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The Best Resources on Artificial Intelligence and Machine Learning


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?


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


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...


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


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?


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?


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.

Future Tense Newsletter: The Four Master Switches


I reach out to you still contemplating the profundity of what Mark Zuckerberg told his congressional inquisitors on Wednesday: "The space of people connecting with other people is a very large space." So large, it even includes newsletters in your inbox. Three clear winners and one loser emerged from Wednesday's Big Tech hearing in Washington. The winners were Rep. Pramila Jayapal, our new "eviscerator in chief"; Alphabet CEO Sundar Pichai's future career as an anger-management therapist; and Tim Wu. When the going gets tough in coming weeks, I will close my eyes and picture the Google CEO soothingly saying "congressman" with infinite patience, as he did at the beginning of all his answers. The more irate the congressional questioner, the more patient, measured, and empathetic his "congressman" sounded.

The Story of an Ensemble Classifier Acquired By Facebook


First, let me tell you how the story started. I was the lead in the machine learning team of a start-up company working on a smart gadget. The gadget was aimed to identify hand gestures based on muscle signals. The gadget had eight sensors sitting on the forearm and recording muscle signals. We were in the early stage and the product was not ready.

Sensor fitted mouth piece lets user control a joystick with their TONGUE to play video games

Daily Mail - Science & tech

A new device lets users control images on a screen with just a flick of the tongue. Designed as a master thesis, [In] Brace is a mouth piece fitted with sensors that detect the location of a small sphere the wearer moves with their tongue. The device is constructed from a customized plastic retainer that is connected to a Wi-Fi transmitter, which is placed around the ear. The creator of [In] Brace sees it as a way to help those with disabilities interact with technology or doing complex tasks where hands are occupied with other devices. The technology was designed and developed by Dorothee Clasen for her thesis in Human-Computer Interaction design and research, Gizmodo reports.