"Many researchers … speculate that the information-processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons. [Artificial neural networks] capture this kind of highly parallel computation based on distributed representations"
– from Machine Learning (Section 4.1.1; page 82) by Tom M. Mitchell, McGraw Hill Companies, Inc. (1997).
The field of machine learning is becoming easier and easier to enter thanks to readily available tools, a wide range of open source datasets, and a community open to sharing ideas and giving advice. Almost everything you need to get started is online; it's just a matter of finding it. To help entry-level enthusiasts get their head around different ML systems and how to implement them, I've put together some of my favorite machine learning tutorials. All of the following articles provide a brief introduction to the systems being covered, talk you through the cleaning, testing, and implementation process, and also provide links to datasets and Gitub repositories so you can follow the same steps on your own. This detailed guide explores transformer architecture by creating a translator that takes an English sentence and translates it to German. It covers data preprocessing, model training, and wraps things up by looking at the results and what could be done to improve the system.
Photos underage girls share on their social media accounts are being faked to appear nude and shared on messaging app Telegram, a new report discovered. The disturbing images are created using a simple'deepfake' bot that can virtually remove clothes using artificial intelligence, according to report authors Sensity. More than 100,000 non-consensual sexual images of 10,000 women and girls have been shared online that were created using the bot between July 2019 and 2020. The majority of the victims were private individuals with photos taken from social media - all were women and some looked'visibly underage', Sensity said. Sensity says what makes this bot particularly scary is how easy it is to use as it just requires the user to upload an image of a girl, click a few buttons and it then uses its'neural network' to determine what would be under the clothes and produce a nude This form of'deepfake porn' isn't new, the technology behind this bot is suspected to be based on an tool produced last year called DeepNude.
Then, this course is for you. The Common mistake by a data scientist is Applying the tools without the intuition of how it works and behaves. Having the solid foundation of mathematics will help you to understand how each algorithm work, its limitations and its underlying assumptions. With this, you will have an edge over your peers and makes you more confident in all the applications of Machine Learning, Data Science, and Deep Learning. As a common saying: It always pays to know the machinery under the hood, rather than being a guy who is just behind the wheel with no knowledge about the car.
This article is part of "Deconstructing artificial intelligence," a series of posts that explore the details of how AI applications work. One of the things that caught my eye at Nvidia's flagship event, the GPU Technology Conference (GTC), was Maxine, a platform that leverages artificial intelligence to improve the quality and experience of video-conferencing applications in real-time. Maxine used deep learning for resolution improvement, background noise reduction, video compression, face alignment, and real-time translation and transcription. In this post, which marks the first installation of our "deconstructing artificial intelligence" series, we will take a look at how some of these features work and how they tie-in with AI research done at Nvidia. We'll also explore the pending issues and the possible business model for Nvidia's AI-powered video-conferencing platform.
Historians and nostalgic residents alike take an interest in how cities were constructed and how they developed -- and now there's a tool for that. Google AI recently launched the open-source browser-based toolset "rǝ," which was created to enable the exploration of city transitions from 1800 to 2000 virtually in a three-dimensional view. Google AI says the name rǝ is pronounced as "re-turn" and derives its meaning from "reconstruction, research, recreation and remembering." This scalable system runs on Google Cloud and Kubernetes and reconstructs cities from historical maps and photos. There are three main components to the toolset. Warper is a crowdsourcing platform,where users can upload photos of historical print maps and georectify them to match real world coordinates.
MIT researchers have identified a brain pathway critical in enabling primates to effortlessly identify objects in their field of vision. The findings enrich existing models of the neural circuitry involved in visual perception and help to further unravel the computational code for solving object recognition in the primate brain. Led by Kohitij Kar, a postdoc at the McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, the study looked at an area called the ventrolateral prefrontal cortex (vlPFC), which sends feedback signals to the inferior temporal (IT) cortex via a network of neurons. The main goal of this study was to test how the back-and-forth information processing of this circuitry -- that is, this recurrent neural network -- is essential to rapid object identification in primates. The current study, published in Neuron and available via open access, is a followup to prior work published by Kar and James DiCarlo, the Peter de Florez Professor of Neuroscience, the head of MIT's Department of Brain and Cognitive Sciences, and an investigator in the McGovern Institute and the Center for Brains, Minds, and Machines.
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.
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.
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."
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.