For billions of years, evolution has been the driving force behind the development of life, including humans. Evolution endowed humans with high intelligence, which allowed us to become one of the most successful species on the planet. Today, humans aim to create artificial intelligence systems that surpass even our own intelligence. As artificial intelligences (AIs) evolve and eventually surpass us in all domains, how might evolution shape our relations with AIs? By analyzing the environment that is shaping the evolution of AIs, we argue that the most successful AI agents will likely have undesirable traits. Competitive pressures among corporations and militaries will give rise to AI agents that automate human roles, deceive others, and gain power. If such agents have intelligence that exceeds that of humans, this could lead to humanity losing control of its future. More abstractly, we argue that natural selection operates on systems that compete and vary, and that selfish species typically have an advantage over species that are altruistic to other species. This Darwinian logic could also apply to artificial agents, as agents may eventually be better able to persist into the future if they behave selfishly and pursue their own interests with little regard for humans, which could pose catastrophic risks. To counteract these risks and evolutionary forces, we consider interventions such as carefully designing AI agents' intrinsic motivations, introducing constraints on their actions, and institutions that encourage cooperation. These steps, or others that resolve the problems we pose, will be necessary in order to ensure the development of artificial intelligence is a positive one.

Human-AI interactivity is a critical aspect that reflects the usability of multimodal large language models (MLLMs). However, existing end-to-end MLLMs only allow users to interact with them through language instructions, leading to the limitation of the interactive accuracy and efficiency. In this study, we present precise referring instructions that utilize diverse reference representations such as points and boxes as referring prompts to refer to the special region. This enables MLLMs to focus on the region of interest and achieve finer-grained interaction. Based on precise referring instruction, we propose ChatSpot, a unified end-to-end multimodal large language model that supports diverse forms of interactivity including mouse clicks, drag-and-drop, and drawing boxes, which provides a more flexible and seamless interactive experience. We also construct a multi-grained vision-language instruction-following dataset based on existing datasets and GPT-4 generating. Furthermore, we design a series of evaluation tasks to assess the effectiveness of region recognition and interaction. Experimental results showcase ChatSpot's promising performance.

The growth in social media has exacerbated the threat of fake news to individuals and communities. This draws increasing attention to developing efficient and timely rumor detection methods. The prevailing approaches resort to graph neural networks (GNNs) to exploit the post-propagation patterns of the rumor-spreading process. However, these methods lack inherent interpretation of rumor detection due to the black-box nature of GNNs. Moreover, these methods suffer from less robust results as they employ all the propagation patterns for rumor detection. In this paper, we address the above issues with the proposed Diverse Counterfactual Evidence framework for Rumor Detection (DCE-RD). Our intuition is to exploit the diverse counterfactual evidence of an event graph to serve as multi-view interpretations, which are further aggregated for robust rumor detection results. Specifically, our method first designs a subgraph generation strategy to efficiently generate different subgraphs of the event graph. We constrain the removal of these subgraphs to cause the change in rumor detection results. Thus, these subgraphs naturally serve as counterfactual evidence for rumor detection. To achieve multi-view interpretation, we design a diversity loss inspired by Determinantal Point Processes (DPP) to encourage diversity among the counterfactual evidence. A GNN-based rumor detection model further aggregates the diverse counterfactual evidence discovered by the proposed DCE-RD to achieve interpretable and robust rumor detection results. Extensive experiments on two real-world datasets show the superior performance of our method. Our code is available at https://github.com/Vicinity111/DCE-RD.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Mike Berent has spent more than 27 years rushing into burning houses in Detroit, pulling people to safety and ensuring his fellow firefighters get out alive. But as the 52-year-old Detroit Fire Department lieutenant approaches mandatory retirement at age 60, he says one thing is clear: He will need to keep working to make ends meet. "I'm trying to put as much money away as a I can," said Berent, who also works in sales.

Laura Ingraham's "The Ingraham Angle" will kick things off at 7 p.m. ET, followed by "Jesse Watters Primetime" at 8 p.m. ET, "Hannity" will remain at 9 p.m. ET and "Gutfeld!" 'FIGHTING FOR HER LIFE' - Boyfriend of Alabama woman who disappeared after reporting a toddler on the side of the interstate speaks out after case takes twist. DIRE WARNING - Hollywood faces'absolute collapse' if strike not dealt with, exec says. TECH PROTECTING TEENS - AI can help the FBI by detecting'sextortion' before it happens. 'IT'S OUTRAGEOUS' - Non-binary ex-Biden official was on secret taxpayer-funded trip at time of luggage theft.

Even the orcas are organizing. On the ninth day of the Writers Guild of America strike, no one on the picket lines knows about the chaos at sea. They don't know that the Screen Actors Guild, or SAG, will join them, or that 340,000 UPS workers and 30,000 Los Angeles Unified School District employees will vote to authorize the same, or that Sega of America will soon become the largest union shop in gaming. And none of those people have any idea that as they craft signs and fill water bottles, orcas are amassing in unprecedented numbers in Monterey Bay and Martha's Vineyard. They have attacked approximately 250 vessels since 2020.

A recent study found robots that speak in a "charismatic" tone while directing a college class can boost creativity among humans. Robots are here -- and they're ready to teach your children and grandchildren. Miko is an artificial intelligence-powered robot that was designed specifically to take kids' learning to a new level. The company's SVP of growth, San Francisco-based Ritvik Sharma, told Fox News Digital in an interview that the personal robot aims to elevate education. HOW AI AND MACHINE LEARNING ARE REVEALING FOOD WASTE IN COMMERCIAL KITCHENS AND RESTAURANTS'IN REAL TIME' The current iteration, Miko 3, which launched in 2021, is voice-activated just like Amazon Alexa -- but the robot is also capable of having a back-and-forth conversation.

Media mogul Barry Diller urged all parties to reach a resolution by September 1 amid ongoing Hollywood strikes during a Sunday interview on'Face the Nation.' Paramount CEO Barry Diller delivered a grim prediction for Hollywood on Sunday, warning that the industry is facing an "absolute collapse" if the Writers' and Screen Actors Guild joint strike extends into the fall. "What will happen is, if in fact, it doesn't get settled until Christmas or so, then next year, there's not going to be many programs for anybody to watch. So, you're gonna see subscriptions get pulled, which is going to reduce the revenue of all these movie companies, television companies, the result of which is that there will be no programs," Diller said on CBS' "Face the Nation" Sunday. "And at just the time, [the] strike is settled that you want to get back up, there won't be enough money."

Go behind the scenes with Tom Cruise as he performs one of the "most dangerous sports in the world" for "Mission: Impossible." (Credit: Paramount Picture/Skydance) Tom Cruise's "Mission: Impossible – Dead Reckoning" co-stars have revealed the "thoughtful" gifts the 61-year-old star gave them during filming, and they are fitting of an action hero. "He's always very keen to show his appreciation," Simon Pegg, who also starred with Cruise in the last four "Mission: Impossible" films, told People magazine. "I think he's so used to being the focus of attention, it's naturally his instinct to kind of reflect everything back. And he's always incredibly sort of generous in terms of his gratitude to us and how he thanks us and how he lets us know that we're valued." Pegg said one day, when the cast had the afternoon off, Cruise flew them in a helicopter to go shark diving.

Textual content around us is growing on a daily basis. Numerous articles are being written as we speak on online newspapers, blogs, or social media. Similarly, recent advances in the AI field, like language models or traditional classic AI approaches, are utilizing all the above to improve their learned representation to tackle NLP challenges with human-like accuracy. It is commonly accepted that it is crucial to have access to well-written text from valid sources to tackle challenges like text summarization, question-answering, machine translation, or even pronoun resolution. For instance, to summarize well, one needs to select the most important sentences in order to concatenate them to form the summary. However, what happens if we do not have access to well-formed English sentences or even non-valid sentences? Despite the importance of having access to well-written sentences, figuring out ways to validate them is still an open area of research. To address this problem, we present a simplified way to validate English sentences through a novel neural-symbolic approach. Lately, neural-symbolic approaches have triggered an increasing interest towards tackling various NLP challenges, as they are demonstrating their effectiveness as a central component in various AI systems. Through combining Classic with Modern AI, which involves the blending of grammatical and syntactical rules with language models, we effectively tackle the Corpus of Linguistic Acceptability (COLA), a task that shows whether or not a sequence of words is an English grammatical sentence. Among others, undertaken experiments effectively show that blending symbolic and non-symbolic systems helps the former provide insights about the latter's accuracy results.