Fox News Flash top headlines for Nov. 24 are here. Check out what's clicking on FoxNews.com The prosthetic legs of a double amputee and soon-to-be high school wrestling captain were stolen from a gym closet in California last week, putting his dreams of winning a state championship or even wrestling this season in doubt. Brett Winters, a senior at Pacific High School in San Bernardino, California, was born without tibia bones in his legs. As a baby, his mother was told by doctors that Winters could either spend life in a wheelchair or amputate his legs.

Alicia Carriquiry Distinguished Professor and President's Chair in Statistics Director of CSAFE, Iowa State University In the US criminal justice system, jurors choose between two competing hypothesis: the suspect is the source of the evidence found at the crime scene or s/he is not. The likelihood ratio framework, which relies on Bayes' theorem for assessing the probative value of evidence, is difficult to implement in practice, when evidence is in the form of an image. Machine learning provides a good alternative for determining whether the evidence supports the proposition that the suspect may have been its source. We illustrate these ideas using information about the surface topography of bullet lands.

Scientists have created a computer algorithm that can take an underwater photograph and automatically readjust its colors to compensate for the distorting effect of water on light. Researchers Derya Akkaynak and Tali Trebitz started work on the technology – called Sea-thru -- more than three years ago. Akkaynak told Business Insider via email that Sea-thru's mission is to enable huge, artificial intelligence-powered analysis of underwater images. The algorithm effectively adjusts underwater images to make them look like they were taken in broad daylight, making them easier for AI software to analyze. "On underwater images, AI methods generally perform poorly or inconsistently, because water degrades images too severely for automated analysis," she said.

A journalist rode in a driverless vehicle last month. The tricked-out minivan navigated busy city streets, made an unprotected left-hand turn and even reached speeds of 45 mph. According to a TechCrunch report, it was a completely uneventful, yet remarkable ride in a Waymo fully autonomous vehicle. FRANKFURT AM MAIN, GERMANY - SEPTEMBER 12: John Krafcik, CEO of Waymo, speaks at the opening event ... [ ] of the IAA 2019 Frankfurt Auto Show on September 12, 2019 in Frankfurt am Main, Germany. The IAA will be open to the public from September 12 through 22. (Photo by Sean Gallup/Getty Images) Self-driving vehicles reached peak hype a year ago.

Archaeologists in Yamagata, Japan said they have discovered a cluster of enormous, ancient geoglyphs in Southern Peru. The discovery was made with the help of cutting-edge artificial intelligence (AI) technology. A research group at Yamagata University identified 143 new geoglyphs etched into the desert terrains of Nazca in southern Peru. The giant land art pieces, known as the Nazca Lines, depict human-like figures and a variety of animals including birds, fish, snakes, foxes, felines, and camelids. Many can only be identified from the air due to their large size.

Everybody knows (or should know) by now that machine learning (which is what most current artificial intelligence actually amounts to) is subject to bias. Last week, the New York Times had the idea of asking three prominent experts in the field to talk about the bias problem, in particular the ways that social bias can be reflected and amplified in dangerous ways by the technology to discriminate against, or otherwise damage, certain social groups. At first sight, the resulting article looked like a run-of-the-mill review of what has become a common topic – except for one thing: the three experts were all women. One, Daphne Koller, is a co-founder of the online education company Coursera; another, Olga Russakovsky, is a Princeton professor who is working to reduce bias in ImageNet, the data set that powered the current machine-learning boom; the third, Timnit Gebru, is a research scientist at Google in the company's ethical AI team. Reading the observations of these three women brought to the surface a thought that's been lurking at the back of my mind for years.

How often do you think you're touched by data science in some form or another? Finding your way to this article likely involved a whole bunch of data science (whooaa). To simplify things a bit, I'll explain what data science means to me. "Data Science is the art of applying scientific methods of analysis to any kind of data so that we can unlock important information." If we unpack that, all data science really means is to answer questions by using math and science to go through data that's too much for our brains to process.

This post is by Anton Buchner, a senior consultant with TrinityP3. Anton is one of Australia's leaders in data-driven marketing. Helping navigate through the bells, whistles and hype to identify genuine marketing value when it comes to technology, digital activity, and the resulting data footprint. And the Artificial Intelligence (AI) space is no exception. Over the past few years we've seen the rise and rise of AI discussion and solutions in marketing. I have spent the past month talking to a wide variety of industry thought leaders and experts in the AI space – from business, agency, and tech vendor perspectives. With the aim of identifying how Australian marketers are using AI solutions to enhance and anticipate consumer interaction. In this post, I would like to share some of their experiences and learnings to date. However, before we jump in, as I'm sure most of you know, AI dates back decades. Let's take a quick look back at how AI emerged.

This is Part 2 of the series "Transforming Your Enterprise for the Experience Economy" Consumer demands and preferences have always driven how, when, and what products businesses deliver. But for much of modern history, "giving the people what they want" has been more reactive than predictive. Today, the data and technology exist to drive proactive, customer-centric decision-making and personalization, ultimately allowing companies to enhance enterprise supply chain operations to reduce friction, anticipate customer needs, and create amazing customer experiences. But how do you cultivate and activate data to create a customer-centric supply chain? To find out, we ask Kirk Borne, principal data scientist for Booz Allen Hamilton, to share the top data insights necessary to digitize supply chains and put customers at the center of each step along the journey.