Machines are taking over, even venturing into the dark arts of beer brewing in an artificial intelligence experiment at Lot Fourteen.

The ever-increasing amount of user-generated content online has led, in recent years, to an expansion in research and investment in automated content analysis tools. Scrutiny of automated content analysis has accelerated during the COVID-19 pandemic, as social networking services have placed a greater reliance on these tools due to concerns about health risks to their moderation staff from in-person work. At the same time, there are important policy debates around the world about how to improve content moderation while protecting free expression and privacy. In order to advance these debates, we need to understand the potential role of automated content analysis tools. This paper explains the capabilities and limitations of tools for analyzing online multimedia content and highlights the potential risks of using these tools at scale without accounting for their limitations. It focuses on two main categories of tools: matching models and computer prediction models. Matching models include cryptographic and perceptual hashing, which compare user-generated content with existing and known content. Predictive models (including computer vision and computer audition) are machine learning techniques that aim to identify characteristics of new or previously unknown content.

Claim detection and verification are crucial for news understanding and have emerged as promising technologies for mitigating misinformation in news. However, most existing work focus on analysis of claim sentences while overlooking crucial background attributes, such as the claimer, claim objects, and other knowledge connected to the claim. In this work, we present NewsClaims , a new benchmark for knowledge-aware claim detection in the news domain. We re-define the claim detection problem to include extraction of additional background attributes related to the claim and release 529 claims annotated over 103 news articles. In addition, NewsClaims aims to benchmark claim detection systems in emerging scenarios, comprising unseen topics with little or no training data. Finally, we provide a comprehensive evaluation of various zero-shot and prompt-based baselines for this new benchmark.

Given the growing use of Artificial Intelligence (AI) and machine learning (ML) methods across all aspects of environmental sciences, it is imperative that we initiate a discussion about the ethical and responsible use of AI. In fact, much can be learned from other domains where AI was introduced, often with the best of intentions, yet often led to unintended societal consequences, such as hard coding racial bias in the criminal justice system or increasing economic inequality through the financial system. A common misconception is that the environmental sciences are immune to such unintended consequences when AI is being used, as most data come from observations, and AI algorithms are based on mathematical formulas, which are often seen as objective. In this article, we argue the opposite can be the case. Using specific examples, we demonstrate many ways in which the use of AI can introduce similar consequences in the environmental sciences. This article will stimulate discussion and research efforts in this direction. As a community, we should avoid repeating any foreseeable mistakes made in other domains through the introduction of AI. In fact, with proper precautions, AI can be a great tool to help {\it reduce} climate and environmental injustice. We primarily focus on weather and climate examples but the conclusions apply broadly across the environmental sciences.

Cultural code-switching concerns how we adjust our overall behaviours, manners of speaking, and appearance in response to a perceived change in our social environment. We defend the need to investigate cultural code-switching capacities in artificial intelligence systems. We explore a series of ethical and epistemic issues that arise when bringing cultural code-switching to bear on artificial intelligence. Building upon Dotson's (2014) analysis of testimonial smothering, we discuss how emerging technologies in AI can give rise to epistemic oppression, and specifically, a form of self-silencing that we call 'cultural smothering'. By leaving the socio-dynamic features of cultural code-switching unaddressed, AI systems risk negatively impacting already-marginalised social groups by widening opportunity gaps and further entrenching social inequalities.

… computer vision and deep learning to scan warehouse shelves with speed … in sensors, machine vision, AI, computing power and new hardware.

Large, AI-powered language models (LLMs) like OpenAI's GPT-3 have enormous potential in the enterprise. For example, GPT-3 is now being used in over 300 apps by thousands of developers to produce more than 4.5 billion words per day. And Naver, the company behind the eponymous search engine Naver, is employing LLMs to personalize search results on the Naver platform -- following on the heels of Bing and Google. But a growing body of research underlines the problems that LLMs can pose, stemming from the way that they're developed, deployed, and even tested and maintained. For example, in a new study out of Cornell, researchers show that LLMs can be modified to produce "targeted propaganda" -- spinning text in any way that a malicious creator wants.

Many of us who have uploaded images of our faces and the faces of our friends and family to openly-licensed platforms on the Web may have inadvertently contributed to a massive and growing database for AI facial recognition. So how are our faces being used? So have we all thrown away our privacy and assumption of innocence for a selfie? The film is Web Monetized, with all streaming payments going to the Surveillance Technology Oversight Project (S.T.O.P.) On the GftW Community Forum, we have been streaming funds to S.T.O.P. since July. So far, we have generated almost $200 in micropayments to support their work.

Oscar Isaac gets quite naked, and then he gets quite dead. Timothée Chalamet, as Paul Atreides, falls in love with a girl, played by Zendaya, who's on screen for all of seven minutes. They barely speak; most of their courtship proceeds in visions and hazy dreams--the safest of social distances. Not that most real-world teens would even play Seven Minutes in Heaven these days. If they didn't already prefer to achieve sexual awakening as Paul does--remotely--then the past two years of Covid-19 protocols will have inculcated in their psyches the belief that a policy of No Touching is not only law-abiding but, for a lot of them, ideal.

You don't have to own a robot vacuum or a digital assistant like Alexa or Siri to use artificial intelligence. In fact, AI has become part of our everyday lives in ways we don't even notice, let alone control. When you check your news feed on Facebook or search the internet on Google, you're interacting with AI. It offers great benefits, like robots assisting during surgery, but also gives rise to troubling moral questions. Henley, the author or coauthor of more than twenty books, brings an impressive background to this weighty topic.