Features

Features

• 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

• 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

• Threats of AI include malicious use against enterprises

  As sophisticated tools become easier to use, enterprises need to protect themselves against AI threats to ensure they do not become the victims of malicious attacks.  Continue Reading

• A look at the leading artificial intelligence infrastructure products

  The artificial intelligence infrastructure market is young and varied, with enterprise AI vendors offering everything from cloud services to powerful, and expensive, hardware.  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

• 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

• 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 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