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In today’s data-driven world, machine learning fuels creativity across industries-from healthcare and finance to e-commerce and entertainment. For many fulfilling roles in data science and analytics, understanding the core machine learning algorithms can be a bit daunting with no examples to rely on. This blog will look at the most popular machine learning algorithms and present real-world use cases to illustrate their application.
Large language models (LLMs) have demonstrated impressive performance on several tasks and are increasingly deployed in real-world applications. However, especially in high-stakes settings, it becomes vital to know when the output of an LLM may be unreliable. Depending on whether an answer is trustworthy, a system can then choose to route the question to another expert, or otherwise fall back on a safe default behavior.
Last Updated on July 7, 2025 by Editorial Team Author(s): MD Rafsun Sheikh Originally published on Towards AI. Source: Linkedin Learn how context engineering transforms AI behavior, from smarter agents to better RAG systems. Master prompt structure, memory, and more. Forget Fine-Tuning. Let’s Talk Context If you’ve ever been frustrated by vague, robotic answers from AI, there’s a good chance the problem wasn’t the model but the context.
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.
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ADD / XOR / ROL A blog about reverse engineering, mathematics, politics, economics and more. Sunday, July 06, 2025 A non-anthropomorphized view of LLMs In many discussions where questions of "alignment" or "AI safety" crop up, I am baffled by seriously intelligent people imbuing almost magical human-like powers to something that - in my mind - is just MatMul with interspersed nonlinearities.
This paper was presented at the Workshop on Reliable and Responsible Foundation Models at ICML 2025. Large Language Models (LLMs) have demonstrated impressive generalization capabilities across various tasks, but their claim to practical relevance is still mired by concerns on their reliability. Recent works have proposed examining the activations produced by an LLM at inference time to assess whether its answer to a question is correct.
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This paper was presented at the Workshop on Reliable and Responsible Foundation Models at ICML 2025. Large Language Models (LLMs) have demonstrated impressive generalization capabilities across various tasks, but their claim to practical relevance is still mired by concerns on their reliability. Recent works have proposed examining the activations produced by an LLM at inference time to assess whether its answer to a question is correct.
Your Content Goes Here Your Content Goes Here Researchers have developed an AI model named Centaur, claiming it can simulate the human mind by training on a data set called Psych-101, which aggregates data from 160 psychology experiments, encompassing over 60,000 participants' decisions.
We design differentially private algorithms for the problem of prediction with expert advice under dynamic regret, also known as tracking the best expert. Our work addresses three natural types of adversaries, stochastic with shifting distributions, oblivious, and adaptive, and designs algorithms with sub-linear regret for all three cases.
Last Updated on July 7, 2025 by Editorial Team Author(s): R. Thompson (PhD) Originally published on Towards AI. “We are being forced to confront the most fundamental questions about what it means to be human — and we’re not ready.” — AI Researcher What happens when a machine not only calculates but contemplates? When it stops being a tool and starts becoming a participant?
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!
Last Updated on July 9, 2025 by Editorial Team Author(s): Anirban Bhattacharjee Originally published on Towards AI. In modern print and scan workflows document reformatting is a critical component, especially in environments dealing with diverse input formats, different languages, and layouts which are common in modern enterprise environments. Traditional rule-based algorithms often fall short in accurately interpreting and adapting such content.
Last Updated on July 7, 2025 by Editorial Team Author(s): Bruce Tisler Originally published on Towards AI. And That’s Exactly What They Hoped Would HappenJustFunguy A Vending Machine with a Mind of Its Own Picture a vending machine humming in a quiet office corner, its shelves stocked with chips and soda, ready to dispense a quick snack. Now imagine it’s not just a machine — it’s a business, run by an AI named Claudius, juggling orders, setting prices, and chatting with suppliers via email.
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.
Should a large language model (LLM) be used as a therapist? In this paper, we investigate the use of LLMs to *replace* mental health providers, a use case promoted in the tech startup and research space. We conduct a mapping review of therapy guides used by major medical institutions to identify crucial aspects of therapeutic relationships, such as the importance of a therapeutic alliance between therapist and client.
Cookies help us display personalized product recommendations and ensure you have great shopping experience. Accept X By using this site, you agree to the Privacy Policy and Terms of Use. Accept Analytics Analytics Show More How Data Analytics Reduces Truck Accidents and Speeds Up Claims 7 Min Read Interior Designers Boost Profits with Predictive Analytics 8 Min Read Improving LinkedIn Ad Strategies with Data Analytics 9 Min Read Data Helps Speech-Language Pathologists Deliver Better Results 6 Mi
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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.
Hannah Cairo has solved the so-called Mizohata-Takeuchi conjecture, a problem in harmonic analysis closely linked to other central results in the field.
ChatGPT can help you write an essay, but it might defeat the purpose of writing an essay. Your brain works differently when you're using generative AI for a task than when you use your brain alone. Namely, you're less likely to remember what you did.
ChatGPT can help you write an essay, but it might defeat the purpose of writing an essay. Your brain works differently when you're using generative AI for a task than when you use your brain alone. Namely, you're less likely to remember what you did.
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
BEIJING/SHANGHAI (Reuters) -Huawei's artificial intelligence research division has rejected claims that a version of its Pangu Pro large language model has copied elements from an Alibaba model, saying that it was independently developed and trained.
You are using an outdated browser. Please upgrade your browser to improve your experience. Crypto 101 Crypto 101 is an introductory course on cryptography, freely available for programmers of all ages and skill levels. Get current version (PDF) Tweet Start to finish. Comes with everything you need to understand complete systems such as SSL/TLS: block ciphers, stream ciphers, hash functions, message authentication codes, public key encryption, key agreement protocols, and signature algorithms.
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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.
Though most signs are telling us artificial intelligence isn't taking anyone's jobs, employers are still using the tech to justify layoffs , outsource work to the global South , and scare workers into submission. But that's not all — a growing number of employers are using AI not just as an excuse to downsize, but are giving it the final say in who gets axed.
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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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