How I Prepared for a Data Science Interview at a Large Tech CompanyKDnuggets And got the job.
And got the job. Read More
How Amazon Search increased ML training twofold using AWS Batch for Amazon SageMaker Training jobsArtificial Intelligence In this post, we show you how Amazon Search optimized GPU instance utilization by leveraging AWS Batch for SageMaker Training jobs. This managed solution enabled us to orchestrate machine learning (ML) training workloads on GPU-accelerated instance families like P5, P4, and others. We will also provide a step-by-step walkthrough of the use case implementation.
In this post, we show you how Amazon Search optimized GPU instance utilization by leveraging AWS Batch for SageMaker Training jobs. This managed solution enabled us to orchestrate machine learning (ML) training workloads on GPU-accelerated instance families like P5, P4, and others. We will also provide a step-by-step walkthrough of the use case implementation. Read More
OpenAI Introduces IndQA: A Culture Aware Benchmark For Indian LanguagesMarkTechPost How can we reliably test whether large language models actually understand Indian languages and culture in real world contexts? OpenAI has released IndQA, a benchmark that evaluates how well AI models understand and reason about questions that matter in Indian languages across cultural domains. Why IndQA? OpenAI states that about 80 percent of people worldwide
The post OpenAI Introduces IndQA: A Culture Aware Benchmark For Indian Languages appeared first on MarkTechPost.
How can we reliably test whether large language models actually understand Indian languages and culture in real world contexts? OpenAI has released IndQA, a benchmark that evaluates how well AI models understand and reason about questions that matter in Indian languages across cultural domains. Why IndQA? OpenAI states that about 80 percent of people worldwide
The post OpenAI Introduces IndQA: A Culture Aware Benchmark For Indian Languages appeared first on MarkTechPost. Read More
How to Build a Model-Native Agent That Learns Internal Planning, Memory, and Multi-Tool Reasoning Through End-to-End Reinforcement LearningMarkTechPost In this tutorial, we explore how an agent can internalize planning, memory, and tool use within a single neural model rather than relying on external orchestration. We design a compact, model-native agent that learns to perform arithmetic reasoning tasks through reinforcement learning. By combining a stage-aware actor-critic network with a curriculum of increasingly complex environments,
The post How to Build a Model-Native Agent That Learns Internal Planning, Memory, and Multi-Tool Reasoning Through End-to-End Reinforcement Learning appeared first on MarkTechPost.
In this tutorial, we explore how an agent can internalize planning, memory, and tool use within a single neural model rather than relying on external orchestration. We design a compact, model-native agent that learns to perform arithmetic reasoning tasks through reinforcement learning. By combining a stage-aware actor-critic network with a curriculum of increasingly complex environments,
The post How to Build a Model-Native Agent That Learns Internal Planning, Memory, and Multi-Tool Reasoning Through End-to-End Reinforcement Learning appeared first on MarkTechPost. Read More
Why Nonparametric Models Deserve a Second LookTowards Data Science Discover how nonparametric conditional distributions unify regression, classification, and synthetic data generation—without assuming functional forms.
The post Why Nonparametric Models Deserve a Second Look appeared first on Towards Data Science.
Discover how nonparametric conditional distributions unify regression, classification, and synthetic data generation—without assuming functional forms.
The post Why Nonparametric Models Deserve a Second Look appeared first on Towards Data Science. Read More
How to Evaluate Retrieval Quality in RAG Pipelines (part 2): Mean Reciprocal Rank (MRR) and Average Precision (AP)Towards Data Science Evaluating the retrieval quality of your RAG pipeline with binary, order-aware measures
The post How to Evaluate Retrieval Quality in RAG Pipelines (part 2): Mean Reciprocal Rank (MRR) and Average Precision (AP) appeared first on Towards Data Science.
Evaluating the retrieval quality of your RAG pipeline with binary, order-aware measures
The post How to Evaluate Retrieval Quality in RAG Pipelines (part 2): Mean Reciprocal Rank (MRR) and Average Precision (AP) appeared first on Towards Data Science. Read More
AI Papers to Read in 2025Towards Data Science Reading suggestions to keep you up-to-date with the latest and classic breakthroughs in AI and Data Science.
The post AI Papers to Read in 2025 appeared first on Towards Data Science.
Reading suggestions to keep you up-to-date with the latest and classic breakthroughs in AI and Data Science.
The post AI Papers to Read in 2025 appeared first on Towards Data Science. Read More
We Didn’t Invent Attention — We Just Rediscovered ItTowards Data Science How selective amplification emerged across evolution, chemistry, and AI through convergent mathematical solutions
The post We Didn’t Invent Attention — We Just Rediscovered It appeared first on Towards Data Science.
How selective amplification emerged across evolution, chemistry, and AI through convergent mathematical solutions
The post We Didn’t Invent Attention — We Just Rediscovered It appeared first on Towards Data Science. Read More
ARC-GEN: A Mimetic Procedural Benchmark Generator for the Abstraction and Reasoning Corpuscs.AI updates on arXiv.org arXiv:2511.00162v2 Announce Type: new
Abstract: The Abstraction and Reasoning Corpus remains one of the most compelling and challenging benchmarks for tracking progress toward achieving Artificial General Intelligence. In contrast to other evaluation datasets designed to assess an agent’s task-specific skills or accumulated knowledge, the ARC-AGI suite is specifically targeted at measuring skill acquisition efficiency, a trait that has (so far) been lacking in even the most sophisticated machine learning systems. For algorithms that require extensive intra-task exemplars, a significant constraint imposed by ARC-AGI is the modest cardinality of its demonstration set, comprising a small number of $langle$ input, output $rangle$ grids per task specifying the corresponding transformation. To embellish the space of viable sample pairs, this paper introduces ARC-GEN, an open-source procedural generator aimed at extending the original ARC-AGI training dataset as faithfully as possible. Unlike prior efforts, our generator is both exhaustive (covering all four-hundred tasks) and mimetic (more closely honoring the distributional properties and characteristics embodied in the initial ARC-AGI-1 release). We also discuss the use of this generator in establishing a static benchmark suite to verify the correctness of programs submitted to the 2025 Google Code Golf Championship.
arXiv:2511.00162v2 Announce Type: new
Abstract: The Abstraction and Reasoning Corpus remains one of the most compelling and challenging benchmarks for tracking progress toward achieving Artificial General Intelligence. In contrast to other evaluation datasets designed to assess an agent’s task-specific skills or accumulated knowledge, the ARC-AGI suite is specifically targeted at measuring skill acquisition efficiency, a trait that has (so far) been lacking in even the most sophisticated machine learning systems. For algorithms that require extensive intra-task exemplars, a significant constraint imposed by ARC-AGI is the modest cardinality of its demonstration set, comprising a small number of $langle$ input, output $rangle$ grids per task specifying the corresponding transformation. To embellish the space of viable sample pairs, this paper introduces ARC-GEN, an open-source procedural generator aimed at extending the original ARC-AGI training dataset as faithfully as possible. Unlike prior efforts, our generator is both exhaustive (covering all four-hundred tasks) and mimetic (more closely honoring the distributional properties and characteristics embodied in the initial ARC-AGI-1 release). We also discuss the use of this generator in establishing a static benchmark suite to verify the correctness of programs submitted to the 2025 Google Code Golf Championship. Read More
Advancing AI Challenges for the United States Department of the Air Forcecs.AI updates on arXiv.org arXiv:2511.00267v1 Announce Type: new
Abstract: The DAF-MIT AI Accelerator is a collaboration between the United States Department of the Air Force (DAF) and the Massachusetts Institute of Technology (MIT). This program pioneers fundamental advances in artificial intelligence (AI) to expand the competitive advantage of the United States in the defense and civilian sectors. In recent years, AI Accelerator projects have developed and launched public challenge problems aimed at advancing AI research in priority areas. Hallmarks of AI Accelerator challenges include large, publicly available, and AI-ready datasets to stimulate open-source solutions and engage the wider academic and private sector AI ecosystem. This article supplements our previous publication, which introduced AI Accelerator challenges. We provide an update on how ongoing and new challenges have successfully contributed to AI research and applications of AI technologies.
arXiv:2511.00267v1 Announce Type: new
Abstract: The DAF-MIT AI Accelerator is a collaboration between the United States Department of the Air Force (DAF) and the Massachusetts Institute of Technology (MIT). This program pioneers fundamental advances in artificial intelligence (AI) to expand the competitive advantage of the United States in the defense and civilian sectors. In recent years, AI Accelerator projects have developed and launched public challenge problems aimed at advancing AI research in priority areas. Hallmarks of AI Accelerator challenges include large, publicly available, and AI-ready datasets to stimulate open-source solutions and engage the wider academic and private sector AI ecosystem. This article supplements our previous publication, which introduced AI Accelerator challenges. We provide an update on how ongoing and new challenges have successfully contributed to AI research and applications of AI technologies. Read More
