In this paper we introduce and discuss an approach for multi-agent-oriented visual programming. This aims at enabling individuals without programming experience but with knowledge in specific target domains to design and (re)configure autonomous software. We argue that, compared to procedural programming, it should be simpler for users to create programs when agent abstractions are employed. The underlying rationale is that these abstractions, and specifically the belief-desire-intention architecture that is aligned with human practical reasoning, match more closely with people's everyday experience in interacting with other agents and artifacts in the real world. On top of this, we designed and implemented a visual programming system for agents that hides the technicalities of agent-oriented programming using a blocks-based visual development environment that is built on the JaCaMo platform. To further validate the proposed solution, we integrate the Web of Things (WoT) to let users create autonomous behaviour on top of physical mashups of devices, following the trends in industrial end-user programming. Finally, we report on a pilot user study where we verified that novice users are indeed able to make use of this development environment to create multi-agent systems to solve simple automation tasks.

What if the patterns hidden within dialogue reveal more about communication than the words themselves? We introduce Conversational DNA, a novel visual language that treats any dialogue -- whether between humans, between human and AI, or among groups -- as a living system with interpretable structure that can be visualized, compared, and understood. Unlike traditional conversation analysis that reduces rich interaction to statistical summaries, our approach reveals the temporal architecture of dialogue through biological metaphors. Linguistic complexity flows through strand thickness, emotional trajectories cascade through color gradients, conversational relevance forms through connecting elements, and topic coherence maintains structural integrity through helical patterns. Through exploratory analysis of therapeutic conversations and historically significant human-AI dialogues, we demonstrate how this visualization approach reveals interaction patterns that traditional methods miss. Our work contributes a new creative framework for understanding communication that bridges data visualization, human-computer interaction, and the fundamental question of what makes dialogue meaningful in an age where humans increasingly converse with artificial minds.

A quick internet search on this topic will usually produce images of smiling, or perplexed, people looking into a computer. Often these people are surrounded by an array of intimidating holograms associated with spatial computing that barely yet exist in our real working lives. In many cases, images of a white, male hand – one finger extended – meets the elegant finger of a white, humanoid robot. But this is not what really happens at work. We know that visualising the future is hard, because we've used some of these images at the Digit Centre ourselves in the past.

With the introduction of transformer-based models for vision and language tasks, such as LLaVA and Chameleon, there has been renewed interest in the discrete tokenized representation of images. These models often treat image patches as discrete tokens, analogous to words in natural language, learning joint alignments between visual and human languages. However, little is known about the statistical behavior of these visual languages - whether they follow similar frequency distributions, grammatical structures, or topologies as natural languages. In this paper, we take a natural-language-centric approach to analyzing discrete visual languages and uncover striking similarities and fundamental differences. We demonstrate that, although visual languages adhere to Zipfian distributions, higher token innovation drives greater entropy and lower compression, with tokens predominantly representing object parts, indicating intermediate granularity. We also show that visual languages lack cohesive grammatical structures, leading to higher perplexity and weaker hierarchical organization compared to natural languages. Finally, we demonstrate that, while vision models align more closely with natural languages than other models, this alignment remains significantly weaker than the cohesion found within natural languages. Through these experiments, we demonstrate how understanding the statistical properties of discrete visual languages can inform the design of more effective computer vision models.

Artificial intelligence startup OpenAI has been teasing its new AI video generator, Sora, on social media in recent weeks. Last week, it revealed that it had also given actors and directors in Hollywood a first look at the technology – and a chance to try it out – before Sora is launched publicly. OpenAI published a blog post on March 24 titled Sora's First Impressions, showcasing the work that several creative studios and directors had produced using the video generator. Some media experts speculate that Sora will be extremely disruptive to the film creative industry. Al Jazeera spoke to one executive who works in Hollywood, who asked us not to reveal his identity due to the sensitive nature of the subject.

We introduce text2fabric, a novel dataset that links free-text descriptions to various fabric materials. The dataset comprises 15,000 natural language descriptions associated to 3,000 corresponding images of fabric materials. Traditionally, material descriptions come in the form of tags/keywords, which limits their expressivity, induces pre-existing knowledge of the appropriate vocabulary, and ultimately leads to a chopped description system. Therefore, we study the use of free-text as a more appropriate way to describe material appearance, taking the use case of fabrics as a common item that non-experts may often deal with. Based on the analysis of the dataset, we identify a compact lexicon, set of attributes and key structure that emerge from the descriptions. This allows us to accurately understand how people describe fabrics and draw directions for generalization to other types of materials. We also show that our dataset enables specializing large vision-language models such as CLIP, creating a meaningful latent space for fabric appearance, and significantly improving applications such as fine-grained material retrieval and automatic captioning.

Visual language data such as plots, charts, and infographics are ubiquitous in the human world. However, state-of-the-art vision-language models do not perform well on these data. We propose MatCha (Math reasoning and Chart derendering pretraining) to enhance visual language models' capabilities in jointly modeling charts/plots and language data. Specifically, we propose several pretraining tasks that cover plot deconstruction and numerical reasoning which are the key capabilities in visual language modeling. We perform the MatCha pretraining starting from Pix2Struct, a recently proposed image-to-text visual language model. On standard benchmarks such as PlotQA and ChartQA, the MatCha model outperforms state-of-the-art methods by as much as nearly 20%. We also examine how well MatCha pretraining transfers to domains such as screenshots, textbook diagrams, and document figures and observe overall improvement, verifying the usefulness of MatCha pretraining on broader visual language tasks.

Recently, "AI" machine-learning technologies have been creeping their way into artistic fields in both entertaining and harmful ways. While some AI content creators are just making videos for harmless fun, others, like the creators of a recent AI-generated anime short, wrongfully believe they've democratized the animation industry when they've really just come up with a more technologically demanding method of plagiarizing other artists. Earlier this week, Corridor Digital, a Los Angeles-based production studio that creates pop culture YouTube videos, uploaded a video called "Anime Rock, Paper, Scissors." Written and directed by Niko Pueringer and Sam Gorski, it revolves around two twins vying for the throne left vacant by their recently deceased father. By leveraging the machine-learning text-to-image model Stable Diffusion, Corridor Digital gave camera footage filmed in front of a green screen a dramatic anime-like appearance.

It is well recognized that sensory perceptions and language have interconnections through numerous studies in psychology, neuroscience, and sensorial linguistics. Set in this rich context we ask whether the use of sensorial language in writings is part of linguistic style? This question is important from the view of stylometrics research where a rich set of language features have been explored, but with insufficient attention given to features related to sensorial language. Taking this as the goal we explore several angles about sensorial language and style in collections of lyrics, novels, and poetry. We find, for example, that individual use of sensorial language is not a random phenomenon; choice is likely involved. Also, sensorial style is generally stable over time - the shifts are extremely small. Moreover, style can be extracted from just a few hundred sentences that have sensorial terms. We also identify representative and distinctive features within each genre. For example, we observe that 4 of the top 6 representative features in novels collection involved individuals using olfactory language where we expected them to use non-olfactory language.

From abstract art, digital painting, complex sculpture, architectural visualisation or 5 years old hand drawing, whatever you ask for, the AI makes it, or at least tries its best to. Does that cheapen the value of someone who puts their hands in researching, planning, sketching, developing their artworks; versus an AI just learning that from having studied millions of existing images and figuring out what we humans like to look at, and replicating that in mere minutes? You can create artwork without specifying colour or even form of your subject, and simply allowing the AI to take the reins, while you only provide inspirations and mood you wanted to achieve. It's not perfect though, as you will quickly notice; it can not, at the moment at least, understand nuance interactions between human culture, and everything that is presented in the resulting image is based on learning from what people created. Allowing it to run without supervision will in turn create a visual language in our society that might encourage bigotry and biases, and limit true creativity that inspire progression through diversity.