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Machine Learning


Machine Learning

#artificialintelligence

The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications. This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated version of Andrew's pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)


Doctors, Get Ready for Your AI Assistants

WIRED

In 2023, radiologists in hospitals around the world will increasingly use medical images--which include x-rays and CT, MRI, and PET scans--that have been first read and evaluated by AI machines. Gastroenterologists will also be relying on machine vision during colonoscopies and endoscopies to pick up polyps that would otherwise be missed. This progress has been made possible by the extensive validation of "machine eyes"--deep neural networks trained with hundreds of thousands of images that can accurately pick up things human experts can't. This story is from the WIRED World in 2023, our annual trends briefing. Read more stories from the series here--or download or order a copy of the magazine.


U.S. wobbles under burden of keeping too many classified secrets

The Japan Times

The discovery of classified documents at the homes of Donald Trump, Joe Biden and Mike Pence has rekindled a debate about an old habit of the U.S. government -- slapping millions of documents every year with labels of "secret," "top secret" and other confidential designations. Nuclear secrets, names of spies, diplomatic cables: governments everywhere carefully protect information that could compromise security, names of agents or relations with other nations. But in the United States, the machinery of secrecy works overtime. Every year, some 50 million decisions are made on whether to mark government documents as "confidential," "secret" or "top secret," according to several experts. However, "an awful lot of classified documents are not that sensitive," said Bruce Riedel, a former CIA officer currently at the Brookings Institution think tank.


Fusing batch normalisation and convolution for faster inference

#artificialintelligence

Fusing adjacent convolution (Conv)and batch normalisation (BN)layers is a practical way of boosting inference speed. Batch normalisation is one of the most important regularisation techniques in the modern deep learning field.


Could an Emerging Deep Learning Modality Enhance CCTA Assessment of Coronary Artery Disease?

#artificialintelligence

Keya Medical has launched the DeepVessel FFR, a software device that utilizes deep learning to facilitate fractional flow reserve (FFR) assessment based on coronary computed tomography angiography (CCTA). Cleared by the Food and Drug Administration (FDA), the DeepVessel FFR provides a three-dimensional coronary artery tree model and estimates of FFR CT value after semi-automated review of CCTA images, according to Keya Medical. The company said the DeepVessel FFR has demonstrated higher accuracy than other non-invasive tests and suggested the software could help reduce invasive procedures for coronary angiography and stent implantation in the diagnostic workup and subsequent treatment of coronary artery disease. Joseph Schoepf, M.D., FACR, FAHA, FNASCI, the principal investigator of a recent multicenter trial to evaluate DeepVessel FFR, says the introduction of the modality in the United States dovetails nicely with recent guidelines for the diagnosis of chest pain. "I am excited to see the implementation of DeepVessel FFR. It comes together with the 2021 ACC/AHA Chest Pain Guidelines' recognition of the elevated diagnostic role of CCTA and FFR CT for the non-invasive evaluation of patients with stable or acute chest pain," noted Dr. Schoepf, a professor of Radiology, Medicine, and Pediatrics at the Medical University South Carolina.


AI Unleashes its Power to Conquer Cybercrime: A New Era in Cybersecurity

#artificialintelligence

Cybersecurity is a critical issue in today's digital age, as cybercriminals continue to find new ways to infiltrate our systems and steal sensitive information. As the threat of cybercrime looms, it's becoming increasingly clear that traditional cybersecurity methods are no longer enough. But there's hope on the horizon: Artificial Intelligence (AI) is revolutionizing the way we think about cybersecurity and defend against cybercrime. One of the biggest benefits of AI in cybersecurity is its ability to detect and respond to threats in real-time. We all know that traditional cybersecurity methods rely on pre-defined rules and signatures to identify and block malicious activity. But as cybercriminals continue to evolve and find new ways to evade detection, it's becoming clear that this approach is no longer enough.


Data Science & Machine Learning Trends You Cannot Ignore

#artificialintelligence

Digital transformation has become the new mantra for companies to thrive in the digital age. Data science and machine learning are two major assets in the digital transformation era. Digital transformation has become a necessity for businesses. It is the way forward for all businesses, regardless of size and scope. However, it should be more than simply digitizing your processes.


Multi-modal deep learning in less than 15 lines of code - KDnuggets

#artificialintelligence

For many machine learning use-cases, organizations rely solely on tabular data and tree-based models like XGBoost and LightGBM. This is because deep learning is simply too hard for most ML teams. As a result, teams miss out on valuable signals hidden within unstructured data like text and images. New declarative machine learning systems--like open-source Ludwig started at Uber--provide a low-code approach to automating ML that enables data teams to build and deploy state-of-the-art models faster with a simple configuration file. Specifically, Predibase--the leading low-code declarative ML platform--along Ludwig make it easy to build multi-modal deep learning models in 15 lines of code.


Deepfakes: Faces created by AI now look more real than genuine photos

#artificialintelligence

Even if you think you are good at analyzing faces, research shows many people cannot reliably distinguish between photos of real faces and images that have been computer-generated. This is particularly problematic now that computer systems can create realistic-looking photos of people who don't exist. Recently, a fake LinkedIn profile with a computer-generated profile picture made the news because it successfully connected with US officials and other influential individuals on the networking platform, for example. Counter-intelligence experts even say that spies routinely create phantom profiles with such pictures to home in on foreign targets over social media. These deep fakes are becoming widespread in everyday culture which means people should be more aware of how they're being used in marketing, advertising and social media.


How To Become A Machine Learning Expert: A Beginner's Guide - AI Summary

#artificialintelligence

This book is for people with a beginner or intermediate background in machine learning who want to learn something new. You won't need to solve any proofs or run any code while reading. With 30 questions and answers on key concepts in machine learning and AI, this book provides bite-sized nuggets for your journey from machine learning beginner to expert. Even experienced machine learning researchers and practitioners will encounter something new that they can add to their arsenal of techniques.