Comparing the Top 6 OCR (Optical Character Recognition) Models/Systems in 2025MarkTechPost Optical character recognition has moved from plain text extraction to document intelligence. Modern systems must read scanned and digital PDFs in one pass, preserve layout, detect tables, extract key value pairs, and work with more than one language. Many teams now also want OCR that can feed RAG and agent pipelines directly. In 2025, 6
The post Comparing the Top 6 OCR (Optical Character Recognition) Models/Systems in 2025 appeared first on MarkTechPost.
Optical character recognition has moved from plain text extraction to document intelligence. Modern systems must read scanned and digital PDFs in one pass, preserve layout, detect tables, extract key value pairs, and work with more than one language. Many teams now also want OCR that can feed RAG and agent pipelines directly. In 2025, 6
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From Classical Models to AI: Forecasting Humidity for Energy and Water Efficiency in Data CentersTowards Data Science From ARIMA to N-BEATS: Comparing forecasting approaches that balance accuracy, interpretability, and sustainability
The post From Classical Models to AI: Forecasting Humidity for Energy and Water Efficiency in Data Centers appeared first on Towards Data Science.
From ARIMA to N-BEATS: Comparing forecasting approaches that balance accuracy, interpretability, and sustainability
The post From Classical Models to AI: Forecasting Humidity for Energy and Water Efficiency in Data Centers appeared first on Towards Data Science. Read More
A Coding Implementation of a Comprehensive Enterprise AI Benchmarking Framework to Evaluate Rule-Based LLM, and Hybrid Agentic AI Systems Across Real-World TasksMarkTechPost In this tutorial, we develop a comprehensive benchmarking framework to evaluate various types of agentic AI systems on real-world enterprise software tasks. We design a suite of diverse challenges, from data transformation and API integration to workflow automation and performance optimization, and assess how various agents, including rule-based, LLM-powered, and hybrid ones, perform across these
The post A Coding Implementation of a Comprehensive Enterprise AI Benchmarking Framework to Evaluate Rule-Based LLM, and Hybrid Agentic AI Systems Across Real-World Tasks appeared first on MarkTechPost.
In this tutorial, we develop a comprehensive benchmarking framework to evaluate various types of agentic AI systems on real-world enterprise software tasks. We design a suite of diverse challenges, from data transformation and API integration to workflow automation and performance optimization, and assess how various agents, including rule-based, LLM-powered, and hybrid ones, perform across these
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How to Create AI-ready APIs?MarkTechPost Postman recently released a comprehensive checklist and developer guide for building AI-ready APIs, highlighting a simple truth: even the most powerful AI models are only as good as the data they receive—and that data comes through your APIs. If your endpoints are inconsistent, unclear, or unreliable, models waste time fixing bad inputs instead of producing
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Postman recently released a comprehensive checklist and developer guide for building AI-ready APIs, highlighting a simple truth: even the most powerful AI models are only as good as the data they receive—and that data comes through your APIs. If your endpoints are inconsistent, unclear, or unreliable, models waste time fixing bad inputs instead of producing
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LongCat-Flash-Omni: A SOTA Open-Source Omni-Modal Model with 560B Parameters with 27B activated, Excelling at Real-Time Audio-Visual InteractionMarkTechPost How do you design a single model that can listen, see, read and respond in real time across text, image, video and audio without losing the efficiency? Meituan’s LongCat team has released LongCat Flash Omni, an open source omni modal model with 560 billion parameters and about 27 billion active per token, built on the
The post LongCat-Flash-Omni: A SOTA Open-Source Omni-Modal Model with 560B Parameters with 27B activated, Excelling at Real-Time Audio-Visual Interaction appeared first on MarkTechPost.
How do you design a single model that can listen, see, read and respond in real time across text, image, video and audio without losing the efficiency? Meituan’s LongCat team has released LongCat Flash Omni, an open source omni modal model with 560 billion parameters and about 27 billion active per token, built on the
The post LongCat-Flash-Omni: A SOTA Open-Source Omni-Modal Model with 560B Parameters with 27B activated, Excelling at Real-Time Audio-Visual Interaction appeared first on MarkTechPost. Read More
Author: Derrick D. JacksonTitle: Founder & Senior Director of Cloud Security Architecture & RiskCredentials: CISSP, CRISC, CCSPLast updated October 31th, 2025 What Are AI Hallucinations? When Air Canada’s chatbot fabricated a bereavement fare policy in 2024, the company was held legally liable. That case set a clear precedent: you’re responsible for what your AI systems confidently […]
Artificial Intelligence vs Augmented Intelligence The term “Artificial Intelligence” (AI) dominates headlines and boardroom talks. It brings to mind self-driving cars, robotic warehouses, and software that seems to think on its own. Much of the attention around AI focuses on automation and autonomy. But a growing movement is focused on partnership instead of replacement. This […]
DeepAgent: A Deep Reasoning AI Agent that Performs Autonomous Thinking, Tool Discovery, and Action Execution within a Single Reasoning ProcessMarkTechPost Most agent frameworks still run a predefined Reason, Act, Observe loop, so the agent can only use the tools that are injected in the prompt. This works for small tasks, but it fails when the toolset is large, when the task is long, and when the agent must change strategy in the middle of reasoning.
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Most agent frameworks still run a predefined Reason, Act, Observe loop, so the agent can only use the tools that are injected in the prompt. This works for small tasks, but it fails when the toolset is large, when the task is long, and when the agent must change strategy in the middle of reasoning.
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The Pearson Correlation Coefficient, Explained SimplyTowards Data Science A simple explanation of the Pearson correlation coefficient with examples
The post The Pearson Correlation Coefficient, Explained Simply appeared first on Towards Data Science.
A simple explanation of the Pearson correlation coefficient with examples
The post The Pearson Correlation Coefficient, Explained Simply appeared first on Towards Data Science. Read More
Google AI Unveils Supervised Reinforcement Learning (SRL): A Step Wise Framework with Expert Trajectories to Teach Small Language Models to Reason through Hard ProblemsMarkTechPost How can a small model learn to solve tasks it currently fails at, without rote imitation or relying on a correct rollout? A team of researchers from Google Cloud AI Research and UCLA have released a training framework, ‘Supervised Reinforcement Learning’ (SRL), that makes 7B scale models actually learn from very hard math and agent
The post Google AI Unveils Supervised Reinforcement Learning (SRL): A Step Wise Framework with Expert Trajectories to Teach Small Language Models to Reason through Hard Problems appeared first on MarkTechPost.
How can a small model learn to solve tasks it currently fails at, without rote imitation or relying on a correct rollout? A team of researchers from Google Cloud AI Research and UCLA have released a training framework, ‘Supervised Reinforcement Learning’ (SRL), that makes 7B scale models actually learn from very hard math and agent
The post Google AI Unveils Supervised Reinforcement Learning (SRL): A Step Wise Framework with Expert Trajectories to Teach Small Language Models to Reason through Hard Problems appeared first on MarkTechPost. Read More