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
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.
The post DeepAgent: A Deep Reasoning AI Agent that Performs Autonomous Thinking, Tool Discovery, and Action Execution within a Single Reasoning Process appeared first on MarkTechPost.
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.
The post DeepAgent: A Deep Reasoning AI Agent that Performs Autonomous Thinking, Tool Discovery, and Action Execution within a Single Reasoning Process appeared first on MarkTechPost. Read More
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
How to Build an End-to-End Data Engineering and Machine Learning Pipeline with Apache Spark and PySparkMarkTechPost In this tutorial, we explore how to harness Apache Spark’s techniques using PySpark directly in Google Colab. We begin by setting up a local Spark session, then progressively move through transformations, SQL queries, joins, and window functions. We also build and evaluate a simple machine-learning model to predict user subscription types and finally demonstrate how
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In this tutorial, we explore how to harness Apache Spark’s techniques using PySpark directly in Google Colab. We begin by setting up a local Spark session, then progressively move through transformations, SQL queries, joins, and window functions. We also build and evaluate a simple machine-learning model to predict user subscription types and finally demonstrate how
The post How to Build an End-to-End Data Engineering and Machine Learning Pipeline with Apache Spark and PySpark appeared first on MarkTechPost. 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
Build reliable AI systems with Automated Reasoning on Amazon Bedrock – Part 1Artificial Intelligence Enterprises in regulated industries often need mathematical certainty that every AI response complies with established policies and domain knowledge. Regulated industries can’t use traditional quality assurance methods that test only a statistical sample of AI outputs and make probabilistic assertions about compliance. When we launched Automated Reasoning checks in Amazon Bedrock Guardrails in preview at
Enterprises in regulated industries often need mathematical certainty that every AI response complies with established policies and domain knowledge. Regulated industries can’t use traditional quality assurance methods that test only a statistical sample of AI outputs and make probabilistic assertions about compliance. When we launched Automated Reasoning checks in Amazon Bedrock Guardrails in preview at Read More
Too much screen time may be hurting kids’ heartsArtificial Intelligence News — ScienceDaily More screen time among children and teens is linked to higher risks of heart and metabolic problems, particularly when combined with insufficient sleep. Danish researchers discovered a measurable rise in cardiometabolic risk scores and a metabolic “fingerprint” in frequent screen users. Experts say better sleep and balanced daily routines can help offset these effects and safeguard lifelong health.
More screen time among children and teens is linked to higher risks of heart and metabolic problems, particularly when combined with insufficient sleep. Danish researchers discovered a measurable rise in cardiometabolic risk scores and a metabolic “fingerprint” in frequent screen users. Experts say better sleep and balanced daily routines can help offset these effects and safeguard lifelong health. Read More
Anthropic’s New Research Shows Claude can Detect Injected Concepts, but only in Controlled LayersMarkTechPost How do you tell whether a model is actually noticing its own internal state instead of just repeating what training data said about thinking? In a latest Anthropic’s research study ‘Emergent Introspective Awareness in Large Language Models‘ asks whether current Claude models can do more than talk about their abilities, it asks whether they can
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How do you tell whether a model is actually noticing its own internal state instead of just repeating what training data said about thinking? In a latest Anthropic’s research study ‘Emergent Introspective Awareness in Large Language Models‘ asks whether current Claude models can do more than talk about their abilities, it asks whether they can
The post Anthropic’s New Research Shows Claude can Detect Injected Concepts, but only in Controlled Layers appeared first on MarkTechPost. Read More
