In the marketplace for artificial intelligence technology, giant companies like Google, Amazon, and Microsoft offer a powerful, centralized approach: They sell access to platforms for machine learning that hoover up vast amounts of users' personal and proprietary information and use that data to train AI models. A new development called federated learning offers an alternative to the centralized model. It promises to distribute the power of machine learning to mobile phones, IoT devices, and other equipment on the network edge. The payoff: Better performance and enhanced data security. By distributing AI training to the edge, "you speed up the training process significantly, and you get better accuracy," says Marcin Rojek, co‑founder at byteLAKE, a Poland‑based company working on federated learning solutions using Internet of Things (IoT) devices.

How are changing demographics upsetting the CHRO's role? You have more of Gen Z coming into the workforce, and many organizations are clamoring for the talents they have, like digital skills and advanced analytics. But in the war for technical talent, they're finding it hard to get these people. One big reason is that in this generation, unlike previous generations, fewer people want to work for large established companies. And younger workers are more interested in the experience they can have at work.

As companies expand the use of artificial intelligence, so does the vocabulary used to describe how AI applications and tools are changing every business function across industries. Following is a glossary of 25 terms that cover the basic vocabulary of AI in the enterprise. Adversarial machine learning A technique whereby developers introduce purposefully deceptive data inputs to machine learning algorithms to trick them into making incorrect conclusions or decisions. Adversarial data can be used as part of advanced algorithm training; hackers can also use it wreak havoc with AI applications. AI council Some companies have created special groups to aid executives in implementing AI initiatives.

California‑based Farmers Insurance has invested aggressively in AI in recent years. One project frees up time for claim adjusters by using image recognition to detect anomalies and fraud in auto insurance claims. Farmers has deployed AI chatbots to interact with customers in its contact centers. Finally, Farmers uses robotic process automation (RPA) to automate mundane back‑office processes. Most of these projects bubbled up from below, often without unified support from senior management, says Tom Davenport, a fellow of the MIT Initiative on the Digital Economy and a senior advisor to Deloitte Analytics. So, Farmers execs formed two committees to start driving AI decision‑making from the top--with dual roles for the business side and IT.

These numbers help explain why so many organizations are turning to virtual assistants and other AI tools to close the gap. One third of companies in the U.S. and Europe have introduced AI technologies in customer service, according to our survey. We found that 74% of those organizations are now able to provide always‑on customer support. Additional benefits include freeing up the time of human agents and processing high‑volume, repetitive tasks more efficiently. Juniper Research estimates that AI‑powered chatbots will save companies $8 billion annually by 2022, which helps explain why the chatbot market is growing by 25% a year.

What do you think universities should start doing to prepare college grads for a very different labor market ahead? It will depend on the institutions and the individuals involved. Let me offer two guesses. First, we'll need to break down lots of barriers. Take the boundary between work experiences and academic, classroom experiences.