Intel Corp. Chief Executive Patrick Gelsinger is guiding the chip giant through a period of industry upheaval. On the one hand, U.S. semiconductor makers are grappling with softening demand for chips amid inflation and recession fears, and facing new government restrictions on certain exports to China. On the other hand, the industry is about to get more than $50 billion in subsidies to help it shift more production to the U.S. from Asia, thanks to the bipartisan Chips and Science Act that President Biden signed into law over the summer. Under Mr. Gelsinger, who lobbied heavily for the legislation, Intel is going on an expansion spree, investing heavily in new factories in Ohio and other places. The company has also moved forward with an initial public offering of Mobileye, its automated-driving unit.

The regulatory approach towards artificial intelligence is currently the subject of heated debate among policy makers. This regulatory debate is, however, dominated by a one-dimensional viewpoint, in which the digital forest cannot always be seen for its trees. Contrary to popular belief, however, artificial intelligence does not in and of itself constitute a regulatory problem. Artificial intelligence (AI) is not a new phenomenon. Various AI applications, such as machine vision, have been in use in Finland for several decades.

As AI spreads throughout the enterprise, organizations are having a difficult time balancing the benefits against the risks. AI is already baked into a range of tools, from IT infrastructure management to DevOps software to CRM suites, but most of those tools were adopted without an AI risk-mitigation strategy in place. Of course, it's important to remember that the list of potential AI benefits is every bit as long as the risks, which is why so many organizations skimp on risk assessments in the first place. Many organizations have already made serious breakthroughs that wouldn't have been possible without AI. For instance, AI is being deployed throughout the health-care industry for everything from robot-assisted surgery to reduced drug dosage errors to streamlined administrative workflows.

This work introduces a natural language inference (NLI) dataset that focuses on the validity of statements in legal wills. This dataset is unique because: (a) each entailment decision requires three inputs: the statement from the will, the law, and the conditions that hold at the time of the testator's death; and (b) the included texts are longer than the ones in current NLI datasets. We trained eight neural NLI models in this dataset. All the models achieve more than 80% macro F1 and accuracy, which indicates that neural approaches can handle this task reasonably well. However, group accuracy, a stricter evaluation measure that is calculated with a group of positive and negative examples generated from the same statement as a unit, is in mid 80s at best, which suggests that the models' understanding of the task remains superficial. Further ablative analyses and explanation experiments indicate that all three text segments are used for prediction, but some decisions rely on semantically irrelevant tokens. This indicates that overfitting on these longer texts likely happens, and that additional research is required for this task to be solved.

The U.K.'s privacy watchdog has warned against use of so-called "emotion analysis" technologies for anything more serious than kids' party games, saying there's a discrimination risk attached to applying "immature" biometric tech that makes pseudoscientific claims about being able to recognize people's emotions using AI to interpret biometric data inputs. Such AI systems'function', if we can use the word, by claiming to be able to'read the tea leaves' of one or more biometric signals, such as heart rate, eye movements, facial expression, skin moisture, gait tracking, vocal tone etc, and perform emotion detection or sentiment analysis to predict how the person is feeling -- presumably after being trained on a bunch of visual data of faces frowning, faces smiling etc (but you can immediately see the problem with trying to assign individual facial expressions to absolute emotional states -- because no two people, and often no two emotional states, are the same; hence hello pseudoscience!). The watchdog's deputy commissioner, Stephen Bonner, appears to agree that this high tech nonsense must be stopped -- saying today there's no evidence that such technologies do actually work as claimed (or that they will ever work). "Developments in the biometrics and emotion AI market are immature. They may not work yet, or indeed ever," he warned in a statement. "While there are opportunities present, the risks are currently greater.

By imitating human thinking, cognition, learning and computing, artificial intelligence (AI) can accomplish specific tasks, including the completion of certain research with development results. Whether an AI system can be considered an inventor on a Taiwan patent has been widely discussed in the legal profession in recent years. On this issue, the Supreme Administrative Court says "No" in its 2022 Shang Zi No. 55 decision. In said case, the applicant listed an AI as an inventor on the patent application. In turn, after informing the applicant in writing that the patent application was incomplete since a human inventor was not listed, and requesting the applicant to provide a human inventor, the Taiwan Intellectual Property Office (TIPO) rendered an administrative disposition to dismiss the patent application because the applicant still failed to provide a human inventor within the specified time period.

With the advent of the ability of artificial intelligence ("AI") to alter an individual's voice and image (whether in deepfakes or expressly fictional works), it is critical to determine who – if anyone – owns the right to do so, particularly when the voice or image is clearly identified with a fictional character from an existing film. This issue is highlighted by the recent license by James Earl Jones (the voice of Darth Vader) of his voice to an AI company. While articles state that the license of his voice was for use by Disney (the owner of the Star Wars franchise), the transaction raises the following questions: (a) could anyone use his voice without permission and (b) could James Earl Jones have licensed his voice to third parties for use in other films, particularly if used in the distinctive manner of Darth Vader? This article will refer to the individual whose voice or image is at issue as the "Individual," the licensee of AI rights as the "AI Licensee," the new AI work incorporating the voice or image as the "AI Work," and any prior work that the voice or image is taken from, or resembles elements of, as the "Prior Work." Let's first deal with the right of publicity.

On October 3, renowned South Korean illustrator Kim Jung Gi passed away unexpectedly at the age of 47. He was beloved for his innovative ink-and-brushwork style of manhwa, or Korean comic-book art, and famous for captivating audiences by live-drawing huge, intricate scenes from memory. Just days afterward, a former French game developer, known online as 5you, fed Jung Gi's work into an AI model. He shared the model on Twitter as an homage to the artist, allowing any user to create Jung Gi-style art with a simple text prompt. The artworks showed dystopian battlefields and bustling food markets -- eerily accurate in style, and, apart from some telltale warping, as detailed as Jung Gi's own creations.

ABSTRACT Micro-mobility is promising to contribute to sustainable cities in the future with its efficiency and low cost. To better design such a sustainable future, it is necessary to understand the trends and challenges. Thus, we examined people's opinions on micro-mobility in the US and the EU using Tweets. We used topic modeling based on advanced natural language processing techniques and categorized the data into seven topics: promotion and service, mobility, technical features, acceptance, recreation, infrastructure and regulations. Furthermore, using sentiment analysis, we investigated people's positive and negative attitudes towards specific aspects of these topics and compared the patterns of the trends and challenges in the US and the EU. We found that 1) promotion and service included the majority of Twitter discussions in the both regions, 2) the EU had more positive opinions than the US, 3) micro-mobility devices were more widely used for utilitarian mobility and recreational purposes in the EU than in the US, and 4) compared to the EU, people in the US had many more concerns related to infrastructure and regulation issues. These findings help us understand the trends and challenges and prioritize different aspects in micro-mobility to improve their safety and experience across the two areas for designing a more sustainable micro-mobility future. INTRODUCTION The growth of transportation has raised the need for compact, flexible, and more sustainable forms of transportation. Recent developments in the micro-mobility industry show that these devices might address this issue and offer people safer and cheaper trips with reduced travel time. According to the Society of Automotive Engineers (SAE) definition (Society of Automotive Engineers, 2019), micro-mobility refers to a range of small, less than 500 pounds (227 kg) lightweight, fully motorized or motor-assisted devices operating at a speed below 30 mph (48 km/h) and ideal for trips up to 10 km. Typical examples include e-bikes, e-scooters, e-unicycles and e-skateboards, and some of them are widely used as personal or shared transportation devices (Price, Blackshear, Blount Jr, & Sandt, 2021). The global micro-mobility market has been increasing over the years. According to the NACTO (National Association of City Transportation Officials, 2020), 136 million trips were generated by shared micro-mobility in 2019 in the U.S., which was 60% more than 2018. Thus, micro-mobility devices can be well integrated into the overall urban design process of smart and sustainable transportation in the near future. With the sustainable design and development goal, we should not only consider technical challenges and requirements (e.g., battery and material), but also complement and constrain the design and development process by social, infrastructural, and political schemes for a sustainable future (Jiao, Luo, Malmqvist, Johan, & Summers, 2022).

Manufacturers are finding AI is no longer the answer to automating operations and improving product quality. While AI can increase the defect detection rate by up to 90% over human inspection, it's useless if manufacturers cannot obtain the information they need when they need it. Without a faster process, they continue to run the risk of unplanned shutdowns and production errors. "The challenge manufacturers have with AI is actually validating and verifying the return of investment," says Shunichi Kagaya, Senior Engineer at Hitachi, a leader in digital and IoT solutions. "Manufacturers understand the value of data and they want to use it. But they don't understand how."