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Study warns deepfakes can fool facial recognition


Deepfakes, or AI-generated videos that take a person in an existing video and replace them with someone else's likeness, are multiplying at an accelerating rate. According to startup Deeptrace, the number of deepfakes on the web increased 330% from October 2019 to June 2020, reaching over 50,000 at their peak. That's troubling not only because these fakes might be used to sway opinion during an election or implicate a person in a crime, but because they've already been abused to generate pornographic material of actors and defraud a major energy producer. Open source tools make it possible for anyone with images of a victim to create a convincing deepfake, and a new study suggests that deepfake-generating techniques have reached the point where they can reliably fool commercial facial recognition services. In a paper published on the preprint server,

15 Python Projects: From Beginner To Full-Stack - Comp Sci Central


The best way to learn python is by creating a project. These are 10 of the best python projects for beginner to intermediate Python programmers. These python projects will not only help you learn the fundamentals, but you'll have a lot of fun creating these. With each of these 10 python projects, there is a full video tutorial that walks you step-by-step from start to finish. These projects have been hand-selected and range from beginner to intermediate.

How I'd Learn Data Science If I Were To Start All Over Again


A couple of days ago I started thinking if I had to start learning machine learning and data science all over again where would I start? The funny thing was that the path that I imagined was completely different from that one that I actually did when I was starting. I'm aware that we all learn in different ways. Some prefer videos, others are ok with just books and a lot of people need to pay for a course to feel more pressure. And that's ok, the important thing is to learn and enjoy it. So, talking from my own perspective and knowing how I learn better I designed this path if I had to start learning Data Science again.

Computer Vision: Python OCR & Object Detection Quick Starter


Free Coupon Discount - Computer Vision: Python OCR & Object Detection Quick Starter Quick Starter for Optical Character Recognition, Image Recognition Object Detection and Object Recognition using Python Created by Abhilash Nelson Students also bought Deep Learning Prerequisites: Logistic Regression in Python Deep Learning: Convolutional Neural Networks in Python Deep Learning A-Z: Hands-On Artificial Neural Networks The Complete Self-Driving Car Course - Applied Deep Learning The Complete Neural Networks Bootcamp: Theory, Applications Preview this Udemy Course GET COUPON CODE Description Hi There! welcome to my new course'Optical Character Recognition and Object Recognition Quick Start with Python'. This is the third course from my Computer Vision series. Image Recognition, Object Detection, Object Recognition and also Optical Character Recognition are among the most used applications of Computer Vision. Using these techniques, the computer will be able to recognize and classify either the whole image, or multiple objects inside a single image predicting the class of the objects with the percentage accuracy score. Using OCR, it can also recognize and convert text in the images to machine readable format like text or a document.

How AI Could Track and Use Your Emotions


Artificial intelligence can now gauge human emotions, and it's being used in everything from education to marketing, experts say. Your emotions could potentially be tracked using your Wi-Fi router and analyzed by AI, according to a new study from London's Queen Mary University. Researchers used radio waves like those used in Wi-Fi to measure heart and breathing rate signals, which could determine how a person is feeling. The study shows just how pervasive emotion-monitoring could become. "In education, AI could be used in adapting content to serve the needs of each child best," Kamilė Jokubaitė, CEO and founder of Attention Insight, who was not involved in the study, said in an email interview.

A deepfake future is closer than you think. Should we be worried?


Deepfakes have started to appear everywhere – from viral celebrity face swaps to impersonations of political leaders. Millions got their first taste of the technology when they saw former US president Barack Obama using an expletive to describe then-president Donald Trump, or actor Bill Hader shape shifting on a late-night talk show. Earlier this week, social media went into a frenzy after deepfakes surfaced of actor Tom Cruise in a series of TikTok videos that appear to show him doing a magic trick and playing golf, all with a smoothness that was unsettlingly realistic. This isn't even a super high quality deepfake and I'm willing to bet that it could fool most people. Now imagine the quality of deepfake a government agency could produce.

What's All The Buzz About 'Deep Nostalgia'


Bhagat Singh, Marie Curie, Charles Darwin, and other historical figures were momentarily'brought back to life' via Deep Nostalgia – an AI tool released by the genealogy website, MyHeritage. Kind of surreal to take a photo of the singularly inspiring Bhagat Singh -- a revolutionary voice in 1920s India, who was hung by the British in 1931, at the age of 24 -- run it through the Heritage AI algorithm, and see him reanimated. When Ken Burns meets Deep Fake: MyHeritage is offering a tool dubbed #DeepNostalgia, meant to animate old family pictures. Holy Darwin this #deepfake is so scary, Mr. Darwin!!#DeepNostalgia Deep Nostalgia created quite a furore of late, with animated pictures of historical figures running rife in social media.

How to Build an End-to-End Deep Learning Portfolio Project


It was in the late December 2020 when one evening, I was casually scrolling through my Twitter timeline that I caught a tweet from a famous YouTuber that I followed and I paused. He had tweeted about how it was a pain to go through the huge number of comments that each of this videos received and how too often, so many good comments -- to which he would've really loved to reply to -- get lost in the sheer volume. Being a data science practitioner, I was intrigued by the idea of efficiently handling such a huge inflow of comments on videos. Upon thinking about it for a few hours, I was ready to believe that it really was a genuine problem. It was then that the idea of doing a project based on that particular use case was born.

AIhub monthly digest: February 2021


Welcome to the second of our monthly digests, designed to keep you up-to-date with the happenings in the AI world. You can catch up with any AIhub stories you may have missed, get the low-down on recent conferences, and generally immerse yourself in all things AI. You may be aware that we are running a focus series on the UN sustainable development goals (SDG). Each month we tackle a different SDG and cover some of the AI research linked to that particular goal. In February it was the turn of climate action.

Learning Machine Learning -- Week 0


We use machine learning when simple code will not work, but how do we exactly define problems in this field? We have defined the types of algorithms and now we'll define the types of models that these algorithms have. The types of problem that need supervised learning are: - Classification: "Is this a thing or another one?". We have data and we have to classify it into categories with predetermined labels. When the classification is between 2 options it's called binary classification, when is between more than 2 options it's called multi-class classification.