January 2026

Seasonal Switch 2 sales show significant slowing as annual cycle sunsets

Seasonal Switch 2 sales show significant slowing as annual cycle sunsets

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Nintendo’s Switch 2 was an unmitigated market success for Nintendo following its launch last June, selling a record-setting 3.5 million units worldwide in its first four days and reaching over 10 million shipments in just under four months. But a new report from The Game Business suggests that frenzied initial sales pace may have slowed significantly in many markets during the system’s crucial first holiday season. The report suggests that US Switch 2 sales were down about 35 […]

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Safe Continual Reinforcement Learning Methods for Nonstationary Environments. Towards a Survey of the State of the Art

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This work provides a state-of-the-art survey of continual safe online reinforcement learning (COSRL) methods. We discuss theoretical aspects, challenges, and open questions in building continual online safe reinforcement learning algorithms. We provide the taxonomy and the details of continual online safe reinforcement learning methods based on the type of safe learning mechanism that takes adaptation to nonstationarity into account. We categorize safety constraints formulation for online reinforcement learning algorithms, and finally, we discuss prospects for creating reliable, safe […]

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Senior ML Engineer aiming for RL research in ~1.5 years — roadmap, DSA prep, and time management?

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Hi everyone, I’m a Senior Machine Learning Engineer planning a focused transition into Reinforcement Learning research over the next 12–18 months, and I’d really value advice from people who’ve done this alongside full-time work. Background (brief): • B.Tech + M.Tech (strong math/PDEs) • ~2+ years in ML/DS (forecasting, optimization, CNNs) • Currently building LLM-based agents & multi-agent systems in fintech (orchestration, tools, OpenAI/Anthropic, knowledge graphs),via AI automation I’m comfortable with Python, PyTorch, probability, linear algebra, and optimization. Why […]

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How To Buy Pepeto ($PEPETO) Before It Becomes The Next Pepecoin (PEPE)

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PEPE turned people who did things quickly to be millionaires. That’s not hyperbole. The returns that the early investors got transformed lives. PEPE is currently trading at a price of $0.000006619 and has a market capitalization of $2.78B, and such geometric returns are more difficult to recreate mathematically. Pepeto ($PEPETO) offers a similar opportunity, currently available at $0.000000176 during presale. The difference? You are reading it before the people find out about it. This guide walks through exactly […]

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Fine-tuning vision-language models on memory-constrained devices

Fine-tuning vision-language models on memory-constrained devices

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Fine-tuned vision-language models (VLMs) have shown remarkable performance across many computer vision tasks. However, backpropagation — the standard method for adjusting model weights during fine tuning, which works backward from output error — is computationally expensive and thus impractical on resource-constrained edge devices. An alternative is fine-tuning strategies that rely solely on forward passes, significantly lowering the computational requirements. Zeroth-order (ZO) estimation is one such method, but existing ZO-based VLM fine-tuning methods remain substantially inferior to backpropagation-based training […]

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Exploring Student Expectations and Confidence in Learning Analytics

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Learning Analytics (LA) is nowadays ubiquitous in many educational systems, providing the ability to collect and analyze student data in order to understand and optimize learning and the environments in which it occurs. On the other hand, the collection of data requires to comply with the growing demand regarding privacy legislation. In this paper, we use the Student Expectation of Learning Analytics Questionnaire (SELAQ) to analyze the expectations and confidence of students from different faculties regarding the processing […]

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Detect and redact personally identifiable information using Amazon Bedrock Data Automation and Guardrails

Detect and redact personally identifiable information using Amazon Bedrock Data Automation and Guardrails

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Organizations handle vast amounts of sensitive customer information through various communication channels. Protecting Personally Identifiable Information (PII), such as social security numbers (SSNs), driver’s license numbers, and phone numbers has become increasingly critical for maintaining compliance with data privacy regulations and building customer trust. However, manually reviewing and redacting PII is time-consuming, error-prone, and scales poorly as data volumes grow. Organizations face challenges when dealing with PII scattered across different content types – from texts to images. Traditional […]

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Speed meets scale: Load testing SageMakerAI endpoints with Observe.AI’s testing tool

Speed meets scale: Load testing SageMakerAI endpoints with Observe.AI’s testing tool

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This post is cowritten with Aashraya Sachdeva from Observe.ai. You can use Amazon SageMaker to build, train and deploy machine learning (ML) models, including large language models (LLMs) and other foundation models (FMs). This helps you significantly reduce the time required for a range of generative AI and ML development tasks. An AI/ML development cycle typically involves data pre-processing, model development, training, testing and deployment lifecycles. By using SageMaker, your data science and ML engineering teams can offload […]

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Quantitative mapping from conventional MRI using self-supervised physics-guided deep learning: applications to a large-scale, clinically heterogeneous dataset

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Magnetic resonance imaging (MRI) is a cornerstone of clinical neuroimaging, yet conventional MRIs provide qualitative information heavily dependent on scanner hardware and acquisition settings. While quantitative MRI (qMRI) offers intrinsic tissue parameters, the requirement for specialized acquisition protocols and reconstruction algorithms restricts its availability and impedes large-scale biomarker research. This study presents a self-supervised physics-guided deep learning framework to infer quantitative T1, T2, and proton-density (PD) maps directly from widely available clinical conventional T1-weighted, T2-weighted, and FLAIR MRIs. […]

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A Data-Driven Predictive Framework for Inventory Optimization Using Context-Augmented Machine Learning Models

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Demand forecasting in supply chain management (SCM) is critical for optimizing inventory, reducing waste, and improving customer satisfaction. Conventional approaches frequently neglect external influences like weather, festivities, and equipment breakdowns, resulting in inefficiencies. This research investigates the use of machine learning (ML) algorithms to improve demand prediction in retail and vending machine sectors. Four machine learning algorithms. Extreme Gradient Boosting (XGBoost), Autoregressive Integrated Moving Average (ARIMA), Facebook Prophet (Fb Prophet), and Support Vector Regression (SVR) were used to […]

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