After 48 full hours, Amazon Prime Day 2022 is officially over. We hope you were able to find useful discounts among the Kindles, Echo Dots, Stasher bags, LifeStraws, and other great deals we found sifting through Amazon. If you missed out, fear not, a few great deals remain--at least for now. Below are the best remaining deals from all of our Prime Day 2022 coverage. Special offer for Gear readers: Get a 1-Year Subscription to WIRED for $5 ($25 off). This includes unlimited access to WIRED.com and our print magazine (if you'd like). Subscriptions help fund the work we do every day. If you buy something using links in our stories, we may earn a commission. This helps support our journalism. We like Google's Pixel 6 Pro (9/10, WIRED Recommends) because it has a spectacular camera system, with a great 4X optical zoom, a sharp 120-Hz screen refresh rate with an AMOLED panel, and plenty of useful smart software features powered by Google's Tensor processor. Oh, and a battery that generally lasts a full day.

Artificial Intelligence (AI), machine learning, decentralised finance, and sustainability currently present some of the largest gaps between …

An artificial intelligence-powered system newly cleared by the FDA, … Using its deep learning AI models and algorithms, Eye-Sync was able to …

It was the year 2014 when Ian Goodfellow pitted two separate Neural Networks against each other. And thus were invented GANs. Now, all manner of industries is scrambling to make sure they are in a prime position to take advantage of AI-generated content. For the photography industry -- from stock images through to high-end fashion spreads -- there is a heady mix of excitement about the potential time, and therefore money, savings that AI could offer; and a frisson about the creative potential. The stock image industry is projected to reach US$6.4 Trillion by 2027.

AI technologies in the future will help increase demand for skills insulated from automation, such as creativity, leadership, and organisational …

Machine-learning based investing approach adapts well to changing market conditions:, says Kanika Agarrwal, Upside AI co-founder, …

Shopping for a new way to enjoy your favorite music, podcasts, or TV shows? Below you'll find the best headphones and speakers we've seen on sale this Prime Day, which ends in a few hours (11:59 pm PT, to be exact). Be sure to check out our guides to the Best Wireless Headphones, Best Noise-Canceling Headphones, Best Workout Headphones, and Best Bluetooth Speakers for more information about what's hot right now. The WIRED Gear team tests products year-round. We sorted through hundreds of thousands of deals by hand to make these picks.

Amazon Prime Day is winding down, ending in just a few hours (11:59 pm PT to be exact). Most of the deals are for Prime subscribers, but there are great discounts for non-members as well. We've spent hours combing through thousands of deals; these are our favorites on WIRED-tested gear, from Alexa-enabled speakers and robot vacs to laptops and tablets. The WIRED Gear team tests products year-round. We sorted through hundreds of thousands of deals by hand to make these picks. Crossed out products are out of stock or no longer discounted. Our Amazon Prime Day coverage page has the latest stories, and our Prime Day Shopping Tips will help you avoid bad deals. You can also get a 1-year subscription to WIRED for $5 here. Updated July 13: We've added new deals, including the OnePlus 9 Pro, Logitech K380 Keyboard, and Bose QuietComfort 45 Headphones. We've also removed expired deals and updated prices. If you buy something using links in our stories, we may earn a commission. This helps support our journalism. We've got a separate roundup of more great home and kitchen deals right here. There are blenders, and then there are Vitamix blenders. I was skeptical, but like my fellow Gear reviewer Joe Ray, the Vitamix made me a blender person.

Audio-based automatic speech recognition (ASR) degrades significantly in noisy environments and is particularly vulnerable to interfering speech, as the model cannot determine which speaker to transcribe. Audio-visual speech recognition (AVSR) systems improve robustness by complementing the audio stream with the visual information that is invariant to noise and helps the model focus on the desired speaker. However, previous AVSR work focused solely on the supervised learning setup; hence the progress was hindered by the amount of labeled data available. In this work, we present a self-supervised AVSR framework built upon Audio-Visual HuBERT (AV-HuBERT), a state-of-the-art audio-visual speech representation learning model. On the largest available AVSR benchmark dataset LRS3, our approach outperforms prior state-of-the-art by ~50% (28.0% vs. 14.1%) using less than 10% of labeled data (433hr vs. 30hr) in the presence of babble noise, while reducing the WER of an audio-based model by over 75% (25.8% vs. 5.8%) on average.

Over 60,000 songs are released on Spotify every day, and the competition for the listener's attention is immense. In that regard, the importance of captivating and inviting cover art cannot be underestimated, because it is deeply entangled with a song's character and the artist's identity, and remains one of the most important gateways to lead people to discover music. However, designing cover art is a highly creative, lengthy and sometimes expensive process that can be daunting, especially for non-professional artists. For this reason, we propose a novel deep-learning framework to generate cover art guided by audio features. Inspired by VQGAN-CLIP, our approach is highly flexible because individual components can easily be replaced without the need for any retraining. This paper outlines the architectural details of our models and discusses the optimization challenges that emerge from them. More specifically, we will exploit genetic algorithms to overcome bad local minima and adversarial examples. We find that our framework can generate suitable cover art for most genres, and that the visual features adapt themselves to audio feature changes. Given these results, we believe that our framework paves the road for extensions and more advanced applications in audio-guided visual generation tasks.