Personalized, real-time chatbots could share conspiracy theories in increasingly credible and persuasive ways, researchers say, smoothing out human errors like poor syntax and mistranslations and advancing beyond easily discoverable copy-paste jobs. Soon after ChatGPT debuted last year, researchers tested what the artificial intelligence chatbot would write after it was asked questions peppered with conspiracy theories and false narratives. The results -- in writings formatted as news articles, essays and television scripts -- were so troubling that the researchers minced no words. "This tool is going to be the most powerful tool for spreading misinformation that has ever been on the internet," said Gordon Crovitz, a co-chief executive of NewsGuard, a company that tracks online misinformation and conducted the experiment last month. "Crafting a new false narrative can now be done at dramatic scale, and much more frequently -- it's like having A.I. agents contributing to disinformation."

Netflix is rolling out changes to account sharing in Canada, New Zealand, Portugal and Spain after trialing the change in Latin America. If you live in one of these countries, you must set a primary location for where you use it. Then, if you have friends or family who want to share your account, you'll have to subscribe to either the Standard or Premium tier and pay a fee ($8 in Canada and New Zealand, €4 in Portugal and €6 in Spain) for up to two extra users outside of your home. In Netflix's words, "Today, over 100 million households are sharing accounts – impacting our ability to invest in great new TV and films." It's not clear how new regions will take to the policy.

In 2020, researchers at the Center on Terrorism, Extremism and Counterterrorism at the Middlebury Institute of International Studies found that GPT-3, the underlying technology for ChatGPT, had "impressively deep knowledge of extremist communities" and could be prompted to produce polemics in the style of mass shooters, fake forum threads discussing Nazism, a defense of QAnon and even multilingual extremist texts. OpenAI uses machines and humans to monitor content that is fed into and produced by ChatGPT, a spokesman said. The company relies on both its human A.I. trainers and feedback from users to identify and filter out toxic training data while teaching ChatGPT to produce better-informed responses. OpenAI's policies prohibit use of its technology to promote dishonesty, deceive or manipulate users or attempt to influence politics; the company offers a free moderation tool to handle content that promotes hate, self-harm, violence or sex. But at the moment, the tool offers limited support for languages other than English and does not identify political material, spam, deception or malware.

I can already feel it coming. This is going to be the year of "generative AI" the way 2012 was the year of Instagram. The way every year since 2017 has been the year of "blockchain revolution" or the "pivot to video." Best to get clear-eyed and figure out what all the hype is about. After all, Google and Microsoft (and probably others) are now officially in the race to out-AI one another.

Interpretable machine learning offers insights into what factors drive a certain prediction of a black-box system. A large number of interpreting methods focus on identifying explanatory input features, which generally fall into two main categories: attribution and selection. A popular attribution-based approach is to exploit local neighborhoods for learning instance-specific explainers in an additive manner. The process is thus inefficient and susceptible to poorly-conditioned samples. However, they can only interpret single-class predictions and many suffer from inconsistency across different settings, due to a strict reliance on a pre-defined number of features selected. This work exploits the strengths of both methods and proposes a framework for learning local explanations simultaneously for multiple target classes. Our model explainer significantly outperforms additive and instance-wise counterparts on faithfulness with more compact and comprehensible explanations. We also demonstrate the capacity to select stable and important features through extensive experiments on various data sets and black-box model architectures. Black-box machine learning systems enjoy a remarkable predictive performance at the cost of interpretability. This trade-off has motivated a number of interpreting approaches for explaining the behavior of these complex models. Such explanations are particularly useful for high-stakes applications such as healthcare (Caruana et al., 2015; Rich, 2016), cybersecurity (Nguyen et al., 2021) or criminal investigation (Lipton, 2018). While model interpretation can be done in various ways (Mothilal et al., 2020; Bodria et al., 2021), our discussion will focus on feature importance or saliency-based approach - that is, to assign relative importance weights to individual features w.r.t the model's prediction on an input example. Features here refer to input components interpretable to humans; for high-dimensional data such as texts or images, features can be a bag of words/phrases or a group of pixels/super-pixels (Ribeiro et al., 2016). Explanations are generally made by selecting top K features with the highest weights, signifying K most important features to a black-box's decision.

Quotes are critical for establishing credibility in news articles. A direct quote enclosed in quotation marks has a strong visual appeal and is a sign of a reliable citation. Unfortunately, this journalistic practice is not strictly Figure 1: The central idea of QuoteCSE is based on followed, and a quote in the headline is often journalism principles, where quotes from news headlines "contextomized." Such a quote uses words out and body text should be matched. The proposed of context in a way that alters the speaker's contrastive learning framework maximizes the intention so that there is no semantically semantic similarity between the headline quote and the matching quote in the body text. We present matched quote in the body text while minimizing the QuoteCSE, a contrastive learning framework similarity for other unmatched quotes in the same or that represents the embedding of news quotes other articles.

Deep lens optimization has recently emerged as a new paradigm for designing computational imaging systems, however it has been limited to either simple optical systems consisting of a single DOE or metalens, or the fine-tuning of compound lenses from good initial designs. Here we present a deep lens design method based on curriculum learning, which is able to learn optical designs of compound lenses ab initio from randomly initialized surfaces, therefore overcoming the need for a good initial design. We demonstrate this approach with the fully-automatic design of an extended depth-of-field computational camera in a cellphone-style form factor, highly aspherical surfaces, and a short back focal length.

'Gutfeld!' panelists weigh in on the'fake boycott' by trans activists of a'Harry Potter' themed video game online, accusing author J.K. Rowling of being'transphobic.' Online media personalities are buzzing about a possible trans character in the highly anticipated and highly rated game "Hogwarts Legacy." The game, which is set in the "Harry Potter" universe, has dominated pre-order sales on Steam and other game platforms in recent weeks. "Hogwarts Legacy" is scheduled to be released on Friday. But some players are complaining that a seemingly transgender character named Sirona Ryan is designed to "pivot the conversation away" from author J.K. Rowling, according to TheGamer.

Three months ago, ChatGPT debuted--the first artificial-intelligence bot to produce original content virtually indistinguishable from that of a human brain. Now, the creators of that software are beta testing a new tool that can (or so they say) determine whether a text was written by a person or a machine. The applications could be many, from identifying disinformation campaigns to detecting when a job candidate is has used AI for a cover letter. But experts worry the software will only create more challenges for leaders already caught in an AI rabbit hole. "The mushing of original thinking and discernment and artificial intelligence is dangerous for employees, managers, and leaders," says Andrés Tapia, a senior client partner and global diversity, equity, and inclusion strategist at Korn Ferry.

At I/O 2022, Google revealed an Immersive View feature for Maps that uses computer vision and AI to combine Street View and aerial photography into a 3D format. The idea is to create a detailed perspective of buildings and other aspects of the environment. The feature is rolling out in five cities today. You'll be able to check it out in London, Los Angeles, New York, San Francisco and Tokyo. Google started offering a preview of the feature in those cities in September.