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• What is Fréchet inception distance (FID)?

  Fréchet inception distance (FID) is a metric for quantifying the realism and diversity of images generated by generative adversarial networks (GANs). Continue Reading

• What is Q-learning?

  Q-learning is a machine learning approach that enables a model to iteratively learn and improve over time by taking the correct action. Continue Reading

• What is computational linguistics? Definition and career info

  Computational linguistics (CL) is the application of computer science to the analysis and comprehension of written and spoken language. 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

• What is autonomous AI?

  Autonomous artificial intelligence (AI) is a branch of AI in which systems and tools are advanced enough to act with limited human oversight and involvement. Continue Reading

• Definitions to Get Started

  • What is automated machine learning (AutoML)?
  • What are AI agents? Types and examples
  • What is an intelligent agent? Definition, use cases and benefits
  • What is natural language processing (NLP)?

  • What is a neural network?
  • What is edge AI?
  • What is AgentOps? What it does and how it powers AI agents
  • What is automated reasoning? How is it used in AI?

  View All Definitions

• What is a knowledge graph in ML (machine learning)?

  In the realm of machine learning (ML), a knowledge graph is a graphical representation that captures the connections between different entities.Continue Reading

• What is an AI prompt engineer?

  An AI prompt engineer is an expert in creating text-based prompts or cues that can be interpreted and understood by large language models and generative AI tools.Continue Reading

• What is image-to-image translation?

  Image-to-image translation is a generative artificial intelligence (AI) technique that translates a source image into a target image while preserving certain visual properties of the original image.Continue Reading

• How to run LLMs locally: Hardware, tools and best practices

  Local deployments of large language models offer advantages, including privacy, speed and customization -- but organizations need the right tools and infrastructure to succeed.Continue Reading

• What is AI art and how is it created?

  Artificial intelligence art (AI art) is any form of art that has been created or enhanced with AI tools.Continue Reading

• What is responsible AI?

  Responsible AI is an approach to developing and deploying artificial intelligence (AI) from both an ethical and legal point of view. The goal of responsible AI is to use AI in a safe, trustworthy and ethical fashion.Continue Reading

• What is conversational AI (conversational artificial intelligence)?

  Conversational AI, also known as conversational artificial intelligence, is a type of AI that enables computers to understand, process and generate human language.Continue Reading

• 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

• 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

• 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 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)? Definition, Types, Examples & Use Cases

  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

• 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

• 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 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 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

• 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

• AI winter

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

• 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

• 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 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 machine translation?

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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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 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

• 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

• 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

• 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

• 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

• 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

• deconvolutional networks (deconvolutional neural networks)

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

• sustainable AI

  Sustainable AI is the use of artificial intelligence systems that operate in ways contingent with sustainable business practices.Continue Reading

• Exploring GPT-3 architecture

  With 175 billion parameters, GPT-3 is one of the largest and most well-known neural networks available for natural language applications. Learn why people are so pumped about it.Continue Reading

• algorithmic transparency

  Algorithmic transparency is openness about the purpose, structure and underlying actions of the algorithms used to search for, process and deliver information.Continue Reading

• How AI in weather prediction can aid human intelligence

  AI and machine learning models are becoming more widely used in climate prediction and disaster preparedness to aid experts without replacing them.Continue Reading

• Weighing quantum AI's business potential

  Quantum AI has the potential to revolutionize business computing, but logistic complexities create sizeable obstacles for near-term adoption and success.Continue Reading

• knowledge engineering

  Knowledge engineering is a field of artificial intelligence (AI) that tries to emulate the judgment and behavior of a human expert in a given field.Continue Reading

• How AI ethics is the cornerstone of governance

  The concept of AI ethics ensures that AI systems provide accuracy and reliability. Businesses will benefit from adopting AI ethics strategies of their own.Continue Reading

• dropout

  Dropout refers to data, or noise, that's intentionally dropped from a neural network to improve processing and time to results.Continue Reading

• Training GANs relies on calibrating 2 unstable neural networks

  Understanding the complexities and theory of dueling neural networks can carve out a path to successful GAN training.Continue Reading

• UX defines chasm between explainable vs. interpretable AI

  From deep learning to simple code, all algorithms should be transparent. The frameworks of AI interpretability and explainability aim to make machine learning understandable to humans.Continue Reading

• How to build a neural network from the ground floor

  Deep learning is powering the development of AI. To build your own neural network, start by understanding the basics: how neural networks learn, correlate and stack with data.Continue Reading

• How to develop a successful, modern AI infrastructure

  Before AI can revolutionize business processes or decision-making, companies need a strong foundation. These tools, platforms and applications help enterprises get started with AI.Continue Reading

• Neural network applications in business run wide, fast and deep

  Neural network uses are starting to emerge in the enterprise. This handbook examines the growing number of businesses reporting gains from implementing this technology.Continue Reading