The convergence of Information Technology and Operational Technology has exposed Industrial Control Systems to adaptive, intelligent adversaries that render static defenses obsolete. This paper introduces the Adversarial Resilience Co-evolution (ARC) framework, addressing the "Trinity of Trust" comprising model fidelity, data integrity, and analytical resilience. ARC establishes a co-evolutionary arms race within a Fortified Secure Digital Twin (F-SCDT), where a Deep Reinforcement Learning "Red Agent" autonomously discovers attack paths while an ensemble-based "Blue Agent" is continuously hardened against these threats. Experimental validation on the Tennessee Eastman Process (TEP) and Secure Water Treatment (SWaT) testbeds demonstrates superior performance in detecting novel attacks, with F1-scores improving from 0.65 to 0.89 and detection latency reduced from over 1200 seconds to 210 seconds. A comprehensive ablation study reveals that the co-evolutionary process itself contributes a 27% performance improvement. By integrating Explainable AI and proposing a Federated ARC architecture, this work presents a necessary paradigm shift toward dynamic, self-improving security for critical infrastructure.

Is an artificial intelligence revolution about to transform mathematics? Some prominent mathematicians think so, thanks to automated tools that can help write proofs suddenly showing impressive leaps in capability, with the potential to change the way maths research is done. Around 100 of the world's top mathematicians gathered at the University of Cambridge in June for a conference whose theme was based on whether computers might help mathematicians resolve some long-standing problems over how to check that their proofs were correct. This process, known as formalisation, doesn't necessarily have to involve artificial intelligence, and indeed a similar meeting held at Cambridge in 2017 made no mention of AI. But eight years later, AI has come on by leaps and bounds, most notably with the success of large language models powering tools like ChatGPT.

Interest modeling in recommender system has been a constant topic for improving user experience, and typical interest modeling tasks (e.g. multi-interest, long-tail interest and long-term interest) have been investigated in many existing works. However, most of them only consider one interest in isolation, while neglecting their interrelationships. In this paper, we argue that these tasks suffer from a common "interest amnesia" problem, and a solution exists to mitigate it simultaneously. We figure that long-term cues can be the cornerstone since they reveal multi-interest and clarify long-tail interest. Inspired by the observation, we propose a novel and unified framework in the retrieval stage, "Trinity", to solve interest amnesia problem and improve multiple interest modeling tasks. We construct a real-time clustering system that enables us to project items into enumerable clusters, and calculate statistical interest histograms over these clusters. Based on these histograms, Trinity recognizes underdelivered themes and remains stable when facing emerging hot topics. Trinity is more appropriate for large-scale industry scenarios because of its modest computational overheads. Its derived retrievers have been deployed on the recommender system of Douyin, significantly improving user experience and retention. We believe that such practical experience can be well generalized to other scenarios.

In a big sense, machine intelligence is a suprahuman techno-mind, cybernetic superintelligence, or global intelligence, being ontological, omniscient, omnipresent, omnipotent. Its practical definition as mimicking human brains, minds or intelligent behavior is naive and myopic, and guided by our anthropomorphic mentality and commercial interest. Broadly, 4OAI is about designing, developing and deploying real intelligence, without human biological limitations of power, memory, thinking and acting. So, General AI and ML is to demonstrate godly features, as omnipotence and omniscience and omnipresence, with the power to create a new environment, smart, intelligent and digital, easily manipulating and controlling any matter, energy and information. The AI Trinity of Reality, Data and Mind, in which the three parts, AI hardware, AI software, AI mindware, distinct, yet one "substance, essence or nature", is to replace the Holy Trinity.

The fast-changing customer demands in modern society seek flexibility, innovation and a rapid response from manufacturers and organisations that, in order to respond to market needs, are creating tools and processes in order to adopt an approach that welcomes change. That approach is found to be Agile Manufacturing – and the Trinity project is the magnet that connects every segment of agile with everyone involved, creating a network that supports people, organisations, production and processes. The main objective of TRINITY is to create a network of multidisciplinary and synergistic local digital innovation hubs (DIHs) composed of research centres, companies, and university groups that cover a wide range of topics that can contribute to agile production: advanced robotics as the driving force and digital tools, data privacy and cyber security technologies to support the introduction of advanced robotic systems in the production processes. The Trinity project is funded by Horizon 2020 the European Union research and innovation programme. Currently, Trinity brings together a network of 16 Digital Innovation Hubs (DIHs) and so far has 37 funded projects with 8.1 million euros in funding.

We present a no-code Artificial Intelligence (AI) platform called Trinity with the main design goal of enabling both machine learning researchers and non-technical geospatial domain experts to experiment with domain-specific signals and datasets for solving a variety of complex problems on their own. This versatility to solve diverse problems is achieved by transforming complex Spatio-temporal datasets to make them consumable by standard deep learning models, in this case, Convolutional Neural Networks (CNNs), and giving the ability to formulate disparate problems in a standard way, eg. With an intuitive user interface, a feature store that hosts derivatives of complex feature engineering, a deep learning kernel, and a scalable data processing mechanism, Trinity provides a powerful platform for domain experts to share the stage with scientists and engineers in solving business-critical problems. It enables quick prototyping, rapid experimentation and reduces the time to production by standardizing model building and deployment. In this paper, we present our motivation behind Trinity and its design along with showcasing sample applications to motivate the idea of lowering the bar to using AI.

When a star's variety of hair styles is the real star of a movie, you know it's a sign of trouble. So it is, unfortunately, with "The Matrix Resurrections," which makes poignant use of hair cuts and color to mark the eighteen years separating the new film from the last installment in the "Matrix" trilogy. Little else in the new film is as moving. The action picks up where the last one left off. There, Neo (Keanu Reeves), having saved the last human city, the underground realm of Zion, died from the effort.

Apple has been slowly but surely creating a name for itself in the low-code/no-code movement. This July, the Cupertino-based company announced the launch of Trinity AI, a no-code platform for complex spatial datasets. Trinity enables machine learning researchers and non-AI devs to tailor complex spatiotemporal datasets to fit deep learning models. Back in 2019, Apple revealed SwiftUI, a programming language that required much less coding than the Swift language. With the release of Trinity, Apple doubles down on its effort to significantly lower the threshold for non-devs and non-ML devs.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Apple has been slowly but surely creating a name for itself in the low-code/no-code movement. This July, the Cupertino-based company announced the launch of Trinity AI, a no-code platform for complex spatial datasets. Trinity enables machine learning researchers and non-AI devs to tailor complex spatiotemporal datasets to fit deep learning models. Back in 2019, Apple revealed SwiftUI, a programming language that required much less coding than the Swift language.