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• computational linguistics (CL)

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

• What is generative AI? Everything you need to know

  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

• 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

• 8 top generative AI tool categories for 2024

  Need a generative AI-specific tool for your organization's development project? Explore the major categories these tools fall into and their capabilities. Continue Reading

• Definitions to Get Started

  • computational linguistics (CL)
  • What is generative AI? Everything you need to know
  • OpenAI
  • deep tech

  • Google Bard
  • natural language generation (NLG)
  • adversarial machine learning
  • robo-advisor

  View All Definitions

• 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

• Google Bard

  Google Bard is an AI-powered chatbot tool designed by Google to simulate human conversations using natural language processing and machine learning.Continue Reading

• 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

• Compare 8 prompt engineering tools

  To get the most out of large language models, developers and other users rely on prompt engineering techniques to achieve their desired output. Review 8 tools that can help.Continue Reading

• 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

• 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 to build a winning AI strategy, explained by experts

  Executives are aware of the value artificial intelligence in its many forms can bring to enterprises yet devising a viable AI strategy can be as complex as the technology itself.Continue Reading

• robo-advisor

  A robo-advisor is a virtual financial advisor powered by artificial intelligence (AI) that employs an algorithm to deliver an automated selection of financial advisory services.Continue Reading

• 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

• artificial superintelligence (ASI)

  Artificial superintelligence (ASI) is a software-based system with intellectual powers beyond those of humans across a comprehensive range of categories and fields of endeavor.Continue Reading

• artificial intelligence (AI)

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

• 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

• 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

• 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

• 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

• backpropagation algorithm

  Backpropagation, or backward propagation of errors, is an algorithm that is designed to test for errors working back from output nodes to input nodes.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

• 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

• prompt engineering

  Prompt engineering is an AI engineering technique encompassing the process of refining LLMs with specific prompts and recommended outputs, as well as the process of refining input to various generative AI services to generate text or images.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

• retrieval-augmented generation

  Retrieval-augmented generation (RAG) is an AI framework that retrieves data from external sources.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

• 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

• Amazon Bedrock (AWS Bedrock)

  Amazon Bedrock -- also known as AWS Bedrock -- is a machine learning platform used to build generative artificial intelligence (AI) applications on the Amazon Web Services cloud computing platform.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

• AI prompt

  An artificial intelligence (AI) prompt is a mode of interaction between a human and a large language model that lets the model generate the intended output.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

• 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

• 10 prompt engineering tips and best practices

  Asking the right questions is key to using generative AI effectively. Learn 10 tips for writing clear, useful prompts, including mistakes to avoid and advice for image generation.Continue Reading

• 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

• LangChain

  LangChain is an open source framework that lets software developers working with artificial intelligence (AI) and its machine learning subset combine large language models with other external components to develop LLM-powered applications.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

• 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

• 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

• anomaly detection

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

• 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 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

• 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

• 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

• What are machine learning models? Types and examples

  Training data and algorithms are key, but there are many learning techniques, processes and practices that influence the selection, care and feeding of machine learning models.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

• 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

• Prompt engineering vs. fine-tuning: What's the difference?

  Prompt engineering and fine-tuning are both practices used to optimize AI output. But the two use different techniques and have distinct roles in model training.Continue Reading

• neural network

  A neural network is a machine learning (ML) model designed to mimic the function and structure of the human brain.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

• GPT-3

  GPT-3, or the third-generation Generative Pre-trained Transformer, is a neural network machine learning model trained using internet data to generate any type of text.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

• clustering in machine learning

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

• reinforcement learning

  Reinforcement learning is a machine learning training method based on rewarding desired behaviors and punishing undesired ones.Continue Reading

• linear regression

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

• 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

• 4 types of learning in machine learning explained

  Factoring performance, accuracy, reliability and explainability, data scientists consider supervised, unsupervised, semi-supervised and reinforcement models to reach best outcomes.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

• 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

• 4 main types of artificial intelligence: Explained

  How close are we to creating an artificial superintelligence that surpasses the human mind? Though we aren't close, the pace is quickening as we develop more advanced types of AI.Continue Reading

• machine translation

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

• supervised learning

  Supervised learning is an approach to creating artificial intelligence (AI) where a computer algorithm is trained on input data that has been labeled for a particular output.Continue Reading

• deep learning

  Deep learning is a type of machine learning and artificial intelligence (AI) that imitates the way humans gain certain types of knowledge.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

• Artificial Intelligence as a Service (AIaaS)

  Artificial Intelligence as a Service (AIaaS) is the third-party offering of artificial intelligence (AI) outsourcing.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

• 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

• 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

• automated machine learning (AutoML)

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

• variational autoencoder (VAE)

  A variational autoencoder (VAE) is a generative AI algorithm that uses deep learning to generate new content, detect anomalies and remove noise.Continue Reading

• The role of AI parameters in the enterprise

  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

• Artificial intelligence vs. human intelligence: How are they different?

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

• responsible AI

  Responsible AI is an approach to developing and deploying artificial intelligence (AI) from both an ethical and legal point of view.Continue Reading

• 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

• machine learning bias (AI bias)

  Machine learning bias, also sometimes called 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 process.Continue Reading

• artificial intelligence (AI) governance

  Artificial intelligence governance is the legal framework for ensuring AI and machine learning technologies are researched and developed with the goal of helping humanity navigate the adoption and use of these systems in ethical and responsible ways.Continue Reading

• 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

• singularity

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

• What is trustworthy AI and why is it important?

  What are the tenets of trustworthy AI and how do the funders and developers of AI ensure they're upheld?Continue Reading

• multimodal AI

  Multimodal AI is artificial intelligence that combines multiple types, or modes, of data to create more accurate determinations, draw insightful conclusions or make more precise predictions about real-world problems.Continue Reading

• 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

• conversational AI

  Conversational AI is a type of artificial intelligence that enables computers to understand, process and generate human language.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

• AI art (artificial intelligence art)

  AI art (artificial intelligence art) is any form of digital art created or enhanced with AI tools.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

• History of generative AI innovations spans 9 decades

  ChatGPT's debut has prompted widespread publicity and controversy surrounding generative AI, a subset of artificial intelligence that's deep-rooted in historic milestones.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

• 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