The rapid normalization of artificial intelligence is forcing a reckoning with how much of the future is being shaped by hype rather than utility. The writers Charles Duhigg, Cal Newport, and Anna Wiener join Tyler Foggatt for a conversation about artificial intelligence and the promises, myths, and anxieties surrounding it. The discussion was recorded before a live audience at The New Yorker Festival this fall. They explore the gap between Silicon Valley's sweeping claims and what generative A.I. can actually do today; how people are using the technology for work, creativity, and emotional support; and why the tech's most immediate political consequences may be the hardest to grapple with. " The Biggest Threat to the 2026 Economy Is Still Donald Trump," by John Cassidy What Can We Do Instead?," by Jay Caspian Kang When an Ivy League school turned against a student .

Meta Tells Its Metaverse Workers to Use AI to'Go 5X Faster' Mark Zuckerberg's metaverse chief is urging employees to adopt AI across every workflow as part of a broader shift inside the company. Meta CEO Mark Zuckerberg says most of the company's code will be written by AI in the next 18 months. A Meta executive in charge of building the company's metaverse products told employees that they should be using AI to "go 5X faster" according to an internal message obtained by 404 Media. "Metaverse AI4P: Think 5X, not 5%," the message, posted by Vishal Shah, Meta's VP of Metaverse, said (AI4P is AI for Productivity). The idea is that programmers should be using AI to work five times more efficiently than they are currently working--not just using it to go 5 more efficiently.

Semi-supervised learning has received considerable attention for its potential to leverage abundant unlabeled data to enhance model robustness. Pseudo labeling is a widely used strategy in semi supervised learning. However, existing methods often suffer from noise contamination, which can undermine model performance. To tackle this challenge, we introduce a novel Synergy-Guided Regional Supervision of Pseudo Labels (SGRS-Net) framework. Built upon the mean teacher network, we employ a Mix Augmentation module to enhance the unlabeled data. By evaluating the synergy before and after augmentation, we strategically partition the pseudo labels into distinct regions. Additionally, we introduce a Region Loss Evaluation module to assess the loss across each delineated area. Extensive experiments conducted on the LA dataset have demonstrated superior performance over state-of-the-art techniques, underscoring the efficiency and practicality of our framework.

On Monday, 404 Media's Samantha Cole reported that the 2.4 trillion company asked workers to download videos from YouTube, Netflix and other datasets to develop commercial AI projects. The graphics card maker is among the tech companies appearing to have adopted a "move fast and break things" ethos as they race to establish dominance in this feverish, too-often-shameful AI gold rush. The training was reportedly to develop models for products like its Omniverse 3D world generator, self-driving car systems and "digital human" efforts. NVIDIA defended its practice in an email to Engadget. The company equated the practice to a person's right to "learn facts, ideas, data, or information from another source and use it to make their own expression."

This one went smoothly -- no claims of rampant rigging, no significant taint of skulduggery -- due in large part to the defeat of deepfakes, democracy's newest enemy. Is such a future possible? So far, neither government nor the tech industry has agreed on effective guardrails against deepfakes. But this FAQ (from five years in the future) shows that the events of 2024 may well force the issue -- and that a solution is possible. Why did it take so long to find an effective way to fight deepfakes?

We are an equal-opportunity employer committed to building a respectful and empowering work environment for all people to freely express themselves amongst colleagues who embrace diversity in all respects. Including fresh voices and unique points of view in all aspects of our business not only creates an environment where we can all grow and thrive but also increases our potential to produce work that better represents--and resonates with--the world around us.

"Data-Driven Thinking" is written by members of the media community and contains fresh ideas on the digital revolution in media. Over the past few years, advertising has become far more data-driven. AI is playing a large role in the transformation, helping advertisers measure campaign efficacy and transform data into actionable insights. But AI is far from infallible. The technology reflects human biases.

Artificial intelligence (AI) was seen as one of the top current investment priorities and was thought to continue to attract investment in the healthcare sector in the upcoming two years, according to GlobalData's latest report'Digital Transformation and Emerging Technology in the Healthcare Industry – 2022 Edition'. In this survey-based report tracker, digital media was prioritised as a top current investment target, with 53% of surveyed respondents confirming that their companies are currently investing in this technology. It was followed by AI, social media and big data (Figure 1). Compared with last year's data, digital media saw the biggest increase in current investment, up by 22% from last year. AI ( 9% from 2021), social media ( 8%) and big data ( 5%) have also gained since last year, besides trending as very popular technologies for investment priorities for several years.

A drone attack has hit a major Russian oil refinery near the border with Ukraine, the plant's management said, sending a ball of flame and black smoke billowing into the sky and prompting the suspension of operations. Officials at the Novoshakhtinsk oil refinery in Russia's Rostov region said the first drone attacked at 8:40am (05:40GMT), hitting a crude distillation unit, triggering a blast and ball of fire. The second attack, at 9:23am, targeted crude oil reservoirs at the refinery, the largest supplier of oil products in southern Russia, but caused no fire, according to plant management. No one was reported injured. Russian regions bordering Ukraine have reported attacks and shelling after Moscow sent its troops into its neighbour on February 24 for what it still calls a "special military operation".

Fractures are widely developed in hydrocarbon reservoirs and constitute the accumulation spaces and transport channels of oil and gas. Fracture detection is a fundamental task for reservoir characterization. From prestack seismic gathers, anisotropic analysis and inversion were commonly applied to characterize the dominant orientations and relative intensities of fractures. However, the existing methods were mostly based on the vertical aligned facture hypothesis, it is impossible for them to recognize fracture dip. Furthermore, it is difficult or impractical for existing methods to attain the real fracture densities. Based on data-driven deep learning, this paper designed a convolutional neural network to perform prestack fracture detection. Capitalizing on the connections between seismic responses and fracture parameters, a suitable azimuth dataset was firstly generated through fracture effective medium modeling and anisotropic plane wave analyzing. Then a multi-input and multi-output convolutional neural network was constructed to simultaneously detect fracture density, dip and strike azimuth. The application on a practical survey validated the effectiveness of the proposed CNN model.