Get started

Get started

Bring yourself up to speed with our introductory content.

• 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 seen as evolution of artificial intelligence

  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

• artificial intelligence

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

• AI vs. machine learning vs. deep learning: Key differences

  AI terms are often used interchangeably, but they are not the same. Understand the difference between artificial intelligence, machine learning and deep learning. Continue Reading

• 4 main types of AI explained

  The emergence of artificial superintelligence will change humanity, but it's not happening soon. Here are the types of AI leading up to that new reality. Continue Reading

• Definitions to Get Started

  • artificial intelligence
  • intelligent process automation (IPA)
  • machine teaching
  • language modeling

  • crypto-agility
  • neuromorphic computing
  • face detection
  • BERT language model

  View All Definitions

• intelligent process automation (IPA)

  Intelligent process automation (IPA) is a combination of technologies used to manage and automate digital processes.Continue Reading

• Science fiction vs. reality: A robotics industry overview

  Robots have made their way into industrial, manufacturing and military settings, but the robots of science fiction remain a long-term goal rather than a reality.Continue Reading

• machine teaching

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

• crypto-agility

  Crypto-agility, or cryptographic agility, is a data encryption practice used by organizations to ensure a rapid response to a cryptographic threat.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

• 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.Continue Reading

• How and why are our devices listening to us?

  Consumers are utilizing digital voice assistants and smartphones but may not realize how frequently companies listen in and sift through all the data these devices create.Continue Reading

• BERT language model

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

• How to overcome 4 major challenges in AI adoption

  While companies are stuck in the research phase of AI, a few simple infrastructure analyzations can jumpstart the process -- and ensure successful deployment.Continue Reading

• cognitive search

  Cognitive search is a new generation of enterprise search that uses artificial intelligence technologies to improve users' search queries and extract relevant information from multiple, diverse data sets.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

• 3 in-demand AI skills that boost data scientists' development

  AI encompasses a wide range of disciplines, from advanced math to application development, and building a strong AI team starts with incredibly skilled data scientists.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

• deep learning

  Deep learning is a type of machine learning (ML) and artificial intelligence (AI) that imitates the way humans gain certain types of knowledge. At its simplest, deep learning can be thought of as a way to automate predictive analytics.Continue Reading

• data scientist

  A data scientist is a professional responsible for collecting, analyzing and interpreting extremely large amounts of data. The data scientist role is an offshoot of several traditional technical roles, including mathematician, scientist, ...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

• supervised learning

  Supervised learning, in the context of artificial intelligence (AI) and machine learning, is a type of system in which both input and desired output data are provided.Continue Reading

• artificial neural network (ANN)

  In information technology (IT), a neural network is a system of hardware and/or software patterned after the operation of neurons in the human brain.Continue Reading

• Machine learning platform architecture demands deep analysis

  This handbook offers advice on choosing machine learning platforms and using them to get accurate and meaningful information from analytics applications.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

• Computer vision AI looks beyond the narrow into the mainstream

  This handbook looks at computer vision in the enterprise, with examples of business applications and advice on deploying systems that incorporate the AI technology.Continue Reading

• Reinforcement learning applications provide focused models

  Goal-driven AI uses trial-and-error learning methods to find optimal solutions to enterprise problems, while distancing themselves from requiring human maintenance.Continue Reading

• social robot

  A social robot is an artificial intelligence (AI) system that is designed to interact with humans and other robots.Continue Reading

• An animated guide to creating an AI business strategy

  Using AI and machine learning to automate and augment business processes is the future of successful work. Without a solid AI business strategy, leaders can risk underperformance.Continue Reading

• 3 ways to create an AI ethics framework for responsible tech

  AI can often reflect the biases and limits of its human developers. Experts say diversity, review boards and a strong AI ethics framework will lead the way toward ethical AI.Continue Reading

• Enterprise AI collaboration tools take tips from dating apps

  Enterprise AI collaboration is turning to an unlikely source for inspiration: dating apps that have long used machine-learning based personalization and communication.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

• Data science and machine learning platforms advance analytics

  Data science platforms include a variety of technologies for machine learning and other advanced analytics uses. This handbook examines them and how they can be used.Continue Reading

• RPA in banking gives fintech a competitive edge

  RPA in banking is setting its sights on fintech and flexible banking to compete with traditional banking. Community banks still see hurdles despite potential to wield RPA.Continue Reading

• AI in fitness offers virtual trainers and customized wearables

  From Fitbits to virtual support, wellness enterprises are positioning AI as a useful tool. Using AI in fitness clubs and products can enhance user comfort and personalization.Continue Reading

• AI as a service democratizes benefits of new tech tools

  The emergence of AI-as-a-service tools is helping more enterprises access the benefits of AI, not just the leading-edge tech companies that pioneered the technology.Continue Reading

• AI in the construction industry refurbishes trade procedures

  From design to reducing workplace injury, AI in the construction industry is changing manual labor jobs. Deploying cobots and AI systems is creating visible business value.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

• AI for customer experience powers gains at enterprises

  Customer experience is growing more central to enterprises' digital strategies, and AI is increasingly driving much of their engagement and retention efforts.Continue Reading

• Implementing RPA boosts company growth and employee satisfaction

  Implementing RPA to augment your workforce can feel overwhelming. Ditch theoretical application conversations, and read a rundown of one executive's experience implementing RPA.Continue Reading

• The new AI frontier: Hyperpersonalized automated advertising

  AI-powered automated advertising is being utilized to connect consumer to products, leading to more sales. Simply put: Hyperpersonalized content is taking over the ad space.Continue Reading

• Enterprises deploy AI in mining projects to improve analytics

  By implementing AI in mining processes, enterprises are able to utilize data algorithms and automated machines to preserve the sensitive environment and their human workforce.Continue Reading

• Automated help desk processes improve enterprise-level ITSM

  By using AI-enabled systems to create an automated help desk, enterprises can streamline IT support while predicting, managing and solving common user issues.Continue Reading

• Using AI in manufacturing processes surges quality and design

  The addition of AI in manufacturing leads to increased workflow -- from design to production. Production plants are turning to technology to supplement the manufacturing skills gap.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

• Business robotics moves off of the manufacturing floor

  New uses of robotics are opening up in businesses, as applications for the technology begin to expand beyond its traditional place in manufacturing plants.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

• How AI process automation helps simplify enterprise workflows

  The growing trend of putting AI in process automation tools is helping companies make their workflows more intelligent, offering an upgrade over traditional process automation tools.Continue Reading

• Use of AI in pharma grows as drug-makers see big benefits

  The use of AI in clinical trials, drug discovery and manufacturing is growing and, despite a few barriers, drug companies are expected to continue rolling out the technology.Continue Reading

• Convert unstructured data to structured data with machine learning

  With access to powerful compute power and advances in machine learning, unstructured data is becoming easier and cheaper for businesses to turn into usable sources of insight.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

• RPA strategy takes advantage of fast-growing market

  The RPA market is rapidly growing, and it's little wonder why. Using RPA best practices, businesses can deploy RPA quickly and potentially use it to save time and money.Continue Reading

• AI for education brings benefits to burdened school staff

  Using AI in education can have a dramatic impact on the way teachers use their time and the manner in which students are served individually. Expect the trend to continue.Continue Reading

• AI and jobs collide as automation looms

  AI automation will eliminate a broad swath of today's jobs over time, but some jobs are likely to disappear sooner than others due to the uneven pace of technology development.Continue Reading

• Automated journalism creeps into newsrooms leaning on AI

  The use of AI in journalism is gaining steam and, at this early stage, newsrooms are still looking at how the tools can be used to help reporters tell deeper stories.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

• AI in restaurants takes customer service to the next level

  Restaurants face challenges that include low margins and high turnover, but the use of chatbots is helping some restaurant chains address these perennial difficulties.Continue Reading

• Data science in healthcare demands dual focus, expert says

  Symphony Post Acute Network is seeing success blending analytics for clinical and business operations, a change compared to how healthcare systems have traditionally used analytics.Continue Reading

• cognitive computing

  Cognitive computing systems use computerized models to simulate the human cognition process to find solutions in complex situations where the answers may be ambiguous and uncertain.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

• The threats of AI must be taken seriously to prevent harm

  The risks of AI use are growing as the technology becomes more pervasive. Rather than laugh off the threats, businesses should move to mitigate them before they become headaches.Continue Reading

• Labeled data brings machine learning applications to life

  The types of data being collected for analytics use are increasing, but traditional structured data is a good match for machine learning. Gartner's Svetlana Sicular explains why.Continue Reading

• GPU cloud tools take complexity out of machine learning infrastructure

  While talk of AI on GPUs is abuzz, actually building a machine learning infrastructure remains a dark art. A startup's PaaS is looking to automate parts of the process.Continue Reading

• AI for recruiting helps companies land top job candidates

  More enterprises are using AI in hiring today, a practice that can help surface strong applicants. But there are several pitfalls businesses need to watch for.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

• AI in insurance forces big changes to traditional industry

  Insurance companies using AI are forcing firms in this traditional industry to grapple with new technology and evaluate emerging risks that could impact their bottom lines.Continue Reading

• AI applications in healthcare smooth providers' operations

  Healthcare providers are embracing AI systems at an increasing rate as the potential benefits for both patient care and operational management become more apparent.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

• Industries evaluate collaborative robot applications

  Robotics has traditionally been viewed as a threat to jobs, but putting AI in robots can make machines more collaborative and empower human workers to be more effective.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

• 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

• New data science platforms aim to be workflow, collaboration hubs

  Oracle's acquisition of DataScience.com is shining a spotlight on workbench-style platforms designed to centralize advanced analytics work by teams of data scientists.Continue Reading

• Addressing the ethical issues of AI is key to effective use

  Enterprises must confront the ethical implications of AI use as they increasingly roll out technology that has the potential to reshape how humans interact with machines.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

• Combination of blockchain and AI makes models more transparent

  Blockchain technology could play an important role in helping enterprises develop more explainable AI applications, something that is frequently lacking today.Continue Reading

• New Intel toolkit OpenVINO supports deep learning on CPUs

  A new developer kit from Intel seeks to lower the bar for doing deep learning on CPUs and other types of chips to extract more intelligence from video.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

• Avoid bias in algorithms for best AI results

  In this podcast, we examine leading thoughts on the problem of AI bias and how to mitigate some of the most common sources of unfair treatment of users of AI applications.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

• AI implementation is a winner-take-all race, analyst says

  Your AI strategy should focus on growth rather than efficiency, says McKinsey analyst Jacques Bughin -- advice that enterprises rarely hear when launching projects.Continue Reading

• Value of NLP applications varies for different AI uses

  Chatbots and virtual assistants are built on sophisticated component pieces, like NLP tools and automated bot technology, which can be implemented on their own in some use cases.Continue Reading

• Why Intuit aims chatbot design at a narrow set of tasks

  A data scientist at Intuit details the finance software vendor's approach to building chatbots -- and explains why it's limiting them to some basic customer service activities.Continue Reading

• How to keep your implementation of AI free from algorithm bias

  When implementing AI, it's important to focus on the quality of training data and model transparency in order to avoid potentially damaging bias in models.Continue Reading

• Humans and AI tools go hand in hand in analytics applications

  Companies are keeping data analysts and other workers in the loop with AI applications to check the results generated by automated algorithms for accuracy, relevance and missing info.Continue Reading

• Artificial intelligence in business strategies, uses

  SearchEnterpriseAI delivers news, tips and strategic advice on applying artificial intelligence technologies in the enterprise to improve products, services and operations.Continue Reading

• Slow pace for AI implementation is a prudent business strategy

  Enterprises eyeing AI development need to keep expectations under control and make sure projects align with business priorities to get real value from the technology.Continue Reading

• predictive modeling

  Predictive modeling is a process that uses data mining and probability to forecast outcomes.Continue Reading

• artificial personality (AP)

  An artificial personality (AP) is a collection of characteristics, tendencies and behavioral quirks assigned to a chatbot, digital assistant, robot or video game character.Continue Reading

• natural language generation (NLG)

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