Machine learning

While most executives expect AI to have a transformative impact on economic growth and competitiveness, executives may need to temper near-term expectations.

Leaders of the finance organizations want to be leaders in developing data and digital strategy, according to Accenture survey.

For AI in insurance, the conversations have moved beyond the use cases and on to the pragmatic details.

Technologies such as autonomous vehicles, AI, wearables, the Internet of Things, and more have emerged as power players.

One of the most important aspects of data management is ensuring data quality. Without this, capabilities such as machine learning and advanced analytics might yield faulty results.

While several emerging technologies are grabbing plenty of headlines this year, there are many “digital disruptions” that may catch chief information officers off guard.

Data trash engineers, algorithm bias auditors, virtual identity defenders and cyber calamity forecasters are among the jobs that will play critical roles in the new digital workforce.

Many carriers are leveraging some type of artificial intelligence to solve widely variant business problems.

Many insurers are hesitant to invest in AI without proof that these theoretically smart systems will yield real-world returns.

AI-powered assistants will change the customer experience for the better, but first they must achieve a more human level of service, according to panelists.

DimensionalMechanics, Immuta and Octopai are among the leading vendors in the machine learning space, according to a new report from Gartner.

While AI already impacts many areas of our lives, the majority of businesses are only beginning to understand the potential of AI for their industries.

AI is slated to provide a large source of value and a key driver of competitive advantage going forward.

Despite growing interest in the various technologies that make up artificial intelligence, many organizations are concerned they might be increasing security vulnerability with their AI investments.

The personal lines P&C giant is making significant digital investments.

AI bias may come from incomplete datasets or incorrect values. Bias may also emerge through interactions overtime, skewing the machine’s learning. Moreover, a sudden business change, such as a new law or business rule, or ineffective training algorithms can also cause bias.

New data-science hire will help CDO Sastry Durvasula operationalize the broker’s wealth of information.

The amount of revenue generated by emerging digital services, especially AI, is expected to double in 5 years, the research firm says.

While it is possible to pair robotic-process automation technology with machine learning technology, it’s important to consider them separately.

Founder Stan Smith says the company's data resources position it to help a new range of clients.