How to Build an Agentic Voice AI Assistant that Understands, Reasons, Plans, and Responds through Autonomous Multi-Step IntelligenceMarkTechPost In this tutorial, we explore how to build an Agentic Voice AI Assistant capable of understanding, reasoning, and responding through natural speech in real time. We begin by setting up a self-contained voice intelligence pipeline that integrates speech recognition, intent detection, multi-step reasoning, and text-to-speech synthesis. Along the way, we design an agent that listens
The post How to Build an Agentic Voice AI Assistant that Understands, Reasons, Plans, and Responds through Autonomous Multi-Step Intelligence appeared first on MarkTechPost.
In this tutorial, we explore how to build an Agentic Voice AI Assistant capable of understanding, reasoning, and responding through natural speech in real time. We begin by setting up a self-contained voice intelligence pipeline that integrates speech recognition, intent detection, multi-step reasoning, and text-to-speech synthesis. Along the way, we design an agent that listens
The post How to Build an Agentic Voice AI Assistant that Understands, Reasons, Plans, and Responds through Autonomous Multi-Step Intelligence appeared first on MarkTechPost. Read More
Nested Learning: A New Machine Learning Approach for Continual Learning that Views Models as Nested Optimization Problems to Enhance Long Context ProcessingMarkTechPost How can we build AI systems that keep learning new information over time without forgetting what they learned before or retraining from scratch? Google Researchers has introduced Nested Learning, a machine learning approach that treats a model as a collection of smaller nested optimization problems, instead of a single network trained by one outer loop.
The post Nested Learning: A New Machine Learning Approach for Continual Learning that Views Models as Nested Optimization Problems to Enhance Long Context Processing appeared first on MarkTechPost.
How can we build AI systems that keep learning new information over time without forgetting what they learned before or retraining from scratch? Google Researchers has introduced Nested Learning, a machine learning approach that treats a model as a collection of smaller nested optimization problems, instead of a single network trained by one outer loop.
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Power Analysis in Marketing: A Hands-On IntroductionTowards Data Science Part 1: What is statistical power and how do we compute it?
The post Power Analysis in Marketing: A Hands-On Introduction appeared first on Towards Data Science.
Part 1: What is statistical power and how do we compute it?
The post Power Analysis in Marketing: A Hands-On Introduction appeared first on Towards Data Science. Read More
Prior Labs Releases TabPFN-2.5: The Latest Version of TabPFN that Unlocks Scale and Speed for Tabular Foundation ModelsMarkTechPost Tabular data is still where many important models run in production. Finance, healthcare, energy and industry teams work with tables of rows and columns, not images or long text. Prior Labs now extends this space with TabPFN-2.5, a new tabular foundation model that scales in context learning to 50,000 samples and 2,000 features while keeping
The post Prior Labs Releases TabPFN-2.5: The Latest Version of TabPFN that Unlocks Scale and Speed for Tabular Foundation Models appeared first on MarkTechPost.
Tabular data is still where many important models run in production. Finance, healthcare, energy and industry teams work with tables of rows and columns, not images or long text. Prior Labs now extends this space with TabPFN-2.5, a new tabular foundation model that scales in context learning to 50,000 samples and 2,000 features while keeping
The post Prior Labs Releases TabPFN-2.5: The Latest Version of TabPFN that Unlocks Scale and Speed for Tabular Foundation Models appeared first on MarkTechPost. Read More
Anthropic Turns MCP Agents Into Code First Systems With ‘Code Execution With MCP’ ApproachMarkTechPost Agents that use the Model Context Protocol MCP have a scaling problem. Every tool definition and every intermediate result is pushed through the context window, which means large workflows burn tokens and hit latency and cost limits fast. Anthropic’s new ‘code execution with MCP’ pattern restructures this pipeline by turning MCP tools into code level
The post Anthropic Turns MCP Agents Into Code First Systems With ‘Code Execution With MCP’ Approach appeared first on MarkTechPost.
Agents that use the Model Context Protocol MCP have a scaling problem. Every tool definition and every intermediate result is pushed through the context window, which means large workflows burn tokens and hit latency and cost limits fast. Anthropic’s new ‘code execution with MCP’ pattern restructures this pipeline by turning MCP tools into code level
The post Anthropic Turns MCP Agents Into Code First Systems With ‘Code Execution With MCP’ Approach appeared first on MarkTechPost. Read More
How to Build an Advanced Multi-Page Reflex Web Application with Real-Time Database, Dynamic State Management, and Reactive UIMarkTechPost In this tutorial, we build an advanced Reflex web application entirely in Python that runs seamlessly inside Colab. We design the app to demonstrate how Reflex enables full-stack development with no JavaScript, just reactive Python code. We create a complete notes-management dashboard featuring two pages, real-time database interactions, filtering, sorting, analytics, and user personalization. We
The post How to Build an Advanced Multi-Page Reflex Web Application with Real-Time Database, Dynamic State Management, and Reactive UI appeared first on MarkTechPost.
In this tutorial, we build an advanced Reflex web application entirely in Python that runs seamlessly inside Colab. We design the app to demonstrate how Reflex enables full-stack development with no JavaScript, just reactive Python code. We create a complete notes-management dashboard featuring two pages, real-time database interactions, filtering, sorting, analytics, and user personalization. We
The post How to Build an Advanced Multi-Page Reflex Web Application with Real-Time Database, Dynamic State Management, and Reactive UI appeared first on MarkTechPost. Read More
Connect Amazon Bedrock agents to cross-account knowledge basesArtificial Intelligence Organizations need seamless access to their structured data repositories to power intelligent AI agents. However, when these resources span multiple AWS accounts integration challenges can arise. This post explores a practical solution for connecting Amazon Bedrock agents to knowledge bases in Amazon Redshift clusters residing in different AWS accounts.
Organizations need seamless access to their structured data repositories to power intelligent AI agents. However, when these resources span multiple AWS accounts integration challenges can arise. This post explores a practical solution for connecting Amazon Bedrock agents to knowledge bases in Amazon Redshift clusters residing in different AWS accounts. Read More
Google AI Releases ADK Go: A New Open-Source Toolkit Designed to Empower Go Developers to Build Powerful AI AgentsMarkTechPost How do you build reliable AI agents that plug into your existing Go services without bolting on a separate language stack? Google has just released Agent Development Kit for Go. Go developers can now build AI agents with the same framework that already supports Python and Java, while keeping everything inside a familiar Go toolchain
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How do you build reliable AI agents that plug into your existing Go services without bolting on a separate language stack? Google has just released Agent Development Kit for Go. Go developers can now build AI agents with the same framework that already supports Python and Java, while keeping everything inside a familiar Go toolchain
The post Google AI Releases ADK Go: A New Open-Source Toolkit Designed to Empower Go Developers to Build Powerful AI Agents appeared first on MarkTechPost. Read More
Democratizing AI: How Thomson Reuters Open Arena supports no-code AI for every professional with Amazon BedrockArtificial Intelligence In this blog post, we explore how TR addressed key business use cases with Open Arena, a highly scalable and flexible no-code AI solution powered by Amazon Bedrock and other AWS services such as Amazon OpenSearch Service, Amazon Simple Storage Service (Amazon S3), Amazon DynamoDB, and AWS Lambda. We’ll explain how TR used AWS services to build this solution, including how the architecture was designed, the use cases it solves, and the business profiles that use it.
In this blog post, we explore how TR addressed key business use cases with Open Arena, a highly scalable and flexible no-code AI solution powered by Amazon Bedrock and other AWS services such as Amazon OpenSearch Service, Amazon Simple Storage Service (Amazon S3), Amazon DynamoDB, and AWS Lambda. We’ll explain how TR used AWS services to build this solution, including how the architecture was designed, the use cases it solves, and the business profiles that use it. Read More
Evaluating Synthetic Data — The Million Dollar QuestionTowards Data Science Learn how to evaluate synthetic data quality using the Maximum Similarity Test — a simple, quantitative approach for assessing fidelity, utility, and privacy in synthetic datasets.
The post Evaluating Synthetic Data — The Million Dollar Question appeared first on Towards Data Science.
Learn how to evaluate synthetic data quality using the Maximum Similarity Test — a simple, quantitative approach for assessing fidelity, utility, and privacy in synthetic datasets.
The post Evaluating Synthetic Data — The Million Dollar Question appeared first on Towards Data Science. Read More
