Features

Features

• 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

• GDPR regulations put premium on transparent AI

  As the EU's GDPR regulations go into effect, enterprises must focus on building transparency in AI applications so that algorithms' decisions can be explained.  Continue Reading

• Getting to machine learning in production takes focus

  Bridging the gap between training and production is one of the biggest machine learning development hurdles enterprises face, but some are finding ways to streamline the process.  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

• How one company thinks chatbots and AI can change insurance

  Insurance agency management company In-Fi is hoping AI chatbots can streamline homeowner insurance applications and bring the process in line with customers' expectations.  Continue Reading

• Harman's plan for AI in cars moving full speed ahead

  Can an AI virtual assistant in every car save the auto industry from sluggish sales and an uncertain future? Audio component manufacturer Harman argues that it can help.  Continue Reading

• More curiosity could help narrow AI tools handle broader uses

  Today, engineers are developing AI tools primarily for individual applications, but programming a facsimile of curiosity into algorithms could help make them more general purpose.  Continue Reading

• Designing and building artificial intelligence infrastructure

  Building an artificial intelligence infrastructure requires a serious look at storage, networking and AI data needs, combined with deliberate and strategic planning.  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

• AI virtual assistant tools prove better for customer service than chatbots

  AI virtual assistant software is increasingly surpassing chatbots and natural language search, as enterprises see deeper value in enabling true conversation.  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

• IT, finance and marketing have uses for AI -- do you?

  AI in healthcare improves patient outcomes. AI in IT aids employee compliance and security. AI in logistics, AI in marketing, AI in finance -- learn how your company can use AI.  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

• AI functionality limited today but could be a game-changer

  Limited AI capabilities could soon give way to technology that is truly transformative for enterprises, surpassing the overhyped functionality that we see today.  Continue Reading

• Big data throws bias in machine learning data sets

  AI holds massive potential for good, but it also amplifies negative outcomes if data scientists don't recognize data biases and correct them in machine learning data sets.  Continue Reading

• Insurer's machine learning use case: Changing driver behavior

  Machine learning tools can be put to use for more than targeted marketing and product recommendations. Auto insurer HiRoad is using them to help create safer drivers.  Continue Reading

• Machine vision makes paper a thing of the past for insurers

  The insurance industry is buried in paper-based processes. Former MetLife CIO Gary Hoberman aims to change that with a platform that runs on AI and machine vision.  Continue Reading

• How to do a machine learning platform comparison

  Experts share their top criteria for choosing the right machine learning vendor in a market that has become crowded and confusing in the last couple years.  Continue Reading

• Chatbot applications must get better at chatting to engage users

  Today's AI chatbots are good at taking orders and delivering scripted responses, but experts say tomorrow's chatbots need to be more conversational in order to deliver bigger value.  Continue Reading

• Wayfair's chief architect talks AI-driven innovation, impactful IT

  Wayfair sells home furnishings, but under the covers, it's a tech juggernaut. Chief Architect Ben Clark explains how AI-driven innovation propels the business.  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 tools and techniques will get more pervasive, tech exec says

  The past year was the first time we saw AI tools have a real impact in businesses. That trend will continue in 2018, says the vice president of engineering at online real estate site Trulia.  Continue Reading

• Automating machine learning puts analytical models on autopilot

  With data scientists in short supply and the value of machine learning models growing ever more evident, software vendors are increasingly looking to automate machine learning.  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

• At AT&T, CDO responsibilities to include all things AI

  At most companies, the chief data officer role tends to focus on data governance and management issues, but at AT&T, AI is set to be a big part of the executive's remit.  Continue Reading

• Advisory board: The future of artificial intelligence in data centers

  IT should understand how to use AI and ML capabilities to increase efficiency in the data center. Explore predictions and recommendations from our advisory board.  Continue Reading

• Semantic technology underpins conversational AI, other big data uses

  Unsung and unheralded, semantic technology is a key component in artificial intelligence and other big data applications. Yet, like AI, it still faces hurdles to going mainstream.  Continue Reading

• Natural language generation software making inroads in enterprises

  Natural language generation tools are gradually gaining a foothold in enterprises, as businesses deploy emerging artificial intelligence software.  Continue Reading

• AI hype doesn't stop Trulia from using new analytics tools

  Online real estate listing site Trulia is using artificial intelligence tools in meaningful ways, overcoming what many people see as distracting hype.  Continue Reading

• AI chatbots can provide business value when used wisely

  Investments in AI chatbots can pay off for businesses, but customer service teams still need to keep people around to handle complex issues and provide emotional empathy and an engaging voice.  Continue Reading

• AI in manufacturing beneficial, but adoption slow

  Artificial intelligence is a natural fit for the sensor-filled manufacturing industry, but it's not commonplace. What's taking manufacturers so long?  Continue Reading

• Google's cloud-based platform aims machine learning, AI at enterprise

  Google's enterprise cloud platform is starting to become a factor in business IT thanks to strong data preparation, machine learning and AI capabilities.  Continue Reading

• Analytics VP: AI projects must be built on a good data foundation

  Companies may be eager to speed up AI implementations to get to the exciting parts, but the less-hyped aspects of data governance can't be rushed, says LendingTree's head of analytics.  Continue Reading

• EBay joins partnership addressing concerns around AI in business

  In joining a group that is looking to tackle some of the biggest problems related to the use of AI in business, tech giants like eBay are putting aside competition, at least for now. An eBay IT exec explains why.  Continue Reading

• Make room for AI applications in the data center architecture

  For AI applications, the future is now. But implementing artificial intelligence in an enterprise data center presents obstacles for network, storage and compute infrastructures.  Continue Reading

• Analytics teams eye machine learning use cases to boost business

  Experienced users of machine learning tools share how their organizations are using the technology to solve a variety of analytics problems in their businesses and for customers.  Continue Reading

• What businesses need to know about cognitive computing systems

  While cognitive computing tools have come far in utility, they still have some important gaps. Understanding these hitches may be key to getting value from the platforms.  Continue Reading

• Machine learning platform helps IT workers find experts at their firm

  Collokia runs on top of a browser and uses machine learning to understand the skills of IT workers and allow employees to find colleagues with the most expertise on a topic.  Continue Reading

• With AI tools, enterprises need to differentiate between hype and value

  Enterprises can reap real value by implementing AI applications, but seeing that value through the fog of hype can be difficult, says one expert.  Continue Reading

• EBay uses machine learning techniques to translate listings

  To help connect users from different countries and bridge the language barrier, eBay is using machine learning tools to automatically translate item listings.  Continue Reading

• AI chatbot apps to infiltrate businesses sooner than you think

  Artificial intelligence chatbots aren't the norm yet, but within the next five years, there's a good chance the sales person emailing you won't be a person at all.  Continue Reading

• How to explain the business benefits of advanced machine learning

  The business value of machine learning algorithms isn't always obvious. As the top data scientist at one analytics services provider knows, they often require some explanation to business audiences.  Continue Reading

• Job losses from artificial intelligence software seen as unlikely

  There's been a lot of discussion about how likely artificial intelligence applications are to destroy jobs, but one expert says the impact will be small and beneficial.  Continue Reading

• Reality check needed to assess AI applications

  When assessing the reality behind today's AI technology, businesses need to think about how it can perform in specific tasks rather than hoping for a do-it-all tool.  Continue Reading

• Deep machine learning drives Loop AI quest

  Loop AI Labs' Bart Peintner discusses the transformational impact of deep learning technology, artificial intelligence, and how these tech trends will reshape industries.  Continue Reading

• Data lakes swim with golden information for analytics

  First we had data. Then we had big data. Now we have data lakes. Will the murky depths prove bountiful?  Continue Reading