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unsupervised learning
Unsupervised learning refers to the use of artificial intelligence (AI) algorithms to identify patterns in data sets containing data points that are neither classified nor labeled. Continue Reading
supervised learning
Supervised learning is an approach to creating artificial intelligence, where the program is given labeled input data and the expected output results. 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
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machine learning
Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Continue Reading
AI evolution timeline: A decade perspective
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
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
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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
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