We've all been there: you take a photo, and it's just not quite right. You moved too much, or the lighting was bad, or the subject was blurry. Everything happens so quickly, and you haven't had time to adjust to the conditions, so you try again… and you fail. Oftentimes, you fail to capture a moment that happens only once a lifetime. There's no second chance to take a picture of your child's first steps, your son blowing out his birthday candles, or your wife crossing a finish line -- either you capture it now, or you never will.

Have you ever thought about how a painter sat down and painted his picture? Have you ever thought about how he can bring his hand to hold the pen, the reflection of his feelings and mind from an abstract world and to dominate his pen by using his hands naively? Have you ever felt the reality of that music shaped by the emotions that a pianist feels while touching the piano keys with his hands while composing his composition .. The beautiful paintings we see and the emotional music that takes us to other realms is almost like breaking away from this reality we live in and traveling to different worlds .. Is art unique to us? These days Deep Learning has also started to make its own art and it does it really well. In these days, where image processing technology is advancing very rapidly, we are constantly encountering new discoveries, we know that no matter how advanced in image processing technology with Deep Learning, human intelligence has not been reached yet.

The weaponisation of deepfakes against politicians or nation states has become something we're simply going to have to live with,

Generative adversarial networks(GANs) took the Machine Learning field by storm last year with those impressive fake human-like faces. Bonus Point* They are basically generated from nothing. Irrefutably, GANs implements implicit learning methods where the model learns without the data directly passing through the network, unlike those explicit techniques where weights are learned directly from the data. Okay, suppose in the city of Rio de Janeiro, money forging felonies are increasing so a department is appointed to check in these cases. Detectives are expected to classify the legit ones and fake ones.

Currently, deep reinforcement learning (RL) shows impressive results in complex gaming and robotic environments. Often these results are achieved at the expense of huge computational costs and require an incredible number of episodes of interaction between the agent and the environment. There are two main approaches to improving the sample efficiency of reinforcement learning methods - using hierarchical methods and expert demonstrations. In this paper, we propose a combination of these approaches that allow the agent to use low-quality demonstrations in complex vision-based environments with multiple related goals. Our forgetful experience replay (ForgER) algorithm effectively handles errors in expert data and reduces quality losses when adapting the action space and states representation to the agent's capabilities. Our proposed goal-oriented structuring of replay buffer allows the agent to automatically highlight sub-goals for solving complex hierarchical tasks in demonstrations. Our method is universal and can be integrated into various off-policy methods. It surpasses all known existing state-of-the-art RL methods using expert demonstrations on various model environments. The solution based on our algorithm beats all the solutions for the famous MineRL competition and allows the agent to mine a diamond in the Minecraft environment.

Lying has never looked so good, literally. Concern over increasingly sophisticated technology able to create convincingly faked videos and audio, so-called'deepfakes', is rising around the world. But at the same time they're being developed, technologists are also fighting back against the falsehoods. "The concern is that there will be a growing movement globally to undermine the quality of the information sphere and undermine the quality of discourse necessary in a democracy," Eileen Donahoe, a member of the Transatlantic Commission on Election Integrity, told CNBC in December 2018. She said deepfakes are potentially the next generation of disinformation.

In late March, a judge in Wiesbaden, Germany, found herself playing the uncomfortable role of art critic. On trial before her were two men accused of forging paintings by artists including Kazimir Malevich and Wassily Kandinsky, whose angular, abstract compositions can now go for eight-figure prices. The case had been in progress for three and a half years and was seen by many as a test. A successful prosecution could help end an epidemic of forgeries – so-called miracle pictures that appear from nowhere – that have been plaguing the market in avant-garde Russian art. But as the trial reached its climax, it disintegrated into farce.

In 2014, Ian Goodfellow and his colleagues at the University of Montreal published a stunning paper introducing the world to GANs, or generative adversarial networks. Through an innovative combination of computational graphs and game theory they showed that, given enough modeling power, two models fighting against each other would be able to co-train through plain old backpropagation. The models play two distinct (literally, adversarial) roles. Given some real data set R, G is the generator, trying to create fake data that looks just like the genuine data, while D is the discriminator, getting data from either the real set or G and labeling the difference. Goodfellow's metaphor (and a fine one it is) was that G was like a team of forgers trying to match real paintings with their output, while D was the team of detectives trying to tell the difference.

The best models around are based on generative adversarial networks. Clune says that GAN is composed of two neural networks playing a game of cops and robbers--or cops and forgers, rather. These neural networks are commonly referred to as "deep neural networks"--they take data and combine them through a series of many transformations. For instance, GAN is often given images of tumors and then asked to predict whether they are cancerous. The high number of transformations is what makes a neural network "deep."