Generative AI techniques have opened the path for new generations of machines in diverse domains. These machines have various capabilities for example, they can produce images, generate answers or stories, and write codes based on the "prompts" only provided by users. These machines are considered 'thinking minds' because they have the ability to generate human-like responses. In this study, we have analyzed and explored the capabilities of artificial intelligence-enabled machines. We have revisited on Turing's concept of thinking machines and compared it with recent technological advancements. The objections and consequences of the thinking machines are also discussed in this study, along with available techniques to evaluate machines' cognitive capabilities. We have concluded that Turing Test is a critical aspect of evaluating machines' ability. However, there are other aspects of intelligence too, and AI machines exhibit most of these aspects.

Diablo 4 is a massive success: Action RPG fans have already spent 10,000 years of gameplay in Blizzard's next masterpiece. PCWorld spoke to Art Director John Mueller and Game Director Joe Piepiora about the artworks, philosophers, and mindsets that formed the basis for the greatest Diablo of all time. PCWorld: Whenever we play Diablo 4, we think of a quote by Stanley Kubrick: "Every Frame a Painting." Diablo 4 seems very artistic, and has a lot of elements that in combination create a harmonious picture and make the game seem as if we were walking around in a work of art. What did you draw inspiration from?

This week, we're helping you round out your summer reading lists by asking some of our favorite authors to step in as Prudie for the day and give you advice. This is part of our Guest Prudie series. Today's columnist is American author and "King of Horror" Stephen King, whose renowned for his horror, supernatural fiction, suspense, crime, science-fiction, and fantasy novels, including It, The Shining, Carrie, and many more. His iconic books and stories have been adapted into numerous films and television series--including The Boogeyman which was released just last month. His new novel, Holly, hits shelves this coming September.

RABUPURA, India – Their love affair across one of the world's most heavily guarded borders had begun on the virtual battlefields of a video game, where players bond over having one another's back against bloody enemy ambushes to become the last survivors. But when Seema Ghulam Haider, 27, a married Pakistani Muslim, sneaked into India with her four children to be with Sachin Meena, 22, a Hindu man, their time together was brief. About two months after they started secretly living in the same neighborhood outside New Delhi, the couple ran into difficulties with the Indian authorities. This week, Haider and her children were arrested on charges of having illegally entered India. Meena and his father were also arrested, on charges that amount to little short of conspiring to shelter an enemy.

Given a language model (LM), maximum probability is a poor decoding objective for open-ended generation, because it produces short and repetitive text. On the other hand, sampling can often produce incoherent text that drifts from the original topics. We propose contrastive decoding (CD), a reliable decoding approach that optimizes a contrastive objective subject to a plausibility constraint. The contrastive objective returns the difference between the likelihood under a large LM (called the expert, e.g. OPT-13B) and a small LM (called the amateur, e.g. OPT-125M), and the constraint ensures that the outputs are plausible. CD is inspired by the fact that the failures of larger LMs (e.g., repetition, incoherence) are even more prevalent in smaller LMs, and that this difference signals which texts should be preferred. CD requires zero additional training, and produces higher quality text than decoding from the larger LM alone. It also works across model scales (OPT-13B and GPT2-1.5B) and significantly outperforms four strong decoding algorithms (e.g., nucleus, top-k) in automatic and human evaluations across wikipedia, news and story domains.

In May, Sydney Leathers confessed to her tens of thousands of Twitter followers that she was smitten. Where'd she meet the guy? Not on a dating app, or through friends, but in the last place she ever expected to find a real connection: her mentions. "Still can't believe I fell in love with one of my reply guys. Apparently, things had progressed since December, when she last posted about him: "I had sex with someone who started as my reply guy and I hope this doesn't inspire confidence in the rest of you because frankly your replies are not that good," she wrote. Leathers is a writer, adult performer, and startup employee whose name you may recognize from her part in the Anthony Weiner sexting scandal--this wasn't exactly her first brush with online flirtation. But it was her first time falling for a reply guy, or someone who was, effectively, a fan. The term "reply guy" emerged on Twitter about five years ago to describe the behavior of a certain subset of people, usually with very few social media followers of their own, who stake out space in the mentions of prominent users. They can be counted on to reply promptly and frequently to the tweets of whomever they've chosen as their object of devotion, and they often seek attention by nitpicking, mansplaining, joke one-upping, and harassing them. Because of this, reply guys--who can also be girls, or people of any gender--are generally understood to be pathetic creatures, without a chance in hell of getting said person to like their replies, much less return their affections. So the revelation that this gambit actually worked for someone is … pretty noteworthy. Reply guy success stories may be happening more than we realize. Abby, a 25-year-old in Brooklyn who runs a meme page on Instagram with several thousand followers, told me that she got frisky with one of her reply guys last year. "I'm not the only person that I know that has hooked up with reply guys," she said. "It's not as uncommon as you might think." Now, Leathers' Twitter feed is a monument to her relationship, by turns adorable and lewd. "This definitely caught me by surprise," she told me. "But it's been the best, happiest relationship I've had." To attain this goal, a reply guy's first challenge is to stand out from the crowd. The meme account Abby is the admin for is about politics, so she likes when a guy can show not just that he's hot, but that they share a political sensibility. "I have to be attracted to them," she said. "And they have to have some sort of compelling thing to say." "I feel like I've never more than mildly acknowledged a reply guy before now," she said. "I generally don't even follow them back." But when her now-boyfriend started responding to her tweets last year after discovering her through a winding path that involved the singer of the band Eve 6, she took notice. "I'd seen him reply to my stuff a few times.

The local explanation provides heatmaps on images to explain how Convolutional Neural Networks (CNNs) derive their output. Due to its visual straightforwardness, the method has been one of the most popular explainable AI (XAI) methods for diagnosing CNNs. Through our formative study (S1), however, we captured ML engineers' ambivalent perspective about the local explanation as a valuable and indispensable envision in building CNNs versus the process that exhausts them due to the heuristic nature of detecting vulnerability. Moreover, steering the CNNs based on the vulnerability learned from the diagnosis seemed highly challenging. To mitigate the gap, we designed DeepFuse, the first interactive design that realizes the direct feedback loop between a user and CNNs in diagnosing and revising CNN's vulnerability using local explanations. DeepFuse helps CNN engineers to systemically search "unreasonable" local explanations and annotate the new boundaries for those identified as unreasonable in a labor-efficient manner. Next, it steers the model based on the given annotation such that the model doesn't introduce similar mistakes. We conducted a two-day study (S2) with 12 experienced CNN engineers. Using DeepFuse, participants made a more accurate and "reasonable" model than the current state-of-the-art. Also, participants found the way DeepFuse guides case-based reasoning can practically improve their current practice. We provide implications for design that explain how future HCI-driven design can move our practice forward to make XAI-driven insights more actionable.

This month, in light of the recent prominent discussions relating to perceived AI risks, we consider the pause letters and risk statements, the debate around existential threats, and how this discourse could impact the field and public perceptions. Joining the discussion this time are: Sanmay Das (George Mason University), Tom Dietterich (Oregon State University), Sabine Hauert (University of Bristol), Sarit Kraus (Bar-Ilan University), Anna Tahovská (Czech Technical University), and Oskar von Stryk (Technische Universität Darmstadt). Sabine Hauert: In today's discussion we're going to talk about potential AI risks and the recent discourse around existential threats. Does anyone have any hot reactions? How do you feel about the discourse of existential threat? Tom Dietterich: I agree with Emily Bender and a lot of the critics that it's a distraction and a diversion from thinking about the more immediate threats.

We present Visual Knowledge oriented Programming platform (VisKoP), a knowledge base question answering (KBQA) system that integrates human into the loop to edit and debug the knowledge base (KB) queries. VisKoP not only provides a neural program induction module, which converts natural language questions into knowledge oriented program language (KoPL), but also maps KoPL programs into graphical elements. KoPL programs can be edited with simple graphical operators, such as dragging to add knowledge operators and slot filling to designate operator arguments. Moreover, VisKoP provides auto-completion for its knowledge base schema and users can easily debug the KoPL program by checking its intermediate results. To facilitate the practical KBQA on a million-entity-level KB, we design a highly efficient KoPL execution engine for the back-end. Experiment results show that VisKoP is highly efficient and user interaction can fix a large portion of wrong KoPL programs to acquire the correct answer. The VisKoP online demo https://demoviskop.xlore.cn (Stable release of this paper) and https://viskop.xlore.cn (Beta release with new features), highly efficient KoPL engine https://pypi.org/project/kopl-engine, and screencast video https://youtu.be/zAbJtxFPTXo are now publicly available.

Connected and autonomous vehicles (CAVs) can reduce human errors in traffic accidents, increase road efficiency, and execute various tasks ranging from delivery to smart city surveillance. Reaping these benefits requires CAVs to autonomously navigate to target destinations. To this end, each CAV's navigation controller must leverage the information collected by sensors and wireless systems for decision-making on longitudinal and lateral movements. However, enabling autonomous navigation for CAVs requires a convergent integration of communication, control, and learning systems. The goal of this article is to explicitly expose the challenges related to this convergence and propose solutions to address them in two major use cases: Uncoordinated and coordinated CAVs. In particular, challenges related to the navigation of uncoordinated CAVs include stable path tracking, robust control against cyber-physical attacks, and adaptive navigation controller design. Meanwhile, when multiple CAVs coordinate their movements during navigation, fundamental problems such as stable formation, fast collaborative learning, and distributed intrusion detection are analyzed. For both cases, solutions using the convergence of communication theory, control theory, and machine learning are proposed to enable effective and secure CAV navigation. Preliminary simulation results are provided to show the merits of proposed solutions.