We introduce new methods of staining and locking computer vision models, to protect their owners' intellectual property. Staining, also known as watermarking, embeds secret behaviour into a model which can later be used to identify it, while locking aims to make a model unusable unless a secret trigger is inserted into input images. Unlike existing methods, our algorithms can be used to stain and lock pre-trained models without requiring fine-tuning or retraining, and come with provable, computable guarantees bounding their worst-case false positive rates. The stain and lock are implemented by directly modifying a small number of the model's weights and have minimal impact on the (unlocked) model's performance. Locked models are unlocked by inserting a small `trigger patch' into the corner of the input image. We present experimental results showing the efficacy of our methods and demonstrating their practical performance on a variety of computer vision models.

Driven by inherent uncertainty and the sim-to-real gap, robust reinforcement learning (RL) seeks to improve resilience against the complexity and variability in agent-environment sequential interactions. Despite the existence of a large number of RL benchmarks, there is a lack of standardized benchmarks for robust RL. Current robust RL policies often focus on a specific type of uncertainty and are evaluated in distinct, one-off environments. In this work, we introduce Robust-Gymnasium, a unified modular benchmark designed for robust RL that supports a wide variety of disruptions across all key RL components-agents' observed state and reward, agents' actions, and the environment. Offering over sixty diverse task environments spanning control and robotics, safe RL, and multi-agent RL, it provides an open-source and user-friendly tool for the community to assess current methods and foster the development of robust RL algorithms. In addition, we benchmark existing standard and robust RL algorithms within this framework, uncovering significant deficiencies in each and offering new insights.

Distributed control increases system scalability, flexibility, and redundancy. Foundational to such decentralisation is consensus formation, by which decision-making and coordination are achieved. However, decentralised multi-agent systems are inherently vulnerable to disruption. To develop a resilient consensus approach, inspiration is taken from the study of social systems and their dynamics; specifically, the Deffuant Model. A dynamic algorithm is presented enabling efficient consensus to be reached with an unknown number of disruptors present within a multi-agent system. By inverting typical social tolerance, agents filter out extremist non-standard opinions that would drive them away from consensus. This approach allows distributed systems to deal with unknown disruptions, without knowledge of the network topology or the numbers and behaviours of the disruptors. A disruptor-agnostic algorithm is particularly suitable to real-world applications where this information is typically unknown. Faster and tighter convergence can be achieved across a range of scenarios with the social dynamics inspired algorithm, compared with standard Mean-Subsequence-Reduced-type methods.

Meet Madison and Jackson, the AI narrators or "digital voices" soon to be reading some of the audiobooks on Apple Books. They sound nothing like Siri or Alexa or the voice telling you about the unexpected item in the bagging area of your supermarket checkout. They sound warm, natural, animated. With their advanced levels of realism, Apple's new AI voices present the genuine possibility that the listener will be unaware of their artificiality. Even the phrase used in Apple's catalogues of digitally-narrated audiobooks – "this is an Apple Books audiobook narrated by a digital voice based on a human narrator" – is ambiguous.

Adversarial example is a rising way of protecting facial privacy security from deepfake modification. To prevent massive facial images from being illegally modified by various deepfake models, it is essential to design a universal deepfake disruptor. However, existing works treat deepfake disruption as an End-to-End process, ignoring the functional difference between feature extraction and image reconstruction, which makes it difficult to generate a cross-model universal disruptor. In this work, we propose a novel Feature-Output ensemble UNiversal Disruptor (FOUND) against deepfake networks, which explores a new opinion that considers attacking feature extractors as the more critical and general task in deepfake disruption. We conduct an effective two-stage disruption process. We first disrupt multi-model feature extractors through multi-feature aggregation and individual-feature maintenance, and then develop a gradient-ensemble algorithm to enhance the disruption effect by simplifying the complex optimization problem of disrupting multiple End-to-End models. Extensive experiments demonstrate that FOUND can significantly boost the disruption effect against ensemble deepfake benchmark models. Besides, our method can fast obtain a cross-attribute, cross-image, and cross-model universal deepfake disruptor with only a few training images, surpassing state-of-the-art universal disruptors in both success rate and efficiency.

The human questioner may not be sure "I'm good" is a fact. But artificial intelligence (AI) and machine learning (ML) engineers claim that new technologies known as "emotion AI" can observe people and accurately assess how they're feeling. AI is all around us, whether we know it or not. It enables mainstream social media platforms to pitch smart personalization; virtual healthcare assistants to help nurses with burnout prevention; integrated smart assistants in electronic devices to perform various tasks, and much more. Artificial emotional intelligence systems go further.

Recent advances in machine learning (ML) and artificial intelligence (AI) technologies are helping enterprises across industries quickly move from their use cases from the pilot stage to production and operationalization. According to a report by McKinsey & Company, by 2030, businesses that fully absorb AI could double their cash flow, while companies that don't could see a 20% decline*. As market pressures increase, data leaders must move beyond point solutions and assess their entire data science and ML ecosystem when considering new ways to leverage technology and reduce time to market. While the number of available ML frameworks has exploded, developing models remains a complex task involving data acquisition, pre-processing, feature selection, modelling, testing, tuning, deployment, etc. Data science teams need a unified platform that encompasses the complete ML lifecycle, fosters collaboration, and centralizes all data science projects in a secure repository.

The insurance industry in Asia has changed very little in the last few decades. Insurance products have largely been underwritten on the basis of aggregated data with a cost-plus mindset since the advent of the semiconductor! In this period, we have moved from punched card computers to mainframe computers to personal computers to handheld devices. We have transitioned from analog information silos to a highly connected internet-enabled world with near-infinite access to information and near-infinite devices which communicate globally. With the exception of insurance, every other financial product has joined the information age.

When I was a kid, one of my cousins got a DVD player for Christmas. My family watched in awe as he connected it to the TV and inserted a skinny little disc which miraculously played a movie. The next day, my mom wanted to rush out to the store to buy one. VHS' worked fine in my mind. I couldn't understand what the big deal was, so I asked my Mom "Why?"

Health and human services (HHS) agencies often struggle to serve some of society's most needy populations. At many HHS agencies today, tight budgets limit the size of the workforce, even as the volume of caseloads continues to grow. That imbalance makes it hard to provide efficient and effective solutions to address the critical needs of individuals and families, and can leave employees feeling stressed and overworked. Those same employees may also see few opportunities for career development or advancement. High rates of turnover can put a steady stream of inexperienced staff into critical jobs with little training to prepare them.