From books and paintings to movies and musical theatre, artists have often drawn on the stories of the Bible for inspiration. Now, social media users are putting a distinctly modern twist on this trend by using AI to imagine biblical characters as influencers. In these videos, characters such as Adam and Eve, Samson, and David appear to'vlog' their way through the events of the Bible. In one viral video posted to X, a smiling Jesus declares from the cross: 'Yo fam, they don't know that G-O-D is about to BRB.' In another clip, an AI-generated character says: 'Your boy David here. About to yeet this little stone at Goliath and see what happens.' These short AI-generated clips have proven to be wildly popular online, with one TikTok account named theaibibleofficial racking up 26.7 million likes.

Energy-efficient deep neural network (DNN) accelerators are prone to non-idealities that degrade DNN performance at inference time. To mitigate such degradation, existing methods typically add perturbations to the DNN weights during training to simulate inference on noisy hardware. However, this often requires knowledge about the target hardware and leads to a trade-off between DNN performance and robustness, decreasing the former to increase the latter. In this work, we show that applying sharpness-aware training, by optimizing for both the loss value and loss sharpness, significantly improves robustness to noisy hardware at inference time without relying on any assumptions about the target hardware. In particular, we propose a new adaptive sharpness-aware method that conditions the worst-case perturbation of a given weight not only on its magnitude but also on the range of the weight distribution. This is achieved by performing sharpness-aware minimization scaled by outlier minimization (SAMSON). Our approach outperforms existing sharpness-aware training methods both in terms of model generalization performance in noiseless regimes and robustness in noisy settings, as measured on several architectures and datasets.

For the record, I don't own socks with sloths on them. I have three pairs with the CNET logo on them. ChatGPT thinks I might, though, and it also thinks this fact could get me matches on Hinge, or Bumble, or any dating app that has the audacity to ask me for a random fact about myself. Click to read more Love Syncs. Here's a random fact about me: When I tested how ChatGPT might handle rewriting my dating app profile, the experimental AI chatbot tried to turn me into a cringey manic pixie dream girl who forgets to water her "jungle" of houseplants, dances to her favorite "tunes" and is looking for "a fellow weirdo" to go on *shudders* "adventures" with.

For the record, I don't own any socks with sloths on them. I have three pairs with the CNET logo on them. ChatGPT thinks I might, though, and it also thinks this fact could get me matches on Hinge, or Bumble, or any dating app that has the audacity to ask me for a random fact about myself. Click to read more Love Syncs. Here's a random fact about me: When I tested how ChatGPT might handle rewriting my dating app profile, the experimental AI chatbot tried to turn me into a cringey manic pixie dream girl who forgets to water her "jungle" of houseplants, dances to her favorite "tunes" and is looking for "a fellow weirdo" to go on *shudders* "adventures" with.

While contextualized word embeddings have been a de-facto standard, learning contextualized phrase embeddings is less explored and being hindered by the lack of a human-annotated benchmark that tests machine understanding of phrase semantics given a context sentence or paragraph (instead of phrases alone). To fill this gap, we propose PiC -- a dataset of ~28K of noun phrases accompanied by their contextual Wikipedia pages and a suite of three tasks for training and evaluating phrase embeddings. Training on PiC improves ranking models' accuracy and remarkably pushes span-selection (SS) models (i.e., predicting the start and end index of the target phrase) near-human accuracy, which is 95% Exact Match (EM) on semantic search given a query phrase and a passage. Interestingly, we find evidence that such impressive performance is because the SS models learn to better capture the common meaning of a phrase regardless of its actual context. SotA models perform poorly in distinguishing two senses of the same phrase in two contexts (~60% EM) and in estimating the similarity between two different phrases in the same context (~70% EM).

About a year ago, Frankfurt's Lord Mayor Peter Feldmann had the impulse to launch an AI initiative. Stefan Jäger, a speaker in the Lord Mayor's office and honorary board member of the association, let us know what Feldmann had in mind: "As always with new technologies, citizens have difficulty imagining what artificial intelligence actually is in this case. The association wants to acquire and share knowledge. Only transparency will make people curious." And Dr Thorsten Pötter wants to help him do so.

Old school, new school: The Tampa Bay Rays have two pitchers who have started 20 games this year. One is their ace, Blake Snell, whose 2.03 earned-run average ranks second in the American League. The other is Ryne Stanek, a reliever turned “opener” — in his case, a right-hander who works the first inning or so, followed by a left-hander. In a year in which the Rays lost starters Anthony Banda, Jose DeLeon and Brent Honeywell to Tommy John surgery and traded starters Chris Archer and Nathan Eovaldi, the team leads the AL in ERA since May 19, when Sergio Romo debuted as Tampa Bay’s first “opener.” There is no pitching statistic more derided in sabermetrics than wins for a pitcher.

Samson Motors has joined the race to have the first flying cars in the skies, with the world's first 200mph (320km/h) flying sports car, dubbed the Switchblade. The Switchblade can cruise at an altitude of 13,000 feet and at a top speed of 200mph and is fitted with a safety parachute. Samson Motors claims the car will be ready for launch in spring 2018, with prices starting at £90,000 ($120,000). The Switchblade is a three-wheel car, designed by Oregon-based Samson Motors. The car has two seats, and on the ground is 5.1 metres long.

The "great global brain drain" is how futurist Richard Samson describes it. As the century progresses, he predicts, more and more jobs will be sucked up by technology and sophisticated computers, forcing humans to hone skills machines can't duplicate - at least not yet. Qualities such as ethical judgment, compassion, intuition, responsibility, and creativity will be what stand out in an automated world. With ethics issues spiking into the news almost weekly, the idea of a work world in which individual ethical acumen is viewed as an essential job skill is far from outlandish. The signs are already here.