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Japan’s top trade negotiator abruptly canceled a trip to Washington aimed at issuing a joint statement on a tariffs deal with the Trump administration, as a top government spokesman urged the U.S. side to speed up implementation of the agreement.Trade envoy Ryosei Akazawa was scheduled to leave Tokyo for Washington on Thursday for a 10th round of talks, following up on the agreement announced on July 22.But Chief Cabinet Secretary Yoshimasa Hayashi told reporters some details required further consultations, so the trip was postponed.In July, the two sides agreed on a 15% tax on imports of most Japanese goods, effective Aug. 1, down from an earlier 25% rate announced by President Donald Trump as so-called “reciprocal tariffs” on the major U.S. ally. Japanese officials discovered days later that the preliminary deal would add a 15% tariff to other tariffs and objected. Officials in Washington have acknowledged the mistake and agreed to abide by the agreement on a 15% tariff, and to refund any excess import duties that were paid.So far, that hasn’t happened.“We will strongly request the United States to amend its presidential order to correct the reciprocal tariffs and to issue the presidential order to lower tariffs on autos and auto parts,” Hayashi said.In an interview with Fox News earlier this week, U.S. Commerce Secretary Howard Lutnick said Washington was ready to finalize the deal, in which Japan also pledged to invest up to $550 billion in the United States in coming years.Plans for Akazawa to visit Washington are undecided, Hayashi said during a daily briefing, with another nudge at the Trump administration.“Japan and the United States have confirmed the importance of sincere and prompt implementation of the agreement between the two countries,” he said, adding that a deal was essential for the economic security of both countries. Mari Yamaguchi, Associated Press
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E-Commerce
Misshapen eyes and hands with too many fingers once made AI-generated art easy to spot. Now, as the technology advances, its becoming harder to tell human work from machine-made creations. With some fearing the replacement of human creatives, AI-generated art has plenty of detractors. Algorithm aversion, the bias against AI-created work, seems to only be growing, and just 20% of U.S. adults think AI will have a positive impact on arts and entertainment. Artists are among the most vocal opponents, not only because AI is already cutting into their income as more image needs are met by machines, but also because the models may have been trained on their own work. This is a very critical topic, says Philip Rieger, a postdoctoral researcher at the Technical University of Darmstadt. AI can easily imitate and manipulate images, violating artists copyrights, he adds. In response, artists have filed lawsuits alleging AI companies violated intellectual property laws by training on copyrighted works. Others are turning to digital tools to shield their art. But experts say artists’ work remains vulnerablemore than they may realize. How does AI violate artists copyrights? Popular generators like Midjourney and OpenAIs DALL-E train on millions of artworks, from Renaissance classics to modern digital pieces. This massive database allows them to mimic human styles with uncanny accuracy, making it harder than ever to tell the difference. A 2023 Yale University survey found undergraduates could identify whether art was AI-made just 54% of the time. These models rely on copyrighted art under the banner of fair use,” which allows the use of copyrighted work without the owners permission in certain circumstancesfor example, if the work is being used for noncommercial purposes, or if the use doesnt impact the original works value. But experts say that claim doesnt hold up. These models are training on everyones work without their authorization, says Kevin Madigan, senior vice president of policy and government affairs at the Copyright Alliance, an advocacy group for copyright-holders. They have the capability to supplant the need for human created work in the marketplace, which is one of the reasons we argue that it should not be considered fair-use to use those things. Some AI companies, such as OpenAI, further defend their use of copyrighted material by mentioning that artists can opt-out of their work being used. However, copyright advocates say this is not enough. Some companies, like OpenAI, argue that artists can opt out of datasets. Advocates counter thats meaningless if training has already happened. “Copyright is an opt-in regime,” Madigan says. “It’s sort of like putting the toothpaste back in the tube.” Arguments over how AI should be using copyrighted works are continuing to be hashed out in courts across the country and around the world, but little has been done to settle the debate for artists and they have had few legal successes so far. What tools are artists using to protect their work? Since removing artwork from training sets is nearly impossible, some artists are trying to stop AI from accessing it in the first place. Digital tools, like those developed by the University of Chicagos Glaze Project, work to poison images by modifying the pixels that make up digital artworks in a way that is imperceptible to humans but enough to confuse AI models. Its pretty interesting to be looking at, says Hanna Foerster, a computer science PhD student at the University of Cambridge who has studied the security of these models. You can optimize these pixels inside a picture although they seem almost the same to the human eye. One of their tools, Nightshade, works by making AI models see different images than the artist originally rendereda handbag where there once was a cow, for example. Glaze, another of their tools, alters the style instead, making a model see a Jackson Pollock-esque painting where the artist had created a charcoal portrait. These poisons aim to protect artists work once they have already made their way into a training dataset. Others, like robots.txt, work to prevent work from ending up in a dataset. The .txt file tells automated bots combing websites for content where they arent allowed to look. Why arent these anti-AI tools enough? While there are several ways to keep AI models from training on artwork, each comes with trade-offs and vulnerabilities, experts say. Robots.txt, for instance, can block bots scraping the web to build training sets, but it also blocks search engines, hurting artists visibility. Theres a few different issues with that, Madigan says. Its not practical to say, Dont put your work online, because thats basically peoples livelihoods, to have a presence online. The pixel-level poisons that distort images to confuse AI systems also have limits. LightShed, a new tool built by system security researchers, shows just how easily those poisons can be undone. To build LightShed, the researchers studied how these protective programs alter images. They found consistent patterns in the way poisons were applied, and trained their own model to recognize them by feeding it both clean and poisoned versions of the same images. It looks different from image to image, obviously, but its still the same process that is applied, says Foerster, who worked on LightShed during an internship at the Technical University of Darmstadt. Once their model could reliably detect the poison, it simply stripped it awayrevealing the original, unprotected image underneath. The researchers, who presented their work earlier this month at the USENIX Security Symposium, stress that their goal isnt to endanger artists or attack protective tools. Our work wasnt intended as a real attack, neither against the tools nor against the artists, Rieger says. We just wanted to show the need for more robust detection tools and that, basically, people shouldnt trust blindly to these tools. LighShed is only available for research, but the team hopes it will spark stronger safeguards against AI scraping and manipulation. Basically, we are playing nice and informing the people since the bad guys who are not playing as nice as we do wouldnt inform them, Rieger says, noting that the team alerted poison creators to the vulnerabilities they found. They also hope word of their findings reaches artists who may rethink how they protect their work. The future of anti-AI protections Researchers say that creating a foolproof defense against AI models may be nearly impossible, especially as image generation systems grow more advanced. To block tools like LightShed, image poisons would need to be far more random, but that would risk making the image unrecognizable to humans. We have thought about how to construct a kind of perturbation that would be a lot stronger than the ones that exist, but I dont think we can guarantee at all that they wont be broken a different way, Foerster says. Other approaches, such as cryptographic watermarking, could help artists detect when their work has been used in AI-generated content. But its definitely a very difficult problem, Foerster adds. Beyond the technical fixes, some experts argue that the real solution lies in regulation. Madigan says protecting artists will require legislation and legal action to force AI companies to be transparent about their training practices. In particular, Madigan has hope in bipartisan legislation introduced last month by Senators Josh Hawley (R-Mo.) and Richard Blumenthal (D-Conn.) that would hold tech companies accountable for training models on copyrighted materials. AI companies are robbing the American people blind while leaving artists, writers, and other creators with zero recourse, Hawley said in a statement. Its time for Congress to give the American worker their day in court to protect their personal data and creative works. If passed, the bill could give artists new legal tools to sue tech companies for copyright infringement, whether individually or through class action lawsuits, and levy financial penalties against firms that misuse copyrighted material. Even with such challenges, experts stress that artists shouldnt lose hope. The landscape around AI is evolving quicklyand so are the efforts to defend against its risks. Staying on top of these issues, that helps empower the whole community of artists, Madigan says.
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E-Commerce
Snowflake has had a good 24 hours. On Wednesday, August 27, the cloud-based data storage company announced its second-quarter earnings and reported $1.1 billion in revenuea 32% jump year-over-year (YOY). The companys second quarter of fiscal year (FY) 2026 also saw a net revenue retention rate of 125%. Plus, it grew from 606 to 654 customers with more than $1 million in trailing 12-month product revenue. Perhaps most notably, Snowflake has now increased its expected product revenue for FY 2026 to $4.4 billion from $4.3 billion. If met, this figure would signify a 27% growth YOY and beat analysts predictions, according to consensus estimates cited by Reuters. Investors responded positively to the news, with Snowflakes stock price (NYSE: SNOW) jumping about 14% after-hours and into premarket trading on Thursday. What is fueling Snowflakes success? In an earnings call, Snowflakes CEO Sridhar Ramaswamy attributed a great deal of the companys success to AI. Snowflake remains laser focused on our mission to empower every enterprise to achieve its full potential through data and AI, Ramaswamy stated. Were delivering our more than 12,000 customers tremendous value throughout their entire data life cycle with an AI data cloud thats designed to enable faster innovation and remove friction from business operations. Ramaswamy added that Snowflake delivered on our product strategy, introducing incredible new innovations to drive value at each stage of our customers data journey.” “Of course, AI is front and center,” he added. “We are continuing to advance our leadership in enterprise AI with Snowflake Intelligence now in public preview. This platform enables every user to talk to their enterprise data, turning structured and unstructured data into actionable insights through natural language. Shares of Snowflake are up 27% year to date and almost 80% over the last 12 months as of Wednesdays close. Mixed week for AI-adjacent tech However, the AI boom wasnt strong enough to propel every company. Wednesday also saw Nvidia announce its second-quarter earnings for FY 2026, and even a 56% boost in revenue YOYto $46.7 billionwasnt enough to lift its shares. The chip manufacturers stock initially fell 3.5%, though it has almost fully rebounded in premarket trading. The lack of excitement came in part due to Nvidias $41.1 billion in data center revenue. Despite also being up 56% YOY, it failed to meet Wall Streets predicted $41.34 billion, according to consensus estimates cited by CNBC. Nvidia is also facing continued uncertainty about selling its H20 chips to China, despite giving the Trump administration a 15% cut of sales for the go-ahead. The company sold no H20 chips to China during quarter two, and reports indicate that Nvidia has halted production of the chips.
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E-Commerce
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