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El uso de la IA arroja una ventaja competitiva para el 72% de los ejecutivos españoles
En un contexto de hipercompetitividad, ninguna organización quiere quedarse atrás; como consecuencia, el empleo de nuevas tecnologías y soluciones innovadoras en el negocio se ha convertido en una auténtica baza estratégica. Así se desprende del último informe presentado por Experian, un documento que analiza las diferentes perspectivas de los responsables de la toma de decisiones…
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Attention, Spoiled Software Engineers: Take a Lesson from Google’s Programming Language
The language Go hails from an era when programmers had smaller egos and fewer commercial ambitions. My generation of strivers has a lot to learn.
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Can Cellular Automata Be Predicted Without Knowing the Grid? This AI Paper from MIT Unveils LifeGPT: A Topology-Agnostic Transformer Model for Cellular Automata
One of the main challenges in cellular automata (CA) systems, particularly in Conway’s Game of Life (Life), lies in predicting their emergent behavior without explicitly knowing the underlying grid topology. Life and other CA algorithms are computationally simple, yet they generate complex and unpredictable dynamics highly sensitive to initial conditions. This unpredictability complicates the development…
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DCMAC: Demand-Aware Customized Communication for Efficient Multi-Agent Reinforcement Learning
Collaborative Multi-Agent Reinforcement Learning (MARL) has emerged as a powerful approach in various domains, including traffic signal control, swarm robotics, and sensor networks. However, MARL faces significant challenges due to the complex interactions between agents, which introduce non-stationarity in the environment. This non-stationarity complicates the learning process and makes it difficult for agents to adapt…
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Some countries are ending support for EVs. Is it too soon?
Sales of new electric vehicles in Germany have plummeted, dropping nearly 37% in July 2024 from the same month one year ago. One of the main reasons traces back to mid-December 2023, when the German government gave less than one week’s notice before ending its subsidy program for electric vehicles. The program had given drivers…
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Advanced Privacy-Preserving Federated Learning (APPFL): An AI Framework to Address Data Heterogeneity, Computational Disparities, and Security Challenges in Decentralized Machine Learning
Federated learning (FL) is a powerful ML paradigm that enables multiple data owners to train models without centralizing their data collaboratively. This approach is particularly valuable in domains where data privacy is critical, such as healthcare, finance, and the energy sector. The core of federated learning lies in training models on decentralized data stored on…
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Google AI Introduces the Open Buildings 2.5D Temporal Dataset that Tracks Building Changes Across the Global South
Governments and humanitarian organizations need reliable data on building and infrastructure changes over time to manage urbanization, allocate resources, and respond to crises. However, many regions across the Global South need more access to timely and accurate data on buildings, making it difficult to track urban growth and infrastructure development. The absence of this data…
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This Research Paper Discusses Space-Efficient Algorithms for Integer Programming with Few Constraints
Integer Linear Programming (ILP) is the foundation of combinatorial optimization, which is extensively applied across numerous industries to resolve challenging decision-making issues. Under a set of linear equality constraints, an ILP aims to minimize or maximize a linear objective function, with the important condition that all variables must be integers. Even while ILP is an…
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HARP (Human-Assisted Regrouping with Permutation Invariant Critic): A Multi-Agent Reinforcement Learning Framework for Improving Dynamic Grouping and Performance with Minimal Human Intervention
Multi-agent reinforcement learning (MARL) is a field focused on developing systems where multiple agents cooperate to solve tasks that exceed the capabilities of individual agents. This area has garnered significant attention due to its relevance in autonomous vehicles, robotics, and complex gaming environments. The aim is to enable agents to work together efficiently, adapt to…
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Exploring Input Space Mode Connectivity: Insights into Adversarial Detection and Deep Neural Network Interpretability
Input space mode connectivity in deep neural networks builds upon research on excessive input invariance, blind spots, and connectivity between inputs yielding similar outputs. The phenomenon exists generally, even in untrained networks, as evidenced by empirical and theoretical findings. This research expands the scope of input space connectivity beyond out-of-distribution samples, considering all possible inputs.…