Articles about Machine Learning

Notifications are part and parcel of life, being both useful and interesting. But they can also be problematic. Google is aware that the notifications generated by websites can be “spammy or even deceptive”.

The worst offenders can attempt to trick people into sharing personal information or downloading dangerous software. Google is using machine learning to fight back, launching warnings about warnings of unwanted notifications.

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If we are lucky, Artificial intelligence might one day help scientists understand the human brain the same way language models predict the next word in a sentence. And now, that future is closer to becoming reality thanks to a new project from Google Research, Harvard University, and HHMI Janelia. You see, these teams have introduced the Zebrafish Activity Prediction Benchmark, better known as “ZAPBench,” which could help researchers create more accurate models for predicting brain activity.

ZAPBench isn’t just another dataset, folks. Actually, this new tool is based on two hours of brain recordings from larval zebrafish, capturing how roughly 70,000 neurons fired in response to different virtual reality scenarios. These tiny fish were shown various environmental changes, including shifting light patterns and moving water currents, while researchers recorded brain activity at an impressively detailed scale.

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Imagine you had to finish every sentence in every book ever written -- with just your best guess of the next word. That’s how large language models (LLMs) like GPT-4 start learning. 

LLMs use self-supervised learning, meaning they don’t need someone to label or explain the data to them. Instead, they learn by reading vast amounts of text from books, code, academic papers, Wikipedia (and its 57 million+ articles), Reddit forums, and news articles, in addition to billions of others, and then predicting what word comes next in a sentence -- over and over again.

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As AI and machine learning technologies rapidly evolve, IT professionals must continuously adapt their skills to stay competitive in the workforce. This requires not only technical expertise but also a commitment to lifelong learning, including earning relevant certifications and developing crucial soft skills like communication and adaptability.

Companies can support this growth by fostering a culture of continuous learning, offering reskilling and upskilling opportunities, and providing tailored training paths for their employees. By prioritizing ongoing development, businesses can ensure their workforce remains at the forefront of emerging technologies, preparing them for the challenges of the AI-driven future.

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The subway in NYC has received a huge upgrade, but it’s probably not at all what you’d expect. No, it is not coming in the form of new trains or renovations to stations (even though that would be great). Actually, the MTA is testing a system that effectively transforms Google Pixel Android smartphones into powerful diagnostic tools for tracking rail defects!

The project, called “TrackInspect,” attaches Google Pixel phones to subway cars. Then, by using the Android devices’ built-in microphones and motion sensors, it detects vibrations and sound patterns. These sounds can indicate areas of track that may need maintenance. How cool is that?

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OpenAI has just announced GPT-4.5! While the company says the model is its most advanced AI yet, there’s definitely more to this release than meets the eye. Version 4.5 is a clear warning for anyone betting against OpenAI, including Elon Musk.

GPT-4.5 is being labeled a “research preview,” meaning OpenAI is still testing its strengths and weaknesses. With that said, it is apparently smarter, more natural in conversations, and better at avoiding mistakes than previous models. That could signal big trouble for xAI’s Grok, which Musk has been pushing as an alternative to OpenAI’s models.

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How Machine Learning is Innovating Today's World provides a comprehensive understanding of the latest advancements and practical applications of machine learning techniques.

Machine learning (ML), a branch of artificial intelligence, has gained tremendous momentum in recent years, revolutionizing the way we analyze data, make predictions, and solve complex problems. As researchers and practitioners in the field, the editors of this book recognize the importance of disseminating knowledge and fostering collaboration to further advance this dynamic discipline. 

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Fully updated to reflect modern developments in the field, the Fifth Edition of An Introduction to Optimization fills the need for an accessible, yet rigorous, introduction to optimization theory and methods, featuring innovative coverage and a straightforward approach. The book begins with a review of basic definitions and notations while also providing the related fundamental background of linear algebra, geometry, and calculus.

With this foundation, the authors explore the essential topics of unconstrained optimization problems, linear programming problems, and nonlinear constrained optimization. In addition, the book includes an introduction to artificial neural networks, convex optimization, multi-objective optimization, and applications of optimization in machine learning.

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A new report reveals that 54 percent of cybersecurity professionals believe cybercriminals will benefit more from AI than the security industry.

The report by The Chartered Institute of Information Security (CIISec), based on a survey of over 300 cybersecurity professionals, finds 51 percent) of those surveyed believe that AI and machine learning will be the most influential technology in the cybersecurity industry over the coming year.

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With so much data being generated, there is immense scope to extract meaningful information that’ll boost business productivity and profitability. By learning to convert raw data into game-changing insights, you’ll open new career paths and opportunities.

The Data Science Workshop begins by introducing different types of projects and showing you how to incorporate machine learning algorithms in them. You’ll learn to select a relevant metric and even assess the performance of your model. To tune the hyperparameters of an algorithm and improve its accuracy, you’ll get hands-on with approaches such as grid search and random search.

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Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms.

The book covers in-depth mathematical topics, such as regression and classification, illustrated by Python examples. As you make your way through the book, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters.

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In Not With A Bug, But With A Sticker: Attacks on Machine Learning Systems and What To Do About Them, a team of distinguished adversarial machine learning researchers deliver a riveting account of the most significant risk to currently deployed artificial intelligence systems: cybersecurity threats. The authors take you on a sweeping tour -- from inside secretive government organizations to academic workshops at ski chalets to Google’s cafeteria -- recounting how major AI systems remain vulnerable to the exploits of bad actors of all stripes.

Based on hundreds of interviews of academic researchers, policy makers, business leaders and national security experts, the authors compile the complex science of attacking AI systems with color and flourish and provide a front row seat to those who championed this change. Grounded in real world examples of previous attacks, you will learn how adversaries can upend the reliability of otherwise robust AI systems with straightforward exploits.

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Microsoft is delving into the depths of human brain function, trying to replicate its most complex processes in artificial intelligence. While this might sound like progress, it raises a chilling question: Are we on the brink of creating an AI that could outthink and overpower its creators?

In collaboration with top universities, Microsoft Research Asia is spearheading projects that mimic the brain's intricate neural networks. CircuitNet, one such project, isn't just another AI -- it’s a system designed to think like a human. By replicating the brain's connectivity patterns, CircuitNet could potentially surpass current AI capabilities, processing information with an efficiency and accuracy that traditional systems can’t match.

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The collapse of Baltimore's Francis Scott Key Bridge sent shockwaves both nationally and internationally earlier this year. The city is now faced with the significant economic loss of a critical transport channel – and the challenging task of its reconstruction. Engineering and construction specialists anticipate that the rebuild effort could take up to 15 years. As a once-widely utilized structure, there is a deep sense of urgency to expedite the resurrection of the Key Bridge.

But current engineering and construction processes could wreak havoc on the possibility of an expeditious rebuild. The design and planning processes for large architecture, engineering and construction (AEC) projects, specifically critical infrastructure such as airports, bridges and ports, are historically rigid. Operating with a highly formalized approach derived from years of informed engineering practices, the volume of factors to contemplate around the build of a crucial transport structure is astronomical. Identifying and adhering to rigorous regulatory standards is only the first hurdle. Combine this with navigating other variables such as climate change, traffic implications and sourcing highly specialized materials, and suddenly a decade doesn’t seem like an unreasonable timeline for the construction of a major transport hub.

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The course starts by setting the foundation with an introduction to machine learning, Python, and essential libraries, ensuring you grasp the basics before diving deeper.

It then progresses through exploratory data analysis, data scrubbing, and pre-model algorithms, equipping you with the skills to understand and prepare your data for modeling.

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