This concern was likely well-founded. After all, Poincaré, von Neumann, Gauss, and Feynman have all been credited with remarkable contributions to mathematics and physics owing in large part to their tremendously fine numerical intuition, itself iteratively refined through a lifetime of obsessive internal calculation. More recently, philosophers and scientists have begun to wrestle with a set of epistemological concerns that arise from the use of forms of computation in science that are far more powerful than mere calculators. For instance, it has been argued that increasingly routine reliance on artificial intelligence leads scientists to adopt beliefs that are not fully justifiable due to the complexity and opacity of the models that support them. Moreover, it has been argued that the epistemic opacity of these systems limits scientific understanding of the phenomena under investigation, perhaps raising a dark veil between the practice of science and scientific knowledge.

Announced on February 15, 2024, it instantly caught the public's attention by demonstrating the ability to generate dynamic and realistic video clips from text prompts, similar to how OpenAI's DALL-E generates images from text. While Sora is still in a pre-release phase, its potential to revolutionize content creation and disrupt various industries be it media, entertainment, or advertising, has already ignited discussions across online communities. Subreddits such as r/OpenAI, r/technology and r/ChatGPT have emerged as epicentres for technology enthusiasts and critics to openly discuss and share narratives about the latest advancements in AI technologies. Previous studies have explored public perceptions of large language models like ChatGPT and image generators such as DALL-E through analysing online forums. For instance, Talafidaryani and Mora (2024) employed topic modeling techniques on Reddit data to uncover dominant themes surrounding ChatGPT, including its capabilities, limitations, and ethical considerations. Similarly, Zhou and Nabus (2023) investigated discussions on DALL-E, revealing discourse on creative applications, risks of misuse, and comparisons to human artists. However, due to Sora's relatively recent emergence, there is still a lack of research on the narratives and themes emerging from Reddit conversations about this novel technology. By conducting topic modeling analysis on a large corpus of Reddit comments, the study aims to feel that gap and uncover the main topics and themes users are discussing about Sora. These narratives can provide valuable insights into public perceptions, areas of excitement, as well as societal and ethical concerns surrounding around the advent of new generative AI technologies.

Deep generative models have the potential to fundamentally change the way we create high-fidelity digital content but are often hard to control. Prompting a generative model is a promising recent development that in principle enables end-users to creatively leverage zero-shot and few-shot learning to assign new tasks to an AI ad-hoc, simply by writing them down. However, for the majority of end-users writing effective prompts is currently largely a trial and error process. To address this, we discuss the key opportunities and challenges for interactive creative applications that use prompting as a new paradigm for Human-AI interaction. Based on our analysis, we propose four design goals for user interfaces that support prompting. We illustrate these with concrete UI design sketches, focusing on the use case of creative writing. The research community in HCI and AI can take these as starting points to develop adequate user interfaces for models capable of zero- and few-shot learning.

Artificial intelligence (AI) is increasingly utilized in synthesizing visuals, texts, and audio. These AI-based works, often derived from neural networks, are entering the mainstream market, as digital paintings, songs, books, and others. We conceptualize both existing and future human-in-the-loop (HITL) approaches for creative applications and to develop more expressive, nuanced, and multimodal models. Particularly, how can our expertise as curators and collaborators be encoded in AI models in an interactive manner? We examine and speculate on long term implications for models, interfaces, and machine creativity. Our selection, creation, and interpretation of AI art inherently contain our emotional responses, cultures, and contexts. Therefore, the proposed HITL may help algorithms to learn creative processes that are much harder to codify or quantify. We envision multimodal HITL processes, where texts, visuals, sounds, and other information are coupled together, with automated analysis of humans and environments. Overall, these HITL approaches will increase interaction between human and AI, and thus help the future AI systems to better understand our own creative and emotional processes.

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Session 1: Introduction to Tensorflow We'll cover the importance of data with machine and deep learning algorithms, the basics of creating a dataset, how to preprocess datasets, then jump into Tensorflow, a library for creating computational graphs built by Google Research. We'll learn the basic components of Tensorflow and see how to use it to filter images. Session 2: Training A Network W/ Tensorflow We'll see how neural networks work, how they are "trained", and see the basic components of training a neural network. We'll then build our first neural network and use it for a fun application of teaching a neural network how to paint an image. Session 3: Unsupervised And Supervised Learning This session goes deep.

Playing the world's saddest song on the world's tiniest violin is no longer just a sarcastic dream. Using a tiny-radar based chip, the team at Design I/O built a device that detects the movements of this unsympathetic gesture and transforms them into a violin solo. This innovation is based on Google's Project Soli, which uses invisible radar emanating from a chip to recognize finger movements and broad beam radar to detect movement, velocity and distance. Using a tiny-radar based chip, the team at Design I/O has built a device that detects movements of the unsympathetic gesture and transforms them into a violin solo. This invention is based on Google's Project Soli - a tiny radar chip that detects hand gestures Using a tiny-radar based chip, the team at Design I/O has built a device that detects movements of the unsympathetic gesture and transforms them into a violin solo.