FOX News' Eben Brown reports on AI going mainstream in healthcare, which doctors say has the potential to create stronger relationships with patients. Think of everything you do online -- and in real life, too -- that says something about who you are. Your likes, clicks, hobbies and activities all add to the wealth of data points companies already have on you. How is that data used? Let's take a deep look at how they use your conversations to create profiles.

Netflix is giving the Terminator franchise the anime treatment in a new series that's set to hit the streaming platform "soon." The company dropped the first teaser for Terminator: The Anime Series this weekend during its Geeked Week event. Details so far are scant, but we do know it'll be produced by Production IG, the Japanese animation studio behind the original Ghost in the Shell movie and spinoff TV series. Terminator: The Anime Series will take us back to August 1997, when the Skynet AI has first become self-aware and turned against humans. It will feature a cast of new characters, according to Variety. Terminator: The Anime Series is COMING SOON #GeekedWeek pic.twitter.com/mcbxavrn7V

AI expert Marva Bailer tells Fox News Digital how the open availability of artificial intelligence can have negative impacts and talks potential federal legislation to control it. European nations continue to jockey for leadership on artificial intelligence (AI), with Paris announcing it will host the next safety summit shortly after Britain hosted the first one. "The first edition of the Artificial Intelligence Security Summit, organized by the United Kingdom, provides an opportunity to develop international cooperation in the field of security, a crucial issue for the years to come. It was, therefore, natural for France to host the second edition of this summit," French Minister Delegate for the Digital Economy Jean-Noël Barrot said in a press release. The future of AI remains up for grabs, with many nations trying to position themselves at the forefront of the race.

With the growth of online services, the need for advanced text classification algorithms, such as sentiment analysis and biased text detection, has become increasingly evident. The anonymous nature of online services often leads to the presence of biased and harmful language, posing challenges to maintaining the health of online communities. This phenomenon is especially relevant in South Korea, where large-scale hate speech detection algorithms have not yet been broadly explored. In this paper, we introduce "KoMultiText", a new comprehensive, large-scale dataset collected from a well-known South Korean SNS platform. Our proposed dataset provides annotations including (1) Preferences, (2) Profanities, and (3) Nine types of Bias for the text samples, enabling multi-task learning for simultaneous classification of user-generated texts. Leveraging state-of-the-art BERT-based language models, our approach surpasses human-level accuracy across diverse classification tasks, as measured by various metrics. Beyond academic contributions, our work can provide practical solutions for real-world hate speech and bias mitigation, contributing directly to the improvement of online community health. Our work provides a robust foundation for future research aiming to improve the quality of online discourse and foster societal well-being.

Path reasoning methods over knowledge graphs have gained popularity for their potential to improve transparency in recommender systems. However, the resulting models still rely on pre-trained knowledge graph embeddings, fail to fully exploit the interdependence between entities and relations in the KG for recommendation, and may generate inaccurate explanations. In this paper, we introduce PEARLM, a novel approach that efficiently captures user behaviour and product-side knowledge through language modelling. With our approach, knowledge graph embeddings are directly learned from paths over the KG by the language model, which also unifies entities and relations in the same optimisation space. Constraints on the sequence decoding additionally guarantee path faithfulness with respect to the KG. Experiments on two datasets show the effectiveness of our approach compared to state-of-the-art baselines. Source code and datasets: AVAILABLE AFTER GETTING ACCEPTED.

Large language models (LLMs) have been shown to possess impressive capabilities, while also raising crucial concerns about the faithfulness of their responses. A primary issue arising in this context is the management of (un)answerable queries by LLMs, which often results in hallucinatory behavior due to overconfidence. In this paper, we explore the behavior of LLMs when presented with (un)answerable queries. We ask: do models represent the fact that the question is (un)answerable when generating a hallucinatory answer? Our results show strong indications that such models encode the answerability of an input query, with the representation of the first decoded token often being a strong indicator. These findings shed new light on the spatial organization within the latent representations of LLMs, unveiling previously unexplored facets of these models. Moreover, they pave the way for the development of improved decoding techniques with better adherence to factual generation, particularly in scenarios where query (un)answerability is a concern.

The recently introduced Controlled Text Reduction (CTR) task isolates the text generation step within typical summarization-style tasks. It does so by challenging models to generate coherent text conforming to pre-selected content within the input text (``highlights''). This framing enables increased modularity in summarization-like tasks, allowing to couple a single CTR model with various content-selection setups and modules. However, there are currently no reliable CTR models, while the performance of the existing baseline for the task is mediocre, falling short of practical utility. Here, we address this gap by introducing a high-quality, open-source CTR model that tackles two prior key limitations: inadequate enforcement of the content-preservation constraint, and suboptimal silver training data. Addressing these, we amplify the content-preservation constraint in both training, via RL, and inference, via a controlled decoding strategy. Further, we substantially improve the silver training data quality via GPT-4 distillation. Overall, pairing the distilled dataset with the highlight-adherence strategies yields marked gains over the current baseline, of up to 30 ROUGE-L points, providing a reliable CTR model for downstream use.

We evaluate how well LLMs understand African American Language (AAL) in comparison to their performance on White Mainstream English (WME), the encouraged "standard" form of English taught in American classrooms. We measure LLM performance using automatic metrics and human judgments for two tasks: a counterpart generation task, where a model generates AAL (or WME) given WME (or AAL), and a masked span prediction (MSP) task, where models predict a phrase that was removed from their input. Our contributions include: (1) evaluation of six pre-trained, large language models on the two language generation tasks; (2) a novel dataset of AAL text from multiple contexts (social media, hip-hop lyrics, focus groups, and linguistic interviews) with human-annotated counterparts in WME; and (3) documentation of model performance gaps that suggest bias and identification of trends in lack of understanding of AAL features.

Buddy Comedies for Men Who Don't Have Any Meaningful Male Friendships Buddy Comedies That Could've Been Romantic Comedies If Not for Rampant Homophobia Early 2000s Politically Incorrect Comedies You're Still Allowed to Love Because "It Was a Different Time" and "When Did Everyone Get So Sensitive?" Action Thrillers That Will Leave You Asking, "Why Are the Explosions So Loud, but Then I Can't Hear Any of the Words?" Psychological Crime Thrillers with a Reckless Male Detective Who Doesn't Play by the Rules but Who Is Also Vulnerable and Has a Deep, Defining Trauma That, Once Revealed, Will Perfectly Unlock His Character Arc Robot Sci-Fi Movies That Will Make You Say, "Why Are These People Resisting? If I Had Sentient Robot Overlords (and One Day I Hope to), Then, Gee, I'd Simply Relish Not Having to Make Any More Decisions" True-Crime Documentaries That Will Make You Wonder If You're Still Young or Pretty Enough to Be Targeted by a Serial Killer Because It's Midnight, and Your Phone Just Died, so You Can't Even Search Each Movie's Individual Rotten Tomatoes Score, and One of Your Contact Lenses Is Floating So Far Back on Your Eyeball It Is Now Likely Lost Inside of Your Brain, and Besides It's Not Like You're Really Going to Watch the Movie Anyway, You Just Like Knowing You Have Options, and with Us You Always Have Options! Sure the Majority of Them Are Pig-Trough Slop, but as Long as You Dumb Little Piggies Keep Oinking We'll Keep Shovelling It in, and Pretty Soon You Won't Even Notice That Jeff Bridges Isn't Jeff Bridges but, Rather, the Digitized Likeness of Jeff Bridges, Cuz You Dumb Little Slurping Hogs Will Be Too Sauced Off Our Latest Algorithmic Scum-Muck Special to Ever Know the Difference.

ChatGPT seems to have blindsided us all. In less than a year it has proved that it can make writers redundant, which is one of the reasons why the Writers Guild of America recently went on strike, and why a group of novelists, including Jonathan Franzen, Jodi Picoult and George RR Martin, are pursuing a lawsuit against OpenAI, the company that owns the chatbot. Imitation that appears to be original writing. From my experiments, it's obvious that ChatGPT's current level of literary sophistication is weak – it is cliche-prone and generally unconvincing – but who knows how it will develop? Writers like stretching our imaginations, coming up with ideas, working out storylines and plots, creating believable characters, overcoming creative challenges and working on a full-length piece of work over an extended period of time. Most of us write our books ourselves and while we are influenced by other writers, we're not a chatbot that has been trained on hundreds of thousands of novels for the sole purpose of mimicking human creativity. Imagine a future where those who are most adept at getting AI to write creatively will dominate, while we writers who spend a lifetime devoted to our craft are sidelined. OK, this is a worst‑case scenario, but we have to consider it, because ChatGPT and the other Large Language Models (LLMs) out there have been programmed to imagine a future that threatens many creative professions. ChatGPT is already responding to the questions I ask it in seconds, quite reliably. It is an impressive beast, but one that needs to be tamed.