Got a moral quandary you don't know how to solve? Why not turn to the wisdom of artificial intelligence, aka Ask Delphi: an intriguing research project from the Allen Institute for AI that offers answers to ethical dilemmas while demonstrating in wonderfully clear terms why we shouldn't trust software with questions of morality. Ask Delphi was launched on October 14th, along with a research paper describing how it was made. From a user's point of view, though, the system is beguilingly simple to use. Just head to the website, outline pretty much any situation you can think of, and Delphi will come up with a moral judgement. Since Ask Delphi launched, its nuggets of wisdom have gone viral in news stories and on social media.

Modern AI tools, such as generative adversarial networks, have transformed our ability to create and modify visual data with photorealistic results. However, one of the deleterious side-effects of these advances is the emergence of nefarious uses in manipulating information in visual data, such as through the use of deep fakes. We propose a novel architecture for preserving the provenance of semantic information in images to make them less susceptible to deep fake attacks. Our architecture includes semantic signing and verification steps. We apply this architecture to verifying two types of semantic information: individual identities (faces) and whether the photo was taken indoors or outdoors. Verification accounts for a collection of common image transformation, such as translation, scaling, cropping, and small rotations, and rejects adversarial transformations, such as adversarially perturbed or, in the case of face verification, swapped faces. Experiments demonstrate that in the case of provenance of faces in an image, our approach is robust to black-box adversarial transformations (which are rejected) as well as benign transformations (which are accepted), with few false negatives and false positives. Background verification, on the other hand, is susceptible to black-box adversarial examples, but becomes significantly more robust after adversarial training.

There has recently been an explosion of work on spoken dialogue systems, along with an increased interest in open-domain systems that engage in casual conversations on popular topics such as movies, books and music. These systems aim to socially engage, entertain, and even empathize with their users. Since the achievement of such social goals is hard to measure, recent research has used dialogue length or human ratings as evaluation metrics, and developed methods for automatically calculating novel metrics, such as coherence, consistency, relevance and engagement. Here we develop a PARADISE model for predicting the performance of Athena, a dialogue system that has participated in thousands of conversations with real users, while competing as a finalist in the Alexa Prize. We use both user ratings and dialogue length as metrics for dialogue quality, and experiment with predicting these metrics using automatic features that are both system dependent and independent. Our goal is to learn a general objective function that can be used to optimize the dialogue choices of any Alexa Prize system in real time and evaluate its performance. Our best model for predicting user ratings gets an R$^2$ of .136 with a DistilBert model, and the best model for predicting length with system independent features gets an R$^2$ of .865, suggesting that conversation length may be a more reliable measure for automatic training of dialogue systems.

Artificial intelligence is changing how we interact with everything, from food to healthcare, travel and also religion. Experts say major global faiths are discussing their relationship with AI, and some are starting to incorporate this technology into their worship. Robot priests can recite prayers, perform funerals, and even comfort those experiencing a spiritual crisis. BBC Global Religion reporter Sofia Bettiza has taken a look at whether AI's relationship with religion is just a gimmick, or whether it can truly transform how people experience faith.

NoC interconnect IP to be dataflow backbone for image signal processors providing enhanced sensitivity, high-resolution HD imaging through low current, low power in a single-chip solution for the security/surveillance market. Eyenix's imaging solution provides a step-function advance over their previous product, replacing a 3rd-party artificial intelligence (AI) function with a superior capability developed in-house for super-resolution imaging. This is provided in a tightly integrated system design, including functions for image stabilization for mobile usage and image dewarping for wide-angle camera correction. The first application is destined for surveillance camera applications. Eyenix chose Arteris IP on-chip interconnect technology as a part of Eyenix's proprietary image processing chip because it enables Eyenix to design and integrate a complete and superior Eyenix imaging solution without dependency on external IP blocks for the AI function.

… Launches First Global Call for Application: Artificial Intelligence for … a first-of-its-kind innovation lab spearheading the adoption of AI …

… tools that help companies ensure their AI systems and machine learning models are aligned with ethical, regulatory, and business requirements.

The global Machine Learning in Manufacturing research report includes data on the world's largest manufacturers, exporters, and service providers.

Sham Kakade, a machine learning expert at the University of Washington, will begin in January 2022 as the Gordon McKay Professor of Computer …

Using machine learning, the algorithm produces a daily score based on the types of photos used in global news reports.