It's free and easy to nominate yourself or someone you know--here's how. We have some exciting news: Nominations are now open for's 2026 Innovators Under 35 competition. This annual list recognizes 35 of the world's best young scientists and inventors, and our newsroom has produced it for more than two decades. It's free to nominate yourself or someone you know, and it only takes a few moments. We're looking for people who are making important scientific discoveries and applying that knowledge to build new technologies. Or those who are engineering new systems and algorithms that will aid our work or extend our abilities.

But her passion is for paper--with no scissors. Today, she's a tessellation expert who teaches, invents new designs, and writes papers on the underlying math. Madonna Yoder '17 photographed in her studio Ross Mantle When Madonna Yoder '17 was eight years old, she learned how to fold a square piece of paper over and over and over again. After about 16 folds, she held a bird in her hands. The first time she pulled the tail of a flapping crane, she says, she realized: . That first piece was an origami classic, folded by kids at summer camp for generations and many people's first foray into the art form.

The graph represents a network of 17,999 Twitter users whose recent tweets contained "generative AI", or who were replied to, mentioned, retweeted or quoted in those tweets, taken from a data set limited to a maximum of 20,000 tweets, tweeted between 1/1/2023 12:00:00 AM and 2/11/2023 8:47:19 AM. The network was obtained from Twitter on Saturday, 11 February 2023 at 17:02 UTC. The tweets in the network were tweeted over the 3270-day, 20-hour, 55-minute period from Thursday, 27 February 2014 at 19:51 UTC to Saturday, 11 February 2023 at 16:47 UTC. There is an edge for each "replies-to" relationship in a tweet, an edge for each "mentions" relationship in a tweet, an edge for each "retweet" relationship in a tweet, an edge for each "quote" relationship in a tweet, an edge for each "mention in retweet" relationship in a tweet, an edge for each "mention in reply-to" relationship in a tweet, an edge for each "mention in quote" relationship in a tweet, an edge for each "mention in quote reply-to" relationship in a tweet, and a self-loop edge for each tweet that is not from above. The graph's vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm.

The graph represents a network of 6,501 Twitter users whose tweets in the requested range contained "chatbot", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 28 January 2022 at 13:30 UTC. The requested start date was Friday, 28 January 2022 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 7-day, 17-hour, 41-minute period from Thursday, 20 January 2022 at 07:18 UTC to Friday, 28 January 2022 at 01:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

The graph represents a network of 4,023 Twitter users whose tweets in the requested range contained "(Artificial Intelligence) OR #AI", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Thursday, 14 May 2020 at 04:33 UTC. The requested start date was Thursday, 14 May 2020 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 1-day, 1-hour, 46-minute period from Tuesday, 12 May 2020 at 04:35 UTC to Wednesday, 13 May 2020 at 06:22 UTC.

CHICAGO & LONDON--(BUSINESS WIRE)--Artificial Intelligence (AI) is widely expected to drive important benefits across the health system, from increasing efficiency to improving patient outcomes, but it also may be key to making healthcare more human. Benefits range from increasing the amount of time clinicians can spend with patients and on cross-care team collaboration to enhancing the ability to deliver preventative care. According to a new study of more than 900 healthcare professionals in the U.S. and U.K. conducted by MIT Technology Review Insights with GE Healthcare, nearly half of medical professionals surveyed said AI is already increasing their ability to spend time with and provide care to patients. Additionally, more than 78 percent of healthcare business leaders who reported they have deployed AI in their operations also reported that AI has helped drive workflow improvements, streamlining operational and administrative activities and delivering significant efficiencies toward transforming the future of healthcare. "Of any industry, AI could have the most profound benefits on human lives if we can effectively harness it across the healthcare system," said Kieran Murphy, President and CEO, GE Healthcare.

Is Your Phone Listening To You? What does your cell phone know about you? Watch til the E N D, you will be SCARED from your Phone after this episode! Artificial Intelligence is the ability of machines to seemingly think for themselves. AI is demonstrated when a task, formerly performed by a human and thought of as requiring the ability to learn, reason and solve problems, can now be done by a machine.

In pop culture, the combination of business interests and artificial intelligence is something to be feared. It brings to mind Skynet, the malevolent neural network from the Terminator movies that goes to great lengths to destroy its human makers. The reality is different, though. We take advantage of it every time we check out new products recommended by Amazon.com, We have fun with it when we browse Netflix, which uses AI to predict what viewers might like to watch next. We're also increasingly likely to encounter it at work, since businesses of all types are finding ways to use it in industrial, retail, and service operations.

The potential for artificial intelligence to transform health care is huge, but there's a big catch. AI algorithms will need vast amounts of medical data on which to train before machine learning can deliver powerful new ways to spot and understand the cause of disease. That means imagery, genomic information, or electronic health records--all potentially very sensitive information. That's why researchers are working on ways to let AI learn from large amounts of medical data while making it very hard for that data to leak. One promising approach is now getting its first big test at Stanford Medical School in California.

I've always had this vision too that we as humans would maybe perhaps carry with us, let's call it our emotion passport. It's our emotional digital footprint that we control.