. AI models can automatically generate answers to common queries. Example: Document Summarization. Generative AI models can be employed to automatically generate concise summaries of lengthy documents. This can help users quickly grasp the key points and relevant information without having to read through entire documents. |
Table: AI Technology, Description and Examples in the context of search
One of the challenges in writing this short blog has been the overlap between the various AI technologies. Aspects of NLP can be found in ML and LLMs and vice versa but hopefully it goes some way to helping you better understand the main characteristics and use of each of the AI technologies in the context of search. Overall, it is the ability of these technologies to capture contextual information and relationships within language that has made them so valuable in enhancing search results, improving language understanding, and delivering more relevant and accurate information. And this is just the beginning of the impact that AI will have on search. As AI technology continues to improve, we can expect to see even more innovative and powerful ways to search for information. In future blogs in this series, we’ll investigate each of these technologies in more detail.
Next up in the A-Z of Search – Algorithms.
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