Author: Tech Jacks Solutions

Generative AI Purpose-built for Social and Mental Health: A Real-World Pilotcs.AI updates on arXiv.org arXiv:2511.11689v3 Announce Type: replace-cross
Abstract: Generative artificial intelligence (GAI) chatbots built for mental health could deliver safe, personalized, and scalable mental health support. We evaluate a foundation model designed for mental health. Adults completed mental health measures while engaging with the chatbot between May 15, 2025 and September 15, 2025. Users completed an opt-in consent, demographic information, mental health symptoms, social connection, and self-identified goals. Measures were repeated every two weeks up to 6 weeks, and a final follow-up at 10 weeks. Analyses included effect sizes, and growth mixture models to identify participant groups and their characteristic engagement, severity, and demographic factors. Users demonstrated significant reductions in PHQ-9 and GAD-7 that were sustained at follow-up. Significant improvements in Hope, Behavioral Activation, Social Interaction, Loneliness, and Perceived Social Support were observed throughout and maintained at 10 week follow-up. Engagement was high and predicted outcomes. Working alliance was comparable to traditional care and predicted outcomes. Automated safety guardrails functioned as designed, with 76 sessions flagged for risk and all handled according to escalation policies. This single arm naturalistic observational study provides initial evidence that a GAI foundation model for mental health can deliver accessible, engaging, effective, and safe mental health support. These results lend support to findings from early randomized designs and offer promise for future study of mental health GAI in real world settings.

 arXiv:2511.11689v3 Announce Type: replace-cross
Abstract: Generative artificial intelligence (GAI) chatbots built for mental health could deliver safe, personalized, and scalable mental health support. We evaluate a foundation model designed for mental health. Adults completed mental health measures while engaging with the chatbot between May 15, 2025 and September 15, 2025. Users completed an opt-in consent, demographic information, mental health symptoms, social connection, and self-identified goals. Measures were repeated every two weeks up to 6 weeks, and a final follow-up at 10 weeks. Analyses included effect sizes, and growth mixture models to identify participant groups and their characteristic engagement, severity, and demographic factors. Users demonstrated significant reductions in PHQ-9 and GAD-7 that were sustained at follow-up. Significant improvements in Hope, Behavioral Activation, Social Interaction, Loneliness, and Perceived Social Support were observed throughout and maintained at 10 week follow-up. Engagement was high and predicted outcomes. Working alliance was comparable to traditional care and predicted outcomes. Automated safety guardrails functioned as designed, with 76 sessions flagged for risk and all handled according to escalation policies. This single arm naturalistic observational study provides initial evidence that a GAI foundation model for mental health can deliver accessible, engaging, effective, and safe mental health support. These results lend support to findings from early randomized designs and offer promise for future study of mental health GAI in real world settings. Read More  

How Thomson Reuters built an Agentic Platform Engineering Hub with Amazon Bedrock AgentCoreArtificial Intelligence This blog post explains how TR’s Platform Engineering team, a geographically distributed unit overseeing TR’s service availability, boosted its operational productivity by transitioning from manual to an automated agentic system using Amazon Bedrock AgentCore.

 This blog post explains how TR’s Platform Engineering team, a geographically distributed unit overseeing TR’s service availability, boosted its operational productivity by transitioning from manual to an automated agentic system using Amazon Bedrock AgentCore. Read More  

Build agents to learn from experiences using Amazon Bedrock AgentCore episodic memoryArtificial Intelligence In this post, we walk you through the complete architecture to structure and store episodes, discuss the reflection module, and share compelling benchmarks that demonstrate significant improvements in agent task success rates.

 In this post, we walk you through the complete architecture to structure and store episodes, discuss the reflection module, and share compelling benchmarks that demonstrate significant improvements in agent task success rates. Read More  

Google Trends is Misleading You: How to Do Machine Learning with Google Trends DataTowards Data Science Google Trends is one of the most widely used tools for analysing human behaviour at scale. Journalists use it. Data scientists use it. Entire papers are built on it. But there is a fundamental property of Google Trends data that makes it very easy to misuse, especially if you are working with time series or trying to build models, and most people never realise they are doing it.
The post Google Trends is Misleading You: How to Do Machine Learning with Google Trends Data appeared first on Towards Data Science.

 Google Trends is one of the most widely used tools for analysing human behaviour at scale. Journalists use it. Data scientists use it. Entire papers are built on it. But there is a fundamental property of Google Trends data that makes it very easy to misuse, especially if you are working with time series or trying to build models, and most people never realise they are doing it.
The post Google Trends is Misleading You: How to Do Machine Learning with Google Trends Data appeared first on Towards Data Science. Read More  

If You Want to Become a Data Scientist in 2026, Do ThisTowards Data Science Learn from my mistakes and fast track your data science career
The post If You Want to Become a Data Scientist in 2026, Do This appeared first on Towards Data Science.

 Learn from my mistakes and fast track your data science career
The post If You Want to Become a Data Scientist in 2026, Do This appeared first on Towards Data Science. Read More  

Building a Self-Healing Data Pipeline That Fixes Its Own Python ErrorsTowards Data Science How I built a self-healing pipeline that automatically fixes bad CSVs, schema changes, and weird delimiters.
The post Building a Self-Healing Data Pipeline That Fixes Its Own Python Errors appeared first on Towards Data Science.

 How I built a self-healing pipeline that automatically fixes bad CSVs, schema changes, and weird delimiters.
The post Building a Self-Healing Data Pipeline That Fixes Its Own Python Errors appeared first on Towards Data Science. Read More  

How bunq handles 97% of support with Amazon BedrockArtificial Intelligence In this post, we show how bunq upgraded Finn, its in-house generative AI assistant, using Amazon Bedrock to transform user support and banking operations to be seamless, in multiple languages and time zones.

 In this post, we show how bunq upgraded Finn, its in-house generative AI assistant, using Amazon Bedrock to transform user support and banking operations to be seamless, in multiple languages and time zones. Read More  

Using Strands Agents to create a multi-agent solution with Meta’s Llama 4 and Amazon BedrockArtificial Intelligence In this post, we explore how to build a multi-agent video processing workflow using Strands Agents, Meta’s Llama 4 models, and Amazon Bedrock to automatically analyze and understand video content through specialized AI agents working in coordination. To showcase the solution, we will use Amazon SageMaker AI to walk you through the code.

 In this post, we explore how to build a multi-agent video processing workflow using Strands Agents, Meta’s Llama 4 models, and Amazon Bedrock to automatically analyze and understand video content through specialized AI agents working in coordination. To showcase the solution, we will use Amazon SageMaker AI to walk you through the code. Read More  

7 Statistical Concepts Every Data Scientist Should Master (and Why)KDnuggets Understanding data starts with statistics. These 7 statistics concepts give you the foundation to analyze and interpret with confidence.

 Understanding data starts with statistics. These 7 statistics concepts give you the foundation to analyze and interpret with confidence. Read More  

Navigating AI Entrepreneurship: Insights From The Application LayerKDnuggets Through the lens of a serial entrepreneur, this article explores how the AI revolution is shifting from infrastructure to the application layer, where the greatest opportunities lie in solving specialized, data-heavy industry problems rather than perfecting raw technology.

 Through the lens of a serial entrepreneur, this article explores how the AI revolution is shifting from infrastructure to the application layer, where the greatest opportunities lie in solving specialized, data-heavy industry problems rather than perfecting raw technology. Read More