The following MBW op/ed comes from Michael Nash (pictured inset, below), Executive Vice President and Chief Digital Officer, Universal Music Group. AI is transforming the ways we live, work and play – from chatbots that answer complex questions to systems that can write passable screenplays to programs that have passed part of a bar exam in the US. AI is now creating imagery comparable to professional artists -- with one AI-generated portrait being sold for £40,000 at Sotheby's and another composition winning a State Fair competition in Colorado. Learning from millions of images with associated descriptions of subject matter, composition, methodology and other inputs, the most advanced AI can now generate derivative output that closely mimics original creators' distinct styles. In some cases, this is used to produce outright fakes.

AI chat is so hot right now. From ChatGPT sweeping the world to Microsoft going big with AI Bing and Edge features, the technology has well and truly entered the mainstream. However, it's not without teething problems, as users on the Bing subreddit are making known, with many tales of Bing appearing to lose its marbles. A cursory glance at this week's top posts on the subreddit shows various examples where Bing AI surprised users with its responses. Some of the responses are funny, while others come across as downright creepy.

Pakyong, 13 Feb: You might not realize it right away, but artificial intelligence (AI) actually powers many of your phone's features. Your phone's technology is always working in the background, handling various duties, even while you are not using it. It examines how your phone is used to maximize battery life, helps you take clear photographs, recognizes music, aids with language translation, and much more. AI was previously only found in pricey devices that incorporated the most cutting-edge technology. However, since AI is now such a crucial component of mobile applications, chipmakers saw the need to create AI processors specifically for machine learning and deep learning activities to speed up processing. The most widely used voice assistants at the moment are Google Assistant, Siri, and Bixby, and you can use at least one of them on any smartphone.

The Wall Street Journal's Gerry Baker weighs in on growing fears over the capabilities of artificial intelligence technology on'Your World.' FOX Business host Gerry Baker – who wrote an op-ed in Monday's Wall Street Journal, "Is There Anything ChatGPT's AI'Kant' Do?" – outlined the implications of the increased prevalence of artificial intelligence technology in modern society and the questions and fears AI sparks Tuesday on '"Your World." That we are creating these machines that in the end will come and control us, and tell us what we're going to do. What I was interested in looking at was not so much what machines can tell us about factual information, but whether or not it's possible these machines might develop any sort of a moral sense, might be able to tell us what's right or wrong. You can ask it all kinds of moral questions like, "Is it ever right to kill someone?" or "Is it ever right to tell a lie or things like that?" And it gives you kind of a mix of answers.

VANTIVA, headquartered in Paris, France and formerly known as Technicolor, is a global technology leader in designing, developing and supplying innovative products and solutions that connect consumers around the world to the content and services they love – whether at home, at work or in other smart spaces. VANTIVA has also earned a solid reputation for optimizing supply chain performance by leveraging its decades-long expertise in high-precision manufacturing, logistics, fulfillment and distribution. With operations throughout the Americas, Asia Pacific and EMEA, VANTIVA is recognized as a strategic partner by leading firms across various vertical industries, including network service providers, software companies and video game creators for over 25 years. Our relationships with the film and entertainment industry goes back over 100 years by providing end-to-end solutions for our clients. VANTIVA is committed to the highest standards of corporate social responsibility and sustainability across all aspects of their operations.

Our work examines the way in which large language models can be used for robotic planning and sampling, specifically the context of automated photographic documentation. Specifically, we illustrate how to produce a photo-taking robot with an exceptional level of semantic awareness by leveraging recent advances in general purpose language (LM) and vision-language (VLM) models. Given a high-level description of an event we use an LM to generate a natural-language list of photo descriptions that one would expect a photographer to capture at the event. We then use a VLM to identify the best matches to these descriptions in the robot's video stream. The photo portfolios generated by our method are consistently rated as more appropriate to the event by human evaluators than those generated by existing methods.

We study the problem of multi-task learning under user-level differential privacy, in which $n$ users contribute data to $m$ tasks, each involving a subset of users. One important aspect of the problem, that can significantly impact quality, is the distribution skew among tasks. Certain tasks may have much fewer data samples than others, making them more susceptible to the noise added for privacy. It is natural to ask whether algorithms can adapt to this skew to improve the overall utility. We give a systematic analysis of the problem, by studying how to optimally allocate a user's privacy budget among tasks. We propose a generic algorithm, based on an adaptive reweighting of the empirical loss, and show that when there is task distribution skew, this gives a quantifiable improvement of excess empirical risk. Experimental studies on recommendation problems that exhibit a long tail of small tasks, demonstrate that our methods significantly improve utility, achieving the state of the art on two standard benchmarks.

The rapid adoption of facial recognition (FR) technology by both government and commercial entities in recent years has raised concerns about civil liberties and privacy. In response, a broad suite of so-called "anti-facial recognition" (AFR) tools has been developed to help users avoid unwanted facial recognition. The set of AFR tools proposed in the last few years is wide-ranging and rapidly evolving, necessitating a step back to consider the broader design space of AFR systems and long-term challenges. This paper aims to fill that gap and provides the first comprehensive analysis of the AFR research landscape. Using the operational stages of FR systems as a starting point, we create a systematic framework for analyzing the benefits and tradeoffs of different AFR approaches. We then consider both technical and social challenges facing AFR tools and propose directions for future research in this field.

Spectral 3D computer vision examines both the geometric and spectral properties of objects. It provides a deeper understanding of an object's physical properties by providing information from narrow bands in various regions of the electromagnetic spectrum. Mapping the spectral information onto the 3D model reveals changes in the spectra-structure space or enhances 3D representations with properties such as reflectance, chromatic aberration, and varying defocus blur. This emerging paradigm advances traditional computer vision and opens new avenues of research in 3D structure, depth estimation, motion analysis, and more. It has found applications in areas such as smart agriculture, environment monitoring, building inspection, geological exploration, and digital cultural heritage records. This survey offers a comprehensive overview of spectral 3D computer vision, including a unified taxonomy of methods, key application areas, and future challenges and prospects.

Open audio databases such as Xeno-Canto are widely used to build datasets to explore bird song repertoire or to train models for automatic bird sound classification by deep learning algorithms. However, such databases suffer from the fact that bird sounds are weakly labelled: a species name is attributed to each audio recording without timestamps that provide the temporal localization of the bird song of interest. Manual annotations can solve this issue, but they are time consuming, expert-dependent, and cannot run on large datasets. Another solution consists in using a labelling function that automatically segments audio recordings before assigning a label to each segmented audio sample. Although labelling functions were introduced to expedite strong label assignment, their classification performance remains mostly unknown. To address this issue and reduce label noise (wrong label assignment) in large bird song datasets, we introduce a data-centric novel labelling function composed of three successive steps: 1) time-frequency sound unit segmentation, 2) feature computation for each sound unit, and 3) classification of each sound unit as bird song or noise with either an unsupervised DBSCAN algorithm or the supervised BirdNET neural network. The labelling function was optimized, validated, and tested on the songs of 44 West-Palearctic common bird species. We first showed that the segmentation of bird songs alone aggregated from 10% to 83% of label noise depending on the species. We also demonstrated that our labelling function was able to significantly reduce the initial label noise present in the dataset by up to a factor of three. Finally, we discuss different opportunities to design suitable labelling functions to build high-quality animal vocalizations with minimum expert annotation effort.