Can an artificial intelligence (AI) system be a named inventor on a United States patent? No, says a federal appeals court in a decision issued earlier this month. The case, Thaler v. Vidal, arose from two patent applications filed in 2019 by Stephen Thaler, naming an AI system he calls DABUS (for "Device for the Autonomous Bootstrapping of Unified Sentience") as the "inventor." After the U.S. Patent and Trademark Office (PTO) informed Thaler that the applications were incomplete because they did not list a human inventor, he filed a complaint in a federal district court in Virginia. In September 2021, that court ruled against Thaler, citing "the overwhelming evidence that Congress intended to limit the definition of'inventor' to natural persons."

Vivek Wadhwa and Mauritz Kop recently penned an op-ed urging governments around the world to get ahead of the threat posed by the emerging technology known as quantum computing. They even went so far as to title their article "Why Quantum Computing is Even More Dangerous Than Artificial Intelligence." Up front: This one gets a very respectful hard-disagree from me. While I do believe that quantum computing does pose an existential threat to humanity, my reasons differ wildly from those proposed by Wadhwa and Kop. Wadhwa and Kop open their article with a description of AI's failures, potential misuse, and how the media's narrative has exacerbated the danger of AI before it settles on a powerful lead: The world's failure to rein in the demon of AI--or rather, the crude technologies masquerading as such--should serve to be a profound warning.

Deep learning models are known to be vulnerable to adversarial examples that are elaborately designed for malicious purposes and are imperceptible to the human perceptual system. Autoencoder, when trained solely over benign examples, has been widely used for (self-supervised) adversarial detection based on the assumption that adversarial examples yield larger reconstruction errors. However, because lacking adversarial examples in its training and the too strong generalization ability of autoencoder, this assumption does not always hold true in practice. To alleviate this problem, we explore how to detect adversarial examples with disentangled label/semantic features under the autoencoder structure. Specifically, we propose Disentangled Representation-based Reconstruction (DRR). In DRR, we train an autoencoder over both correctly paired label/semantic features and incorrectly paired label/semantic features to reconstruct benign and counterexamples. This mimics the behavior of adversarial examples and can reduce the unnecessary generalization ability of autoencoder. We compare our method with the state-of-the-art self-supervised detection methods under different adversarial attacks and different victim models, and it exhibits better performance in various metrics (area under the ROC curve, true positive rate, and true negative rate) for most attack settings. Though DRR is initially designed for visual tasks only, we demonstrate that it can be easily extended for natural language tasks as well. Notably, different from other autoencoder-based detectors, our method can provide resistance to the adaptive adversary.

Keith J. Dreyer, DO, PhD, FACR, American College of Radiology (ACR) Data Science Institute Chief Science Officer, explains the state of artificial intelligence (AI) in radiology in 2022. Although there are about 200 AI algorithms for medical imaging now cleared by the U.S. Food and Drug Administration (FDA), a recent ACR survey of its members showed AI only has about a 2% market penetration rate. "So, there is about another 98% that fall into the category of potential addressable market," Dreyer said. "Now why is that when there is a lot of enthusiasm and we are past the days from six years ago when radiologists were fearful of losing their jobs to AI because Geoffrey Hinton said we should stop training radiologists because AI will take over in another 5 years. That was in 2016, and are now past the five-year mark and it's ridiculous, because today there is an incredible shortage of radiologists."

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! In 2018, Iowa-based Digital Diagnostics made headlines when it became the first autonomous AI (artificial intelligence) system authorized by the U.S. Food and Drug Administration. It received FDA approval to use AI to autonomously detect diabetic retinopathy in adults with diabetes, without the need for input from a doctor. Its AI-diagnostic system, the IDx-DR, can be used to identify diabetic retinopathy – one of the leading causes of blindness in the U.S. and other developed countries – as well as other serious eye diseases, including macular edema.

WASHINGTON – The Justice Department's search of former President Donald Trump's Florida home was spurred by the discovery that he had held onto a trove of highly classified material that included documents related to the use of "clandestine human sources" in intelligence gathering, according to a redacted version of the affidavit used to obtain the search warrant. The portions of the affidavit made public Friday describe the Justice Department's monthslong push to recover sensitive materials taken from the White House by a former president who viewed state documents as his private property and now faces a department investigating the possibility he illegally obstructed those efforts. 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. If this does not resolve the issue or you are unable to add the domains to your allowlist, please see this support page.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Consolidating their organization's tech stacks, defending budgets and reducing risk are three of the top challenges facing CISOs going into 2023. Identifying which security technologies deliver the most value and defining spending guardrails is imperative. Forrester's 2023 security and risk planning guide provides CISOs prescriptive guidance on which technologies to increase and defend their investments and which to consider paring back spending and investment.

With the increasing digitisation of society and human communication, text data are becoming more important for research in the social and behavioural sciences (Gentzkow, Kelly, and Taddy 2019; Salganik 2019). Advances made in natural language processing (NLP) in particular have led to exciting insights derived from text data (e.g., on emotional responses to the pandemic (Kleinberg, Vegt, and Mozes 2020) or on the rhetoric around immigration in political speeches (Card et al. 2022); for an overview, see (Boyd and Schwartz 2021)). Importantly, the use of computational techniques to quantify and analyse text data has triggered a demand, especially for large datasets (often of several tens of thousands of documents) that can be harnessed for machine learning approaches (e.g., (Socher et al. 2013; Lewis et al. 2020)). That status quo of a need for larger datasets and an appetite to use text data for the study of social science phenomena has resulted in a dilemma: many of the important questions require targeted, primary data collection or access to potentially sensitive data. However, such data are hard to obtain, not because they do not exist but because sharing them is constrained by data protection regulations and ethical concerns. One potential consequence is that research activity may be biased toward topics for which suitable data is more readily available rather than those most important. One of the few viable solutions to this dilemma is automated text anonymisation; that is, the large-scale processing of text data so that individuals cannot be identified from the resulting output. Such a method would allow for the flow of sensitive data so that the staggering potential of text data can be exploited for scientific progress. With this paper and the tool it introduces, we seek to enable researchers to work with such sensitive data in a way that protects the privacy of individuals whilst retaining the usefulness of anonymised data for computational text analysis.

Simulations of high energy density physics are expensive, largely in part for the need to produce nonlocal thermodynamic equilibrium opacities. High-fidelity spectra may reveal new physics in the simulations not seen with low-fidelity spectra, but the cost of these simulations also scale with the level of fidelity of the opacities being used. Neural networks are capable of reproducing these spectra, but neural networks need data to to train them which limits the level of fidelity of the training data. This paper demonstrates that it is possible to reproduce high-fidelity spectra with median errors in the realm of 3% to 4% using as few as 50 samples of high-fidelity Krypton data by performing transfer learning on a neural network trained on many times more low-fidelity data. K. D. Humbird is with Lawrence Livermore National Laboratory, 7000 East Ave, Livermore, CA, 94550 USA, email: humbird1@llnl.gov. In this case, higher fidelity opacity calculations are necessary Inertial confinement fusion (ICF) is currently to capture important physical processes accurately one of the experimental approaches to controlled [4], [5]. In this work, we focus on improving the nuclear fusion.

This report documents the history of research on AI rights and other moral consideration of artificial entities. It highlights key intellectual influences on this literature as well as research and academic discussion addressing the topic more directly. We find that researchers addressing AI rights have often seemed to be unaware of the work of colleagues whose interests overlap with their own. Academic interest in this topic has grown substantially in recent years; this reflects wider trends in academic research, but it seems that certain influential publications, the gradual, accumulating ubiquity of AI and robotic technology, and relevant news events may all have encouraged increased academic interest in this specific topic. We suggest four levers that, if pulled on in the future, might increase interest further: the adoption of publication strategies similar to those of the most successful previous contributors; increased engagement with adjacent academic fields and debates; the creation of specialized journals, conferences, and research institutions; and more exploration of legal rights for artificial entities.