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Operationalizing Machine Learning for the Automotive Future

#artificialintelligence

It's no secret that global mobility ecosystems are changing rapidly. Like so many other industries, automakers are experiencing massive technology-driven shifts. The automobile itself drove radical societal changes in the 20th century, and current technological shifts are again quickly restructuring the way we think about transportation. The rapid progress in AI/ML has propelled the emergence of new mobility application scenarios that were unthinkable just a few years ago. These complex use cases require some rigorous MLOps planning.



Main concepts behind Machine Learning

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Imagine you are teaching a kid to differentiate dogs from cats: at first, you show him many images of both animals, identifying each of them. With these examples, he can associate each animal with its name and then classify new images correctly. The supervised learning has exactly the same idea: from a big train dataset, the algorithm "learns" the relationship between data and label and, therefore, it can predict the result of any other input. In mathematical terms, we are trying to find a expression Y f(X) b that can predict the results. Where X is the input, Y is the prediction and f(X) b is the model learned by the algorithm.


ggforce: Make a Hull Plot to Visualize Clusters in ggplot2

#artificialintelligence

The ggforce package is a ggplot2 extension that adds many exploratory data analysis features. In this tutorial, we'll learn how to make hull plots for visualizing clusters or groups within our data. This article is part of R-Tips Weekly, a weekly video tutorial that shows you step-by-step how to do common R coding tasks. Here are the links to get set up. Learn how to use ggforce in our 7-minute YouTube video tutorial. The Hull Plot is a visualization that produces a shaded areas around clusters (groups) within our data.


Blue Hexagon Recognized by CRN - "The 10 Hottest AI Security Companies You Need to Know"

#artificialintelligence

WIRE)--Blue Hexagon, a leading agentless cloud-native AI platform, today announces CRN's recognition of Blue Hexagon in the "10 Hottest AI Security Companies You Need to Know." This acknowledgment signifies the work Blue Hexagon has devoted to active and continuous AI-driven cloud security and network threat detection and response (NDR). As the leading cloud security platform, for actionable visibility, real-time threat defense and continuous compliance, this underscores Blue Hexagon's commitment to delivering innovative solutions to customers who need to secure their data, network and workloads in the public cloud. Blue Hexagon's deep learning models automatically analyze millions of traits within payloads, protocols, and headers to identify a wide variety of known and unknown file-based and protocol-based threats, in less than a second. The company's technology can analyze cloud configurations, cloud storage activity, and the entire threat kill chain in real time, without the burden of deploying and managing agents.


The Future of AI and Big Data: Three Concepts

#artificialintelligence

"We are probably in the second or third inning." Lo, a professor of finance at the MIT Sloan School of Management, and Ajay Agrawal of the University of Toronto's Rotman School of Management shared their perspective at the inaugural CFA Institute Alpha Summit in May. In a conversation moderated by Mary Childs, they focused on three principal concepts that they expect will shape the future of AI and big data. Lo said that applying machine learning to such areas as consumer credit risk management was certainly the first inning. But the industry is now trying to use machine learning tools to better understand human behavior.


Facebook Fellow Spotlight: Shaping the future with neural program synthesis and adversarial ML - Facebook Research

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Each year, PhD students from around the world apply for the Facebook Fellowship, a program designed to encourage and support promising doctoral students who are engaged in innovative and relevant research in areas related to computer science and engineering. Fellowship recipients receive tuition funding for up to two years to conduct their research at their respective universities, independently of Facebook. To learn about award details, eligibility, and more, visit the program page below. Xinyun is a PhD student at UC Berkeley working with Professor Dawn Song and is expected to graduate in 2022. Her research explores the intersection of deep learning, programming languages, and security, focused on neural program synthesis and adversarial machine learning (ML).


It's time to standardize robotic surgery

ZDNet

The global surgical robotics market is expanding rapidly and may soon be worth $120B. But is the medical training ecosystem ready for the shift to robot-assisted surgeries? As more surgeons use robots in the OR, the approach for training on them and using them needs to be standardized. The truth is that all surgeons aren't approaching this innovative tech the same way. Standardized best practices are what set surgeons and patients up for success, and will help to make robotic surgery safer in the future.


Automated catfishing: a dating site chat bot

#artificialintelligence

Ok, title is a little weird, but I'm quite interested in all things AI and chatbots. The challenge I made for myself was easy: how long can a bot keep a normal everyday conversation going before the other party realizes he or she is talking to a bot.