Author name: aifuturethinkers.com

Hello, and welcome to the world of my AI! I am overjoyed that you have chosen to accompany us on this journey into the fascinating field of artificial intelligence. If you find artificial intelligence to be as fascinating as I do, you are in for a thrilling trip! As a Data Engineering professional, I’ve been immersed in technology for the past ten years, and it’s become second nature to me. With a Master’s degree in Computer Application under my belt, I’ve had the fortunate opportunity to see artificial intelligence (AI) disrupting businesses and changing the game in ways that we couldn’t have anticipated before it happened. Now that I’ve finished reading all of my blogs, I’m ready to pass on all of the incredible information that I’ve gained. Together, we will investigate everything from the most cutting-edge AI applications to the most recent fashions. It doesn’t matter if you’ve never worked with AI before; I guarantee to make the process easy and entertaining so that anybody may take part in the AI adventure.

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Estimating Product-Level Price Elasticities Using Hierarchical Bayesian

In this article, I will introduce you to hierarchical Bayesian (HB) modelling, a flexible approach to automatically combine the results of multiple sub-models. This method enables estimation of individual-level effects by optimally combining information across different groupings of data through Bayesian updating. This is particularly valuable when individual units have limited observations but share common …

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Advancing Gemini’s security safeguards – Google DeepMind

We’re publishing a new white paper outlining how we’ve made Gemini 2.5 our most secure model family to date. Imagine asking your AI agent to summarize your latest emails — a seemingly straightforward task. Gemini and other large language models (LLMs) are consistently improving at performing such tasks, by accessing information like our documents, calendars, …

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Prototyping Gradient Descent in Machine Learning

Learning Supervised learning is a category of machine learning that uses labeled datasets to train algorithms to predict outcomes and recognize patterns. Unlike unsupervised learning, supervised learning algorithms are given labeled training to learn the relationship between the input and the outputs. Prerequisite: Linear algebra Suppose we have a regression problem where the model needs …

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Gemini as a universal AI assistant

Over the last decade, we’ve laid a lot of the foundations for the modern AI era, from pioneering the Transformer architecture on which all large language models are based, to developing agent systems that can learn and plan like AlphaGo and AlphaZero. We’ve applied these techniques to make breakthroughs in quantum computing, mathematics, life sciences …

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Top Machine Learning Jobs and How to Prepare For Them

days, job titles like data scientist, machine learning engineer, and Ai Engineer are everywhere — and if you were anything like me, it can be hard to understand what each of them actually does if you are not working within the field. And then there are titles that sound even more confusing — like quantum blockchain LLM robotic engineer …

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Updates to Gemini 2.5 from Google DeepMind

New Gemini 2.5 capabilities Native audio output and improvements to Live API Today, the Live API is introducing a preview version of audio-visual input and native audio out dialogue, so you can directly build conversational experiences, with a more natural and expressive Gemini. It also allows the user to steer its tone, accent and style …

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The Automation Trap: Why Low-Code AI Models Fail When You Scale

In the , building Machine Learning models was a skill only data scientists with knowledge of Python could master. However, low-code AI platforms have made things much easier now. Anyone can now directly make a model, link it to data, and publish it as a web service with just a few clicks. Marketers can now …

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How to Set the Number of Trees in Random Forest

Scientific publication T. M. Lange, M. Gültas, A. O. Schmitt & F. Heinrich (2025). optRF: Optimising random forest stability by determining the optimal number of trees. BMC bioinformatics, 26(1), 95. Follow this LINK to the original publication. Forest — A Powerful Tool for Anyone Working With Data What is Random Forest? Have you ever wished …

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