If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The information commissioner has expressed concern over the lack of a formal legal framework for the use of facial recognition cameras by the police. A barrister for the commissioner, Elizabeth Denham, told a court the current guidelines around automated facial recognition (AFR) technology were "ad hoc" and a clear code was needed. In a landmark case, Ed Bridges, an office worker from Cardiff, claims South Wales police violated his privacy and data protection rights by using AFR on him when he went to buy a sandwich during his lunch break and when he attended a peaceful anti-arms demonstration. The technology maps faces in a crowd and then compares them with a watchlist of images, which can include suspects, missing people or persons of interest to the police. The cameras have been used to scan faces in large crowds in public places such as streets, shopping centres, football crowds and music events such as the Notting Hill carnival.
A common need when you are analyzing real-world data-sets is determining which data point stand out as being different to all others data points. Such data points are known as anomalies. This article was originally published on Medium by Davis David. In this article, you will learn a couple of Machine Learning-Based Approaches for Anomaly Detection and then show how to apply one of these approaches to solve a specific use case for anomaly detection (Credit Fraud detection) in part two. A common need when you analyzing real-world data-sets is determining which data point stand out as being different to all others data points.
The IIF surveyed 59 institutions (54 banks and 5 insurers) on their exploration and adoption of Machine Learning techniques in Anti-Money Laundering. While the detailed version of our resultant report is limited in its distribution to the regulatory community and those 59 firms, a short-form summary report has also been prepared for public distribution. This study covers the particular purposes of application in the AML space, as well as which types of specific techniques are in scope, firms' maturity in adopting, benefits, challenges and model governance. Our findings indicated that the application of machine learning techniques in AML is spreading quickly across the industry, driven by a dedication to build a stronger and more effective defense system against illicit activity. Significantly, none of the 59 surveyed firms were pursuing machine learning as a means to reduce staff, but rather to gain greater and faster insights that can be made available for their trained AML analysts.
Sarah Runge, Global Head of Financial Crime Compliance Regulatory Strategy for Credit Suisse, joins us on this week's episode of FRT to discuss the benefits and challenges of applying Machine Learning in Anti-Money Laundering and Countering Terrorism Financing (AML/CTF). Sarah highlights the potential that enhanced analytics hold to strengthen the defense mechanisms against financial crime. Today's framework and practices lead to inefficiencies that can make one lose sight of the bigger picture. However, we also explore how technology cannot (and should not) replace the human element and vigilance in a financial institution's safeguarding measures. It should be seen as a way to empower analysts and focus their resources on the cases that need their attention the most.
Machine learning and artificial intelligence were intended to make people more productive, not replace them. The tools are ultimately aimed at engineers who want to develop ways to solve problems more efficiently, at least that's how the Advanced Research Projects Agency -- Energy (ARPA-E) sees it. Engineers have long had tools such as computer-aided design and a lot of vendors provide CAD modeling software. But ARPA-E is going beyond that. Program Director David Tew said his agency hopes to "automate" the intuition and expertise that engineers bring to the table.
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This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.
Stripe uses machine learning to respond to our users' complex, real-world problems. Machine learning powers Radar to block fraud, and Billing to retry failed charges on the network. Stripe serves millions of businesses around the world, and our machine learning infrastructure scores hundreds of millions of predictions across many machine learning models. These models are powered by billions of data points, with hundreds of new models being trained each day. Over time, the volume, quality of data, and number of signals have grown enormously as our models continuously improve in performance.
Authors: Egor Zakharov, Aliaksandra Shysheya, Egor Burkov, Victor Lempitsky Abstract: Several recent works have shown how highly realistic human head images can be obtained by training convolutional neural networks to generate them. In order to create a personalized talking head model, these works require training on a large dataset of images of a single person. However, in many practical scenarios, such personalized talking head models need to be learned from a few image views of a person, potentially even a single image. Here, we present a system with such few-shot capability. It performs lengthy meta-learning on a large dataset of videos, and after that is able to frame few- and one-shot learning of neural talking head models of previously unseen people as adversarial training problems with high capacity generators and discriminators.
There's little doubt that the buzzword of 2018 was artificial intelligence. It was hard to find a headline last year in the tech space that didn't focus on how AI was going to change the world. Scrambling to be part of the revolution, companies claimed every new product was "AI-powered" or had "AI capabilities" to challenge the status quo for operations and innovations. Developers jumped in on machine learning, neural networks, natural language processing and a range of other subfields to innovate and monetize. Now that the hype has calmed a bit and brands are finally starting to implement AI-driven solutions, we are seeing a shift in the way consumers interact with brands -- from hyperpersonalized messages, to self-driving cars, to anticipating the next step in your pizza order.