Adobe Firefly is a deceptively powerful AI playground to generate images, videos, and more. Here's how to make the most of it. 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. Adobe Firefly feels like the best-kept secret in software right now.

Automatic speech recognition (ASR) research is driven by the availability of common datasets between industrial researchers and academics, encouraging comparisons and evaluations. Lib-riSpeech, despite its long success as an ASR benchmark, is now limited by its size and focus on clean, read speech, leading to near-zero word error rates. More recent datasets, including MOSEL, YODAS, Gigaspeech, OWSM, Libriheavy or People's Speech suffer from major limitations including licenses that researchers in the industry cannot use, unreliable transcriptions, incorrect audio data, or the lack of evaluation sets. This work presents the Loquacious Set, a 25,000-hour curated collection of commercially usable English speech. Featuring hundreds of thousands of speakers with diverse accents and a wide range of speech types (read, spontaneous, talks, clean, noisy), the Loquacious Set is designed to work for academics and researchers in the industry to build ASR systems in real-world scenarios.

Freelancer has accused Anthropic, the AI startup behind the Claude large language models, of ignoring its "do not crawl" robots.txt Meanwhile, iFixit CEO Kyle Wiens said Anthropic has ignored the website's policy prohibiting the use of its content for AI model training. Matt Barrie, the chief executive of Freelancer, told The Information that Anthropic's ClaudeBot is "the most aggressive scraper by far." His website allegedly got 3.5 million visits from the company's crawler within a span of four hours, which is "probably about five times the volume of the number two" AI crawler. Similarly, Wiens posted on X/Twitter that Anthropic's bot hit iFixit's servers a million times in 24 hours.

Deep Learning is advancing medical imaging Research and Development (R&D), leading to the frequent clinical use of Artificial Intelligence/Machine Learning (AI/ML)-based medical devices. However, to advance AI R&D, two challenges arise: 1) significant data imbalance, with most data from Europe/America and under 10% from Asia, despite its 60% global population share; and 2) hefty time and investment needed to curate proprietary datasets for commercial use. In response, we established the first commercial medical imaging platform, encompassing steps like: 1) data collection, 2) data selection, 3) annotation, and 4) pre-processing. Moreover, we focus on harnessing under-represented data from Japan and broader Asia, including Computed Tomography, Magnetic Resonance Imaging, and Whole Slide Imaging scans. Using the collected data, we are preparing/providing ready-to-use datasets for medical AI R&D by 1) offering these datasets to AI firms, biopharma, and medical device makers and 2) using them as training/test data to develop tailored AI solutions for such entities. We also aim to merge Blockchain for data security and plan to synthesize rare disease data via generative AI.

At the turn of the century, when the modern web was just emerging and Microsoft was king, a small but growing technology movement posed an existential threat to the company. Steve Ballmer, Microsoft's CEO at the time, called one of its core elements "a cancer that attaches itself" to "everything it touches." The disease was a competing operating system, Linux, and the open-source software it represented: programs that were free for anyone to download, modify, and use, in contrast to expensive, proprietary software such as Microsoft Windows and Office. Open-source software did eventually attach itself to much of the internet--Mozilla Firefox, the Android operating system, and Wikipedia are all "open" projects--but the tech industry managed to turn the egalitarian philosophy into a business opportunity. Trillion-dollar companies use free open-source software to build or enhance their own products.

NEW YORK – Meta is releasing a commercial version of its open-source artificial intelligence model, Llama, the company said Tuesday, giving startups and other businesses a powerful free-of-charge alternative to pricey proprietary models sold by OpenAI and Google. The new version of the model, called Llama 2, will be distributed by Microsoft through its Azure cloud service and will run on the Windows operating system, Meta said in a blog post, referring to Microsoft as "our preferred partner" for the release. The model, which Meta previously provided only to select academics for research purposes, will also be made available via direct download and through Amazon Web Services, Hugging Face and other providers, according to the blog post and a separate Facebook post by Meta CEO Mark Zuckerberg. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

The rumors of a commercially-oriented Meta AI model were true. Meta and Microsoft have teamed up to unveil Llama 2, a next-generation large language (very generalized) AI model intended for both commercial and research purposes. The upgraded open source code places a greater emphasis on responsibility. Developers "red-teamed" models (that is, tested them for safety) and created a transparency schematic to detail potential issues. They also include a responsible use guide, and there's an acceptable use policy to prevent abuses like criminal activity, misleading representations and spam.

Microsoft will release a new version of Meta Platforms' artificial-intelligence language model in a partnership between the tech giants that will make the software available to companies for the first time.

Adobe held a full-court press event in Las Vegas at its annual Summit. In a hall the length of two football fields with five massive video screens ranging lengthwise across the space, the company's event featured a panoply of digital images, all created with its new generative artificial intelligence tool, Firefly (Figure A). At the event, Adobe executives, including CEO Shantanu Narayen, laid out the company's vision and range of ambitions for product innovations. As part of this initiative, Adobe is going all in on generative AI, introducing a raft of new products to a global audience of industry participants, analysts, customers and the press. Anil Chakravarthy, president, digital experience, at Adobe spoke about digital-first, end-to-end customer experience as now central to marketing communications, and that AI and machine learning will be key to Adobe products serving these enterprises.

She wanted to know if I had any suggestions, and asked what I thought all the new advances meant for lawmakers. I've spent a few days thinking, reading, and chatting with the experts about this, and my answer morphed into this newsletter. Though GPT-4 is the standard bearer, it's just one of many high-profile generative AI releases in the past few months: Google, Nvidia, Adobe, and Baidu have all announced their own projects. In short, generative AI is the thing that everyone is talking about. And though the tech is not new, its policy implications are months if not years from being understood.