Reddit announced it will start charging companies to use its huge, ever-growing trove of text to train A.I. chatbots. It's another expense for the fledgling tech and another knock against the "open internet" ideals that Reddit once embodied. If you enjoy this show, please consider signing up for Slate Plus. Slate Plus members get benefits like zero ads on any Slate podcast, bonus episodes of shows like Slow Burn and Dear Prudence--and you'll be supporting the work we do here on What Next TBD. Sign up now at slate.com/whatnextplus to help support our work.

CTA Thematic Programs Director Brian Comiskey and tech strategist, author David Espindola discuss how artificial intelligence will continue to change how businesses operate and the impact on consumers. Artificial intelligence will be capable of producing higher-quality "digital humans" and could use more energy than the entire global workforce by 2025, according to experts. Brian Comiskey, the Director of Thematic Programs for the Consumer Technology Association, revealed the trade association had developed an artificial intelligence working group to navigate the new technology. With his time in thematic indexing, Comiskey noted that "responsible AI" is a growing buzzword in the financial space and has become an increasing priority for leaders across various industries. By 2025, without sustainable AI practices, AI will consume more energy than the human workforce, significantly offsetting carbon-zero gains, according to Gartner's research for their 2023 strategic predictions.

Fox News correspondent Douglas Kennedy has the details on software that could'transform society' on'America's Newsroom.' The use of facial recognition technology, a form of biometric artificial intelligence, is growing across the U.S. as an efficient security system that can identify people based on measuring facial features, but has been hit with some notable criticisms. Police departments, the health care industry, and companies looking to fight back against cyber fraud have rolled out the technology in recent years to bolster security measures. The tech is far from new, with its roots stretching back to the mid-1960s, when researchers in Palo Alto pioneered training computers to recognize faces, and has exploded in use since around 2010. Today, machine learning algorithms - a subset of artificial intelligence that uses data and algorithms to mimic how humans learn - has fine-tuned the technology. The tech can measure and identify facial measurements in a photo or video, and cross-analyze whether two photos or videos show the same person, or even pick a person out in a crowd of people, Amazon Web Services explains.

College students Tabatha Fajardo, Jay Ram and Kyra Varnavas give their take on the development of AI in the classroom on'The Story.' Artificial intelligence experts have advised consumers to use caution and trust their instincts when encountering "hallucinations" from artificial intelligence chatbots. "The number-one piece is common sense," Kayle Gishen, chief technology officer of Florida-based tech company NeonFlux, told Fox News Digital. People should verify what they see, read or find on platforms such as ChatGPT through "established sources of information," he said. AI is prone to making mistakes -- "hallucinations" in tech terminology -- just like human sources. The word "hallucinations" refers to AI outputs "that are coherent but factually incorrect or nonsensical," said Alexander Hollingsworth of Oyova, an app developer and marketing agency in Florida.

Kurt "The CyberGuy" Knutsson describes a situation in which a viewer was hacked and reveals what steps you can take to avoid this from happening to you. When you're the tech-savvy friend, people ask for help. It comes with the territory. And who helps those people? Me! I've always got your back with new tricks, tips and secrets to master your devices.

In the era of social media, people frequently share their own opinions online on various issues and also in the way, get exposed to others' opinions. Be it for selective exposure of news feed recommendation algorithms or our own inclination to listen to opinions that support ours, the result is that we get more and more exposed to opinions closer to ours. Further, any population is inherently heterogeneous i.e. people will hold a varied range of opinions regarding a topic and showcase a varied range of openness to get influenced by others. In this paper, we demonstrate the different behavior put forward by open- and close-minded agents towards an issue, when allowed to freely intermix and communicate. We have shown that the intermixing among people leads to formation of opinion echo chambers i.e. a small closed network of people who hold similar opinions and are not affected by opinions of people outside the network. Echo chambers are evidently harmful for a society because it inhibits free healthy communication among all and thus, prevents exchange of opinions, spreads misinformation and increases extremist beliefs. This calls for reduction in echo chambers, because a total consensus of opinion is neither possible nor is welcome. We show that the number of echo chambers depends on the number of close-minded agents and cannot be lessened by increasing the number of open-minded agents. We identify certain 'moderate'-minded agents, who possess the capability of manipulating and reducing the number of echo chambers. The paper proposes an algorithm for intelligent placement of moderate-minded agents in the opinion-time spectrum by which the opinion echo chambers can be maximally reduced. With various experimental setups, we demonstrate that the proposed algorithm fares well when compared to placement of other agents (open- or close-minded) and random placement of 'moderate'-minded agents.

Critical scholarship has elevated the problem of gender bias in data sets used to train virtual assistants (VAs). Most work has focused on explicit biases in language, especially against women, girls, femme-identifying people, and genderqueer folk; implicit associations through word embeddings; and limited models of gender and masculinities, especially toxic masculinities, conflation of sex and gender, and a sex/gender binary framing of the masculine as diametric to the feminine. Yet, we must also interrogate how masculinities are "coded" into language and the assumption of "male" as the linguistic default: implicit masculine biases. To this end, we examined two natural language processing (NLP) data sets. We found that when gendered language was present, so were gender biases and especially masculine biases. Moreover, these biases related in nuanced ways to the NLP context. We offer a new dictionary called AVA that covers ambiguous associations between gendered language and the language of VAs.

The Japanese notion of "kawaii" or expressions of cuteness, vulnerability, and/or charm is a global cultural export. Work has explored kawaii-ness as a design feature and factor of user experience in the visual appearance, nonverbal behaviour, and sound of robots and virtual characters. In this initial work, we consider whether voices can be kawaii by exploring the vocal qualities of voice assistant speech, i.e., kawaii vocalics. Drawing from an age-inclusive model of kawaii, we ran a user perceptions study on the kawaii-ness of younger- and older-sounding Japanese computer voices. We found that kawaii-ness intersected with perceptions of gender and age, i.e., gender ambiguous and girlish, as well as VA features, i.e., fluency and artificiality. We propose an initial model of kawaii vocalics to be validated through the identification and study of vocal qualities, cognitive appraisals, behavioural responses, and affective reports.

Variational autoencoders (VAEs) are used for transfer learning across various research domains such as music generation or medical image analysis. However, there is no principled way to assess before transfer which components to retrain or whether transfer learning is likely to help on a target task. We propose to explore this question through the lens of representational similarity. Specifically, using Centred Kernel Alignment (CKA) to evaluate the similarity of VAEs trained on different datasets, we show that encoders' representations are generic but decoders' specific. Based on these insights, we discuss the implications for selecting which components of a VAE to retrain and propose a method to visually assess whether transfer learning is likely to help on classification tasks.

The energy inefficiency of the apps can be a major issue for the app users which is discussed on App Stores extensively. Previous research has shown the importance of investigating the energy related app reviews to identify the major causes or categories of energy related user feedback. However, there is no study that efficiently extracts the energy related app reviews automatically. In this paper, we empirically study different techniques for automatic extraction of the energy related user feedback. We compare the accuracy, F1-score and run time of numerous machine-learning models with relevant feature combinations and relatively modern Neural Network-based models. In total, 60 machine learning models are compared to 30 models that we build using six neural network architectures and three word embedding models. We develop a visualization tool for this study through which a developer can traverse through this large-scale result set. The results show that neural networks outperform the other machine learning techniques and can achieve the highest F1-score of 0.935. To replicate the research results, we have open sourced the interactive visualization tool. After identifying the best results and extracting the energy related reviews, we further compare various techniques to help the developers automatically investigate the emerging issues that might be responsible for energy inefficiency of the apps. We experiment the previously used string matching with results obtained from applying two of the state-of-the-art topic modeling algorithms, OBTM and AOLDA. Finally, we run a qualitative study performed in collaboration with developers and students from different institutions to determine their preferences for identifying necessary topics from previously categorized reviews, which shows OBTM produces the most helpful results.