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Machine Learning


Introducing Baskerville (waf!)

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Baskerville is a machine operating on the Deflect network that protects sites from hounding, malicious bots. It's also an open source project that, in time, will be able to reduce bad behaviour on your networks too. Baskerville responds to web traffic, analyzing requests in real-time, and challenging those acting suspiciously. A few months ago, Baskerville passed an important milestone – making its own decisions on traffic deemed anomalous. The quality of these decisions (recall) is high and Baskerville has already successfully mitigated many sophisticated real-life attacks.


NXP Invests in Au-Zone to Enhance Machine Learning Capabilities

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NXP is hoping to improve its machine learning offerings after making a strategic investment in Au-Zone Technologies. The exclusive arrangement specifically concerns Au-Zone's DeepView ML Tool Suite, which will be used to bolster NXP's eIQ Machine Learning software development environment and lead to the creation of new Edge machine learning products. In that regard, the DeepView Suite comes with a graphical user interface (GUI) and workflows that will make it easier to import datasets, and to train neural network models for Edge devices. DeepView's run-time inference engine will give eIQ developers more insight into system memory usage, data movement, and other performance metrics in real time, which will in turn allow them to optimize their model before deploying it in a System-on-Chip (SoC) solution. "This partnership will accelerate the deployment of embedded Machine Learning features," said Au-Zone CEO Brad Scott.


Advancement of AI and Machine Learning in Fraud Prevention

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In June 2020, Visa announced the launch of a digital tool that would help the US financial services institutions with their efforts to counter new account fraud. According to Visa, new account frauds are estimated to be around $10 billion a year. What is that tool, and why is it needed? In this article, we explore what is new from the AI and ML desk to help FIs in fraud detection and prevention.


5 Must-Read Data Science Papers (and How to Use Them) - KDnuggets

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Data science might be a young field, but that doesn't mean you won't face expectations about having an awareness of certain topics. This article covers several of the most important recent developments and influential thought pieces. Topics covered in these papers range from the orchestration of the DS workflow to breakthroughs in faster neural networks to a rethinking of our fundamental approach to problem solving with statistics. The team at Google Research provides clear instructions on antipatterns to avoid when setting up your data science workflow. This paper borrows the metaphor of technical debt from software engineering and applies it to data science.


TensorFlow - Hands-on Machine Learning with TensorFlow

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Created by The Click Reader Preview this Udemy Course - GET COUPON CODE Learn how to build Machine Learning projects in this TensorFlow Course created by The Click Reader. In this course, you will be learning about Scalar as well as Tensors and how to create them using TensorFlow. You will also be learning how to perform various kinds of Tensor operations for manipulating and changing tensor values. You will be performing a total of three Machine Learning projects while learning through this TensorFlow full course: 1. Linear Regression from Scratch You will be learning how to create a Linear Regression model from scratch using TensorFlow. You will be preparing the data, building the model architecture as well as training the model using a custom-made loss function as well as an optimizer.


AI Fairness Isn't Just an Ethical Issue

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The authority that administers A-Level college entrance exams in the UK, Ofqual, recently found itself mired in scandal. Unable to hold live exams because of Covid-19, it designed and employed an algorithm that based scores partly on the historical performance of the schools students attended. The outcry was immediate, as students who were already disadvantaged found themselves further penalized by artificially deflated scores, their efforts disregarded and their futures thrown into disarray. This is far from an isolated incident. Even the world's most sophisticated technology companies have faced similar problems.



How Amex Uses AI To Automate 8 Billion Risk Decisions (And Achieve 50% Less Fraud)

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There are few bigger targets for cyber criminals than credit card companies. Which is why the U.S. alone had over 270,000 reports of credit card fraud in 2019, double the 2017 rate. So what's a credit card company to do? Use artificial intelligence to sniff out fraud and block it. "We believe at American Express that we have the world's largest and most advanced machine learning system in the financial services industry," American Express' VP of risk management Anjali Dewan told me recently on the TechFirst podcast. "And these models are ... monitoring 100% of these transactions and returning 8 billion credit and fraud risk decisions in real time."


An Introduction to SpeedLegal

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I've originally published this article here. In a previous post, I described why Artificial Intelligence (AI) is necessary for businesses and legal professionals who are reviewing legal documents. More people are relying on powerful Machine Learning models to streamline the document review process and make decisions in a fraction of the time. At SpeedLegal, we believe in products that are easy to use and accessible to everyone. Our motto is Answers to your legal concerns need to be two clicks away.


Brain-inspired computing boosted by new concept of completeness

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The next generation of high-performance, low-power computer systems might be inspired by the brain. However, as designers move away from conventional computer technology towards brain-inspired (neuromorphic) systems, they must also move away from the established formal hierarchy that underpins conventional machines -- that is, the abstract framework that broadly defines how software is processed by a digital computer and converted into operations that run on the machine's hardware. This hierarchy has helped enable the rapid growth in computer performance. Writing in Nature, Zhang et al.1 define a new hierarchy that formalizes the requirements of algorithms and their implementation on a range of neuromorphic systems, thereby laying the foundations for a structured approach to research in which algorithms and hardware for brain-inspired computers can be designed separately. The performance of conventional digital computers has improved over the past 50 years in accordance with Moore's law, which states that technical advances will enable integrated circuits (microchips) to double their resources approximately every 18–24 months.