The first time I entered one of these groups, I felt like I had the voyeuristic ability to see into strangers' minds. I was immediately blown away by how imaginative human beings are. But then I was confronted with more disturbing material, namely, President Obama turned into a monkey and many pregnant celebrities. Dozens of prompters are fixated on this old racist trope and this tabloidy cultural fetish. Yes, I realize that pregnant bodies are beautiful. And searching through the Midjourney database some pregnancies --like the Status of Liberty's--seem motivated by curiosity.

On Wednesday, March 29 at 19:30 GMT: As powerful artificial intelligence systems improve their ability to create images, video and text, researchers are increasingly worried about the technology's seemingly inevitable role in disinformation and propaganda campaigns. Advancements in generative AI have the potential to radically alter our information environment to the point where humans – and even machines – may not have the ability to distinguish between content that is AI-generated versus human-made. The proliferation of tools powered by generative AI are making disinformation easier to produce, paving the way for a host of new problems with no clear solutions for online content moderators. AI ethicists and others within the industry are calling for more regulatory measures. And while many have hailed generative AI for its ability to provide highly personalised recommendations, the same online personal data that trains AI programmes could potentially be used to manipulate people en masse via chatbots to share conspiracy theories or foreign propaganda.

We define `ousiometrics' to be the study of essential meaning in whatever context that meaningful signals are communicated, and `telegnomics' as the study of remotely sensed knowledge. From work emerging through the middle of the 20th century, the essence of meaning has become generally accepted as being well captured by the three orthogonal dimensions of evaluation, potency, and activation (EPA). By re-examining first types and then tokens for the English language, and through the use of automatically annotated histograms -- `ousiograms' -- we find here that: 1. The essence of meaning conveyed by words is instead best described by a compass-like power-danger (PD) framework, and 2. Analysis of a disparate collection of large-scale English language corpora -- literature, news, Wikipedia, talk radio, and social media -- shows that natural language exhibits a systematic bias toward safe, low danger words -- a reinterpretation of the Pollyanna principle's positivity bias for written expression. To help justify our choice of dimension names and to help address the problems with representing observed ousiometric dimensions by bipolar adjective pairs, we introduce and explore `synousionyms' and `antousionyms' -- ousiometric counterparts of synonyms and antonyms. We further show that the PD framework revises the circumplex model of affect as a more general model of state of mind. Finally, we use our findings to construct and test a prototype `ousiometer', a telegnomic instrument that measures ousiometric time series for temporal corpora. We contend that our power-danger ousiometric framework provides a complement for entropy-based measurements, and may be of value for the study of a wide variety of communication across biological and artificial life.

Spoken keyword spotting (KWS) is the task of identifying a keyword in an audio stream and is widely used in smart devices at the edge in order to activate voice assistants and perform hands-free tasks. The task is daunting as there is a need, on the one hand, to achieve high accuracy while at the same time ensuring that such systems continue to run efficiently on low power and possibly limited computational capabilities devices. This work presents AraSpot for Arabic keyword spotting trained on 40 Arabic keywords, using different online data augmentation, and introducing ConformerGRU model architecture. Finally, we further improve the performance of the model by training a text-to-speech model for synthetic data generation. AraSpot achieved a State-of-the-Art SOTA 99.59% result outperforming previous approaches.

In this paper, we study the transferability of ImageNet spatial and Kinetics spatio-temporal representations to multi-label Movie Trailer Genre Classification (MTGC). In particular, we present an extensive evaluation of the transferability of ConvNet and Transformer models pretrained on ImageNet and Kinetics to Trailers12k, a new manually-curated movie trailer dataset composed of 12,000 videos labeled with 10 different genres and associated metadata. We analyze different aspects that can influence transferability, such as frame rate, input video extension, and spatio-temporal modeling. In order to reduce the spatio-temporal structure gap between ImageNet/Kinetics and Trailers12k, we propose Dual Image and Video Transformer Architecture (DIViTA), which performs shot detection so as to segment the trailer into highly correlated clips, providing a more cohesive input for pretrained backbones and improving transferability (a 1.83% increase for ImageNet and 3.75% for Kinetics). Our results demonstrate that representations learned on either ImageNet or Kinetics are comparatively transferable to Trailers12k. Moreover, both datasets provide complementary information that can be combined to improve classification performance (a 2.91% gain compared to the top single pretraining). Interestingly, using lightweight ConvNets as pretrained backbones resulted in only a 3.46% drop in classification performance compared with the top Transformer while requiring only 11.82% of its parameters and 0.81% of its FLOPS.

Online discourse covers a wide range of topics and many actors tailor their content to impact online discussions through carefully crafted messages and targeted campaigns. Yet the scale and diversity of online media content make it difficult to evaluate the impact of a particular message. In this paper, we present a new technique that leverages semantic similarity to quantify the change in the discussion after a particular message has been published. We use a set of press releases from environmental organisations and tweets from the climate change debate to show that our novel approach reveals a heavy-tailed distribution of response in online discourse to strategic communications.

Many natural language processing (NLP) tasks rely on labeled data to train machine learning models to achieve high performance. However, data annotation can be a time-consuming and expensive process, especially when the task involves a large amount of data or requires specialized domains. Recently, GPT-3.5 series models have demonstrated remarkable few-shot and zero-shot ability across various NLP tasks. In this paper, we first claim that large language models (LLMs), such as GPT-3.5, can serve as an excellent crowdsourced annotator by providing them with sufficient guidance and demonstrated examples. To make LLMs to be better annotators, we propose a two-step approach, 'explain-then-annotate'. To be more precise, we begin by creating prompts for every demonstrated example, which we subsequently utilize to prompt a LLM to provide an explanation for why the specific ground truth answer/label was chosen for that particular example. Following this, we construct the few-shot chain-of-thought prompt with the self-generated explanation and employ it to annotate the unlabeled data. We conduct experiments on three tasks, including user input and keyword relevance assessment, BoolQ and WiC. The annotation results from GPT-3.5 surpasses those from crowdsourced annotation for user input and keyword relevance assessment. Additionally, for the other two tasks, GPT-3.5 achieves results that are comparable to those obtained through crowdsourced annotation.

As a combination of visual and audio signals, video is inherently multi-modal. However, existing video generation methods are primarily intended for the synthesis of visual frames, whereas audio signals in realistic videos are disregarded. In this work, we concentrate on a rarely investigated problem of text guided sounding video generation and propose the Sounding Video Generator (SVG), a unified framework for generating realistic videos along with audio signals. Specifically, we present the SVG-VQGAN to transform visual frames and audio melspectrograms into discrete tokens. SVG-VQGAN applies a novel hybrid contrastive learning method to model inter-modal and intra-modal consistency and improve the quantized representations. A cross-modal attention module is employed to extract associated features of visual frames and audio signals for contrastive learning. Then, a Transformer-based decoder is used to model associations between texts, visual frames, and audio signals at token level for auto-regressive sounding video generation. AudioSetCap, a human annotated text-video-audio paired dataset, is produced for training SVG. Experimental results demonstrate the superiority of our method when compared with existing textto-video generation methods as well as audio generation methods on Kinetics and VAS datasets.

There is no doubt that GPT-4, the latest iteration of the artificial-intelligence engine created by the company OpenAI, is innovative and cool. It can create a poem in the style of Basho, spell out the chord progression and time signature for a simple tune, and provide a seven-step recipe for a peanut-butter-and-jelly sandwich. When I asked it to write a musical about a narcissistic politician who holds the fate of the world in his hands, it delivered a story in two acts, with a protagonist named Alex Sterling who "navigates a maze of power, manipulation, and the consequences of his decisions," as he sings "Narcissus in the Mirror," "The Price of Power," and about a dozen other invented songs. Those songs appear to have been created out of thin air; certainly, no human conceived them. Still, Alex's story, which "explores themes of self-discovery, redemption, and the responsibility of leadership," is quite familiar.

A new shortcut allows Apple users to'replace' Siri with the revolutionary AI program ChatGPT on their iPhones and iPads. The technique effectively upgrades Siri with the full power of the chatbot, giving it the ability to dictate emails, suggest recipes and even write song lyrics from scratch with a basic prompt of just a few words. The device will continue to respond using Apple's voice -- but it draws on the chatbot's algorithms and is no longer limited in length. Only people who have a premium developer version of ChatGPT will be able to replace their Siri with the AI model, though. Here's how to activate it: The shortcut has been devised by AI software builder Wrigley McKay.