Artificial intelligence might be coming for your next job, just not in the way you feared. The past few years have seen any number of articles that warn about a future where AI and automation drive humans into mass unemployment. To a considerable extent, those threats are overblown and distant. But a more imminent threat to jobs is that of algorithmic bias, the effect of machine learning models making decisions based on the wrong patterns in their training examples. A online game developed by computer science students at New York University aims to educate the public about the effects of AI bias in hiring.
Some of the greatest scientists and inventors of the future are sitting in high school classrooms right now, breezing through calculus and eagerly awaiting freshman year at the world's top universities. They may have already won Math Olympiads or invented clever, new internet applications. We know these students are smart, but are they prepared to responsibly guide the future of technology? Developing safe and beneficial technology requires more than technical expertise -- it requires a well-rounded education and the ability to understand other perspectives. But since math and science students must spend so much time doing technical work, they often lack the skills and experience necessary to understand how their inventions will impact society.
If Bill Gates were to drop out of Harvard University and start a new company today, it would be one that focuses on artificial intelligence, he said in an interview on Monday. The perspective shows that the Microsoft co-founder hasn't lost interest in the technology industry where his company has operated for the past 44 years. "Given my background, I would start an AI company whose goal would be to teach computers how to read, so that they can absorb and understand all the written knowledge of the world. That's an area where AI has yet to make progress, and it will be quite profound when we achieve that goal," Gates told David Rubinstein at an Economic Club of Washington event in the nation's capital on Monday. Gates has invested in Luminous, a start-up developing silicon for AI.
LONG BEACH, CALIFORNIA--Every time you talk, your body moves in sync, whether it's something as subtle as eyes widening or more extreme movements like flailing arms. Now, researchers have designed an artificial intelligence that knows how you're going to move based purely on the sound of your voice. Researchers collected 144 hours of video of 10 people speaking, including a nun, a chemistry teacher, and five TV show hosts (Conan O'Brien, Ellen DeGeneres, John Oliver, Jon Stewart, and Seth Meyers). They used an existing algorithm to produce skeletal figures representing the positions of the speakers' arms and hands. They then trained their own algorithm with the data, so it would predict gestures based on fresh audio of the speakers.
It's not hard to find a good story in the tech industry. The problem is that due to the industry's staggering gender gap, most of these stories center on the struggles and accomplishments of men. In this article, we aim to provide a platform for female technologists to share the stories of how they got into engineering, the biggest challenges they've faced, and their advice to the next generation of women in tech. You'll meet a former geologist turned product manager, an academic who fell in love with data science, a senior tech leader who discovered her dream job after the first two companies she worked for folded, and more. CCC's technology solutions are designed to increase connectedness among companies in the automotive industry, including insurance carriers, manufacturers, parts suppliers and collision repair shops. Ranjini Vaidyanathan was in academia and earned a PhD before realizing she had a passion for data science. While changing focuses wasn't always easy, Vaidyanathan said the transition was made easier by some simple, yet powerful, advice from her mentors. "When the going gets tough, what'll help you pull through is your passion for the technical work." How did you get into engineering? I studied applied science and mathematics before finally switching to data science after my PhD. It took me some time to decide what, exactly, I wanted to pursue. I had been doing pen-and-paper theory work as a student, but after a certain point, I realized I found applied problems more interesting. What's the biggest challenge you've faced in your career, and how have you worked to overcome it? Switching fields from academia to data science was challenging. I had to brush up industry-relevant skills like programming, and also adjust to the paradigm shift in thinking, both in terms of technical and soft skills.
Nearly every industry is using artificial intelligence in one way or another to improve business outcomes. AI holds great promise as new and exciting applications are discovered, but there is a catch. There aren't enough trained AI engineers capable of carrying out the work. Karen Roby talks with Sameer Maskey, a professor of AI at Columbia University and founder of Fusemachines, about the shortage and what can be done. The following is an edited transcript of the interview.
Fox News Flash top headlines for June 13 are here. Check out what's clicking on Foxnews.com Amazon's Alexa devices are recording children without their consent, in violation of laws in at least eight states, according to a lawsuit filed in Seattle. "Alexa routinely records and voiceprints millions of children without their consent or the consent of their parents. This practice violates California law, which prohibits the recording of oral communications without the consent of all parties to the communication," a complaint filed Tuesday on behalf of an 8-year-old boy in California Superior Court states.
Improved business performance is increasingly driven by Data Science and Machine Learning. For that reason, it is of crucial importance that the code powering key business decisions is deemed to be of production quality. Machine learning models which can be deployed effortlessly and operate unattended are far more likely to achieve commercial objectives. At QuantumBlack, we've always asserted that the only useful data science code is production-level. Every data scientist follows their own workflow when solving analytics problems.
Aidan Wen is well on his way toward a career in artificial intelligence. The high school junior already has two semesters of machine-learning courses under his belt. Last summer he competed for a $12,000 prize sponsored by the Radiological Society of North America for the best ML model for spotting signs of pneumonia in lung X-rays. This year, he has entered another competition seeking a system for early detection of earthquakes using audio files. Next, he wants to try his hand at a project using natural language processing.
Nowadays, Machine Learning (ML) is seen as the universal solution to improve the effectiveness of information retrieval (IR) methods. However, while mathematics is a precise and accurate science, it is usually expressed by less accurate and imprecise descriptions, contributing to the relative dearth of machine learning applications for IR in this domain. Generally, mathematical documents communicate their knowledge with an ambiguous, context-dependent, and non-formal language. Given recent advances in ML, it seems canonical to apply ML techniques to represent and retrieve mathematics semantically. In this work, we apply popular text embedding techniques to the arXiv collection of STEM documents and explore how these are unable to properly understand mathematics from that corpus. In addition, we also investigate the missing aspects that would allow mathematics to be learned by computers.