Companies like Google--which revealed at its annual product showcase this week that it is throwing generative AI at everything it has, from Gmail to Photos to Maps--were too busy looking over their shoulders to see the real competition coming, writes Sernau: "While we've been squabbling, a third faction has been quietly eating our lunch." Greater access to these models has helped drive innovation--it can also help catch their flaws. AI won't thrive if just a few mega-rich companies get to gatekeep this technology or decide how it is used. But this open-source boom is precarious. Most open-source releases still stand on the shoulders of giant models put out by big firms with deep pockets.

A government panel of experts started discussions on Thursday on ways to promote and regulate ChatGPT and other generative artificial intelligence tools. "AI has the potential to change the economy and society positively, but it also has risks, so it's important to deal with both appropriately," Prime Minister Fumio Kishida said at the first meeting of the AI strategy panel. "AI is a global topic, and Japan is required to exercise leadership on it as the president of the Group of Seven" major democracies this year, he said, showing eagerness to take the initiative in international efforts to create rules. 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.

Fox News correspondent Grady Trimble has the latest on fears the technology will spiral out of control on'Special Report.' It's not uncommon to catch drivers singing by themselves in their cars, but soon you may see more of them having conversations instead. Automakers around the world are jumping on the artificial intelligence bandwagon and aiming to offer ChatGPT and similar generative AI features in their vehicles soon. "Having an assistant and really being able to use voice that is clear enough that you can ask questions and get answers, I think that's what the artificial intelligence will enable us to do," GM CEO Mary Barra recently told FOX Business. One company that knows a lot about chit-chat on the move is SoundHound, a major supplier of voice recognition systems used by several automakers, including Hyundai and Mercedes-Benz.

In this review, we explore the historical development and future prospects of artificial intelligence (AI) and deep learning in astronomy. We trace the evolution of connectionism in astronomy through its three waves, from the early use of multilayer perceptrons, to the rise of convolutional and recurrent neural networks, and finally to the current era of unsupervised and generative deep learning methods. With the exponential growth of astronomical data, deep learning techniques offer an unprecedented opportunity to uncover valuable insights and tackle previously intractable problems. As we enter the anticipated fourth wave of astronomical connectionism, we argue for the adoption of GPT-like foundation models fine-tuned for astronomical applications. Such models could harness the wealth of high-quality, multimodal astronomical data to serve state-of-the-art downstream tasks. To keep pace with advancements driven by Big Tech, we propose a collaborative, open-source approach within the astronomy community to develop and maintain these foundation models, fostering a symbiotic relationship between AI and astronomy that capitalizes on the unique strengths of both fields.

The open road poses many challenges to autonomous perception, including poor visibility from extreme weather conditions. Models trained on good-weather datasets frequently fail at detection in these out-of-distribution settings. To aid adversarial robustness in perception, we introduce WEDGE (WEather images by DALL-E GEneration): a synthetic dataset generated with a vision-language generative model via prompting. WEDGE consists of 3360 images in 16 extreme weather conditions manually annotated with 16513 bounding boxes, supporting research in the tasks of weather classification and 2D object detection. We have analyzed WEDGE from research standpoints, verifying its effectiveness for extreme-weather autonomous perception. We establish baseline performance for classification and detection with 53.87% test accuracy and 45.41 mAP. Most importantly, WEDGE can be used to fine-tune state-of-the-art detectors, improving SOTA performance on real-world weather benchmarks (such as DAWN) by 4.48 AP for well-generated classes like trucks. WEDGE has been collected under OpenAI's terms of use and is released for public use under the CC BY-NC-SA 4.0 license. The repository for this work and dataset is available at https://infernolia.github.io/WEDGE.

Large generative AI models (LGAIMs), such as ChatGPT, GPT-4 or Stable Diffusion, are rapidly transforming the way we communicate, illustrate, and create. However, AI regulation, in the EU and beyond, has primarily focused on conventional AI models, not LGAIMs. This paper will situate these new generative models in the current debate on trustworthy AI regulation, and ask how the law can be tailored to their capabilities. After laying technical foundations, the legal part of the paper proceeds in four steps, covering (1) direct regulation, (2) data protection, (3) content moderation, and (4) policy proposals. It suggests a novel terminology to capture the AI value chain in LGAIM settings by differentiating between LGAIM developers, deployers, professional and non-professional users, as well as recipients of LGAIM output. We tailor regulatory duties to these different actors along the value chain and suggest strategies to ensure that LGAIMs are trustworthy and deployed for the benefit of society at large. Rules in the AI Act and other direct regulation must match the specificities of pre-trained models. The paper argues for three layers of obligations concerning LGAIMs (minimum standards for all LGAIMs; high-risk obligations for high-risk use cases; collaborations along the AI value chain). In general, regulation should focus on concrete high-risk applications, and not the pre-trained model itself, and should include (i) obligations regarding transparency and (ii) risk management. Non-discrimination provisions (iii) may, however, apply to LGAIM developers. Lastly, (iv) the core of the DSA content moderation rules should be expanded to cover LGAIMs. This includes notice and action mechanisms, and trusted flaggers. In all areas, regulators and lawmakers need to act fast to keep track with the dynamics of ChatGPT et al.

Prognostics and health management (PHM) technology plays a critical role in industrial production and equipment maintenance by identifying and predicting possible equipment failures and damages, thereby allowing necessary maintenance measures to be taken to enhance equipment service life and reliability while reducing production costs and downtime. In recent years, PHM technology based on artificial intelligence (AI) has made remarkable achievements in the context of the industrial IoT and big data, and it is widely used in various industries, such as railway, energy, and aviation, for condition monitoring, fault prediction, and health management. The emergence of large-scale foundation models (LSF-Models) such as ChatGPT and DALLE-E marks the entry of AI into a new era of AI-2.0 from AI-1.0, where deep models have rapidly evolved from a research paradigm of single-modal, single-task, and limited-data to a multi-modal, multi-task, massive data, and super-large model paradigm. ChatGPT represents a landmark achievement in this research paradigm, offering hope for general artificial intelligence due to its highly intelligent natural language understanding ability. However, the PHM field lacks a consensus on how to respond to this significant change in the AI field, and a systematic review and roadmap is required to elucidate future development directions. To fill this gap, this paper systematically expounds on the key components and latest developments of LSF-Models. Then, we systematically answered how to build the LSF-Model applicable to PHM tasks and outlined the challenges and future development roadmaps for this research paradigm.

At Google in 2023, it seems pretty clear that AI itself now is the core product. As my colleague Melissa Heikkilä put it in her report on the company's efforts: Google is throwing generative AI at everything. The company made this point in one demo after another, all morning long. A Gmail demo showed how generative AI can compose an elaborate email to an airline to help you get a refund. The new Magic Editor in Google Photos will not only remove unwanted elements but reposition people and objects in photos, make the sky brighter and bluer, and then adjust the lighting in the photo so that all that doctoring looks natural.

This week, at its annual I/O developer conference in Mountain View, Google showcased a head-spinning number of projects and products powered by or enhanced by AI. They included a new-and-improved version of its chatbot Bard, tools to help you write emails and documents or manipulate images, devices with AI baked in, and a chatbot-like experimental version of Google search. Google's big pivot is, of course, largely fueled not by algorithms but by generative AI FOMO. The appearance last November of ChatGPT--the remarkably clever but still rather flawed chatbot from OpenAI--combined with Microsoft adding the technology to its search engine Bing a few months later, triggered something of a panic at Google. ChatGPT proved wildly popular with users, demonstrating new ways to serve up information that threatened Google's vice grip on the search business and its reputation as the leader in AI.

What use could healthcare have for someone who makes things up, can't keep a secret, doesn't really know anything, and, when speaking, simply fills in the next word based on what's come before? Lots, if that individual is the newest form of artificial intelligence, according to some of the biggest companies out there. Companies pushing the latest AI technology -- known as "generative AI" -- are piling on: Google and Microsoft want to bring types of so-called large language models to healthcare. Big firms that are familiar to folks in white coats -- but maybe less so to your average Joe and Jane -- are equally enthusiastic: Electronic medical records giants Epic and Oracle Cerner aren't far behind. The space is crowded with startups, too.