It only took Matt Laming, a 19-year-old from the United Kingdom, about a month to hit a million followers on Twitter. And all it required was sharing a steady stream of the most outlandish computer-generated images that he and a bunch of internet strangers could think up. In recent weeks, the digital marketing apprentice, better known online as @weirddalle, has shared images depicting things like people vacuuming in the forest, the Demogorgon from Netflix's "Stranger Things" holding a basketball, and a Beanie Baby that looks a lot like Danny DeVito. These and other pictures, which range from ridiculous to disturbing, were created with a freely available artificial intelligence system called Craiyon. To use it, you just type what you'd like it to envision -- "A rainbow lion eating a slice of pizza" -- and it will spit out pictures in response.

Existing datasets used to train deep learning models for narrowband radio frequency (RF) signal classification lack enough diversity in signal types and channel impairments to sufficiently assess model performance in the real world. We introduce the Sig53 dataset consisting of 5 million synthetically-generated samples from 53 different signal classes and expertly chosen impairments. We also introduce TorchSig, a signals processing machine learning toolkit that can be used to generate this dataset. TorchSig incorporates data handling principles that are common to the vision domain, and it is meant to serve as an open-source foundation for future signals machine learning research. Initial experiments using the Sig53 dataset are conducted using state of the art (SoTA) convolutional neural networks (ConvNets) and Transformers. These experiments reveal Transformers outperform ConvNets without the need for additional regularization or a ConvNet teacher, which is contrary to results from the vision domain. Additional experiments demonstrate that TorchSig's domain-specific data augmentations facilitate model training, which ultimately benefits model performance. Finally, TorchSig supports on-the-fly synthetic data creation at training time, thus enabling massive scale training sessions with virtually unlimited datasets.

Netflix shed almost 1 million subscribers during the spring amid tougher competition and soaring inflation that's squeezing household budgets, heightening the urgency behind the video streaming service's effort to launch a cheaper option with commercial interruptions. The April-June contraction of 970,000 accounts, announced Tuesday as part of Netflix's second-quarter earnings report, is by far the largest quarterly subscriber loss in the company's 25-year history. It could have been far worse, though, considering Netflix management released an April forecast calling for a loss of 2 million subscribers during the second quarter. Netflix was probably spared from deeper losses by the ongoing popularity of "Stranger Things," its science fiction/horror series that debuted in 2016. Following the release of the series' fourth season in late May, Netflix said, viewers watched a total of 1.3 billion hours of it over the next four weeks -- more than any other English-language series in the service's history.

Abstract: Artificial Intelligence (AI) and Machine Learning (ML) promise to transform all facets of medicine.

Utilizing machine learning, we find that boosted trees outperform GAM and accurately describe water quality dynamics with less than 1% error.

HiddenLayer has emerged from stealth with $6 million seed funding to protect the machine learning models: it is the first of what may become a new …

The CRN Emerging Vendors 2022 list includes startups developing next-generation AI and machine learning technologies.

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We demonstrate that even with less data and compute, this approach has enabled performance comparable to state-of-the-art deep learning models across …

Machine learning algorithms successfully predicted visual and anatomic outcomes and dosing frequency in patients with macular edema secondary to …