Kara Frederick, tech director at the Heritage Foundation, discusses the need for regulations on artificial intelligence as lawmakers and tech titans discuss the potential risks. FIRST ON FOX: A new GOP-led bill aims to stop U.S. tax dollars from going toward a United Nations-run program that uses artificial intelligence to help weed out content deemed to be misinformation or hate speech. Rep. Ben Cline, R-Va., is introducing the End The U.N. Censorship Act this week. AI has emerged as a top priority for lawmakers on Capitol Hill this year. Its advancements, as well as its pitfalls, have inspired a slew of legislation as Washington, D.C., races to get ahead of the rapidly emerging technology.

Early on in Gabrielle Zevin's Tomorrow, and Tomorrow, and Tomorrow, one of the trio of lead characters gives a fictional interview to a very real video games publication. The troubled but passionate Samson Mazur tells the interviewer, "There is no more intimate act than play, even sex." This is an explosive statement, but a perfect one in the context of a novel that treasures the act of play and holds it sacred. In some ways, this is a thesis statement for Tomorrow, and Tomorrow, and Tomorrow itself: the novel opening its heart, and showing you what it is truly about. Video games are seldom treated in literature as a site of emotion, but in Zevin's work they are the very landscape that the full spectrum of relationships, grief, and love play out in.

Despite pre-trained language models have proven useful for learning high-quality semantic representations, these models are still vulnerable to simple perturbations. Recent works aimed to improve the robustness of pre-trained models mainly focus on adversarial training from perturbed examples with similar semantics, neglecting the utilization of different or even opposite semantics. Different from the image processing field, the text is discrete and few word substitutions can cause significant semantic changes. To study the impact of semantics caused by small perturbations, we conduct a series of pilot experiments and surprisingly find that adversarial training is useless or even harmful for the model to detect these semantic changes. To address this problem, we propose Contrastive Learning with semantIc Negative Examples (CLINE), which constructs semantic negative examples unsupervised to improve the robustness under semantically adversarial attacking. By comparing with similar and opposite semantic examples, the model can effectively perceive the semantic changes caused by small perturbations. Empirical results show that our approach yields substantial improvements on a range of sentiment analysis, reasoning, and reading comprehension tasks. And CLINE also ensures the compactness within the same semantics and separability across different semantics in sentence-level.

Slate has relationships with various online retailers. If you buy something through our links, Slate may earn an affiliate commission. We update links when possible, but note that deals can expire and all prices are subject to change. All prices were up to date at the time of publication. The simplest way to summarize the plot of Ready Player Two is to repeat the plot of its predecessor, Ready Player One, as they are largely the same.

There's a long-running line of children's books where you provide the kid's details – name, age, favourite hobbies – and they all get mail-merged into the narrative, making the youngster the central character in their own story and providing the illusion of personalisation at a low cost. Ready Player Two, the sequel to the hugely popular Ready Player One, offers a similar experience. Like its predecessor, it's a tedious slog through arcane pop culture references – The Silmarillion, the music of Prince, the movies of John Hughes – sprinkled in so lazily that you could replace them with your own favourites, or swap them right out and be left with a much shorter, and probably better book. The action picks up immediately after the events of Ready Player One, which is set in the near-future, in a world where vast swathes of the population spend most of their day living inside a virtual reality simulation called the OASIS, to escape from the poverty, crime and general awfulness of life on Earth. The protagonist, Wade Watts, is a nerdy teenager living in the'stacks' outside Oklahoma City – a shanty-town comprised of literal stacks of trailers and RVs – who devotes all of his time to an in-OASIS treasure hunt devised by billionaire James Halliday, the late co-creator of the simulation, as a Willy Wonka-esque means to find an heir to his fortune.

Dogs were the first domesticated animal, likely originating from human-associated wolves, but their origin remains unclear. Bergstrom et al. sequenced 27 ancient dog genomes from multiple locations near to and corresponding in time to comparable human ancient DNA sites (see the Perspective by Pavlidis and Somel). By analyzing these genomes, along with other ancient and modern dog genomes, the authors found that dogs likely arose once from a now-extinct wolf population. They also found that at least five different dog populations ∼10,000 years before the present show replacement in Europe at later dates. Furthermore, some dog population genetics are similar to those of humans, whereas others differ, inferring a complex ancestral history for humanity's best friend. Science , this issue p. [557][1]; see also p. [522][2] Dogs were the first domestic animal, but little is known about their population history and to what extent it was linked to humans. We sequenced 27 ancient dog genomes and found that all dogs share a common ancestry distinct from present-day wolves, with limited gene flow from wolves since domestication but substantial dog-to-wolf gene flow. By 11,000 years ago, at least five major ancestry lineages had diversified, demonstrating a deep genetic history of dogs during the Paleolithic. Coanalysis with human genomes reveals aspects of dog population history that mirror humans, including Levant-related ancestry in Africa and early agricultural Europe. Other aspects differ, including the impacts of steppe pastoralist expansions in West and East Eurasia and a near-complete turnover of Neolithic European dog ancestry. [1]: /lookup/doi/10.1126/science.aba9572 [2]: /lookup/doi/10.1126/science.abe7823

Machine-learning models trained on normal behavior are showing cracks --forcing humans to step in to set them straight. People weren't just searching, they were buying too--and in bulk. The majority of people looking for masks ended up buying the new Amazon #1 Best Seller, "Face Mask, Pack of 50". When covid-19 hit, we started buying things we'd never bought before. The shift was sudden: the mainstays of Amazon's top ten--phone cases, phone chargers, Lego--were knocked off the charts in just a few days.

When the U.S. began locking down to slow the spread of the coronavirus, Amazon, grocery stores, and wholesale stores like Costco saw an enormous uptick in consumers wanting to buy a few select items. On Amazon, during the week of April 12th to 18th, the top ten search queries were face masks and N95 masks, hand sanitizer, paper products like paper towels and toilet paper, and sanitizing solutions like Lysol spray and Clorox wipes. So many people bought face masks that April's new #1 selling product on Amazon was "Face Mask, Pack of 50". This trend occurred across every single consumer- and business-facing industry and vertical. Consumers started behaving erratically literally overnight, and they haven't stopped behaving abnormally, creating a massive problem for companies who employ artificial intelligence (AI) and machine learning models.

People weren't just searching, they were buying too--and in bulk. The majority of people looking for masks ended up buying the new Amazon #1 Best Seller, "Face Mask, Pack of 50". When covid-19 hit, we started buying things we'd never bought before. The shift was sudden: the mainstays of Amazon's top ten--phone cases, phone chargers, Lego--were knocked off the charts in just a few days. Nozzle, a London-based consultancy specializing in algorithmic advertising for Amazon sellers, captured the rapid change in this simple graph.

Anyone looking for an illustration of how rapidly shopping habits changed when covid-19 hit needed only to glance at the top 10 search terms on Amazon in the week of April 12 to 18. In place of former mainstays like phone cases, phone chargers, and Lego sets were "toilet paper," "face mask," "hand sanitizer," "paper towels," "Lysol spray," "Clorox wipes," "mask," "Lysol," "masks for germ protection," and "N95 mask." People weren't just searching; they were buying, too--and in bulk. The majority of people looking for masks ended up buying the new Amazon #1 best seller, "Face Mask, Pack of 50." Nozzle, a London-based consultancy specializing in algorithmic advertising for Amazon sellers, captured the rapid change back in February in this simple graph.