Science and technology journalists today face challenges in finding newsworthy leads due to increased workloads, reduced resources, and expanding scientific publishing ecosystems. Given this context, we explore computational methods to aid these journalists' news discovery in terms of time-efficiency and agency. In particular, we prototyped three computational information subsidies into an interactive tool that we used as a probe to better understand how such a tool may offer utility or more broadly shape the practices of professional science journalists. Our findings highlight central considerations around science journalists' agency, context, and responsibilities that such tools can influence and could account for in design. Based on this, we suggest design opportunities for greater and longer-term user agency; incorporating contextual, personal and collaborative notions of newsworthiness; and leveraging flexible interfaces and generative models. Overall, our findings contribute a richer view of the sociotechnical system around computational news discovery tools, and suggest ways to improve such tools to better support the practices of science journalists.

This paper proposes a framework for quantitatively evaluating interactive LLMs such as ChatGPT using publicly available data sets. We carry out an extensive technical evaluation of ChatGPT using 23 data sets covering 8 different common NLP application tasks. We evaluate the multitask, multilingual and multi-modal aspects of ChatGPT based on these data sets and a newly designed multimodal dataset. We find that ChatGPT outperforms LLMs with zero-shot learning on most tasks and even outperforms fine-tuned models on some tasks. We find that it is better at understanding non-Latin script languages than generating them. It is able to generate multimodal content from textual prompts, via an intermediate code generation step. Moreover, we find that ChatGPT is 63.41% accurate on average in 10 different reasoning categories under logical reasoning, non-textual reasoning, and commonsense reasoning, hence making it an unreliable reasoner. It is, for example, better at deductive than inductive reasoning. ChatGPT suffers from hallucination problems like other LLMs and it generates more extrinsic hallucinations from its parametric memory as it does not have access to an external knowledge base. Finally, the interactive feature of ChatGPT enables human collaboration with the underlying LLM to improve its performance, i.e, 8% ROUGE-1 on summarization and 2% ChrF++ on machine translation, in a multi-turn "prompt engineering" fashion. We also release codebase for evaluation set extraction.

Best Buy is going head-to-head with Amazon this Cyber Monday and has been touting a price match guarantee. If you have a paid My Best Buy Membership, you can access some exclusive deals, but they're not worth the yearly fee if you haven't already joined. We test products year-round and handpicked these deals. The discount amounts we show are based on actual street prices at retailers in the past few months. Products that are sold out or no longer discounted as of publishing will be crossed out. We'll update this guide periodically. If you buy something using links in our stories, we may earn a commission. This helps support our journalism. The Hisense U8K (8/10, WIRED Recommends) has a mini-LED display that delivers excellent black levels, and it comes with a Google TV interface that's easy to use. The feet are on the ends though, so you'll want to mount it or pair it with a long TV stand.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. In an age of deepfakes and post-truth, as artificial intelligence rose and Elon Musk turned Twitter into X, the Merriam-Webster word of the year for 2023 is "authentic." Lookups for the word are routinely heavy on the dictionary company's site but were boosted to new heights throughout the year, editor at large Peter Sokolowski told The Associated Press in an exclusive interview. "We see in 2023 a kind of crisis of authenticity," he said ahead of Monday's announcement of this year's word.

While we still don't know all the details, there have been reports that researchers at OpenAI had made a "breakthrough" in AI that alarmed staff members. The claim is that they came up with a new way to make powerful AI systems and had created a new model, called Q* (pronounced Q star), that was able to perform grade-school level math. Some at OpenAI reportedly believe this could be a breakthrough in the company's quest to build artificial general intelligence, a much-hyped concept of an AI system that is smarter than humans. And why is grade-school math such a big deal? Our senior AI reporter Melissa Heikkilä called some experts to find out how big of a deal any such breakthrough would really be.

Like most Americans, I like to think of myself as an individual--but a week ago I walked out into a parking lot to find five cars identical in make, model, year, and color to my own. I was glad I remembered my license plate number, and that my key fob would (hopefully) only unlock the correct vehicle. A few days later I found myself in a grocery checkout line, skimming through yet another article in which the writer touted the wonders of "artificial intelligence" and fretted hazily over whether we are nearing the point when AI will be able to produce novels, films, and other creative work, effectively replacing us. When I looked up and over to other people in the line, half of them wore the same shoe brand as me. The truth is that there is no such thing as "artificial intelligence."

Not everyone in Hollywood is happy with the film industry's historic AI deal. A provision allowing for the creation of digital replicas and synthetic performers could, critics argue, decrease the number of jobs available to both performers and crew. This, in turn, could allow big-name stars--and their AI-generated clones--to feature in multiple projects at once, pushing out emerging actors as Hollywood becomes awash with synthetic performers. Feelings are so strong that 14 percent of the national board of the Screen Actors Guild-American Federation of Television and Radio Artists, or SAG-AFTRA for short, actually voted against taking the deal to its general membership for ratification. Leaders of the Directors Guild of America and the Writers Guild of America, in contrast, overwhelmingly agreed to have their members accept the agreements they hammered out with the Alliance of Motion Picture and Television Producers (AMPTP).

Jess Baker, a UK business psychologist and author, says that anyone considering a job share with someone they do not previously know should really find out if they have compatible personalities. "I'd strongly recommend you each complete a personality profiling tool, to increase your level of self-awareness of psychological characteristics, like how you each cope under pressure, how you make decisions, and how you relate to others."

The study investigates the role of 'superspreaders' in shaping opinions within networks, distinguishing three types: A, B, and C. Type A has a significant influence in shaping opinions, Type B acts as a counterbalance to A, and Type C functions like media, providing an objective viewpoint and potentially regulating A and B's influence. The research uses a confidence coefficient and z-score to survey superspreaders' behaviors, with a focus on the conditions affecting group dynamics and opinion formation, including environmental factors and forgetfulness over time. The findings offer insights for improving online communication security and understanding social influence.

Even though online social movements can quickly become viral on social media, languages can be a barrier to timely monitoring and analyzing the underlying online social behaviors (OSB). This is especially true for under-resourced languages on social media like dialectal Arabic; the primary language used by Arabs on social media. Therefore, it is crucial to provide solutions to efficiently exploit resources from high-resourced languages to solve language-dependent OSB analysis in under-resourced languages. This paper proposes to localize content of resources in high-resourced languages into under-resourced Arabic dialects. Content localization goes beyond content translation that converts text from one language to another; content localization adapts culture, language nuances and regional preferences from one language to a specific language/dialect. Automating understanding of the natural and familiar day-to-day expressions in different regions, is the key to achieve a wider analysis of OSB especially for smart cities. In this paper, we utilize content-localization based neural machine translation to develop sentiment and hate classifiers for two low-resourced Arabic dialects: Levantine and Gulf. Not only this but we also leverage unsupervised learning to facilitate the analysis of sentiment and hate predictions by inferring hidden topics from the corresponding data and providing coherent interpretations of those topics in their native language/dialects. The experimental evaluations and proof-of-concept COVID-19 case study on real data have validated the effectiveness of our proposed system in precisely distinguishing sentiments and accurately identifying hate content in both Levantine and Gulf Arabic dialects. Our findings shed light on the importance of considering the unique nature of dialects within the same language and ignoring the dialectal aspect would lead to misleading analysis.