Artificial intelligence models really can help spot cancer and reduce doctors' workload, according to the largest study of its kind. Radiologists who chose to use AI were able to identify an extra 1 in 1000 cases of breast cancer. Alexander Katalinic at the University of Lübeck, Germany, and his colleagues worked with almost 200 certified radiologists to test an AI trained to identify signs of breast cancer from mammograms. The radiologists examined 461,818 women across 12 breast cancer screening sites in Germany between July 2021 and February 2023, and for each person could choose whether or not to use AI. This resulted in 260,739 being checked by AI plus a radiologist, with the remaining 201,079 patients checked by a radiologist alone. Those who elected to use AI successfully detected breast cancer at a rate of 6.7 instances in every 1000 scans – 17.6 per cent higher than the 5.7 per 1000 scans among those who chose not to use AI.

Ninety-four per cent of university exam submissions created using ChatGPT weren't detected as being generated by artificial intelligence, and these submissions tended to get higher scores than real students' work. Peter Scarfe at the University of Reading, UK, and his colleagues used ChatGPT to produce answers to 63 assessment questions on five modules across the university's psychology undergraduate degrees. Students sat these exams at home, so they were allowed to look at notes and references, and they could potentially have used AI although this wasn't permitted. How this moment for AI will change society forever (and how it won't) The AI-generated answers were submitted alongside real students' work, and accounted for, on average, 5 per cent of the total scripts marked by academics. The markers weren't informed that they were checking the work of 33 fake students – whose names were themselves generated by ChatGPT.

Optical Character Recognition (OCR) is an established task with the objective of identifying the text present in an image. While many off-the-shelf OCR models exist, they are often trained for either scientific (e.g., formulae) or generic printed English text. Extracting text from chemistry publications requires an OCR model that is capable in both realms. Nougat, a recent tool, exhibits strong ability to parse academic documents, but is unable to parse tables in PubMed articles, which comprises a significant part of the academic community and is the focus of this work. To mitigate this gap, we present the Printed English and Chemical Equations (PEaCE) dataset, containing both synthetic and real-world records, and evaluate the efficacy of transformer-based OCR models when trained on this resource. Given that real-world records contain artifacts not present in synthetic records, we propose transformations that mimic such qualities. We perform a suite of experiments to explore the impact of patch size, multi-domain training, and our proposed transformations, ultimately finding that models with a small patch size trained on multiple domains using the proposed transformations yield the best performance. Our dataset and code is available at https://github.com/ZN1010/PEaCE.

The tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during self-motion. In this study, we examine whether a simplified linear model of these neurons can be used to esti- mate self-motion from the optic flow. We present a theory for the con- struction of an estimator consisting of a linear combination of optic flow vectors that incorporates prior knowledge both about the distance distri- bution of the environment, and about the noise and self-motion statistics of the sensor. The estimator is tested on a gantry carrying an omnidirec- tional vision sensor. The experiments show that the proposed approach leads to accurate and robust estimates of rotation rates, whereas transla- tion estimates turn out to be less reliable.

In a great plot twist, instead of robots taking our jobs, they're actually helping us get hired. Artificial intelligence is becoming more prevalent in hiring and recruiting. Talent scouts who may have started using AI to test the technical ability of programmers and coders are beginning to expand their use to non-technical roles. Soft skills are more in-demand than ever, but screening a candidate for things like leadership, communication, and empathy can be time-consuming and difficult. Luckily, using psychometrics and attitude testing, AI is now equipped to assess traits like extroversion, conscientiousness, and teamwork.

A new self-driving car service is hitting the streets of Frisco, Texas this week. But, unlike its competitors – many of whom are working to blend in – it seems Drive.ai is doing just about everything it can to stand out. The firm rolled out a fleet of bright orange cars today, each of which is adorned with a blue and white banner that says'Self Driving Vehicle' and four external LED screens to let pedestrians know what it's doing at any given moment. A new self-driving car service is hitting the streets of Frisco, Texas this week. But, unlike its competitors – many of whom are working to blend in – it seems Drive.ai is doing just about everything it can to stand out.

The tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during self-motion. In this study, we examine whether a simplified linear model of these neurons can be used to estimate self-motion from the optic flow. We present a theory for the construction of an estimator consisting of a linear combination of optic flow vectors that incorporates prior knowledge both about the distance distribution of the environment, and about the noise and self-motion statistics of the sensor. The estimator is tested on a gantry carrying an omnidirectional vision sensor. The experiments show that the proposed approach leads to accurate and robust estimates of rotation rates, whereas translation estimates turn out to be less reliable.

The tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during self-motion. In this study, we examine whether a simplified linear model of these neurons can be used to estimate self-motion from the optic flow. We present a theory for the construction of an estimator consisting of a linear combination of optic flow vectors that incorporates prior knowledge both about the distance distribution of the environment, and about the noise and self-motion statistics of the sensor. The estimator is tested on a gantry carrying an omnidirectional vision sensor. The experiments show that the proposed approach leads to accurate and robust estimates of rotation rates, whereas translation estimates turn out to be less reliable.

The tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during self-motion. In this study, we examine whether a simplified linear model of these neurons can be used to estimate self-motionfrom the optic flow. We present a theory for the construction ofan estimator consisting of a linear combination of optic flow vectors that incorporates prior knowledge both about the distance distribution ofthe environment, and about the noise and self-motion statistics of the sensor. The estimator is tested on a gantry carrying an omnidirectional visionsensor. The experiments show that the proposed approach leads to accurate and robust estimates of rotation rates, whereas translation estimatesturn out to be less reliable.