Emerging Tech: Page

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.

By Ed Burns • May 30, 2018

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.

By Ron Schmelzer • May 22, 2018

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.

By George Lawton • April 19, 2018

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.

By Ed Burns • April 18, 2018

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.

By Ed Burns • April 10, 2018

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.

By Torsten Volk • April 6, 2018

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.

By Craig Stedman • April 5, 2018

Hype surrounding AI technologies nears atomic proportions

Amid the infiltration of artificial intelligence into business and society, spawning fears from some of Armageddon, humankind's greatest AI threat may very well come from within.

By Ron Karjian • Feb. 15, 2018

Cognitive computing in healthcare mends doctor-patient gaps

Cognitive computing and healthcare data aggregation may prove to be important bedside companions to doctors treating their patients in the era of medical specialization.

By Scott Robinson • Feb. 15, 2018

AI technologies have come far, but the road is long

Machine learning and other artificial intelligence technologies are poised to offer businesses big benefits, but companies have to walk before they run with AI.

By Bridget Botelho • Feb. 15, 2018

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.

By Ed Burns • Jan. 31, 2018

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.

By Bridget Botelho • Jan. 17, 2018

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.

By Mary Pratt • Jan. 17, 2018

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.

By Jack Vaughan • Jan. 15, 2018

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.

By Ed Burns • Nov. 8, 2017

Mathematician warns against weapons of ‘math’ destruction

In this SearchCIO Q&A, author and data scientist Cathy O'Neil talks about the danger of trusting algorithms too much.

By Nicole Laskowski • June 21, 2017

How Facebook uses deep learning models to engage users

At Facebook it's all about user engagement, and to accomplish this, the company relies heavily on deep learning algorithms to tailor its products to the interests of individuals.

By Ed Burns • June 5, 2017

Why machine learning models require a failover plan

Flawed machine learning models lead to failures and user interruptions. Expert Judith Myerson explains the causes for failures and how a failover plan can improve user experience.

By Judith Myerson • June 2, 2016