This week is starting off strong for deals. If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA Today's newsroom and any business incentives. There is no better way to ease into the work week than with a little online window shopping. And if you're going to distract yourself from your work or your to-do list, you might as well start with what's on sale, right?

Get the best robot vacuum we've ever tested on sale for its lowest price ever. If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA TODAY's newsroom and any business incentives. Tired of looking at dirty floors and carpets but too drained after a long day at work to spend a ton of time cleaning? Robot vacuums are a fantastic alternative because they can help to keep your floors looking tidy in between deeper cleaning sessions and you don't have to lift a finger.

Fox News Flash top headlines for May 6 are here. Check out what's clicking on Foxnews.com A Kentucky man has been charged with murder for fatally punching his 1-year-old son in the head after becoming angry over losing a video game, authorities said Sunday. Anthony Trice, 26, was watching the baby Friday when he grew enraged over losing the game, threw his controller and struck the infant in the head, the Louisville Metro Police Department said. Trice tried to comfort the baby, carrying him into the kitchen, but dropped him, Louisville station WAVE-TV reported.

Composed of long chains of amino acids, proteins perform these myriad tasks by folding themselves into precise 3D structures that govern how they interact with other molecules. Because a protein's shape determines its function and the extent of its dysfunction in disease, efforts to illuminate protein structures are central to all of molecular biology -- and in particular, therapeutic science and the development of lifesaving and life-altering medicines. In recent years, computational methods have made significant strides in predicting how proteins fold based on knowledge of their amino acid sequence. If fully realized, these methods have the potential to transform virtually all facets of biomedical research. Current approaches, however, are limited in the scale and scope of the proteins that can be determined.

It's also frighteningly common: 3.5% of the population aged 40 years or older (about 60.5 million in 2010) has been diagnosed with the disease, and the number is expected to steeply rise in the next year. Early detection and treatment is essential -- glaucoma progresses irreversibly and almost imperceptibly. Toward that end, scientists at IBM Research and New York University describe in a paper a noninvasive technique that uses AI to detect patterns characteristic of glaucoma in retina imaging data. It's scheduled to be presented at the Association for Research in Vision and Ophthalmology later this month in Vancouver. "From a biological point of view, we know there are associations between visual function and retinal structure," wrote senior research scientist and manager at IBM Research Australia Rahil Garnavi in a blog post.

Designing accurate and efficient CNNs for mobile devices is challenging due to the large design space and expensive computational methods. Although many mobile CNNs are available for developers to train and deploy to mobile devices, existing CNN architecture may not be able to achieve the best results for some tasks on mobile devices. Last year, Google introduced an automated mobile neural architecture search (MNAS) approach, and proposed MnasNet based on reinforcement learning to automatically design mobile models. Facebook then proposed FBNet, a differentiable neural architecture search (DNAS) framework to optimize CNN architecture based on a gradient method. Both FBNet and MnasNet introduced automated solutions to change the way deep learning models are designed for mobile.

Ahead of its Build conference, Microsoft today released a slew of new machine learning products and tweaks to some of its existing services. These range from no-code tools to hosted notebooks, with a number of new APIs and other services in-between. The core theme, here, though, is that Microsoft is continuing its strategy of democratizing access to AI. Ahead of the release, I sat down with Microsoft's Eric Boyd, the company's corporate vice president of its AI platform, to discuss Microsoft's take on this space, where it competes heavily with the likes of Google and AWS, as well as numerous, often more specialized startups. And to some degree, the actual machine learning technologies have become table stakes. Everybody now offers pre-trained models, open-source tools and the platforms to train, build and deploy models.

But concerns over growing inequality and the lack of opportunity for many in the labor force--serious matters linked to a variety of structural changes in the economy–are well-founded and need to be addressed, four scholars on artificial intelligence and the economy recently told an audience at Stanford Graduate School of Business. That's not to say that artificial intelligence isn't having a profound effect on many areas of the economy. But understanding the link between the two trends is difficult and it's easy to make misleading assumptions about the kinds of jobs that are in danger of becoming obsolete. "Most jobs are more complex than [many people] realize," said Google's chief economist, Hal Varian, during a forum on the future of work, which was sponsored by the Stanford Institute for Human-Centered Artificial Intelligence. Today's workforce is sharply divided by levels of education, and those who have not gone beyond high school are affected the most by long-term changes in the economy, says David Autor, professor of economics at the Massachusetts Institute of Technology.

When I first became focused on the military and existential concerns of AI in 2012, there was only a small handful of publications and organizations focused on the ethical concerns of AI. MIRI, the Future of Humanity Institute, the Institute for Ethics and Emerging Technologies, and the personal blogs of Ben Goertzel and Nick Bostrom was most of my reading at the time. These limited sources focused mostly on the consequences of artificial general intelligence (i.e. By 2014, artificial intelligence made its way firmly onto the radar of almost everyone in the tech world. New startups began (by 2015) ubiquitously including "machine learning" in their pitch decks, and 3-4-year-old startups were re-branding themselves around the value proposition of "AI." Not until later 2016 did the AI ethics wave make it into the mainstream beyond the level of Elon Musk's tweets. By 2017, some business conferences began having breakout sessions around AI ethics – mostly the practical day-to-day concerns (privacy, security, transparency).

Designing AI systems capable of accurate instance-level landmark recognition (i.e., distinguishing Niagara Falls from just any waterfall) and retrieving images (matching objects in an image to other instances of that object in a catalog) is a longstanding pursuit of Google's AI research division. Last year, it released Google-Landmarks, a landmarks data set it claimed at the time was the world's largest, and hosted two competitions (Landmark Recognition 2018 and Landmark Retrieval 2018) in which more than 500 machine learning researchers participated. Additionally, it's launched two new challenges (Landmark Recognition 2019 and Landmark Retrieval 2019) on Kaggle, its machine learning community, and released the source code and model for Detect-to-Retrieve, a framework for regional image retrieval. "Both instance recognition and image retrieval methods require ever-larger datasets in both the number of images and the variety of landmarks in order to train better and more robust systems," wrote Google AI software engineers Bingyi Cao and Tobias Weyand. "We hope that this dataset will help advance the state-of-the-art in instance recognition and image retrieval."