AI Words And Phrases Data Scientist Should Know

1. Algorithm

An algorithm is a set of rules for solving a problem or performing a task. It consists of well-defined instructions that can be followed to achieve a desired outcome. An Algorithm can used in various fields, such as computer science, data analysis, artificial intelligence, cryptography, and more. There are many types of algorithms, including:

1. • Sorting Algorithms (e.g., QuickSort, MergeSort)
   • Search Algorithms (e.g., Binary Search, Depth-First Search)
   • Graph Algorithms (e.g., Dijkstra’s, Kruskal’s)
   • Machine Learning Algorithms (e.g., Decision Trees, Neural Networks)

2. 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. Good API documentation is crucial for developers to understand how to use the API effectively, including endpoints, request/response formats, and error handling. There are many types of APIs, including:

1. • Web APIs: Accessed over HTTP/HTTPS, commonly used for web services (e.g., RESTful APIs, GraphQL).
   • Library APIs: Functions or methods provided by programming libraries or frameworks.
   • Operating System APIs: Allow applications to interact with the operating system (e.g., file management, network access).
   • Hardware APIs: Enable communication with hardware components (e.g., device drivers).

3. Artificial intelligence (AI)

4. Big data

Big data refers to the vast volumes of structured and unstructured data that are generated every second from various sources, such as social media, sensors, transactions. Big data has the potential to drive significant advancements and innovations across industries. Through data mining, powerful AI software can analyze these large databases to identify patterns and draw conclusions. An access to big data allows AI solutions grow more intelligent and deliver more human-like interactions. Several technologies and frameworks are designed to handle big data, including:

1. • Hadoop: An open-source framework for distributed storage and processing.
   • Apache Spark: A fast and general-purpose cluster-computing system.
   • NoSQL Databases: Such as MongoDB and Cassandra, which are designed to handle unstructured data.

5. Black box

The term “black box” can refer to different concepts depending on the context. In Artificial Intelligence and Machine Learning, Black Box refers to systems where the internal workings are not easily interpretable or understandable. While these models can make accurate predictions, it can be challenging to understand how they arrive at those predictions. Black box describes an AI system whose inner workings are impossible to view. Humans can’t find out how black box AI comes to a specific decision — only inputs and outputs can be observed. For instance, ChatGPT is an example of an AI black box. It’s impossible to tell which answer it will give and why it gives any specific answer over another.

14. Clustering

Clustering in linguistics refers to the grouping of similar linguistic elements, such as words or phrases, based on shared characteristics or contexts. This can occur at various levels, including phonetics, syntax, or semantics. For instance, in phonetics, consonant clusters occur when two or more consonants appear together in a syllable. In semantics, clustering might involve categorizing words with related meanings, which helps in understanding language patterns and structures. Overall, clustering aids in organizing linguistic data and analyzing language use in different contexts.

15. Conversational AI

Conversational AI refers to technologies that enable machines to engage in human-like dialogue. This includes chatbots, virtual assistants, and other applications that use natural language processing (NLP) and machine learning to understand and generate human language. Key features of conversational AI include:

1. Natural Language Understanding (NLU): The ability to comprehend user input in natural language, recognizing intent and context.
2. Natural Language Generation (NLG): The capability to produce coherent and contextually relevant responses.
3. Dialog Management: Managing the flow of conversation, maintaining context, and ensuring logical progression in interactions.
4. Learning and Adaptation: The ability to improve responses over time based on user interactions and feedback.

‍Learn all the differences between chatbots and conversational AI in our Chatbot vs Conversational AI blog post.

16. Conversational SMS

Conversational SMS is a text messaging system that allows customers and businesses to have natural, personalized two-way interactions. These conversations can be facilitated by either AI or human agents.

17. Conversational user interface (CUI)

A conversational user interface (also known as CUI or Conversational UI) is what allows computers to mimic conversations with real humans. These interfaces use Natural Language Processing (see below) to interpret incoming voice or text and reply with a response.

The two primary types of CUIs are voice assistants (like Siri and Alexa) and chatbots.

18. Data mining

Data mining is the analysis of large databases to generate new information. Through data mining, AI tools become more effective at solving a wider variety of problems.

19. Decision tree

A decision tree is a structure of responses that help a chatbot give specific answers to customer questions. By asking a series of questions, known as branches, chatbots can use a decision tree to narrow down a customer’s goal.

20. Deep learning

Deep learning is a type of machine learning in which multiple layers of networks are used to train algorithms using large data sets. As opposed to traditional machine learning, deep learning can understand unstructured data more effectively, often leading to higher-quality results.

21. Deepfake

A deepfake is a video of someone in which their face has been altered by AI to make them look like another person.

Deepfakes are often used maliciously to defame or spread misinformation about a person.

22. Emergent behavior

Also known as emergence, emergent behavior occurs when an AI system does something surprising or more complex than it was programmed to do. Emergence can be either dangerous or beneficial, but it’s always unexpected and difficult to predict.

Picture a flock of birds moving together in an intricate pattern — no single bird is directing them, yet they collectively create elaborate formations. This is emergent behavior.

23. Evolutionary algorithm

An evolutionary algorithm (EA) uses mechanisms inspired by nature — think survival of the fittest — to solve problems better. Chatbots that use EAs test out and compare different possible responses to a question to determine the optimal way to answer a prompt.

24. Explainable AI (XAI)

Explainable AI refers to transparent systems that let people oversee how decisions or predictions are made by AI. XAI is the opposite of black box AI, whose inner workings aren’t easy to understand.

XAI is also known as interpretable AI or explainable machine learning (XML).

25. Fuzzy logic

Fuzzy logic is an approach to computing based on varying degrees of truth as opposed to a binary true or false approach. Whenever chatbots have to respond to unclear or vague instructions, those built to incorporate fuzzy logic will come up with better, more natural responses.

26. Gemini

Gemini, known as Bard until February 2024, is Google’s generative AI chatbot. Many see it as a top rival to OpenAI’s ChatGPT.

It’s powered by a large language model (LLM) of the same name, which was unveiled in December 2023 and serves as the successor to LaMDA.

27. Generative AI

Generative AI is an umbrella term for any artificial intelligence that can create new content (like text or images) using the data it was trained on. This is different from “traditional” AI, which uses patterns to make predictions.

ChatGPT and Bard are examples of advanced generative AI. What makes this technology appealing is that it can produce content that is indistinguishable from that created by humans — allowing people to have natural conversations.

Meanwhile, traditional AI is typically used in technology like the bubble tree. In this example, users can only select from a limited number of pre-defined options, with the program trying to ultimately predict your end need based on a series of prompts (like an elaborate game of 21 Questions).

To see how generative AI is transforming customer service, sign up for our interactive webinar, Generative AI in Customer Support: Benefits & Practical Applications.

28. Generative AI chatbot

A generative AI chatbot is a type of chatbot powered by AI models that enable it to create unique responses in real time. Unlike rule-based chatbots that rely on predefined scripts, generative AI chatbots can come up with their own replies that are more varied, contextually relevant, and conversational.

29. GPT-4

Short for Generative Pre-Trained Transformer 4, GPT-4 is a language model released by OpenAI in March 2023 capable of producing human-like responses. It serves as the basis of ChatGPT.

It performs at a much higher level than its predecessor, GPT-3.

30. GPT-4o

Officially named GPT-4 Omni, GPT-4o is is a language model announced by OpenAI in May 2024. Capable of processing and generating text, images, and audio, GPT-4o is expected to perform twice as fast as GPT-4.

31. Grok

Grok is a generative AI chatbot developed by xAI, which was founded by Elon Musk. Released in beta in November 2023, Grok has been dubbed the anti-woke chatbot since xAI has programmed it to answer questions on any topic and has given it “a rebellious streak.”

32. Grounding

Grounding is the process of determining how factual a response generated by a chatbot actually is. Generative AI chatbots can deliver convincing answers — even when they’re wrong — so companies need to ground responses to ensure high levels of accuracy.

33. Guardrails

Guardrails are rules that limit the actions AI can take or the answers it can give users. They ensure AI handles data properly and doesn’t generate false or unethical content.

For businesses, guardrails help prevent AI hallucinations and ensure chatbots deliver answers that are accurate and on-brand.

34. Hallucinate

Hallucinating occurs when a chatbot provides a nonsensical, irrelevant, or blatantly false answer. AI chatbots hallucinate due to limitations in their training data or LLM (see large language model).

Think of the most bizarre, least helpful answers you’ve received from a chatbot — those are hallucinations.

FUN FACT: Concerns over the accuracy of AI-generated answers prompted both the Cambridge Dictionary and Dictionary.com to  name ‘hallucinate’ as their 2023 Word of the Year.

35. Heuristic

A heuristic is a problem-solving technique that’s meant to quickly find an acceptable solution when picking an optimal solution is too time-consuming. AI tools use heuristic shortcuts to determine the best decision based on available data.

36. Intent

Intent is the goal a human has when interacting with a machine. When a customer asks a chatbot about the location of their package, for example, a powerful AI tool would be able to recognize the user’s intent as obtaining information about their order status.

By correctly identifying a user’s intent, a chatbot can generate specific responses tailored to a person’s unique needs, helping them accomplish a particular task more quickly.

37. Interactive voice response (IVR)

Interactive voice response technology allows telephone users to speak with a computer-operated system that recognizes what they’re saying

Anytime you call a business and receive an automated reply presenting a pre-recorded menu of options, you’re interacting with an IVR system.

38. Knowledge base (KB)

A knowledge base is a set of data available for a program to draw on to perform a task or give a response. The larger the knowledge base an AI application has access to, the wider the range of problems it can solve.

It’s important to note that an AI program can only pull from the knowledge base it was given. For many online companies, an FAQ page serves as the basis for their knowledge base.

39. Language model (LM)

A language model is a neural network trained to generate sentences. By looking at a question, previously selected words, and even grammar cues (such as optimal character count), it creates a response designed to mimic human speech.

Generative AI tools, such as ChatGPT and Bard, use language models to create unique, rephrased answers to questions. This way, users get the same information without receiving cookie-cutter responses.

“Language models lie at the heart of AI. They’re what computers use to summarize information, translate text, analyze user sentiment, and more.”
– Michał Partyka, VP of Engineering, Zowie

40. LaMDA

Short for Language Model for Dialogue Applications, LaMDA is a group of conversational language models developed by Google in 2021. The LaMDA name is also given to a chatbot built using these models.

In 2022, LaMDA grew in popularity after a Google engineer claimed the chatbot had become sentient.

41. Large language model (LLM)

A large language model is a deep-learning algorithm that recognizes and generates content after training on massive amounts of data. The larger the data set is, the more effective a language model will be at understanding, translating, and predicting text.

Robust LLMs are why chatbots like ChatGPT can deliver impressive responses to a wide range of topics.

42. Machine learning (ML)

Machine learning is a subfield of artificial intelligence that involves teaching computers to perform new tasks without requiring explicit programming.

Thanks to machine learning, chatbots can self-improve without constant human maintenance and identify additional questions to automate on their own.

43. Narrow AI

Narrow AI is a kind of technology that uses a learning algorithm to perform a single task that humans can do. Narrow AI tools can’t apply any knowledge gained from the execution of one task to others.

All AI in existence today is narrow.

44. Natural language generation (NLG)

NLG is a subset of NLP that focuses on the outputs a chatbot gives to people.

NLG determines how logical, appropriate, and human-like a chatbot’s replies are.

45. Natural language processing (NLP)

Natural language processing is a program’s ability to interpret written and spoken human language. It allows computers to understand what people are saying, including their tone and intent.

Natural language processing is what enables chatbots to detect how a customer feels or what they’re trying to achieve, whether they’re frustrated and want to complain or simply trying to complete a purchase.

Find out how NLP can be leveraged in customer service in our blog post, How Does an NLP Chatbot Actually Work?

46. Natural language understanding (NLU)

NLU is a subset of NLP concerned with how well a chatbot comprehends the meaning behind the words people are using.

NLU is how accurately an AI tool takes the words it’s given and converts them into messages a chatbot can recognize.