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Why the future of AI isn’t bigger — it’s smarter, smaller, and more transparent The Hidden Cost of Today’s AI Large AI models are powerful. But they come with serious trade-offs. Training a single large-scale model can cost millions of dollars in compute resources alone. For example, training a model like GPT-3 is estimated to have cost over $4 million. Newer, larger models can cost significantly more — some reports suggest upwards of $50–100 million when factoring in research, hardware, […]

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This study examines the multifaceted application of machine learning and artificial intelligence (AI) in two key, dynamically developing sectors: cryptocurrency market capitalisation forecasting and customer service optimisation. An analysis of the effectiveness of various regression models (Linear, Lasso, and Decision Tree Regression) in predicting the market capitalisation of 3 leading cryptocurrencies shows that a model’s success is highly dependent on the specific characteristics of the asset. While linear models achieve exceptional accuracy (R2>0.99) for most major and liquid […]

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Every developer who’s built an IoT pipeline has hit this wall: the code that worked beautifully with ten sensors publishing once per second collapses under production load. Your database starts lagging, the message queue backs up, and suddenly you’re troubleshooting at 2 AM trying to figure out why 5000 devices at 1Hz brought everything to its knees. Your code is fine. The broker config is fine. The architecture isn’t. MQTT streams data continuously; SQL databases thrive on batched writes. Most tutorials […]

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This work constructs, analyzes and simulates a modified SIR epidemiological model for the spread of a generic long-time disease, in which the coefficients of infectivity and death rate are system variables. Diseases, such as COVID-19, have demonstrated very clearly that infectivity and death rates can change over time, even for the same variant of the virus, due to vaccination, improved treatments, better analysis, better medications, etc. This motivates us to model a generic disease where the infectivity and […]

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Supervised fine-tuning (SFT) is a common first stage of LLM post-training, teaching the model to follow instructions and shaping its behavior as a helpful assistant. At the same time, SFT may harm the fundamental capabilities of an LLM, particularly after long pretraining: a phenomenon known as catastrophic overtraining (Springer et al., 2025). To understand overtraining, we first investigate catastrophic forgetting in finetuning through the lens of implicit regularization of the learning rate. For models trained to the same […]

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We have Chat GPT Enterprise edition for our org. We have created and deployed client interactions summaries in various workflows and also a chatbot which responds to our questions. My problem, LLM does not remember chat beyond last 3 instances and that too it has to be same session. Once session is over, no memory! Second problem, we have provided Thumbs up and down to users to provide us feedback but how we make LLM learn from this […]

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Early identification of Autism Spectrum Disorder (ASD) traits in infants is crucial for early intervention, which can greatly improve the child’s quality of life. Solutions that use voice analysis offer a promising non-invasive way to detect ASD. However, most current studies depend on extracting specific voice markers from certain datasets and do not include validation across different groups. In this paper, we propose a supervised contrastive learning method for identifying ASD based on infant vocalizations. We extend the […]

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We introduce Metric-Aware Principal Component Analysis (MAPCA), a unified framework for scale-invariant representation learning based on the generalised eigenproblem max Tr(W^T Sigma W) subject to W^T M W = I, where M is a symmetric positive definite metric matrix. The choice of M determines the representation geometry. The canonical beta-family M(beta) = Sigma^beta, beta in [0,1], provides continuous spectral bias control between standard PCA (beta=0) and output whitening (beta=1), with condition number kappa(beta) = (lambda_1/lambda_p)^(1-beta) decreasing monotonically to […]

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Recent reinforcement learning (RL) approaches have advanced radiology report generation (RRG), yet two core limitations persist: (1) report-level rewards offer limited evidence-grounded guidance for clinical faithfulness; and (2) current methods lack an explicit self-improving mechanism to align with clinical preference. We introduce clinically aligned Evidence-aware Self-Correcting Reinforcement Learning (ESC-RL), comprising two key components. First, a Group-wise Evidence-aware Alignment Reward (GEAR) delivers group-wise, evidence-aware feedback. GEAR reinforces consistent grounding for true positives, recovers missed findings for false negatives, and […]

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