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• What is a generative adversarial network (GAN)?

  A generative adversarial network (GAN) is a machine learning (ML) model in which two neural networks compete by using deep learning methods to become more accurate in their predictions. Continue Reading

• Types of AI algorithms and how they work

  AI algorithms can help businesses gain a competitive advantage. Learn the main types of AI algorithms, how they work, and why companies must thoroughly evaluate benefits and risks. Continue Reading

• Interpretability vs. explainability in AI and machine learning

  Understanding how AI models make decisions is challenging, but two concepts -- interpretability and explainability -- can shed light on model outputs. Continue Reading

• How to effectively manage AI projects in 12 steps

  AI is a high priority for companies but results often fall short of expectations. These 12 steps will help you successfully manage AI projects and deliver business value. Continue Reading

• How to create a winning AI strategy for your business

  To deliver real business value, AI projects must be aligned with organizational goals. Here is a 10-step program for developing an effective AI strategy, plus sample templates. Continue Reading

• Definitions to Get Started

  • What is a generative adversarial network (GAN)?
  • What is Gen AI? Generative AI explained
  • What is a decision tree in machine learning?
  • What is AI? Artificial Intelligence explained

  • What is embodied AI? How it powers autonomous systems
  • What is agentic AI? Complete guide
  • What is linear regression?
  • What is deep learning and how does it work?

  View All Definitions

• Artificial intelligence vs. human intelligence: Differences explained

  Artificial intelligence is humanlike. There are differences, however, between natural and artificial intelligence. Here are three ways AI and human cognition diverge.Continue Reading

• What is Gen AI? Generative AI explained

  Generative AI is a type of artificial intelligence technology that can produce various types of content, including text, imagery, audio and synthetic data.Continue Reading

• What is a decision tree in machine learning?

  A decision tree is a flow chart created by a computer algorithm to make decisions or numeric predictions based on information in a digital data set.Continue Reading

• What is AI? Artificial Intelligence explained

  Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems.Continue Reading

• Responsible AI vs. ethical AI: What's the difference?

  Ethical AI establishes principles for AI development and use, while responsible AI ensures they're implemented in practice. Learn how the two differ and complement each other.Continue Reading

• AI parameters: Explaining their role in AI model performance

  What is the correlation between the number of parameters and an AI model's performance? It's not as straightforward as the parameter-rich generative AI apps would have us believe.Continue Reading

• What is embodied AI? How it powers autonomous systems

  Embodied AI refers to artificial intelligence systems that interact with and can learn from their environments using a suite of technologies that include sensors, motors, machine learning and natural language processing.Continue Reading

• What is agentic AI? Complete guide

  Agentic AI refers to artificial intelligence systems that are capable of autonomous action and decision-making.Continue Reading

• The history of artificial intelligence: Complete AI timeline

  From the Turing test's introduction to ChatGPT's celebrated launch, AI's historical milestones have forever altered the lifestyles of consumers and operations of businesses.Continue Reading

• What is linear regression?

  Linear regression is a statistical technique that identifies the relationship between the mean value of one variable and the corresponding values of one or more other variables.Continue Reading

• What is deep learning and how does it work?

  Deep learning is a type of machine learning (ML) and artificial intelligence (AI) that trains computers to learn from extensive data sets in a way that simulates human cognitive processes.Continue Reading

• Explore the role of training data in AI and machine learning

  AI and machine learning models use a variety of learning methods to process and analyze data -- but regardless of how a model is trained, data quality and relevance are crucial.Continue Reading

• What is a neural network?

  A neural network is a machine learning (ML) model designed to process data in a way that mimics the function and structure of the human brain.Continue Reading

• What is an AI accelerator?

  An AI accelerator is a type of hardware device that can efficiently support AI workloads.Continue Reading

• Trustworthy AI explained with 12 principles and a framework

  To be considered trustworthy, AI systems should meet these 12 principles and employ a four-step framework to ensure the use of AI is ethical, lawful and robust.Continue Reading

• What is machine learning bias (AI bias)?

  Machine learning bias, also known as 'algorithm bias' or 'AI bias,' is a phenomenon that occurs when an algorithm produces results that are systemically prejudiced due to erroneous assumptions in the machine learning (ML) process.Continue Reading

• What businesses should know about OpenAI's GPT-4o model

  Enterprise use cases are driving the need for faster AI response times, better data handling and cost optimization. OpenAI attempts to meet that need with GPT-4o.Continue Reading

• What business leaders should know about EU AI Act compliance

  AI compliance expert Arnoud Engelfriet shares key takeaways from his book 'AI and Algorithms,' describing the EU AI Act's effects on innovation, risk management and ethical AI.Continue Reading

• AI model optimization: How to do it and why it matters

  Challenges like model drift and operational inefficiency can plague AI models. These model optimization strategies can help engineers improve performance and mitigate issues.Continue Reading

• What is singularity in technology and AI?

  In technology, the singularity describes a hypothetical future where technology growth is out of control and irreversible.Continue Reading

• What is the Turing Test?

  A Turing Test is a method of inquiry in artificial intelligence (AI) for determining whether or not a computer is capable of thinking like a human being.Continue Reading

• What is narrow AI (weak AI)?

  Narrow AI is an application of artificial intelligence technologies to enable a high-functioning system that replicates -- and perhaps surpasses -- human intelligence for a dedicated purpose.Continue Reading

• What is natural language processing (NLP)?

  Natural language processing (NLP) is the ability of a computer program to understand human language as it’s spoken and written -- referred to as natural language.Continue Reading

• 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

• 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

• 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

• 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 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

• How to detect AI-generated content

  AI- or human-generated? To test their reliability, six popular generative AI detectors were asked to judge three pieces of content. The one they got wrong may surprise you.Continue Reading

• Generative AI vs. predictive AI: Understanding the differences

  Generative AI and predictive AI vary in how they handle use cases and unstructured and structured data, respectively. Explore the benefits and limitations of each.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

• GitHub Copilot vs. ChatGPT: How do they compare?

  Copilot and ChatGPT are generative AI tools that can help coders be more productive. Learn about their strengths and weaknesses, as well as alternative coding assistants.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

• What is Google Gemini (formerly Bard)

  Google Gemini -- formerly called Bard -- is an artificial intelligence (AI) chatbot tool designed by Google to simulate human conversations using natural language processing (NLP) and machine learning.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

• Gemma

  Gemma is a collection of lightweight open source generative AI models designed mainly for developers and researchers.Continue Reading

• data poisoning (AI poisoning)

  Data or AI poisoning attacks are deliberate attempts to manipulate the training data of artificial intelligence and machine learning models to corrupt their behavior and elicit skewed, biased or harmful outputs.Continue Reading

• OpenAI

  OpenAI is a private research laboratory that aims to develop and direct artificial intelligence (AI) in ways that benefit humanity as a whole.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

• semantic search

  Semantic search is a data searching technique that uses natural language processing (NLP) and machine learning algorithms to improve the accuracy of search results by considering the searcher's intent and the contextual meaning of the terms used in ...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

• prompt chaining

  Prompt chaining is a technique used when working with generative AI models in which the output from one prompt is used as input for the next.Continue Reading

• Embedding models for semantic search: A guide

  Embedding models in semantic search are changing how we interact with information by going beyond keyword matching to capture meaning and relationships in text and other data.Continue Reading

• How to use Perplexity AI: Tutorial, pros and cons

  AI-powered search engine Perplexity offers a conversational tone and much-needed source citations -- but it's not perfect. Learn how the tool works and how to start using it.Continue Reading

• vision language models (VLMs)

  Vision language models (VLMs) combine machine vision and semantic processing techniques to make sense of the relationship within and between objects in images.Continue Reading

• neuro-symbolic AI

  Neuro-symbolic AI combines neural networks with rules-based symbolic processing techniques to improve artificial intelligence systems' accuracy, explainability and precision.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