The city of Henderson is hoping artificial intelligence could make their roads safer by cutting back on the number of crashes.

The Special Operations Command plans to apply artificial intelligence into its operations to help the U.S. stay ahead of Russia, China and other …

A lack of skilled talent is the biggest obstacle for organizations looking to take advantage of artificial intelligence (AI), according to a SnapLogic report.

Walmart has opened a store in Levittown, N.Y. that is intended to showcase the power of artificial intelligence. The store, announced last week, is packed with video cameras, digital screens, and over 100 servers, making it appear more like a corporate data center than a discount retailer. All that machinery helps Walmart automatically track inventory so that it knows when toilet paper is running low or that milk needs restocking. The company's goal is to create "a glimpse into the future of retail," when computers rather than humans are expected to do a lot of retail's grunt work. Walmart's push into artificial intelligence highlights how retailers are increasingly adding data crunching to their brick and mortar stores.

When ML models need to be regularly updated in production, a host of challenges emerges. No one tool can do it all for you - organizations using a mix of Git, Makefiles, ad hoc scripts and reference files for reproducibility.

Microsoft's latest breakthrough could make DNA-based data centers possible

Abstract: In this work, we propose a non-autoregressive seq2seq model that converts text to spectrogram. It is fully convolutional and obtains about 17.5 times speed-up over Deep Voice 3 at synthesis while maintaining comparable speech quality using a WaveNet vocoder. Interestingly, it has even fewer attention errors than the autoregressive model on the challenging test sentences. Furthermore, we build the first fully parallel neural text-to- speech system by applying the inverse autoregressive flow (IAF) as the parallel neural vocoder. Our system can synthesize speech from text through a single feed-forward pass. We also explore a novel approach to train the IAF from scratch as a generative model for raw waveform, which avoids the need for distillation from a separately trained WaveNet.

I saw a cool take yesterday, that we listen to music because it's a meaningful (it has patterns) adversarial example to our neural network. If we humans fit a good model of the world through exploration (I think the free energy principle says something like that? I don't totally get it), then we need to have incentives to find examples that counter our model, since that's the way we improve. Maybe art in general exploit something like that. Except some art is more simply explained because it just exploits things that help us/our genes survive (beautiful bodies, green sceneries).

Google Assistant can draw on voice command, as seen here at the Google I/O conference in 2018, with the help of machine learning techniques. Artificial intelligence systems powered by machine learning have been creating headlines with applications as varied as making restaurant reservations by phone, sorting cucumbers, and distinguishing chihuahuas from muffins. Media buzz aside, many fast-growing startups are taking advantage of machine learning (ML) techniques like neural networks and support vector machines to learn from data, make predictions, improve products, and enhance business decisions. Unfortunately "machine learning theater" – companies pretending to use the technology to make theirs seem more sophisticated for a higher valuation – is also on the rise. Undeniably, ML is transforming businesses and industries, with some more likely to benefit than others.