An invading force, 10,000 strong, marches through a storm toward a fortress built into the side of a mountain. From a distance, the combatants look like ants -- menacing and alarmingly well organized. They rattle their spears and snarl through teeth that have never known modern dentistry, and when lightning strikes, it reveals their sheer numbers. Volleys of arrows fly, swords find their way to the weak spots around breast plates. Bodies on both sides hit the ground. This bloody affair is the Battle of Helm's Deep, from The Lord of the Rings: The Two Towers.

A guy with no background in film or artificial intelligence is working on making an entire movie -- in a provocative attempt to demonstrate that generative AI art models can open ambitious levels of filmmaking to the masses. German tech entrepreneur Fabian Stelzer told PC Magazine that his 70s-style sci-fi film "Salt" will feature all artificial voices except his own, and that generative models will also create the film's footage and sound effects, too. From what Stelzer's been posting to Twitter to tease his progress, the film apears to be about space travelers who encounter a planet with an overgrowth of bizarre salt -- but beyond that basic premise, the neuroscientist is allowing his Twitter followers to determine which specific directions the film will go in a sort of "Choose Your Own Adventure" style for the AI age. "I definitely want to have a'Director's Cut' at some point, or a'Community's Cut,' but the real goal is to transcend the medium of film into something new," he said. "Like enable everyone in the community to eventually use a model that lets them write their own scenes."

The growing societal dependence on social media and user generated content for news and information has increased the influence of unreliable sources and fake content, which muddles public discourse and lessens trust in the media. Validating the credibility of such information is a difficult task that is susceptible to confirmation bias, leading to the development of algorithmic techniques to distinguish between fake and real news. However, most existing methods are challenging to interpret, making it difficult to establish trust in predictions, and make assumptions that are unrealistic in many real-world scenarios, e.g., the availability of audiovisual features or provenance. In this work, we focus on fake news detection of textual content using interpretable features and methods. In particular, we have developed a deep probabilistic model that integrates a dense representation of textual news using a variational autoencoder and bi-directional Long Short-Term Memory (LSTM) networks with semantic topic-related features inferred from a Bayesian admixture model. Extensive experimental studies with 3 real-world datasets demonstrate that our model achieves comparable performance to state-of-the-art competing models while facilitating model interpretability from the learned topics. Finally, we have conducted model ablation studies to justify the effectiveness and accuracy of integrating neural embeddings and topic features both quantitatively by evaluating performance and qualitatively through separability in lower dimensional embeddings.

… Intelligence and Machine Learning or Deep Learning. Databricks Lakehouse stores your data in your comprehensively scalable cloud object store, …

Furthermore, there is deep learning and reinforcement learning. Such procedures constitute the backbone of big data analytics.

Here's Bowie's "Space Oddity," created by YouTube user aidontknow. Their process involved typing the song lyrics into the AI prompt, verse by verse, to generate each image for the video. Sure, the likes of Midjourney, Dall-E, and Stable Fusion will one day leave the uncanny valley behind and provide a never-ending source of perfect stock photos that will look as convincing as the real thing. But there's also a big downside to all this fun: These tools are essentially mining the existing work of designers and photographers who receive no AI licensing fees in a rapidly evolving field that will have negative downstream effects for the industry. Right now, this video demonstrates that AI's often otherworldly results are still limited: well-suited for poetry and fantastic literature, but not anything too literal.

Crosstalk is a traditional Chinese theatrical performance art. It is commonly performed by two performers in the form of a dialogue. With the typical features of dialogues, crosstalks are also designed to be hilarious for the purpose of amusing the audience. In this study, we introduce CrossDial, the first open-source dataset containing most classic Chinese crosstalks crawled from the Web. Moreover, we define two new tasks, provide two benchmarks, and investigate the ability of current dialogue generation models in the field of crosstalk generation. The experiment results and case studies demonstrate that crosstalk generation is challenging for straightforward methods and remains an interesting topic for future works.

Multi-modal knowledge graphs (MKGs) include not only the relation triplets, but also related multi-modal auxiliary data (i.e., texts and images), which enhance the diversity of knowledge. However, the natural incompleteness has significantly hindered the applications of MKGs. To tackle the problem, existing studies employ the embedding-based reasoning models to infer the missing knowledge after fusing the multi-modal features. However, the reasoning performance of these methods is limited due to the following problems: (1) ineffective fusion of multi-modal auxiliary features; (2) lack of complex reasoning ability as well as inability to conduct the multi-hop reasoning which is able to infer more missing knowledge. To overcome these problems, we propose a novel model entitled MMKGR (Multi-hop Multi-modal Knowledge Graph Reasoning). Specifically, the model contains the following two components: (1) a unified gate-attention network which is designed to generate effective multi-modal complementary features through sufficient attention interaction and noise reduction; (2) a complementary feature-aware reinforcement learning method which is proposed to predict missing elements by performing the multi-hop reasoning process, based on the features obtained in component (1). The experimental results demonstrate that MMKGR outperforms the state-of-the-art approaches in the MKG reasoning task.

Self-supervised methods have emerged as a promising avenue for representation learning in the recent years since they alleviate the need for labeled datasets, which are scarce and expensive to acquire. Contrastive methods are a popular choice for self-supervision in the audio domain, and typically provide a learning signal by forcing the model to be invariant to some transformations of the input. These methods, however, require measures such as negative sampling or some form of regularisation to be taken to prevent the model from collapsing on trivial solutions. In this work, instead of invariance, we propose to use equivariance as a self-supervision signal to learn audio tempo representations from unlabelled data. We derive a simple loss function that prevents the network from collapsing on a trivial solution during training, without requiring any form of regularisation or negative sampling. Our experiments show that it is possible to learn meaningful representations for tempo estimation by solely relying on equivariant self-supervision, achieving performance comparable with supervised methods on several benchmarks. As an added benefit, our method only requires moderate compute resources and therefore remains accessible to a wide research community.

For companies using artificial intelligence-powered security technology, protecting users is not only ethical, but it also makes business sense.