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• 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

• Definitions to Get Started

  • deep tech
  • Google Bard
  • natural language generation (NLG)
  • adversarial machine learning

  • OpenAI
  • robo-advisor
  • narrow AI (weak AI)
  • artificial intelligence (AI)

  View All Definitions

• 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

• 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

• 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

• Artificial intelligence data storage planning best practices

  AI storage planning is similar to the storage planning you're used to: Consider capacity, IOPS and reliability requirements for source data and the application's database.Continue Reading

• Gauge your knowledge of cloud providers' AI technologies

  As enterprise interest grows, major cloud providers continue to unveil machine learning and AI services. See how much you know about their offerings with this brief quiz.Continue Reading

• How to use machine learning to build a predictive algorithm

  Machine learning is an invaluable tool for solving business problems, but don't jump into it for predictive analytics without understanding these important factors.Continue Reading

• Machine learning models require DevOps-style workflows

  Big data is driving the use of AI and machine learning. But teams must be swift to embrace DevOps and re-evaluate models, according to Wikibon's James Kobielus.Continue Reading

• AI components make tools more than the sum of their parts

  AI applications, rather than being one monolithic tool, are built around a diverse collection of tools and techniques that combine to produce advanced functionality.Continue Reading