The Beyerdynamic Amiron 300 hit their lowest price yet. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. The Beyerdynamic Amiron 300 (8/10, WIRED Review) are currently marked down to just $180 at Amazon, a full $100 off their list price, and the lowest price we've seen yet for these upgraded earbuds. The Amiron 300 have a classic Beyerdynamic sound profile, which is delightfully rich and smooth, with a mostly neutral base that has good definition at the far ends of the spectrum.

The electric vehicle tax credit is gone, and Tesla's new, more affordable models don't quite close the gap. For nearly two decades, CEO Elon Musk has promised Tesla would make a more affordable electric vehicle, to, as he put it in 2006, "help expedite the move from a mine-and-burn hydrocarbon economy towards a solar electric economy." On Tuesday, Tesla announced a new Model Y and Model 3 Standard, versions of its popular compact SUV and sedan stripped of a few higher-end touches and features to bring the price down to $39,990 and $36,990, respectively. They're both about $5,000 cheaper than the Premium variants, which goes a ways--but not all the way--toward recouping the $7,500 tax credit canceled by the GOP-led Congress this past summer . The price point also puts Tesla's newest models firmly in the "more affordable" EV camp.

The Beats Powerbeats Pro 2 are $50 off on Amazon. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. Beats has been a household name in headphones for years, known for punchy bass and bold styling. The Powerbeats Pro 2 (9/10, WIRED Recommends) use ergonomic over-ear hooks to stay absolutely secure in your ears when you're running, lifting, or just walking the dog.

Large pretrained models can be used as annotators, helping replace or augment crowdworkers and enabling distilling generalist models into smaller specialist models. Unfortunately, this comes at a cost: employing top-of-the-line models often requires paying thousands of dollars for API calls, while the resulting datasets are static and challenging to audit. To address these challenges, we propose a simple alternative: rather than directly querying labels from pretrained models, we task models to generate programs that can produce labels. These programs can be stored and applied locally, re-used and extended, and cost orders of magnitude less. Our system, \textbf{Alchemist}, obtains comparable to or better performance than large language model-based annotation in a range of tasks for a fraction of the cost: on average, improvements amount to a \textbf{12.9}

A Microsoft research team proposes DeepSpeed-MoE, comprising a novel MoE architecture design and model compression technique that reduces MoE model size by up to 3.7x and a highly optimized inference system that provides 7.3x better latency and cost compared to existing MoE inference solutions.

Inflation is going up dramatically right now, and may very well lead to rising interest rates in the future. Improvements in technology can cause a lowering of prices. One example of this is what happened after Alphabet (NASDAQ:GOOG) (NASDAQ:GOOGL) acquired the artificial intelligence start-up Deep Mind seven years ago. The company gave its AI solution the task of how to cool its many data centers. In this clip from "The 5," recorded on Oct. 11, Motley Fool contributor Danny Vena describes what happened next.

This robot is not exactly in the cloud: Wall-E from the movie by Pixar. Bringing a new robot to market is exciting: new capability, new hardware, new services. The problem is when you get to software, where everything feels harder and takes longer than you think it should. Like Tesla's full self-driving, which has all the hardware and intelligence it needs -- with the possible exception of LIDAR -- but is perpetually just ... about ... to ... arrive ... and even so, was recently savaged by Consumer Reports as buggy and ineffective. Hardware is necessary, but software provides the animating intelligence that allows it to do useful, efficient, and safe work.

The nation's top inventors and businesses rely heavily on the United States Patent and Trademark Office to issue patents for inventions and register trademarks for product and intellectual property identification. The agency has come to embrace increasingly more emerging and advanced technologies in recent years to meet its mission, and it is now also enduring a large-scale modernization. USPTO Chief Information Officer Jamie Holcombe is working to help make the agency's systems "better, cheaper and faster." Holcombe joined the agency at the request of Under Secretary of Commerce for Intellectual Property and Director Andrei Iancu, who immediately articulated his aims to "propel the USPTO into the next decade." "He wanted to ensure that all the up-to-date commercial tools were available to the USPTO examiners so that we could conduct our business in the most advanced way possible," Holcombe told Nextgov in the latest episode of Critical Update.

The town of Rotterdam, New York, has only 45 police officers, but technology extends their reach. Each day a department computer logs the license plates of around 10,000 vehicles moving through and around town, using software plugged into a network of cameras at major intersections and commercial areas. "Let's say for instance you had a bank robbed," says Jeffrey Collins, a lieutenant who supervises the department's uniform division. "You can look back and see every car that passed." Officers can search back in time for a specific plate, and also by color, make, and model of car.

Despite investment, research publications and job demand in the field continuing to grow through 2019, technologists are starting to come to terms with potential limitations in what AI can realistically achieve. Meanwhile, a growing movement is grappling with its ethics and social implications, and widespread business adoption remains stubbornly low. As a result, companies and organizations are increasingly pushing tools that commoditize existing predictive and image recognition machine learning, making the tech easier to explain and use for non-coders. Emerging breakthroughs, like the ability to create synthetic data and open-source language processors that require less training than ever, are aiding these efforts. At the same time, the use of AI for nefarious ends like deepfakes and the mass-production of spam are still in their earliest theoretical stages, and troubling reports indicate such dystopia may become more real in 2020.