Emotion Recognition in Conversation (ERC) plays a significant part in Human-Computer Interaction (HCI) systems since it can provide empathetic services. Multimodal ERC can mitigate the drawbacks of uni-modal approaches. Recently, Graph Neural Networks (GNNs) have been widely used in a variety of fields due to their superior performance in relation modeling. In multimodal ERC, GNNs are capable of extracting both long-distance contextual information and inter-modal interactive information. Unfortunately, since existing methods such as MMGCN directly fuse multiple modalities, redundant information may be generated and diverse information may be lost. In this work, we present a directed Graph based Cross-modal Feature Complementation (GraphCFC) module that can efficiently model contextual and interactive information. GraphCFC alleviates the problem of heterogeneity gap in multimodal fusion by utilizing multiple subspace extractors and Pair-wise Cross-modal Complementary (PairCC) strategy. We extract various types of edges from the constructed graph for encoding, thus enabling GNNs to extract crucial contextual and interactive information more accurately when performing message passing. Furthermore, we design a GNN structure called GAT-MLP, which can provide a new unified network framework for multimodal learning. The experimental results on two benchmark datasets show that our GraphCFC outperforms the state-of-the-art (SOTA) approaches.

In the 1991 movie "Terminator 2: Judgment Day," a sentient killer robot travels back in time to stop the rise of artificial intelligence. The robot locates the computer scientist whose work will lead to the creation of Skynet, a computer system that will destroy the world, and convinces him that A.I. development must be stopped immediately. Together, they travel to the headquarters of Cyberdyne Systems, the company behind Skynet, and blow it up. The A.I. research is destroyed, and the course of history is changed--at least, for the rest of the film. In the sci-fi world of "Terminator 2," it's crystal clear what it means for an A.I. to become "self-aware," or to pose a danger to humanity; it's equally obvious what might be done to stop it.

It simply wouldn't feel like a deals holiday without Amazon, nor would it feel like the holiday season without a pile of Prime shipping boxes piling up inside your front door. You'd better put out some snacks for your mail delivery folks--Amazon's Black Friday deals have started earlier, so the package pile might be bigger than ever. We've rounded up the best Black Friday deals we could find. Discounted Alexa speakers, Fire tablets, and Kindles will probably feel familiar, but there are also steep price drops on TVs, laptops, coffee makers, and much more. Check out our Early Black Friday Deals roundup for more. We test products year-round and handpicked these deals. Products that are sold out or no longer discounted as of publishing will be crossed out . We'll update this guide throughout the Black Friday and Cyber Monday weekend. If you buy something using links in our stories, we may earn a commission. This helps support our journalism. The Fire HD 10 is still our favorite Fire tablet. This version has a speedy enough processor and a large screen for you to consume all the Amazon content your heart could want. You can shell out more cash for the Keyboard Case bundle ($60 off), a tablet without advertisements on the lock screen, or both. In our Best Amazon Fire Tablets guide, we say this model has the best value. That's especially true thanks to this deal, which brings the gadget within $5 of the lowest price we've tracked.

It's been a drama-filled week for OpenAI, the creator of ChatGPT. Its wunderkind CEO Sam Altman has been unceremoniously booted out by its board and more than 600 staff members are now threatening to quit unless he's allowed back in. As a writer, I am of course duty-bound to swear on my copy of McNae's Essential Law for Journalists that I did not use OpenAI's chatbot to write this column – or did I? Even if I did, why would I fess up to it? Thanks to disastrously unpopular attempts by the likes of BuzzFeed to create AI-assisted content, its name is mud in the media industry.

Microsoft CEO Satya Nadella on Monday said that he was open to the prospect of Sam Altman returning to OpenAI following his dramatic ouster from the high-profile company last week, as the fallout of his abrupt removal continues. The tech giant had previously announced that it was bringing in Altman and former OpenAI President Greg Brockman to lead a new artificial intelligence research team there, and also appeared prepared to hire other OpenAI employees who choose to follow their former boss out of the company. Brockman resigned from OpenAI in protest over the firing of Altman as CEO. But in an interview with CNBC, Nadella seemed willing to accept a scenario in which Altman would return to his old job, saying it's up to the people of OpenAI to choose to stay there or go over to Microsoft. "I'm open to both options," he said.

Influencer marketing has become a very popular tool to reach customers. Despite the rapid growth in influencer videos, there has been little research on the effectiveness of their constituent features in explaining video engagement. We study YouTube influencers and analyze their unstructured video data across text, audio and images using an "interpretable deep learning" framework that accomplishes both goals of prediction and interpretation. Our prediction-based approach analyzes unstructured data and finds that "what is said" in words (text) is more influential than "how it is said" in imagery (images) or acoustics (audio). Our novel interpretation-based approach is implemented after completion of model prediction by analyzing the same source of unstructured data to measure importance attributed to the video features. We eliminate several spurious relationships in two steps, identifying a subset of relationships which are confirmed using theory. We uncover novel findings that establish distinct associations for measures of shallow and deep engagement based on the dual-system framework of human thinking. Our approach is validated using simulated data, and we discuss the learnings from our findings for influencers and brands.

Heterogeneous unsupervised domain adaptation (HUDA) is the most challenging domain adaptation setting where the feature spaces of source and target domains are heterogeneous, and the target domain has only unlabeled data. Existing HUDA methods assume that both positive and negative examples are available in the source domain, which may not be satisfied in some real applications. This paper addresses a new challenging setting called positive and unlabeled heterogeneous unsupervised domain adaptation (PU-HUDA), a HUDA setting where the source domain only has positives. PU-HUDA can also be viewed as an extension of PU learning where the positive and unlabeled examples are sampled from different domains. A naive combination of existing HUDA and PU learning methods is ineffective in PU-HUDA due to the gap in label distribution between the source and target domains. To overcome this issue, we propose a novel method, predictive adversarial domain adaptation (PADA), which can predict likely positive examples from the unlabeled target data and simultaneously align the feature spaces to reduce the distribution divergence between the whole source data and the likely positive target data. PADA achieves this by a unified adversarial training framework for learning a classifier to predict positive examples and a feature transformer to transform the target feature space to that of the source. Specifically, they are both trained to fool a common discriminator that determines whether the likely positive examples are from the target or source domain. We experimentally show that PADA outperforms several baseline methods, such as the naive combination of HUDA and PU learning.

The AI development community is increasingly making use of hosting intermediaries such as Hugging Face provide easy access to user-uploaded models and training data. These model marketplaces lower technical deployment barriers for hundreds of thousands of users, yet can be used in numerous potentially harmful and illegal ways. In this article, we explain ways in which AI systems, which can both `contain' content and be open-ended tools, present one of the trickiest platform governance challenges seen to date. We provide case studies of several incidents across three illustrative platforms -- Hugging Face, GitHub and Civitai -- to examine how model marketplaces moderate models. Building on this analysis, we outline important (and yet nevertheless limited) practices that industry has been developing to respond to moderation demands: licensing, access and use restrictions, automated content moderation, and open policy development. While the policy challenge at hand is a considerable one, we conclude with some ideas as to how platforms could better mobilize resources to act as a careful, fair, and proportionate regulatory access point.

We attempt to detect out-of-distribution (OOD) text samples though applying Topological Data Analysis (TDA) to attention maps in transformer-based language models. We evaluate our proposed TDA-based approach for out-of-distribution detection on BERT, a transformer-based language model, and compare the to a more traditional OOD approach based on BERT CLS embeddings. We found that our TDA approach outperforms the CLS embedding approach at distinguishing in-distribution data (politics and entertainment news articles from HuffPost) from far out-of-domain samples (IMDB reviews), but its effectiveness deteriorates with near out-of-domain (CNN/Dailymail) or same-domain (business news articles from HuffPost) datasets.

The launch of ChatGPT has garnered global attention, marking a significant milestone in the field of Generative Artificial Intelligence. While Generative AI has been in effect for the past decade, the introduction of ChatGPT has ignited a new wave of research and innovation in the AI domain. This surge in interest has led to the development and release of numerous cutting-edge tools, such as Bard, Stable Diffusion, DALL-E, Make-A-Video, Runway ML, and Jukebox, among others. These tools exhibit remarkable capabilities, encompassing tasks ranging from text generation and music composition, image creation, video production, code generation, and even scientific work. They are built upon various state-of-the-art models, including Stable Diffusion, transformer models like GPT-3 (recent GPT-4), variational autoencoders, and generative adversarial networks. This advancement in Generative AI presents a wealth of exciting opportunities and, simultaneously, unprecedented challenges. Throughout this paper, we have explored these state-of-the-art models, the diverse array of tasks they can accomplish, the challenges they pose, and the promising future of Generative Artificial Intelligence.