The prosperity of social media platforms has raised the urgent demand for semantic-rich services, e.g., event and storyline attribution. However, most existing research focuses on clip-level event understanding, primarily through basic captioning tasks, without analyzing the causes of events across an entire movie. This is a significant challenge, as even advanced multimodal large language models (MLLMs) struggle with extensive multimodal information due to limited context length. To address this issue, we propose a Two-Stage Prefix-Enhanced MLLM (TSPE) approach for event attribution, i.e., connecting associated events with their causal semantics, in movie videos. In the local stage, we introduce an interaction-aware prefix that guides the model to focus on the relevant multimodal information within a single clip, briefly summarizing the single event. Correspondingly, in the global stage, we strengthen the connections between associated events using an inferential knowledge graph, and design an event-aware prefix that directs the model to focus on associated events rather than all preceding clips, resulting in accurate event attribution. Comprehensive evaluations of two real-world datasets demonstrate that our framework outperforms state-of-the-art methods.

During an appearance on "The Ingraham Angle," Jimmy Failla shares his thoughts on the latest interesting development in the world of artificial intelligence. ChatGPT has been a game changer for artificial intelligence, catapulting earlier this year to the fastest-growing web platform ever as millions of people across the world rushed to communicate with a system that can mimic human conversation. The system, however, is unable to respond to current events questions due to having a knowledge cutoff date of September 2021. When Fox News Digital, for example, attempted to ask ChatGPT questions about current events, such as if the Titan submersible implosion could have been prevented or what charges Hunter Biden was hit with this month, the chatbot responded that it does not have knowledge of current events after September 2021. "As an AI language model, I have a knowledge cutoff date because my training data only goes up until September 2021," ChatGPT responded when asked why it does not possess language beyond September 2021.

ChatGPT launched last November and has taken the world by storm, unlike any other technology since the dawn of the smartphone. You have probably seen examples on social media of the AI giving eerily human responses to obscure prompts -- but the chatbot can actually be used to carry out a large number of basic daily tasks that could save you time -- and money. It is easy (and free) to use ChatGPT and can be used to do anything from writing work reports to creating diet plans and helping you apply for jobs. So whether you want to use it on a desktop, tablet or cellphone, DailyMail.com There's a subscription option, but you don't have to pay to use ChatGPT ChatGPT (it stands for Generative Pre-trained Transformer) is a'large language model' which can produce convincing, human-like answers to almost any question.

Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. To create healthy online communities, companies need better strategies to weed out harmful posts. In this VB On-Demand event, AI/ML experts from Cohere and Google Cloud share insights into the new tools changing how moderation is done. Game players experience a staggering amount of online abuse. A recent study found that five out of six adults (18–45) experienced harassment in online multiplayer games, or over 80 million gamers.

Neural networks are designed to mimic the behavior of human brains, to understand various relationships in the data. These networks have the power to understand complex non-linear relationships and can help us to make more intelligent decisions. Neural networks are used in fields like image processing, natural language processing, etc., and can outperform the traditional machine learning algorithms. But one basic drawback with the traditional neural network is that it cannot memorize things. Let's say, we are playing a game and we need a model to predict the next move of a player. This depends a lot on the previous moves and traditional neural networks will not perform well here.

Let's face it, people are spending more time on social media than ever before. In 2018, around 2.65 billion people were using social media worldwide, a number thought to increase to 3.1 billion by 2021. Mosts posts are harmless depictions of life, like status updates, pictures of friends or food, the occasional meme, etc. etc. We give these posts a like, maybe a comment, then keep on scrolling. But sometimes people post about more personal topics and show signs that things in their life are not going well.

Artificial intelligence (AI) promises to change nearly every enterprise workflow, but it isn't changing software and web development just yet. What other developers (and businesses for that matter) should take from this information is that it is OK if you haven't jumped on the AI hype train just yet. However, they should also take note that, despite low adoption rates, there is strong interest in the space, and it could be poised to grow rapidly. While many developers aren't using AI and machine learning tools, 81% said they are interested in learning more about them. Of those developers, 46% said they were specifically interested in automated machine learning, 22% in sentiment analysis and natural language processing, and 21% in hybrid and deep learning models.

There have been recriminations about Secretary Clinton's use of a personal email account and server while she was Secretary of State, but there hasn't been enough examination of why Shadow IT is a bad thing. When an employee uses personal IT resources to do their job, that activity and data is out of view of the employer, in the so-called "shadows." An employer provides computer resources to employees and has a responsibility to maintain the systems, keep track of company data, back it up, secure it, and secure the network and devices. When employees use their personal computers and email accounts for work, it creates security risks. The employer can't secure systems or data it doesn't know about, and individuals might have worse cybersecurity practices than the employer.