Breakthroughs, discoveries, and DIY tips sent every weekday. The first-ever professional eating competition between goats, appropriately called The Great Goat Graze-Off, will take place on July 12 in New York City. The event will feature five invasive plant-eating fiends from the Riverside Park Conservancy--Kash, Rufus, Mallomar, Romeo, and Butterball. "Competitive eating events--whether human or goat--are quirky, high-energy spectacles that tap into our love for friendly competition and fun," Alison Ettinger-DeLong, communications manager at Riverside Park Conservancy, tells Popular Science on behalf of the conservancy team. "People enjoy watching the goats munch with gusto and cheer for their favorites, but the deeper enjoyment comes from seeing animals do what they naturally love (for goats, that's eat!) while learning about their role in environmental care."

Yet realizing measurable business value from AI-powered applications requires a new game plan. Rather, the time is now for organizations to modernize their infrastructure, processes, and application architectures using cloud native technologies to stay competitive. Today's organizations exist in an era of geopolitical shifts, growing competition, supply chain disruptions, and evolving consumer preferences. AI applications can help by supporting innovation, but only if they have the flexibility to scale when needed. Fortunately, by modernizing applications, organizations can achieve the agile development, scalability, and fast compute performance needed to support rapid innovation and accelerate the delivery of AI applications. David Harmon, director of software development for AMD says companies, "really want to make sure that they can migrate their current [environment] and take advantage of all the hardware changes as much as possible."

Two identical black and white pictures of murky shapes sit side-by-side on a computer screen. On the left side, Ismail Baris Turkbey, MD, a radiologist with 15 years of experience, has outlined an area where the fuzzy shapes represent what he believes is a creeping, growing prostate cancer. On the other side of the screen, an artificial intelligence (AI) computer program has done the same--and the results are nearly identical. The black and white image is an MRI scan from someone with prostate cancer, and the AI program has analyzed thousands of them. "The [AI] model finds the prostate and outlines cancer-suspicious areas without any human supervision," Turkbey explains.

Margaret W. Moodey was one of the first women to work at the Smithsonian in science. Beginning around 1900, Moodey worked as a scientific aide in the Smithsonian's Department of Geology. Her work included identifying, classifying, and cataloging samples, including gems and fossils. By 1924, an annual report notes that she "had the entire responsibility and care of the collection of cut gems." Moodey was an important resource for anyone seeking answers about the collection.

Fox News Flash top entertainment and celebrity headlines are here. Check out what clicked this week in entertainment. Angie Harmon is reflecting on the new direction her career has taken since she ended her reign as Jane Clementine Rizzoli on "Rizzoli & Isles." The 49-year-old spoke to reporters on Wednesday about her latest role in the Lifetime original film, "Buried in Barstow," in which Harmon stars as Hazel King, a single mother who is "determined to shield her daughter from the life she once had while protecting and defending those who can't protect themselves." "Plucked off the streets of Las Vegas at 15, Hazel was trained as a hitwoman until a surprising pregnancy drives her to leave it all behind," the show's synopsis explains.

There has recently been an explosion of work on spoken dialogue systems, along with an increased interest in open-domain systems that engage in casual conversations on popular topics such as movies, books and music. These systems aim to socially engage, entertain, and even empathize with their users. Since the achievement of such social goals is hard to measure, recent research has used dialogue length or human ratings as evaluation metrics, and developed methods for automatically calculating novel metrics, such as coherence, consistency, relevance and engagement. Here we develop a PARADISE model for predicting the performance of Athena, a dialogue system that has participated in thousands of conversations with real users, while competing as a finalist in the Alexa Prize. We use both user ratings and dialogue length as metrics for dialogue quality, and experiment with predicting these metrics using automatic features that are both system dependent and independent. Our goal is to learn a general objective function that can be used to optimize the dialogue choices of any Alexa Prize system in real time and evaluate its performance. Our best model for predicting user ratings gets an R$^2$ of .136 with a DistilBert model, and the best model for predicting length with system independent features gets an R$^2$ of .865, suggesting that conversation length may be a more reliable measure for automatic training of dialogue systems.

Artificial Intelligence has been one of the most hyped technologies of the decade, with millions of articles, reports, blogs and conferences discussing the topic from every possible angle. But what's the state of artificial intelligence applications right now – especially when it comes to'real world' uses? While fully intelligent general AI remains a long way off, we have seen some impressive uses of machine learning, chatbots and even image recognition technology being released in the last couple of years. Much of the cutting-edge research is still at the academic level, but we have started to see artificial intelligence applications being launched by and used in narrower application in businesses. Let's look at some examples of how AI is being used in the'real world'.

I started developing AI algorithms for handwriting recognition at my part-time student job while doing my Undergraduate degree in Computer Science. Since then, over the last 20 years or so, I have strived to combine my work in the industry with academic research. I did my Graduate degree in Computer Vision and completed my Ph.D. in Machine Learning while having quite an intensive career in the industry in parallel with my studies. In the industry, I've worked on all kinds of data and applications, including medical imaging, educational multimedia, mobile advertising, financial time series, video, text and speech processing for public safety, and other projects. When I began working with the product and business aspects of R&D, I felt that I needed to strengthen the relevant skills, so I went back to school and got an additional Master's degree in Technology Management.

The introduction of AI in the workplace offers huge potential to boost productivity, efficiency, and accuracy for businesses of all sizes. And while the technology remains in its nascent stages of development, forward-thinking businesses are starting to see the potential it holds for growing their organization and boosting the productivity of their employees. However, machine learning, a practical application of artificial intelligence, is transforming businesses, improving the workdays of their employees, providing a better customer experience and ultimately helping to boost the bottom line. Machine learning is a form of AI in the workplace Machine learning and AI can easily be confused, with the terms being commonly used interchangeably. Machine learning, though, has to do with an application performing a specific task by making predictions or decisions based on the data it has at its disposal, based on learned behavioral patterns.

When we think of project management tools, many of us immediately think of Microsoft Project--arguably the market leader for specialized PPM software. However, in the Software as a Service (SaaS) era, there is more choice than ever regarding project management tools, with more specific solutions targeted at different industries and use cases. There are project tools out there dedicated to task management. There are solutions that are more focused on analyzing project data and presenting it in a visually appealing way. There are project management tools which are focused more on resource planning than task management.