The exponential growth in data has intensified the demand for computational power to train large-scale deep learning models. However, the rapid growth in model size and complexity raises concerns about equal and fair access to computational resources, particularly under increasing energy and infrastructure constraints. GPUs have emerged as essential for accelerating such workloads. This study benchmarks four deep learning models (Conv6, VGG16, ResNet18, CycleGAN) using TensorFlow and PyTorch on Intel Xeon CPUs and NVIDIA Tesla T4 GPUs. Our […]

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Autonomous agents operating in continuous environments must decide not only what to do, but when to act. We introduce a lightweight adaptive temporal control system that learns the optimal interval between cognitive ticks from experience, replacing ad hoc biologically inspired timers with a principled learned policy. The policy state is augmented with a predictive hyperbolic spread signal (a "curvature signal" shorthand) derived from hyperbolic geometry: the mean pairwise Poincare distance among n sampled futures embedded in the Poincare […]

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When an open-source AI project gains 60,000 GitHub stars in 72 hours, triggers a trademark dispute, and becomes a $16 million crypto scam—something extraordinary is happening. The tech world witnessed one of the most dramatic rise-and-fall stories compressed into three weeks in January 2026. A developer built his perfect AI assistant, only to watch it spark legal threats, security warnings, and widespread scams. The assistant’s name changed three times—from Clawdbot to Moltbot to OpenClaw—but the vision remained consistent. […]

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Melanie Mitchell explores the science of complexity, revealing how intricate, adaptive behavior—from ant colonies to consciousness—emerges from simple interactions among many individuals. Drawing on the interdisciplinary approach of the Santa Fe Institute, her award-winning book offers a clear overview of the field’s core ideas, current research, and potential to solve major scientific mysteries.

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arXiv:2602.05639v3 Announce Type: replace-cross Abstract: We introduce Variational Joint Embedding (VJE), a reconstruction-free latent-variable framework for non-contrastive self-supervised learning in representation space. VJE maximizes a symmetric conditional evidence lower bound (ELBO) on paired encoder embeddings by defining a conditional likelihood directly on target representations, rather than optimizing a pointwise compatibility objective. The likelihood is instantiated as a heavy-tailed Student–(t) distribution on a polar representation of the target embedding, where a directional–radial decomposition separates angular agreement from magnitude consistency […]

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Get Started with Rust: Generics Generic programming allows programmers to write general algorithms that work with arbitrary types. It reduces code duplication and provides type safety, which enables us to write more concise and clean code. This approach is also known as parametric polymorphism or templates in other languages. Rust supports two types of generic code: Compile-time generics, similar to C++ templates. Run-time generics, similar to virtual functions in C++ and generics in Java. Serokell has a broad […]

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The current implementation of pneumonia diagnosis remains challenging to achieve better performance and improve results. The aim of this research is to propose an innovative framework for pediatric pneumonia diagnosis that unites three fine-tuned pre-trained CNN models through feature fusion at the EfficientNetB0, RestNet50, and MobileNetV2 to achieve better performance and results. The mixed-model architecture framework provides an ideal solution for time-sensitive clinical applications operating in resource-constrained environments. This research experiment used the Chest X-Ray Images (Pneumonia) dataset, […]

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arXiv:2604.16239v1 Announce Type: new Abstract: In multi-fidelity optimization, biased approximations of varying costs of the target function are available. This paper studies the problem of optimizing a locally smooth function with a limited budget, where the learner has to make a tradeoff between the cost and the bias of these approximations. We first prove lower bounds for the simple regret under different assumptions on the fidelities, based on a cost-to-bias function. We then present the Kometo algorithm which […]

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