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AskDocs Intelligence Dictionary for Beginners: Need-to-Know Terms and Meaning

Here’s your dictionary of the most common and need-to-know terms in artificial intelligence.

Oct 17, 2024

AskDocs Team

Academics that do not adopt artificial intelligence (AI) cannot remain competitive as their more advanced peers. While some are using AI to write content, quickly summarize lengthy and complex documents, and automate repetitive processes, academics and small businesses not using AI could miss several key insights. But there are challenges to relying solely on AI.

According to a study on the use of AI in writing scientific review articles, up to 70% of references cited by AI-only approaches were inaccurate. A lack of understanding of how AI works results in potential misuse, overreliance on potentially inaccurate information, and missed opportunities to effectively leverage AI's capabilities while mitigating its limitations.

In this post, we will explore 32 essential AI terms and phrases that would be useful to know whether you're a tech enthusiast, a business professional, or simply curious about the AI craze. From foundational models to popular services, we will simplify the jargon and provide clear explanations of the most important concepts every beginner should know. By familiarizing yourself with these terms, you'll be better equipped to engage in discussions about AI, understand its implications, and make informed decisions in an increasingly AI-driven world.

32 AI Terms and Phrases You Need to Know:

1. Algorithm

An algorithm is a step-by-step set of instructions that a computer follows to perform a specific task or solve a problem. Algorithms are the backbone of programming, guiding a computer on how to process data, make decisions, and produce desired outcomes. They can range from simple operations, like sorting a list of numbers, to more complex ones, such as recommending personalized content or optimizing routes for delivery services.

For example, Google’s search engine uses algorithms to determine the most relevant results for a user’s query by analyzing keywords, ranking websites based on relevance, and displaying the most useful links. Similarly, a shopping site might use an algorithm to suggest products based on a user’s browsing history.

2. Amazon Lex

Amazon Lex is a service provided by AWS (Amazon Web Services) that enables developers to build conversational interfaces using voice and text. Lex uses the same underlying technology as Amazon Alexa, making it easy to create chatbots or virtual assistants that can understand natural language input, process it, and respond accordingly. Lex supports multiple languages and integrates with other AWS services, such as Lambda and S3, to build more sophisticated workflows.

For example, a business might use Amazon Lex to build a customer service chatbot that helps users troubleshoot technical problems, book appointments, or track orders. By integrating with AWS Lambda, the chatbot can trigger serverless functions that perform real-time data lookups or updates.

3. Application Programming Interface (API)

An Application Programming Interface (API) is a set of rules and protocols that allows different software applications to communicate with each other. APIs define how requests and responses should be formatted and transmitted between systems, enabling integration and data sharing across platforms. APIs can be used to connect web applications, services, databases, and even hardware devices, acting as an intermediary that allows developers to access specific functionalities without exposing the entire software codebase..

For example, a weather app might use an API to request data from a weather service, like sending a request for current conditions based on a user's location. The weather service API then responds with the requested data, such as temperature and forecasts, which the app displays for the user.

4. Artificial General Intelligence (AGI)

Artificial General Intelligence (AGI) refers to the hypothetical development of an AI system that possesses human-level cognitive abilities across a wide range of tasks. Unlike narrow AI, which is designed to excel at specific tasks like language translation or image recognition, AGI would be capable of understanding, learning, reasoning, and applying knowledge across various domains, similar to human intelligence. AGI is often seen as the ultimate goal in AI research, but it remains largely theoretical, with no existing AI systems having reached this level.

For example, an AGI system would not only be able to play a game like chess but also understand literature, solve complex scientific problems, and engage in creative tasks like writing or painting, all with the same depth and versatility as a human being. Achieving AGI would require breakthroughs in machine learning, reasoning, and potentially even consciousness.

5. Artificial Intelligence (AI)

Artificial Intelligence (AI) refers to the development of computer systems that can perform tasks typically requiring human intelligence. These tasks include learning, reasoning, problem-solving, understanding natural language, and even perception or decision-making. AI can be categorized into narrow AI, which is designed for specific tasks (like language translation or facial recognition), and general AI, which aims to replicate human cognitive abilities across a wide range of tasks.

For example, AI is used in virtual assistants like Siri or Alexa, which can understand voice commands and provide relevant responses, or in autonomous vehicles, which use AI to interpret surroundings and make driving decisions. It’s also central to recommendation algorithms used by platforms like Netflix or Amazon to suggest content based on user behavior.

6. Amazon Bedrock

Amazon Bedrock is a fully managed service designed to help organizations quickly build and scale generative AI applications. It provides access to a wide range of foundation models (FMs) from top AI providers, including AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon’s own models, such as Titan. Bedrock enables users to create customized AI solutions without needing to manage the underlying infrastructure, simplifying development while maintaining enterprise-grade security and privacy. With features like Custom Model Import, companies can bring their own AI models or data to further customize outputs.

For example, Amazon Bedrock supports applications across industries like healthcare, where it’s used to enhance clinical documentation, or e-commerce, where it powers advanced customer service bots. The platform also supports Retrieval-Augmented Generation (RAG), which combines foundation models with external data to improve responses, making it ideal for tasks like search and recommendation engines.

7. Amazon Titan

Amazon Titan FMs are smart computer programs that have learned from lots of information. They can do many different jobs and can be used as they are or made even better with a company's own data. There are two types of Titan models. One can write summaries, make new text, answer questions, and find important information. The other type changes words and sentences into numbers that show what the words mean. This helps computers understand language better. It's good for things like giving people personal suggestions or finding information. Amazon made sure these programs are safe to use. They can spot and remove bad content, say no to inappropriate requests, and avoid writing mean or violent things.

8. AI21 Labs Jamba

AI21 Labs' Jamba 1.5 models are designed for enterprises needing efficient long-context handling and high performance. With a 256K token context window, these models are ideal for complex tasks like document summarization and agentic workflows. Built on a hybrid SSM-Transformer architecture, Jamba 1.5 models outperform competitors in speed and accuracy, offering multilingual support and structured JSON output capabilities. These models are available on platforms like Hugging Face, Google Cloud, and more.

For instance, a company can use Jamba to power a virtual assistant that summarizes long reports, enhancing productivity in real-time information access.

9. Anthropic's Claude

Anthropic's Claude is a family of large language models designed for advanced reasoning, code generation, and multilingual processing. The Claude models, including Haiku, Sonnet, and Opus, offer varying levels of performance, with 3.5 Sonnet being the most powerful. These models excel in complex tasks like long-form analysis, coding, and open-ended problem-solving. Claude models also have strong safety and accuracy features, making them popular in business settings where reliability and low hallucination rates are critical.

For instance, Claude Opus can analyze extensive data like research papers or codebases, making it suitable for tasks such as drug discovery or financial forecasting. Amazon plans to integrate Claude in services like Alexa for enhanced AI-driven interactions.

10. AskDocs (Ask Documents)

AskDocs is a powerful AI-powered document assistant that transforms files into an intelligent knowledgebase, providing instant answers and insights from multiple documents in one place. Saving academics and professional time. It offers features like cross-document analysis, quick summaries, and the ability to chat with multiple files simultaneously, making it an invaluable tool for students, researchers, and professionals across various industries. AskDocs stands out by supporting numerous file formats, including PDFs, Word documents, and images, while also offering advanced features like OCR support and voice-activated queries. With its user-friendly interface and ability to embed document chatbots on websites, AskDocs streamlines information retrieval and analysis, allowing users to focus on critical thinking and decision-making rather than manual data extraction. Learn more about how to search for information in a PDF file fast with AskDocs.

For example, a user might upload multiple contracts and AskDocs can generate a comprehensive summary, highlight important clauses, or answer specific questions about the content. This automation saves time and improves accuracy for tasks that typically require manual review .

11. ChatGPT

ChatGPT is an AI chatbot developed by OpenAI, based on the GPT-4 language model. It's designed to assist users by generating human-like text responses to various prompts, answering questions, brainstorming ideas, and even writing or summarizing content. The latest version, GPT-4 Turbo, offers improved speed and can handle a wider range of tasks, including real-time voice conversations and multimodal inputs like images and text. It is widely used in productivity apps, coding, and creative writing, and includes advanced features such as voice mode and data analysis.

For example, users can ask ChatGPT to generate code in multiple programming languages or assist in complex problem-solving. The platform also enables custom chatbot creation through a GPT builder, allowing businesses or individuals to tailor ChatGPT to their specific needs.

12. Citation (AI Providing Citations)

In the context of AI, providing citations refers to the ability of AI systems to support their responses with references or links to the sources of information. This feature is particularly important for ensuring transparency, accuracy, and trust in AI-generated content. By offering citations, AI tools allow users to verify the information by checking the original data or documents from which the AI derived its answers. This is common in search engines, AI chatbots, and systems designed for research or fact-based inquiries.

For example, Perplexity AI, a conversational search engine, provides citations alongside its answers, pulling data from web pages and showing users where the information came from. This improves the credibility of the responses and allows users to dive deeper into the content if needed.

13. Cohere Command

Cohere's Command R and Command R+ models are enterprise-grade large language models (LLMs) designed to tackle complex tasks such as text generation, Retrieval-Augmented Generation (RAG), and conversational AI. These models are optimized for long-context applications, with a 128,000-token context window for handling large inputs. Command R+ particularly excels in high-performance environments, offering improvements in multi-step tool use, structured data analysis, and decision-making around tool selection. These models also support a wide array of languages, including English, French, Spanish, and seven others, making them suitable for multilingual applications.

Command R+ is particularly well-suited for tasks that require real-time information retrieval, such as customer service automation, where it can integrate with external tools and databases. This capability allows businesses to use Command R+ for tasks like automating complex data retrieval or handling multilingual customer queries across multiple regions. It also includes enhanced safety modes for flexible deployment in sensitive environments, enabling more controlled and secure AI responses.

14. DALL-E

DALL-E is a generative AI model from OpenAI that transforms text prompts into images. The latest version, DALL-E 3, is integrated with ChatGPT, allowing users to generate detailed images directly within conversations. DALL-E 3 can create images based on descriptions and allows users to refine images by interacting with ChatGPT. The model excels at interpreting prompts accurately, producing highly realistic or artistic outputs, and includes safety features to prevent harmful content generation. It also allows creators to control the style and format of their images with options like "Natural" or "Vivid" modes.

For example, users can describe a scene like "an astronaut walking on Mars at sunset," and DALL-E 3 will create a vivid or realistic depiction of this scenario. The images generated can be used for personal or commercial purposes, with metadata embedded to identify them as AI-generated.

15. Deep Learning

Deep learning is a subset of machine learning that focuses on using neural networks with multiple layers (hence "deep") to model and solve complex patterns and tasks. These networks, called deep neural networks, are designed to automatically learn features from large amounts of data, which makes deep learning particularly effective for tasks like image recognition, speech processing, and natural language understanding. Deep learning models are often powered by large datasets and advanced computing power, allowing them to excel at tasks that require understanding intricate patterns.

For example, deep learning is used in self-driving cars to recognize pedestrians, road signs, and other vehicles. It’s also at the core of virtual assistants like Siri and Google Assistant, which rely on deep neural networks to understand and generate human speech.

16. Dissertation

A dissertation is an extensive, original research project typically completed as part of a doctoral or master's degree program. It represents a significant contribution to a specific field of study and is intended to demonstrate the student's ability to conduct independent research, critically analyze data, and present findings in a clear and scholarly manner. Dissertations usually consist of several sections, including an introduction, literature review, methodology, results, discussion, and conclusion. They are often reviewed by a committee of experts and must meet high academic standards.

For example, a doctoral student in psychology might write a dissertation on the effects of social media on mental health, conducting experiments, analyzing data, and presenting original findings. The student would be expected to contribute new insights or methodologies to the existing body of research in that area.

17. Evidence-Based

Evidence-based refers to a decision-making process or practice that is grounded in the best available, well-researched evidence. It involves using current, credible data and research to make informed decisions, often combining academic studies, expert knowledge, and real-world experience. Evidence-based approaches are common in fields like medicine, education, and policy-making, ensuring that interventions, treatments, or policies are backed by objective findings rather than anecdotal evidence or intuition.

18. Fine-tuning

Fine-tuning is the process of taking a pre-trained machine learning model and adapting it to a specific task by training it further on a smaller, task-specific dataset. This process is typically done after a model has been trained on a large, general-purpose dataset, allowing the model to adjust its parameters for improved performance on a more focused task, such as sentiment analysis, image recognition, or language translation. Fine-tuning allows for better customization of models without requiring vast amounts of computing power or data, as the base model already has general knowledge.

For example, a company might fine-tune a large language model, like GPT-4, to specialize in customer service queries by providing it with additional training on conversations and FAQs specific to their business. This makes the model more efficient and accurate for their particular use case, without needing to build an AI model from scratch.

19. Google Gemini

Google Gemini is a next-generation AI model family developed by Google, known for its multimodal capabilities. This means it can handle and process various types of input, including text, images, audio, video, and code. Gemini models are used for tasks like answering questions, generating text, summarizing content, and even coding. The models come in different sizes—such as Ultra, Pro, Flash, and Nano—each optimized for different levels of complexity and performance, from lightweight, on-device use to handling complex, large-scale tasks in cloud environments.

For example, the Gemini Pro model is optimized for reasoning and can process large amounts of data, like hours of video or audio, or thousands of lines of code. It's used in a range of applications, including Google Workspace tools like Gmail and Docs, and is designed to help users with writing, coding, and analyzing data

20. Grok

Grok is a conversational AI chatbot developed by Elon Musk's AI startup, xAI. It is designed to engage users with a more playful, irreverent tone compared to other chatbots like ChatGPT. Known for its witty, sometimes rebellious responses, Grok is integrated with X (formerly Twitter) and has real-time access to social media posts, which allows it to provide up-to-the-minute information on recent events. This real-time feature sets it apart from competitors like ChatGPT, which only has data up until more recent model updates.

For example, if a user asks Grok about a trending topic, it can pull in real-time posts from X to show what people are discussing. However, Grok has been criticized for inaccuracies and handling controversial or politically sensitive topics with less caution than its peers..

21. Guardrails

In the context of AI, "guardrails" refer to the safety mechanisms and rules put in place to ensure that AI systems behave responsibly, avoid harmful outputs, and align with ethical standards. These guardrails are designed to prevent AI models from generating inappropriate, biased, or dangerous content and often include safety protocols like content filters, response moderation, and restrictions on certain topics. Guardrails help AI systems remain safe, reliable, and ethical for public and enterprise use.

For example, an AI chatbot with guardrails might refuse to answer queries related to harmful activities, offensive content, or misinformation. Similarly, content filters in AI image generators, like Stable Diffusion or DALL-E, prevent the creation of explicit or harmful images.

22. Hallucinate

In the context of AI, "hallucination" refers to the phenomenon where a model generates content or responses that are factually incorrect, nonsensical, or fabricated. This occurs when the AI confidently provides information that may not be based on any real-world data or reliable sources, often filling in gaps with plausible-sounding but inaccurate details. Hallucination is a known challenge in large language models (LLMs) and generative AI systems, especially in tasks like summarization, question answering, or content generation.

For example, an AI chatbot might "hallucinate" by confidently citing a non-existent study or generating a fabricated quote when asked for factual information. This issue can be particularly problematic in critical fields such as healthcare or legal advice, where accuracy is paramount.

23. LLM (Large Language Model)

An LLM, or Large Language Model, refers to an advanced type of machine learning model that has been trained on vast amounts of text data to understand and generate human-like language. These models use deep learning techniques, particularly neural networks with many layers, to learn the structure, grammar, and meaning of language. LLMs, such as OpenAI’s GPT series, Google’s Gemini, and Meta’s LLaMA, can perform a wide variety of tasks, from text generation and summarization to answering questions and even coding.

For example, GPT-4, an LLM developed by OpenAI, can generate coherent essays, write code, and answer complex questions across numerous subjects. Similarly, LLaMA, an open-source LLM by Meta, is designed for research purposes and supports a wide range of applications, including translation and sentiment analysis.

24. Literature Review

A literature review is a comprehensive summary and critical evaluation of existing research and scholarly works on a specific topic. The purpose of a literature review is to identify gaps in the current knowledge, understand the progression of research over time, and provide a foundation for future studies by summarizing and synthesizing key findings from past research. It typically includes sources like academic journals, books, and other authoritative publications, organized thematically, chronologically, or methodologically. Literature reviews are essential in fields like academia and research to ensure that new studies build on a solid foundation of existing knowledge.

For example, a n AI-assisted literature review on climate change mitigation strategies could efficiently search and summarize large volumes of academic research, governmental reports, and policy papers. By categorizing studies into themes like technological solutions, policy initiatives, and socio-economic impacts, AI can track the evolution of research from foundational climate science to applied mitigation strategies. It can also identify trends, compare methodologies, and highlight under-researched areas, such as the long-term effectiveness of specific policies or technologies in diverse socio-political contexts. This approach streamlines the review process, uncovering insights that guide future research efforts.

25. Meta's LLaMA

LLaMA (Large Language Model Meta AI) is a series of open-source AI models developed by Meta. LLaMA 2, released in partnership with Microsoft, provides models in various sizes and is optimized for use across platforms such as AWS and Azure. These models are designed for a wide range of applications, including text generation, content moderation, and chatbots. LLaMA 2 is available for both commercial and research purposes and emphasizes safety and transparency through open access and responsible use guidelines.

26. Microsoft Copilot

Microsoft Copilot is an AI assistant integrated across various Microsoft platforms, such as Windows, Microsoft 365, and Edge. It's designed to assist users with tasks like answering questions, generating summaries, providing recommendations, and even managing productivity. The latest version of Copilot introduces features like Copilot Voice, allowing users to interact using natural speech, and Copilot Vision, which enables Copilot to view and interpret content on web pages and assist with navigation and suggestions in real-time. Additionally, Copilot Daily acts like a personalized briefing, delivering updates on news, weather, and reminders in a podcast-like format.

For example, while browsing, you can ask Copilot for advice or help with tasks like summarizing a webpage or suggesting actions related to the content you're viewing. These new capabilities aim to make the AI feel more like a conversational partner than just a tool.

27. Mistral AI

Mistral AI is a French startup known for developing cutting-edge, open-source AI models. Their flagship models, like Mistral Large 2, are highly advanced, offering significant improvements in tasks such as code generation, mathematical reasoning, and multilingual capabilities. Released in mid-2024, the Mistral Large 2 model has a large context window (128,000 tokens) and provides enhanced function-calling features, making it suitable for complex workflows like reasoning and data retrieval. Additionally, the company focuses on open-source development, making their models available under licenses like Apache 2.0, promoting accessibility and customization.

For instance, Mistral's Pixtral 12B model adds multimodal capabilities, allowing users to process both text and images, making it useful for applications like image captioning and document analysis. Mistral also offers a free API tier to encourage experimentation and prototyping, making these models more accessible for developers.

28. Ollama

Ollama is an AI platform designed to make running and managing large language models (LLMs) easy, offering models like Llama 2, Mistral, and others. It allows users to run these models locally on their devices with built-in support for macOS, Linux, and Windows. Ollama’s key features include OpenAI compatibility, which lets users run models through OpenAI-like API endpoints, and GPU acceleration, optimizing performance on modern hardware without needing extensive configuration. The platform also supports vision models, such as LLaVA, enabling multimodal tasks like image analysis.

For instance, developers can run models locally on their machines and integrate them with existing applications using standard tools like cURL or Python libraries. This makes Ollama suitable for a wide range of applications, from natural language processing to more specialized tasks like sentiment analysis and text summarization.

29. Perplexity AI

Perplexity AI is a conversational AI tool known for its real-time search capabilities, allowing users to receive up-to-date, fact-based responses by pulling data directly from the web. This makes it particularly useful for current events, research, and financial analysis. Unlike traditional language models, Perplexity emphasizes trust by citing sources for each answer, enhancing its credibility. It also integrates well with other AI tools and is capable of handling more structured tasks such as coding or mathematical problem solving. In 2024, Perplexity saw major updates, including enhanced context length and expanded language support, making it a robust tool for more advanced use cases like enterprise applications.

For instance, users can ask Perplexity about the latest stock prices or trends, and the AI will generate answers supported by sources like financial reports or articles. This focus on transparency and accuracy differentiates it from many other AI-powered assistants.

30. QuestWiz

QuestWiz is an AI-powered tool designed for small and medium-sized businesses to create personalized virtual assistants. These AI agents are trained using a business's specific data, such as documents, webpages, and files, allowing them to efficiently answer customer queries and assist with tasks like documentation, customer support, and sales enablement. QuestWiz supports various data formats including TXT, DOCX, PDF, and CSV, making it versatile for different use cases. The tool is especially useful for automating repetitive tasks like answering frequently asked questions, providing businesses with a 24/7 support solution that enhances customer service.

For example, a small business can embed a QuestWiz AI assistant on its website to handle customer inquiries even outside of business hours. This reduces the need for human intervention in routine support tasks, freeing up time for more complex activities and increasing overall efficiency. QuestWiz also allows users to customize widgets and integrate the assistant seamlessly into their websites.

31. RAG (Retrieval-Augmented Generation)

Retrieval-Augmented Generation (RAG) is an advanced AI technique that combines traditional language models with external information retrieval systems. In this approach, the model generates answers by first retrieving relevant documents or data from an external database or search engine and then using the information to craft a more accurate and contextually grounded response. This method enhances the performance of large language models by integrating real-time or specialized knowledge, which helps overcome limitations like outdated or incomplete model training data.

For example, when a user asks a question that involves recent events, a RAG-based system first retrieves relevant documents, such as news articles, from an external source and then generates a response that includes that information. This makes RAG ideal for applications like search engines, research assistants, and fact-checking tools, where accuracy and up-to-date information are critical.

32. Stable Diffusion

Stable Diffusion is a series of text-to-image models developed by Stability AI, known for generating high-quality images from text prompts. The latest version, Stable Diffusion 3, offers significant improvements in handling complex prompts and generating photorealistic images. It also enhances the ability to render legible text within images, a challenge in earlier versions. Stable Diffusion 3 introduces better prompt adherence, making it more reliable for detailed and specific image requests.

For example, users can prompt the model to generate intricate scenes like "a futuristic city skyline at sunset," and the model will produce highly detailed, visually accurate images. It can also be integrated with platforms like Hugging Face and used through APIs for real-time image generation.