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AI technologies

• 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

• 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

• conversational AI

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

• Definitions to Get Started

  • What is generative AI? Everything you need to know
  • multimodal AI
  • conversational AI
  • AI art (artificial intelligence art)

  • cognitive search
  • Google Bard
  • case-based reasoning (CBR)
  • artificial general intelligence (AGI)

  View All Definitions

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• Dall-E

  Dall-E is a generative AI technology that enables users to create new images with text to graphics prompts.Continue Reading

• face detection

  Face detection, also called facial detection, is an artificial intelligence (AI)-based computer technology used to find and identify human faces in digital images and video.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

• 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

• transformer model

  A transformer model is a neural network architecture that can automatically transform one type of input into another type of output.Continue Reading

• CNN vs. GAN: How are they different?

  Convolutional neural networks and generative adversarial networks are both deep learning models but differ in how they work and are used. Learn the ins and outs of CNNs and GANs.Continue Reading

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

  GitHub Copilot and ChatGPT are generative AI tools that can help coders be more productive. Learn about their strengths, weaknesses and optimal use cases.Continue Reading

• neural radiance field (NeRF)

  Neural radiance fields (NeRF) are a technique that generates 3D representations of an object or scene from 2D images by using advanced machine learning.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 do large language models do in AI?

  To capitalize on generative AI, business IT leaders must understand the features of large language models.Continue Reading

• artificial intelligence (AI)

  Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systemsContinue Reading

• assistive technology (adaptive technology)

  Assistive technology is a set of devices intended to help people who have disabilities.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

• generative modeling

  Generative modeling is the use of artificial intelligence (AI), statistics and probability in applications to produce a representation or abstraction of observed phenomena or target variables that can be calculated from observations.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

• image recognition

  Image recognition, in the context of machine vision, is the ability of software to identify objects, places, people, writing and actions in digital images.Continue Reading

• lemmatization

  Lemmatization is the process of grouping together different inflected forms of the same word.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

• 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

• 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

• reinforcement learning

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

• 4 main types of artificial intelligence: Explained

  AI technology can exceed human performance in many areas, but it is still no match for the human brain. Learn about the four main types of AI.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

• natural language processing (NLP)

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

• Recent developments show us the future of chatbots

  Experts in conversational AI are optimistic about what recent advancements in chatbot technology mean for the future. Despite challenges, these advancements can point the way forward.Continue Reading

• How AI can assist industries in environmental protection efforts

  While technology for environmental protection isn't a new concept, AI advancements empower businesses to achieve sustainable operations.Continue Reading

• How significant is AI's role in Industry 4.0?

  Many examples exist to demonstrate the effectiveness of AI in Industry 4.0. It entails the newest revolution in manufacturing, so naturally advanced tech like AI will play a crucial role.Continue Reading

• stemming

  Stemming is the process of reducing a word to its stem that affixes to suffixes and prefixes or to the roots of words known as "lemmas."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

• Why and when to consider a feature store in machine learning

  Feature stores exist to make data for training machine learning models reusable. Explore both the benefits and challenges of feature stores that organizations can experience.Continue Reading

• Industries leading the way in conversational AI

  Learn how companies in vertical markets are using conversational AI and even partnering with AI developers for software that's tailored to their unique business needs.Continue Reading

• The white-box model approach aims for interpretable AI

  The white-box model approach to machine learning makes AI interpretable since algorithms are easy to understand. Ajay Thampi, author of 'Interpretable AI,' explains this approach.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 (ADC) and digital signal processing (DSP).Continue Reading

• How enterprises can establish an AI-first data strategy

  Enterprises looking to mature in their use of AI must focus on the information they're putting into their models. Their models should create trust in their business.Continue Reading

• How hybrid chatbots improve customer experience

  Hybrid chatbots combine human intelligence with AI used in standard chatbots to improve customer experience. Learn how industries are using them to engage with customers.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

• Stochastic point processes and their practical value

  Data scientists learn and utilize stochastic point processes for myriad pragmatic uses. Data scientist Vincent Granville explains this in his new book.Continue Reading

• 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 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

• cognitive computing

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

• Learn the benefits of interpretable machine learning

  In this excerpt from 'Interpretable Machine Learning with Python,' read how machine learning models and algorithms add value when they are both interpretable and explainable.Continue Reading

• Tips and tricks for deploying TinyML

  A typical TinyML deployment has many software and hardware requirements, and there are best practices that developers should be aware of to help simplify this complicated process.Continue Reading

• The benefits of an AI-first strategy

  Enterprises should put AI first in their business strategies by constantly collecting and using new data to power AI models, argues startup investor Ash Fontana.Continue Reading

• fuzzy logic

  Fuzzy logic is an approach to computing based on "degrees of truth" rather than the usual "true or false" (1 or 0) Boolean logic on which the modern computer is based.Continue Reading

• 10 AI tech trends data scientists should know

  The rising environmental and monetary costs of deep learning are catching enterprises' attention, as are new AI techniques like graph neural networks and contrastive learning.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

• 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

• What is a neural network? Explanation and examples

  In information technology, an artificial neural network is a system of hardware and/or software patterned after the operation of neurons in the human brain.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

• A basic design pattern for image recognition

  Learn how a design pattern based on convolutional neural networks can be adapted to create a visual graphics generator model for image recognition.Continue Reading

• General AI vs. narrow AI comes down to adaptability

  AI today has limited and specific applications, but the continual growth of the technology may just lead to the replication of human intelligence through general AI.Continue Reading

• GPT-3 AI language model sharpens complex text generation

  GPT-3 is the latest natural language generation model, but its acquisition by Microsoft leaves developers wondering when, and how, they'll be able to use the model.Continue Reading

• Modern AI evolution timeline shows a decade of rapid progress

  AI has become an asset for organizations to better understand their business position, and its capabilities have improved dramatically over the past decade.Continue Reading

• Learn the business value of AI's various techniques

  To drive business value from AI, business managers need to distinguish between the various AI techniques, starting with the many flavors of machine learning.Continue Reading

• Use of AI-assisted surgery remains limited despite its benefits

  While AI adoption to assist with surgeries remains limited, the technology holds great potential to increase quality of care and decrease patient risk.Continue Reading

• Neuro-symbolic AI emerges as powerful new approach

  The unification of two antagonistic approaches in AI is seen as an important milestone in the evolution of AI. Read about the efforts to combine symbolic reasoning and deep learning by the field's leading experts.Continue Reading

• language modeling

  Language modeling (LM) is the use of various statistical and probabilistic techniques to determine the probability of a given sequence of words occurring in a sentence.Continue Reading

• BERT language model

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

• The peaks and pitfalls of hyper-personalization marketing

  As consumers begin to revolt against unlimited personal data collection and usage, the longevity of hyper-personalized communication may be cut short.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

• Data visualization process yields 360 AI-driven analytics view

  Data visualization tools find increasing uses as part of AI processes to explore data in the initial stages of model development and make outputs easier to digest.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

• How pattern matching in machine learning powers AI

  Pattern matching may sound like a simple idea, but it's being used to create some highly advanced AI tools, powering everything from image recognition to natural language applications.Continue Reading

• Capital One AI VP discusses AI assistant Eno

  Eno from Capital One is an AI assistant that can give customers real-time banking updates and alerts to possible fraud attempts. In this Q&A, Capital One's VP of conversational AI goes over the basics of Eno.Continue Reading

• How to build better conversational AI bots for business uses

  Conversational AI developers from Google, Uber and Autodesk detail dos and don'ts for designing chatbots and AI assistants that can interact effectively with users.Continue Reading

• Knowledge graph applications in the enterprise gain steam

  As the maturity of knowledge graphs improves, enterprises are finding new ways to incorporate them into business operations, though stumbling blocks remain.Continue Reading

• Enterprises put AI in supply chain to streamline processes

  Supply chain AI is helping enterprises that rely on the movement of physical parts and products streamline their operations and automate tricky last-mile problems.Continue Reading

• AI use in healthcare ramps up for app maker Cognoa

  Applications of AI in healthcare have been relatively restricted due to regulatory and data challenges, but one startup is finding ways to make AI effective.Continue Reading

• Social media and AI beef up personalization in marketing

  Enterprises are increasing their use of AI in social media marketing, helping produce more targeted content. As applications evolve, some surprising use cases are emerging.Continue Reading

• AI in real estate smooths paper-based processes

  The use of AI applications in real estate aims to make the paper-based processes of buying and selling property more reliable and repeatable.Continue Reading

• How AI in e-commerce makes vendors more responsive to customers

  AI tools are giving a boost to personalization in e-commerce as vendors find machine learning tools can make ads and experiences more relevant to their customers.Continue Reading

• AI in hospitality industry helps smooth travel turbulence

  A growing number of hotels using AI are reporting streamlined customer service and improved cross-sell opportunities, but the biggest benefits likely lay ahead.Continue Reading

• Customer support chatbots set to transform service functions

  AI-enabled chatbots are helping enterprises improve their customer service functions by automating some tasks, enabling human workers to focus on what really matters.Continue Reading

• Computer vision technology helps Trulia link buyers to homes

  In this podcast, Trulia's vice president of engineering discusses the importance of computer vision applications to the website's overall goal of helping buyers find homes.Continue Reading

• Deep learning use cases aren't limited to big tech companies

  Industries that are not traditionally technology-driven are starting to find ways to use deep learning, proving the tools aren't just for large tech companies.Continue Reading

• Tech experts weigh in on the AI hype cycle

  AI expectations couldn't be any higher. Read why leading industry experts believe the hype is deserved and what developers can do to deliver on the technology's weighty promise.Continue Reading

• Enterprises explore AI voice assistant technology

  Intelligent voice assistant devices, so popular among consumers, are starting to make their way into enterprises, but businesses need to be mindful of several challenges.Continue Reading

• Generative adversarial networks could be most powerful algorithm in AI

  The emergence of generative adversarial networks has been called one of the most interesting successes in recent AI development and could make AI applications more creative.Continue Reading

• Limits of AI today push general-purpose tools to the horizon

  The future of AI should be focused on more general-purpose tools, but developers have a long way to go before achieving the kind of AI movies taught us to expect.Continue Reading

• Machine learning still big at Stripe despite deep learning hype

  Classical machine learning methods are getting overshadowed in today's AI landscape, but problems with deep learning are keeping them relevant at payment processor Stripe.Continue Reading

• Implementing deep learning requires a creative approach

  Using deep learning in an effective way requires creative problem-solving and a team approach that goes beyond simply hiring data scientists, experts say.Continue Reading

• AI in call centers amplifies customer voice

  Speech analytics use cases involving customer contact centers show how AI technology can make sense out of messy human language, helping businesses along the way.Continue Reading

• Limitations of neural networks grow clearer in business

  AI often means neural networks, but intensive training requirements are prompting enterprises to look for alternatives to neural networks that are easier to implement.Continue Reading