Millions of dollars are being spent to develop artificial intelligence software that reads x-rays and other medical scans in hopes it can spot things doctors look for but sometimes miss, such as lung cancers. A new study reports that these algorithms can also see something doctors don't look for on such scans: a patient's race. The study authors and other medical AI experts say the results make it more crucial than ever to check that health algorithms perform fairly on people with different racial identities. Complicating that task: The authors themselves aren't sure what cues the algorithms they created use to predict a person's race. Evidence that algorithms can read race from a person's medical scans emerged from tests on five types of imagery used in radiology research, including chest and hand x-rays and mammograms.

There are myriad articles on artificial intelligence and its application in business. As AI continues to grow and permeate seemingly every aspect of business, it's important to cut through the noise and focus on where AI fits in your organization and how to best implement it. Through this experience, I've learned a few ways leaders can determine their own approach to AI. Broadly speaking, AI is a branch of computer science concerned with replicating human intelligence in machines. Depending on whether you run a business-to-consumer or business-to-business company, you might find some types of AI more relevant to your business than others. In B2B, AI is all about data and analysis to make better-informed decisions.

There are myriad articles on artificial intelligence and its application in business. As AI continues to grow and permeate seemingly every aspect of business, it's important to cut through the noise and focus on where AI fits in your organization and how to best implement it. Through this experience, I've learned a few ways leaders can determine their own approach to AI. Broadly speaking, AI is a branch of computer science concerned with replicating human intelligence in machines. Depending on whether you run a business-to-consumer or business-to-business company, you might find some types of AI more relevant to your business than others. In B2B, AI is all about data and analysis to make better-informed decisions.

There are myriad articles on artificial intelligence and its application in business. As AI continues to grow and permeate seemingly every aspect of business, it's important to cut through the noise and focus on where AI fits in your organization and how to best implement it. Through this experience, I've learned a few ways leaders can determine their own approach to AI. Broadly speaking, AI is a branch of computer science concerned with replicating human intelligence in machines. Depending on whether you run a business-to-consumer or business-to-business company, you might find some types of AI more relevant to your business than others. In B2B, AI is all about data and analysis to make better-informed decisions.

In services, businesses and even our personal lives, artificially intelligent bots are becoming commonplace. But with every technological advancement comes concerns, and the growing use of artificial intelligence is an especially hot topic. Part of the fear around AI is the mystery behind what its capabilities are. How does a bot make a decision? Can it have likes or dislikes?

Artificial intelligence has produced sound so realistic it can fool human ears. Using machine learning, AI learned how to recreate the precise sounds of objects being hit with a drumstick after MIT researchers fed thousands of examples. Human subjects had difficulty distinguishing between the algorithm's sounds and real ones - and actually chose the AI's twice as many times during the online study. MIT researchers fed a massive audiovisual dataset of objects being hit with a drumstick to their AI, which detected patterns in the sounds and learned how to recreate each one. Although there are limitations, human subjects had difficulty distinguishing between the algorithm's sounds and real ones In order to train this sound-producing algorithm, MIT needed to feed it sounds to learn.

Artificial intelligence has produced sound so realistic it can fool human ears. Using machine learning, AI learned how to recreate the precise sounds of objects being hit with a drumstick after MIT researchers fed thousands of examples. Human subjects had difficulty distinguishing between the algorithm's sounds and real ones - and actually chose the AI's twice as many times during the online study. MIT researchers fed a massive audiovisual dataset of objects being hit with a drumstick to their AI, which detected patterns in the sounds and learned how to recreate each one. Although there are limitations, human subjects had difficulty distinguishing between the algorithm's sounds and real ones In order to train this sound-producing algorithm, MIT needed to feed it sounds to learn. About 1,000 videos with an estimated 46,000 sounds that represent different objects being hit, scraped and prodded with a drumstick were recorded.