When poet John Keats wrote in "Ode on a Grecian Urn" that "beauty is truth, truth beauty," he probably didn't have AI influencers in mind. Back in April, Fanvue, an AI-infused creator platform that falls somewhere between OnlyFans and Cameo in terms of services, launched what it's calling the "world's first beauty pageant for AI creators." On Monday, the World AI Creator Awards announced the contest's 10 semifinalists. Drawn from a pool of more than 1,500 applicants, they are vying for the chance to make a liar out of Keats--and a prize package valued at about 20,000. Amongst those 10 finalists, you'll find Seren Ay, a stunning Turkish redhead who is sometimes pictured doing jobs traditionally held by men in her country, like electrical lineman or firefighter.

The graph represents a network of 1,442 Twitter users whose tweets in the requested range contained "#selfdrivingcars", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 23 November 2022 at 13:57 UTC. The requested start date was Wednesday, 23 November 2022 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 27-day, 9-hour, 46-minute period from Wednesday, 26 October 2022 at 11:18 UTC to Tuesday, 22 November 2022 at 21:05 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

Robots operating at night using conventional vision cameras face significant challenges in reconstruction due to noise-limited images. Previous work has demonstrated that burst-imaging techniques can be used to partially overcome this issue. In this paper, we develop a novel feature detector that operates directly on image bursts that enhances vision-based reconstruction under extremely low-light conditions. Our approach finds keypoints with well-defined scale and apparent motion within each burst by jointly searching in a multi-scale and multi-motion space. Because we describe these features at a stage where the images have higher signal-to-noise ratio, the detected features are more accurate than the state-of-the-art on conventional noisy images and burst-merged images and exhibit high precision, recall, and matching performance. We show improved feature performance and camera pose estimates and demonstrate improved structure-from-motion performance using our feature detector in challenging light-constrained scenes. Our feature finder provides a significant step towards robots operating in low-light scenarios and applications including night-time operations.

Explainable artificial intelligence has rapidly emerged since lawmakers have started requiring interpretable models for safety-critical domains. Concept-based neural networks have arisen as explainable-by-design methods as they leverage human-understandable symbols (i.e. concepts) to predict class memberships. However, most of these approaches focus on the identification of the most relevant concepts but do not provide concise, formal explanations of how such concepts are leveraged by the classifier to make predictions. In this paper, we propose a novel end-to-end differentiable approach enabling the extraction of logic explanations from neural networks using the formalism of First-Order Logic. The method relies on an entropy-based criterion which automatically identifies the most relevant concepts. We consider four different case studies to demonstrate that: (i) this entropy-based criterion enables the distillation of concise logic explanations in safety-critical domains from clinical data to computer vision; (ii) the proposed approach outperforms state-of-the-art white-box models in terms of classification accuracy.

The graph represents a network of 2,344 Twitter users whose tweets in the requested range contained "#FinServ", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Monday, 09 November 2020 at 00:43 UTC. The requested start date was Sunday, 08 November 2020 at 01:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 12-day, 15-hour, 56-minute period from Sunday, 25 October 2020 at 01:05 UTC to Friday, 06 November 2020 at 17:01 UTC.

We propose to explain the behavior of black-box prediction methods (e.g., deep neural networks trained on image pixel data) using causal graphical models. Specifically, we explore learning the structure of a causal graph where the nodes represent prediction outcomes along with a set of macro-level "interpretable" features, while allowing for arbitrary unmeasured confounding among these variables. The resulting graph may indicate which of the interpretable features, if any, are possible causes of the prediction outcome and which may be merely associated with prediction outcomes due to confounding. The approach is motivated by a counterfactual theory of causal explanation wherein good explanations point to factors which are "difference-makers" in an interventionist sense. The resulting analysis may be useful in algorithm auditing and evaluation, by identifying features which make a causal difference to the algorithm's output.

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The Cognitive Computing Era will change what it means to be a business as much or more than the introduction of modern Management by Taylor, Sloan and Drucker. Are you sure you want to view these Tweets? What is an #IoT Intelligent Electronic Sensor? Why investing in #AI is one of the biggest commercial opportunities for businesses. According to a recent @PwC report, the UK's GDP will be up to 10.3% higher in 2030 as a result of #ArtificialIntelligence By @technative http://hubs.ly/H0l9WpR0

We had great meetup "Building Trust in the Black Box: An Introduction to AI Explainability". Thank you for your interest. He will talk about "Empathy: The Most Crucial, Least Discussed Data Science Superpower". You can also sign up for our newsletter to be informed about our events, workshops, and articles. He will talk about "Empathy: The Most Crucial, Least Discussed Data Science Superpower".