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Cloak your photos with this AI privacy tool to fool facial recognition

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Ubiquitous facial recognition is a serious threat to privacy. The idea that the photos we share are being collected by companies to train algorithms that are sold commercially is worrying. Anyone can buy these tools, snap a photo of a stranger, and find out who they are in seconds. But researchers have come up with a clever way to help combat this problem. The solution is a tool named Fawkes, and was created by scientists at the University of Chicago's Sand Lab.


Recommender Systems and Deep Learning in Python

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Recommender Systems and Deep Learning in Python 4.6 (1,635 ratings) Course Ratings are calculated from individual students' ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately. What do I mean by "recommender systems", and why are they useful? Let's look at the top 3 websites on the Internet, according to Alexa: Google, YouTube, and Facebook. Recommender systems form the very foundation of these technologies. They are why Google is the most successful technology company today.


What we know about IoT Security? – DocTooLss

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We read a lot about IoT, but not clear what exactly it means, although we know about its definition so here we explain in simple terms. IoT is basically connecting of computing devices, mechanical, digital machines, objects, and people with one another. Ex: wirelessly connecting devices such as smart speakers i.e. our very own Amazon Alexa or Google Home, smart TVs, Apple Watch, internet-connected baby monitors, video doorbells, and even toys, CCTV camera's controlled by smartphones. The technology that is concerned with safeguarding the connected devices and networks in the internet of things (IoT). IoT is a concept based on the idea of everyday physical objects with the ability to communicate directly over the Internet.


The Best Resources on Artificial Intelligence and Machine Learning

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Half of this crazy year is behind us and summer is here. Over the years, we machine learning engineers at Ximilar have gathered a lot of interesting ML/AI material from which we draw. I have chosen the best ones from podcasts to online courses that I recommend to listen to, read, and check. Some of them are introductory, others more advanced. However, all of them are high-quality ones made by the best people in the field and they are worth checking.


How Does Social Media Use Artificial Intelligence?

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As technology develops, AI is making its way into every aspect of our lives. From self-driving cars, Alexa, Google Home and even traffic cameras – AI is everywhere! And as people are beginning to recognise its presence as well as its usage of our data, they're asking how else it is infiltrating their lives. And one part of our lives in which its role is quite significant is social media. It's helping our technology and our social platforms to become more intelligent; using our details and behaviour to become more individualised and give the user the best experience.


Top 10 Python Libraries that Every Data Scientist Must Know

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Python is one of the most popular and widely known programming languages that has replaced many programming languages in the industry. It is one of the most loved programming languages that data science professionals use more because it is an ocean of libraries. Python is known as the beginner's level programming language because of its simplicity and easiness, its programming syntax is simple to learn and is of high level compared to C, Java, and C . Pytorch is an open source library, it basically a replacement of Numpy. PyTorch comes with higher-level functionality useful for building a deep neural network.


Life Imitates Orwell...

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And I am talking Season 3. Or Amazon's hit, The Handmaid's Tale? Do you just binge and veg out or are you like me, and see how easily we could, and are, slipping into these worlds? After watching shows like this I often find myself reflecting back on George Orwell's 1984. It proves more eerily prophetic with each passing year. This Season, I fear, the writers of Westworld are almost scripting our future lives. You may not have caught it, but it is all in there.


Don't Forget what 'Deep' & 'Learning' Actually Mean

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Think critically about whether you need to apply deep-learning to your datasets. Deep Learning, one of the "hottest" things in AI, has a way of seeping into popular culture as this mysterious, software that can make seemingly amazing classifications at human-level accuracy in Computer Vision, speech recognition, or play games like Go, recommend our favorite movies, and the like. But deep learning has crucial pitfalls, when it drives cars that sadly, more than once, have injured or killed their drivers or pedestrians because of silly image-recognition mistakes. Or, when deep learning is used for face-recognition ––something that clearly has adverse effects on people of color, LGBT, and other marginalized groups –– and if deep learning's face-prediction is used by institutions of power with a history of racism, LGBT-phobia, and tossed back and forth between private companies and governments –– deep-learning's pitfalls become frighteningly magnified. Another example is when Facebook's deep-learning neural translation machine led to the illegal arrest of a Palestinian man because of a post he made, at the end of 2017.


Why transfer learning works or fails?

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During the NIPS tutorial talk given in 2016, Andrew Ng said that transfer learning -- a subarea of machine learning where the model is learned and then deployed in related, yet different, areas -- will be the next driver of machine learning commercial success in the years to come. This statement would be hard to contest as avoiding learning large-scale models from scratch would significantly reduce the high computational and annotation efforts required for it and save data science practitioners lots of time, energy, and, ultimately, money. As an illustration of these latter words, consider Facebook's DeepFace algorithm that was the first to achieve a near-human performance in face verification back in 2014. The neural network behind it was trained on 4.4 million labeled faces -- an overwhelming amount of data that had to be collected, annotated, and then trained on for 3 full days without taking into account the time needed for fine-tuning. It won't be an exaggeration to say that most of the companies and research teams without Facebook's resources and deep learning engineers would have to put in months or even years of work to complete such a feat, with most of this time spent on collecting an annotated sample large enough to build such an accurate classifier.


Algorithms can help fight COVID-19. But at what cost?

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This past spring, as billions of people languished at home under lockdown and stared at gloomy graphs, Linda Wang and Alexander Wong, scientists at DarwinAI, a Canadian startup that works in the field of artificial intelligence, took advantage of their enforced break: In collaboration with the University of Waterloo, they helped develop a tool to detect COVID-19 infection by means of X-rays. Using a database of thousands of images of lungs, COVID-Net – as they called the open-access artificial neural network – can detect with 91 percent certainty who is ill with the virus. In the past, we would undoubtedly have been suspicious of, or at least surprised by, a young company (DarwinAI was established in 2018) with no connection to radiology, having devised such an ambitious tool within mere weeks. But these days, we know it can be done. Networks that draw on an analysis of visual data using a technique known as "deep learning" can, with relative flexibility, adapt themselves to decipher any type of image and provide results that often surpass those obtained by expert radiologists.