"The global AI agenda: Promise, reality, and a future of data sharing" is an MIT Technology Review Insights report produced in partnership with Genesys and Philips. It was developed through a global survey conducted in January and February 2020 of over 1,000 executives across 11 different sectors and a series of interviews with experts having specific responsibility for or knowledge of AI. The article below is an extract of the full report. This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review's editorial staff.

Walmart this morning unveiled a new "store of the future" and test grounds for emerging technologies, including AI-enabled cameras and interactive displays. The store, a working concept called the Intelligent Retail Lab -- or "IRL" for short -- operates out of a Walmart Neighborhood Market in Levittown, N.Y. The store is open to customers and is one of Walmart's busiest Neighborhood Market stores, containing more than 30,000 items, the retailer says, which allows it to test out technology in a real-world environment. Similar to Amazon Go's convenience stores, the store has a suite of cameras mounted in the ceiling. But unlike Amazon Go, which is a grab-and-go store with smaller square footage, Walmart's IRL spans 50,000 square feet of retail space and is staffed by more than 100 employees.

Inside one of Walmart's busiest Neighborhood Market grocery stores, high resolution cameras suspended from the ceiling point to a table of bananas. They can tell how ripe the bananas are from their color. When a banana starts to bruise, the cameras send an alert to a worker. Normally, that task would have relied on the subjective assessment of a human, who likely doesn't have time to inspect every piece of fruit. Welcome to Walmart's Intelligent Retail Lab -- the retail giant's biggest attempt to digitize the physical store.

Each quarter, the editors of SearchFinancialApplications recognize new software for innovation or market impact. This quarter, that product is Performance for Performance. Enterprises can use Kanjoya performance review software to obtain a synopsis of an employee's strengths, consensus on areas for improvement and a summary of all the feedback provided by people who review the employee. Elaine Chang, director of products at Kanjoya, based in San Francisco, said Kanjoya's natural language processing and machine learning algorithms continually analyze performance reviews as they are submitted inside the software. The cloud-based tool provides a simple dashboard that displays an employee's "opportunity areas," or items for improvement, and strengths.

The focus of HCOMP 2014 was the crowd worker. While crowdsourcing is motivated by the promise of leveraging people's intelligence and diverse skillsets in computational processes, the human aspects of this workforce are all too often overlooked. Instead, workers are frequently viewed as interchangeable components that can be statistically managed to eek out reasonable outputs.We are quickly moving past and rejecting these notions, and beginning to understand that it is sometimes the very abstractions that we introduce to make human computation feasible, e.g., abstracting humans behind APIs or isolating workers from others in order to ensure independent input, that can lead to the problems that we then set about trying to solve, e.g., poor or inconsistent quality work. Creating a brighter future for crowd work will require new socio-technical systems that not only decompose tasks, recruit and coordinate workers, and make sense of results, but also find interesting tasks for people to contribute to, structure tasks so that workers learn from them as they go, and eventually automate mundane parts of work. Research in artificial intelligence will be vital for achieving this future.