Generalisation and efficiency ofmodelrepairment In practice the numberoffailure casesmight be large or even infinite. Optimisation For all experiments, we employ the same training scheme unless otherwise stated.

Pandora's Box is a central problem in decision making under uncertainty that can model various real life scenarios. In this problem we are given n boxes, each with a fixed opening cost, and an unknown value drawn from a known distribution, only revealed if we pay the opening cost. Our goal is to find a strategy for opening boxes to minimize the sum of the value selected and the opening cost paid.In this work we revisit Pandora's Box when the value distributions are correlated, first studied in [CGT+20]. We show that the optimal algorithm for the independent case, given by Weitzman's rule, directly works for the correlated case. In fact, our algorithm results in significantly improved approximation guarantees compared to the previous work, while also being substantially simpler. We also show how to implement the rule given only sample access to the correlated distribution of values. Specifically, we find that a number of samples that is polynomial in the number of boxes is sufficient for the algorithm to work.

It used to be just a sci-fi nightmare scenario, but today, AI phishing is real, and it's costing companies millions. We've already touched upon this one, but the Hong Kong phishing scam that targeted an employee at Arup deserves a deeper dive. The employee was tricked by deepfake versions of her CFO and colleagues into transferring HK 200 million across 15 transactions. The case has been widely reported and confirmed by the Hong Kong police. Every face and voice was AI-generated.

We investigate reinforcement learning in the Game Of Hidden Rules (GOHR) environment, a complex puzzle in which an agent must infer and execute hidden rules to clear a 6$\times$6 board by placing game pieces into buckets. We explore two state representation strategies, namely Feature-Centric (FC) and Object-Centric (OC), and employ a Transformer-based Advantage Actor-Critic (A2C) algorithm for training. The agent has access only to partial observations and must simultaneously infer the governing rule and learn the optimal policy through experience. We evaluate our models across multiple rule-based and trial-list-based experimental setups, analyzing transfer effects and the impact of representation on learning efficiency.

Reinforcement learning based fine-tuning of large language models (LLMs) on human preferences has been shown to enhance both their capabilities and safety behavior. However, in cases related to safety, without precise instructions to human annotators, the data collected may cause the model to become overly cautious, or to respond in an undesirable style, such as being judgmental. Additionally, as model capabilities and usage patterns evolve, there may be a costly need to add or relabel data to modify safety behavior. We propose a novel preference modeling approach that utilizes AI feedback and only requires a small amount of human data. Our method, Rule Based Rewards (RBR), uses a collection of rules for desired or undesired behaviors (e.g.

SINGAPORE--China's top internet regulator proposed rules Tuesday to control artificial-intelligence tools similar to ChatGPT, putting it at the forefront of efforts by governments worldwide to tame the new technology amid concerns over the challenges it poses.

Machine learning, at its core, is concerned with algorithms that transform information into actionable intelligence. This fact makes machine learning well-suited to the present-day era of Big Data. Without machine learning, it would be nearly impossible to keep up with the massive stream of information. Given the growing prominence of R a cross-platform, zero-cost statistical programming environment there has never been a better time to start using machine learning. R offers a powerful but easy-to-learn set of tools that can assist you with finding data insights.

It is our human nature to tinker with rules, as anyone who has argued over dubious interpretations of the game Monopoly will attest. Rule-breaking may hold the key to giving us an edge over machines that, for all their emerging capabilities, are tied to fixed directives.

I've never been a gambler. Outcomes are determined more on luck than skill and that just makes me queasy. Sometimes, I feel like companies view #AI the same way, like they are hedging their bets and maybe even expecting defeat. Today I want you to weigh in. Why do you think that, according to a study by @BCG (Boston Consulting Group), only one in 10 companies have found success with AI? Seems like we should have better odds than that.

Artificial intelligence (AI) provides thorough data analysis, automates business processes, and engages with customers and employees. The adoption of AI has been particularly widespread in the business world. From workflow management to trend predictions, AI has numerous use cases. Sometimes, I feel like companies view AI the same way, like they are hedging their bets and maybe even expecting defeat. Today I want you to weigh in.