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• What is natural language generation (NLG)?

  Natural language generation (NLG) is the use of artificial intelligence (AI) programming to produce written or spoken narratives from a data set. Continue Reading

• What is unsupervised learning?

  Unsupervised learning is a type of machine learning (ML) technique that uses artificial intelligence (AI) algorithms to identify patterns in data sets that are neither classified nor labeled. Continue Reading

• neuromorphic computing

  Neuromorphic computing is a method of computer engineering in which elements of a computer are modeled after systems in the human brain and nervous system. Continue Reading

• What is language modeling?

  Language modeling, or LM, is the use of various statistical and probabilistic techniques to determine the probability of a given sequence of words occurring in a sentence. Language models analyze bodies of text data to provide a basis for their word... Continue Reading

• AI winter

  AI winter is a quiet period for artificial intelligence research and development. Continue Reading

• Definitions to Get Started

  • What is OpenAI?
  • What is neural radiance field (NeRF)?
  • What is Gemma? Google's open sourced AI model explained
  • What is GenAI? Generative AI explained

  • What is lemmatization?
  • What is Perplexity AI?
  • What are diffusion models?
  • What is an autoregressive model?

  View All Definitions

• What is artificial general intelligence (AGI)?

  Artificial general intelligence (AGI) is the representation of generalized human cognitive abilities in software so that, faced with an unfamiliar task, the AI system could find a solution.Continue Reading

• expert system

  An expert system is a computer program that uses artificial intelligence (AI) technologies to simulate the judgment and behavior of a human or an organization that has expertise and experience in a particular field.Continue Reading

• How do big data and AI work together?

  Enterprises are leaning on big data to train AI algorithms and, in turn, are using AI to understand big data. The results are pushing business operations forward.Continue Reading

• What is supervised learning?

  Supervised learning is a subcategory of machine learning (ML) and artificial intelligence (AI) where a computer algorithm is trained on input data that has been labeled for a particular output.Continue Reading

• Simplify enterprise AI integration with a centralized AI hub

  For enterprises looking to scale their AI projects, centralized AI hubs and governance can simplify integration, streamline operations and ensure consistency.Continue Reading

• What is a backpropagation algorithm?

  A backpropagation algorithm, or backward propagation of errors, is an algorithm that's used to help train neural network models.Continue Reading

• How to use and run Jupyter Notebook: A beginner's guide

  Learn how to create your first project with Jupyter Notebook, a popular platform for presenting data science and machine learning work with interactive code, text and visuals.Continue Reading

• How and why to run machine learning workloads on Kubernetes

  Running ML model development and deployment on Kubernetes is an absolute must in a world where decoupling workloads can optimize resources and cut costs.Continue Reading

• 15 top applications of artificial intelligence in business

  The use of AI in business applications and operations is expanding. Learn about where enterprises are applying AI and the benefits AI applications are driving.Continue Reading

• Few-shot learning explained: What you should know

  Training data quality and availability aren't always a given in machine learning projects. When data is limited, costly or nonexistent, few-shot learning can help.Continue Reading

• What is regression in machine learning?

  Regression in machine learning helps organizations forecast and make better decisions by revealing the relationships between variables. Learn how it's applied across industries.Continue Reading

• What is adversarial machine learning?

  Adversarial machine learning is a technique used in machine learning (ML) to fool or misguide a model with malicious input.Continue Reading

• What is machine translation?

  Machine translation technology enables the conversion of text or speech from one language to another using computer algorithms.Continue Reading

• What is anomaly detection? An overview and explanation

  Anomaly detection is the process of identifying data points, entities or events that fall outside the normal range.Continue Reading

• What is clustering in machine learning and how does it work?

  Clustering is a data science technique in machine learning that groups similar rows in a data set.Continue Reading

• What is natural language understanding (NLU)?

  Natural language understanding (NLU) is a branch of artificial intelligence (AI) that uses computer software to understand input in the form of sentences using text or speech.Continue Reading

• CNN vs. RNN: How are they different?

  Convolutional and recurrent neural networks have distinct but complementary capabilities and use cases. Compare each model architecture's strengths and weaknesses in this primer.Continue Reading

• What is machine vision?

  Machine vision is the ability of a computer to see; it employs one or more video cameras, analog-to-digital conversion and digital signal processing.Continue Reading

• What is fine-tuning in machine learning and AI?

  Fine-tuning is the process of taking a pretrained machine learning model and further training it on a smaller, targeted data set.Continue Reading

• Machine learning vs. neural networks: What's the difference?

  Though machine learning and neural networks are both forms of AI, neural networks are a specific type of ML algorithm. Learn more about their similarities and differences.Continue Reading

• Machine learning regularization explained with examples

  Regularization in machine learning refers to a set of techniques used by data scientists to prevent overfitting. Learn how it improves ML models and prevents costly errors.Continue Reading

• What is boosting in machine learning?

  Boosting is a technique used in machine learning that trains an ensemble of so-called weak learners to produce an accurate model, or strong learner. Learn how it works.Continue Reading

• Microsoft 365 Copilot features and architecture explained

  Microsoft's new assistant adds generative AI to the workplace, using various features and architectural components for automated suggestions, content creation and data insights.Continue Reading

• What is Bayes' theorem? How is it used in machine learning?

  Bayes' theorem is a mathematical formula used in probability theory to calculate conditional probability, i.e., the revised likelihood of an outcome occurring given the knowledge of a related condition or previous outcome.Continue Reading

• What is reinforcement learning?

  Reinforcement learning (RL) is a machine learning training method that trains software to make certain desired actions.Continue Reading

• How to build the business case for AI initiatives

  Building a compelling business case for AI requires attention to business pain points, financial and risk considerations, and collaboration with the CFO.Continue Reading

• gradient descent

  Gradient descent is an optimization algorithm that refines a machine learning (ML) model's parameters to create a more accurate model.Continue Reading

• Learn how to create a machine learning pipeline

  Well-considered machine learning pipelines provide a structured approach to AI development in modern IT environments, ensuring uniformity, speed and business alignment.Continue Reading

• Attributes of open vs. closed AI explained

  What's the difference between open vs. closed AI, and why are these approaches sparking heated debate? Here's a look at their respective benefits and limitations.Continue Reading

• Generative models: VAEs, GANs, diffusion, transformers, NeRFs

  Choosing the right GenAI model for the task requires understanding the techniques each uses and their specific talents. Learn about VAEs, GANs, diffusion, transformers and NerFs.Continue Reading

• large language model operations (LLMOps)

  Large language model operations (LLMOps) is a methodology for managing, deploying, monitoring and maintaining LLMs in production environments.Continue Reading

• Supervised vs. unsupervised learning explained by experts

  Learn the characteristics of supervised learning, unsupervised learning and semisupervised learning and how they're applied in machine learning projects.Continue Reading

• automated machine learning (AutoML)

  Automated machine learning (AutoML) is the process of applying machine learning models to real-world problems using automation.Continue Reading

• A guide to deploying AI in edge computing environments

  Deploying AI at the edge is increasingly popular due to processing speed and other benefits. Consider hosting requirements, latency budget and platform options to get started.Continue Reading

• self-driving car (autonomous car or driverless car)

  A self-driving car -- sometimes called an autonomous car or driverless car -- is a vehicle that uses a combination of sensors, cameras, radar and artificial intelligence (AI) to travel between destinations without a human operator.Continue Reading

• How to build a machine learning model in 7 steps

  Building a machine learning model is a multistep process involving data collection and preparation, training, evaluation, and ongoing iteration. Follow these steps to get started.Continue Reading

• AI, copyright and fair use: What you need to know

  As AI technology advances, U.S. and international copyright laws are struggling to keep pace, raising legal and ethical questions about ownership and AI-generated content.Continue Reading

• Compare natural language processing vs. machine learning

  Both natural language processing and machine learning identify patterns in data. What sets them apart is NLP's language focus vs. ML's broader applicability to many AI processes.Continue Reading

• The different types of machine learning explained

  Rigorous experimentation is key to building machine learning models. Learn about the main types of ML models and the many factors that go into training the right one for the task.Continue Reading

• data splitting

  Data splitting is when data is divided into two or more subsets. Typically, with a two-part split, one part is used to evaluate or test the data and the other for training the model.Continue Reading

• machine learning engineer (ML engineer)

  A machine learning engineer (ML engineer) is a person in IT who focuses on researching, building and designing self-running artificial intelligence (AI) systems to automate predictive models.Continue Reading

• telepresence robot

  A telepresence robot is a robotic device that enables a user to maintain a virtual presence in a remote location.Continue Reading

• How to build an MLOps pipeline

  Machine learning initiatives involve multiple complex workflows and tasks. A standardized pipeline can streamline this process and maximize the benefits of an MLOps approach.Continue Reading

• robot economy

  The robot economy is a scenario in which most of the labor required to sustain human life is automated.Continue Reading

• How to identify and manage AI model drift

  The training data and algorithms used to build AI models have a shelf life. Detecting and correcting model drift ensures that these systems stay accurate, relevant and useful.Continue Reading

• Best practices for getting started with MLOps

  As AI and machine learning become increasingly popular in enterprises, organizations need to learn how to set their initiatives up for success. These MLOps best practices can help.Continue Reading

• facial recognition

  Facial recognition is a category of biometric software that maps an individual's facial features to confirm their identity.Continue Reading

• What is the inception score (IS)?

  The inception score (IS) is a mathematical algorithm used to measure or determine the quality of images created by generative AI through a generative adversarial network (GAN).Continue Reading

• What are graph neural networks (GNNs)?

  Graph neural networks (GNNs) are a type of neural network architecture and deep learning method that can help users analyze graphs, enabling them to make predictions based on the data described by a graph's nodes and edges.Continue Reading

• What are vector embeddings?

  Vector embeddings are numerical representations that capture the relationships and meaning of words, phrases and other data types.Continue Reading

• What are masked language models (MLMs)?

  Masked language models (MLMs) are used in natural language processing (NLP) tasks for training language models.Continue Reading

• Tips for planning a machine learning architecture

  When planning a machine learning architecture, organizations must consider factors such as performance, cost and scalability. Review necessary components and best practices.Continue Reading

• Mixture-of-experts models explained: What you need to know

  By combining specialized models to handle complex tasks, mixture-of-experts architectures can improve efficiency and accuracy for large language models and other AI systems.Continue Reading

• How to build an enterprise generative AI tech stack

  Generative AI tech stacks consist of key components like LLMs, vector databases and fine-tuning tools. The right tech stack can help enterprises maximize their generative AI ROI.Continue Reading

• How to get started with machine learning

  Machine learning roles are rapidly evolving and require a diverse range of skills. Looking to join the field? Start by exploring job responsibilities and required experience.Continue Reading

• The need for common sense in AI systems

  Building explainable and trustworthy AI systems is paramount. To get there, computer scientists Ron Brachman and Hector Levesque suggest infusing common sense into AI development.Continue Reading

• Prompt engineering tips for ChatGPT and other LLMs

  Master the art of prompt engineering -- from basic best practices to advanced strategies -- with practical tips to get more precise, relevant output from large language models.Continue Reading

• BERT language model

  BERT language model is an open source machine learning framework for natural language processing (NLP).Continue Reading

• Improve AI security by red teaming large language models

  Cyberattacks such as prompt injection pose significant security risks to LLMs, but implementing red teaming strategies can test models' resistance to various cyberthreats.Continue Reading

• The role of trusted data in building reliable, effective AI

  Without quality data, creating and managing AI systems is an uphill battle. Methods such as zero-copy integration and primary key consistency can ensure trusted data for better AI.Continue Reading

• Retrieval-Augmented Language Model pre-training

  A Retrieval-Augmented Language Model, also referred to as REALM or RALM, is an artificial intelligence language model designed to retrieve text and then use it to perform question-based tasks.Continue Reading

• Video guide to generative AI

  Generative AI has the potential to revolutionize technology. Learn about popular interfaces such as ChatGPT, the future of generative AI and its effects on businesses.Continue Reading

• ChatGPT explained in a minute

  ChatGPT is an AI-powered chatbot developed by OpenAI. With its ability to communicate in natural language patterns, it can create various types of content for many use cases.Continue Reading

• How AI is advancing assistive technology

  Recent advances in generative AI could revolutionize assistive technology. For people relying on assistive tools, AI-powered devices could usher in a new era of accessibility.Continue Reading

• What is sentiment analysis?

  Learn how AI is used to perform sentiment analysis, the different categories of sentiment that can be identified and how the analysis can be used to improve customer satisfaction.Continue Reading

• What is natural language processing (NLP)?

  NLP enables computers to understand language like humans. This video explores its techniques, applications and challenges, highlighting its importance in businesses.Continue Reading

• Explore the impact of data science in business workflows

  Data science and machine learning are reshaping business workflows and customer experiences, ushering in an era of highly tailored services and predictive strategies.Continue Reading

• How an AI governance framework can strengthen security

  Learn how AI governance frameworks promote security and compliance in enterprise AI deployments with essential components such as risk analysis, access control and incident response.Continue Reading

• deep tech

  Deep technology, or deep tech, refers to advanced technologies based on some form of substantial scientific or engineering innovation.Continue Reading

• How to become an MLOps engineer

  Explore the key responsibilities and skills needed for a career in MLOps, which focuses on managing ML workflows throughout the model lifecycle.Continue Reading

• A guide to ChatGPT Enterprise use cases and implementation

  ChatGPT Enterprise promises powerful generative AI capabilities for business use cases, but successful implementation requires careful planning for security, costs and integration.Continue Reading

• How do LLMs like ChatGPT work?

  AI expert Ronald Kneusel explains how transformer neural networks and extensive pretraining enable large language models like GPT-4 to develop versatile text generation abilities.Continue Reading

• Demystifying AI with a machine learning expert

  In this interview, author Ronald Kneusel discusses his new book 'How AI Works,' the recent generative AI boom and tips for those looking to enter the AI field.Continue Reading

• AI watermarking

  AI watermarking is the process of embedding a recognizable, unique signal into the output of an artificial intelligence model, such as text or an image, to identify that content as AI generated.Continue Reading

• data dignity

  Data dignity, also known as data as labor, is a theory positing that people should be compensated for the data they have created.Continue Reading

• ambient intelligence (AmI)

  Ambient intelligence, sometimes referred to as AmI, is the element of a pervasive computing environment that enables it to interact with and respond appropriately to the humans in that environment.Continue Reading

• neural net processor

  A neural net processor is a central processing unit (CPU) that holds the modeled workings of how a human brain operates on a single chip.Continue Reading

• How to source AI infrastructure components

  Rent, buy or repurpose AI infrastructure? The right choice depends on an organization's planned AI projects, budget, data privacy needs and technical personnel resources.Continue Reading

• neurosynaptic chip

  A neurosynaptic chip, also known as a cognitive chip, is a computer processor that is designed to function more like a biological brain than a typical central processing unit (CPU).Continue Reading

• IBM Watson supercomputer

  Watson was a supercomputer designed and developed by IBM. This advanced computer combined artificial intelligence (AI), automation and sophisticated analytics capabilities to deliver optimal performance as a 'question answering' machine.Continue Reading

• Why and how to use Google Colab

  Whether you're looking to gain experience or you're already an expert data scientist, Google Colab can help boost ML and AI initiatives. Follow this tutorial to learn the basics.Continue Reading

• Lessons on integrating generative AI into the enterprise

  At Generative AI World 2023, various industries convened to explore existing and potential generative AI use cases. Review insights from one company's implementation experience.Continue Reading

• Build a natural language processing chatbot from scratch

  In this excerpt from the book 'Natural Language Processing in Action,' you'll walk through the steps of creating a simple chatbot to understand how to start building NLP pipelines.Continue Reading

• Q&A: How to start learning natural language processing

  In this Q&A, 'Natural Language Processing in Action' co-author Hobson Lane discusses how to start learning NLP, including benefits and challenges of building your own pipelines.Continue Reading

• Why and how to develop a set of responsible AI principles

  Enterprise AI use raises a range of pressing ethical issues. Learn why responsible AI principles matter and explore best practices for enterprises developing an AI framework.Continue Reading

• Compare machine learning vs. software engineering

  Although machine learning has a lot in common with traditional programming, the two disciplines have several key differences, author and computer scientist Chip Huyen explains.Continue Reading

• How data quality shapes machine learning and AI outcomes

  Data quality directly influences the success of machine learning models and AI initiatives. But a comprehensive approach requires considering real-world outcomes and data privacy.Continue Reading

• cognitive computing

  Cognitive computing is the use of computerized models to simulate the human thought process in complex situations where the answers might be ambiguous and uncertain.Continue Reading

• machine teaching

  Machine teaching is the practice of infusing context -- and often business consequences -- into the selection of training data used in machine learning (ML) so that the most relevant outputs are produced by the ML algorithms.Continue Reading

• Use cases show the combined potential of AI and blockchain

  AI and blockchain are both hot topics in IT, yet used for different purposes. However, enterprises across various sectors can now combine both technologies to their advantage.Continue Reading

• automated reasoning

  Automated reasoning is the area of computer science concerned with applying reasoning in the form of logic to computing systems.Continue Reading

• cognitive search

  Cognitive search represents a new generation of enterprise search that uses artificial intelligence (AI) technologies to improve users' search queries and extract relevant information from multiple diverse data sets.Continue Reading

• case-based reasoning (CBR)

  Case-based reasoning (CBR) is an experience-based approach to solving new problems by adapting previously successful solutions to similar problems.Continue Reading

• deconvolutional networks (deconvolutional neural networks)

  Deconvolutional networks are convolutional neural networks (CNN) that work in a reversed process.Continue Reading

• cognitive modeling

  Cognitive modeling is an area of computer science that deals with simulating human problem-solving and mental processing in a computerized model.Continue Reading