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Machine learning isn’t just a niche tool anymore. It drives decisions that affect billions of dollars and millions of lives. No matter whether you’re approving a loan, forecasting global demand, or suggesting the right seller strategy, the models behind those choices need to be accurate, fair and explainable. That’s where Hatim Kagalwala comes in.
Last Updated on July 10, 2025 by Editorial Team Author(s): Anubha Bhaik Originally published on Towards AI. Designing a Scalable Multi-Agent AI System for Operational Intelligence Source: Image generated by author using DALL·E In the past year, there’s been a lot of discussion about AI agents — how specialized systems can analyze, plan, and act together to solve problems.
Driven by steady progress in deep generative modeling, simulation-based inference (SBI) has emerged as the workhorse for inferring the parameters of stochastic simulators. However, recent work has demonstrated that model misspecification can compromise the reliability of SBI, preventing its adoption in important applications where only misspecified simulators are available.
A new report from Incogni evaluates the data privacy practices of today’s most widely used AI platforms. As generative AI and large language models (LLMs) become deeply embedded in everyday tools and services, the risk of unauthorized data collection and sharing has surged. Incogni’s researchers analyzed nine leading platforms using 11 criteria to understand which systems offer the most privacy-friendly experience.
Speaker: Jason Chester, Director, Product Management
In today’s manufacturing landscape, staying competitive means moving beyond reactive quality checks and toward real-time, data-driven process control. But what does true manufacturing process optimization look like—and why is it more urgent now than ever? Join Jason Chester in this new, thought-provoking session on how modern manufacturers are rethinking quality operations from the ground up.
Last Updated on July 10, 2025 by Editorial Team Author(s): Lihi Gur Arie, PhD Originally published on Towards AI. Introduction Training a high-performing image classifier typically requires large amounts of labeled data. But what if you could achieve top-tier results with minimal data and light training? DINOv2 is a powerful vision foundation model that generates rich image representation vectors, also known as embeddings.
Summary: Batch size in deep learning controls how much data a model processes before updating. It impacts training speed, memory, and accuracy. Understanding it helps improve model performance. Learn how steps, epochs, and batch size work together and how to choose the right batch size for your deep learning project. Introduction If you’ve ever trained a deep learning model or even just heard the term thrown around, you’ve likely come across the word batch size.
Author(s): Ojasva Goyal Originally published on Towards AI. The secret sauce has a name — actually, two names: CUDA and cuDNN. Image by Kevin Ache on Unsplash The Superhero Origin Story Picture this: It’s 2006, and NVIDIA realizes their graphics cards have untapped superpowers. They’re not just for making video games look pretty, these GPUs contain thousands of tiny cores that could solve complex problems if only someone would give them the chance.
Author(s): Ojasva Goyal Originally published on Towards AI. The secret sauce has a name — actually, two names: CUDA and cuDNN. Image by Kevin Ache on Unsplash The Superhero Origin Story Picture this: It’s 2006, and NVIDIA realizes their graphics cards have untapped superpowers. They’re not just for making video games look pretty, these GPUs contain thousands of tiny cores that could solve complex problems if only someone would give them the chance.
Skip to main content Skip to secondary menu Skip to primary sidebar Skip to footer Geeky Gadgets The Latest Technology News Home Top News AI Apple Android Technology Guides Gadgets Hardware Gaming Autos Deals About The Hidden Flaw in RAG Systems That’s Costing You Accuracy : Cut RAG Hallucinations 1:55 pm July 9, 2025 By Julian Horsey What if the very systems designed to enhance accuracy were the ones sabotaging it?
Last Updated on July 10, 2025 by Editorial Team Author(s): Dulan Jayawickrama Originally published on Towards AI. 5 Real-World Uses of TinyML and Edge AI You Should Know Generated by AI using OpenAI’s image generation tools. Imagine your phone or watch with a little brain of its own. TinyML and Edge AI mean exactly that–running machine learning on the device itself.
With the shift toward de-escalating surgery in breast cancer, prediction models incorporating imaging can reassess the need for surgical axillary staging. This study employed advancements in deep learning to comprehensively evaluate routine mammograms for preoperative lymph node metastasis prediction. Mammograms and clinicopathological data from 1265 cN0 T1–T2 breast cancer patients (primary surgery, no neoadjuvant therapy) were retrospectively collected from three Swedish institutions.
Author(s): Pawel Rzeszucinski, PhD Originally published on Towards AI. The Curious Case of Large Language Models: How to Raise a Well-Behaving Model “My name is Benjamin Button, and I was born under unusual circumstances. While everyone else was aging, I was gettin’ younger… all alone.”, Benjamin Button [Image source: author] Introduction Much like Benjamin Button, who was born old and had to grow into the world in reverse, large language models (LLMs) are born with a kind of overwhelming maturi
ETL and ELT are some of the most common data engineering use cases, but can come with challenges like scaling, connectivity to other systems, and dynamically adapting to changing data sources. Airflow is specifically designed for moving and transforming data in ETL/ELT pipelines, and new features in Airflow 3.0 like assets, backfills, and event-driven scheduling make orchestrating ETL/ELT pipelines easier than ever!
Enterprises adopting advanced AI solutions recognize that robust security and precise access control are essential for protecting valuable data, maintaining compliance, and preserving user trust. As organizations expand AI usage across teams and applications, they require granular permissions to safeguard sensitive information and manage who can access powerful models.
French AI startup Mistral is negotiating with investors, including Abu Dhabi’s MGX fund, to secure up to $1 billion in equity funding, according to Bloomberg. Concurrently, Mistral is engaging with French financial institutions, such as Bpifrance SACA, to obtain several hundred million euros in debt financing. These discussions aim to bolster Mistral’s financial position within the global artificial intelligence sector.
Wearable devices record physiological and behavioral signals that can improve health predictions. While foundation models are increasingly used for such predictions, they have been primarily applied to low-level sensor data, despite behavioral data often being more informative due to their alignment with physiologically relevant timescales and quantities.
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Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. With the 3.0 release, the top-requested features from the community were delivered, including a revamped UI for easier navigation, stronger security, and greater flexibility to run tasks anywhere at any time.
We design new differentially private algorithms for the problems of adversarial bandits and bandits with expert advice. For adversarial bandits, we give a simple and efficient conversion of any non-private bandit algorithms to private bandit algorithms. Instantiating our conversion with existing non-private bandit algorithms gives a regret upper bound of O(KTε)Oleft(frac{sqrt{KT}}{sqrt{varepsilon}}right)O(εKT), improving upon the existing upper bound O(KTlog(KT)ε)Oleft(frac{sqrt{KT log(KT)}}
This post was co-written with Le Vy from Parcel Perform. Access to accurate data is often the true differentiator of excellent and timely decisions. This is even more crucial for customer-facing decisions and actions. A correctly implemented state-of-the-art AI can help your organization simplify access to data for accurate and timely decision-making for the customer-facing business team, while reducing the undifferentiated heavy lifting done by your data team.
A Turkish court has ordered a nationwide block on access to Grok, the artificial intelligence chatbot developed by Elon Musk’s xAI, following allegations that the tool generated insulting content about prominent figures, including President Recep Tayyip Erdoğan. The move marks Türkiye’s first-ever ban on an AI tool. The Ankara Chief Public Prosecutor’s Office initiated an investigation and sought the access ban after the chatbot reportedly produced offensive responses when prom
Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. With the 3.0 release, the top-requested features from the community were delivered, including a revamped UI for easier navigation, stronger security, and greater flexibility to run tasks anywhere at any time.
Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter 5 Ways to Transition Into AI from a Non-Tech Background You have a non-tech background? Sure, you can transition into AI.
Clinical trial leaders have worked to avoid a diversity crisis for decades. When they use AI, they may spend far less time screening while maintaining the same level of accuracy. There’s a bigger issue, though, and that’s who gets screened in the first place. Traditional recruitment methods often miss entire communities, leaving incomplete data about how treatments work across different populations.
Many enterprises are using large language models (LLMs) in Amazon Bedrock to gain insights from their internal data sources. Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.
Author(s): Towards AI Editorial Team Originally published on Towards AI. If you’ve watched the first two tutorials in the 10-hour LLM Primer, you already know what prompting can do, and you’ve seen how retrieval takes it a step further. But if you’ve ever hit a wall with tone, domain accuracy, or stubborn hallucinations, you already know the truth: Sometimes, a clever prompt (context) isn’t enough.
In Airflow, DAGs (your data pipelines) support nearly every use case. As these workflows grow in complexity and scale, efficiently identifying and resolving issues becomes a critical skill for every data engineer. This is a comprehensive guide with best practices and examples to debugging Airflow DAGs. You’ll learn how to: Create a standardized process for debugging to quickly diagnose errors in your DAGs Identify common issues with DAGs, tasks, and connections Distinguish between Airflow-relate
Generative AI continues to reshape how businesses approach innovation and problem-solving. Customers are moving from experimentation to scaling generative AI use cases across their organizations, with more businesses fully integrating these technologies into their core processes. This evolution spans across lines of business (LOBs), teams, and software as a service (SaaS) providers.
9 reasons why work data is the single most valuable data source for LLM training, uniquely capable of propelling LLM performance to unprecedented heights.
IBM has announced a new line of data center chips and servers, introduced on Tuesday, designed to enhance power efficiency and streamline artificial intelligence integration within business operations. This marks the initial significant update to IBM’s “Power” chip line since 2020. These new Power11 chips are specifically engineered for data centers, historically competing with offerings from Intel and Advanced Micro Devices.
Amazon Bedrock Knowledge Bases offers a fully managed Retrieval Augmented Generation (RAG) feature that connects large language models (LLMs) to internal data sources. This feature enhances foundation model (FM) outputs with contextual information from private data, making responses more relevant and accurate. At AWS re:Invent 2024, we announced Amazon Bedrock Knowledge Bases support for natural language querying to retrieve structured data from Amazon Redshift and Amazon SageMaker Lakehouse.
Speaker: Alex Salazar, CEO & Co-Founder @ Arcade | Nate Barbettini, Founding Engineer @ Arcade | Tony Karrer, Founder & CTO @ Aggregage
There’s a lot of noise surrounding the ability of AI agents to connect to your tools, systems and data. But building an AI application into a reliable, secure workflow agent isn’t as simple as plugging in an API. As an engineering leader, it can be challenging to make sense of this evolving landscape, but agent tooling provides such high value that it’s critical we figure out how to move forward.
Author(s): Daniel Voyce Originally published on Towards AI. Photo by Fabrizio Chiagano on Unsplash If you have read any of my previous articles you will see that more often than not I try and self-host my infrastructure (because as a perpetual startup CTO, I am cheap by nature). I have been pretty heavily utilising GraphRAG (Both Microsofts version and my own home-grown version) for the past year and I am always amazed at how much a small increase in document complexity can blow out budgets.
Sadhasivam Mohanadas Enterprise Architect|Quantum-AI Researcher|AI & Digital Health Leader |Member:IEEE/IET/BCS|Innovating tech for humanity Quantum computing exists beyond the realm of science fiction.
Last Updated on July 10, 2025 by Editorial Team Author(s): dave ginsburg Originally published on Towards AI. In the fast-paced world of marketing, gaining a rapid yet rigorous financial snapshot of your competitors can mean the difference between seizing an opportunity and missing the mark. Beyond press releases and news blips, the best source of unfiltered insight often lies in a company’s own SEC filings — specifically the 10-Q quarterly reports.
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Speaker: Andrew Skoog, Founder of MachinistX & President of Hexis Representatives
Manufacturing is evolving, and the right technology can empower—not replace—your workforce. Smart automation and AI-driven software are revolutionizing decision-making, optimizing processes, and improving efficiency. But how do you implement these tools with confidence and ensure they complement human expertise rather than override it? Join industry expert Andrew Skoog as he explores how manufacturers can leverage automation to enhance operations, streamline workflows, and make smarter, data-dri
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