In a bid to tackle the spread of fake news and misleading videos created using artificial intelligence (AI) and bots, China has released new rules that ban online video and audio providers from using deep learning to produce fake news without a proper disclosure. Failing to provide a disclosure that the post in question was created with AI or VR technology is now a criminal offence, according to the Chinese government. The rules go into effect on January 1st, 2020, and will be enforced by the Cyberspace Administration of China, The Verge reported. The regulation comes about one-and-a-half months after California introduced legislation to make political deepfakes illegal, outlawing the creation or distribution of videos, images, or audio of politicians doctored to resemble real footage within 60 days of an election. The new regulation published said that both providers and users of online video news and audio services are not allowed to use new technologies such as deep learning and virtual reality to create, distribute and broadcast fake news, according to South China Morning Post.

The most important question is how to train the deep convolutional networks? And quest to answer this question led to the invention of the ResNets. As per the traditional approach to train deeper convolutional neural networks, we increase the number of layers. And as per our knowledge on convolutional neural networks, the below should happen -- With the increase in the layers, the network should have low training error. But this was what didn't happen!

With new neural network architectures popping up every now and then, it's hard to keep track of them all. Knowing all the abbreviations being thrown around (DCIGN, BiLSTM, DCGAN, anyone?) can be a bit overwhelming at first. So I decided to compose a cheat sheet containing many of those architectures. Most of these are neural networks, some are completely different beasts. Though all of these architectures are presented as novel and unique, when I drew the node structures… their underlying relations started to make more sense. The Neural Network Zoo (download or get the poster). One problem with drawing them as node maps: it doesn't really show how they're used. For example, variational autoencoders (VAE) may look just like autoencoders (AE), but the training process is actually quite different. The use-cases for trained networks differ even more, because VAEs are generators, where you insert noise to get a new sample. AEs, simply map whatever they get as input to the closest training sample they "remember".

SUBSCRIBE OUR CHANNEL It's hard to believe how far we've come. We launched just a few years ago with our surveillance drones, and in this short amount of time, we've retooled cutting-edge artificial intelligence and machine learning techniques, such as deep learning and convolution neural networks, into hardware systems that governments and peace-keeping agencies can use to keep their troops safe. Now the artificial intelligence does all of the work. Our autonomous weapons are small, fast, accurate, and unstoppable. And they are just the beginning.

This post was originally published on the Shoprunner Engineering blog here feel free to check it out and at some of the other work our teams are doing. Our ShopRunner Data Science team allows all members to have a quarterly hack week. It is important for data science teams to keep innovating so once per quarter team members are allowed to spend a week working on more speculative projects of their choice. For my 2019 Q3 hack week I decided to build a series of generator models to attempt to create fake products. Generator models are models commonly trained to create realistic images or text based on real world examples. This project may seem fairly outlandish, which it is, but my general idea is that if we can create strong generator models that can capture the diversity of our product catalog then we could use these generators to augment low frequency classes within our catalog for other deep learning projects such as taxonomy classification or attribute tagging.

Text-generating neural networks like OpenAI's GPT-2 often raise questions about the dangers of fake text: Can a machine write text that's convincingly, deceptively human? As a comedy writer, I'm more interested in the opposite question: Can a machine produce words that no human would ever write? Can it help me write things that I would never write? Write With Transformer is a web app that lets you write in collaboration with a text-generating neural network. It's a demo for Transformers, a state-of-the-art software library developed and maintained by Hugging Face.

This is less of an AI question and more of a question about game theory. First, some terminology: In the world of Go, we have the concepts of the'Go God' and the'Go Devil'. The Go God always plays perfectly in response to the board state, but doesn't assume anything about its opponent. The Go Devil, on the other hand, has full knowledge of the sort of player it is playing against, and plays optimally to defeat that player. Clearly the Go Devil is stronger than the Go God, because when playing against the Go God (or against another Go Devil) he plays as if he is another Go God, and against all imperfect players he plays at least as well as the Go God and in some cases better.

As AI has become one of the hottest must-have technologies, companies have rushed to build deep learning solutions to almost every problem. The industry has become particularly fixated with monolithic models and end-to-end learning in which algorithms are simply fed a database of training examples and turned loose on their problem domain without ever requiring human assistance. Yet, as Waymo reminds us, when it comes to building truly robust complex deep learning systems that must interact with the chaotic cacophony of the real world and seamlessly operate alongside humans, the most successful systems today combine multiple deep learning models together with traditional hand-coded rulesets. Like all forms of machine learning, deep learning offers a seductively simplistic beginner experience that requires little effort from newcomers to produce reasonably high-quality results right from the start. The problem lies in the long road of incremental improvements to make those out-of-the-box models sufficiently robust and accurate for production use. This seductive beginner's simplicity, coupled with almost a century of science fiction portrayals of machine intelligences that can learn like humans, has led many companies to focus their efforts on building massive singular all-encompassing end-to-end AI models that can do absolutely everything the company needs with one model without ever requiring a moment of human assistance.

The two areas of Artificial Intelligence, namely machine learning and deep learning, raise more questions than an entire field combined, mainly because these two areas are often mixed up and used interchangeably when referring to statistical modeling of data; however, the techniques used in each are different and you need to understand the distinctions between these data modeling paradigms in order to refer to them by their corresponding name. In this article, we'll explain the definitions of artificial intelligence, machine learning, deep learning, and neural networks, briefly overview each of those categories, explain how they work, and finish with an explicit comparison of machine learning vs deep learning. Artificial Intelligence (hereafter referred to as AI) is the intelligence demonstrated by machines as opposed to the natural intelligence of humans. AI can be further classified into three different systems: analytical, human-inspired, and humanized artificial intelligence. Analytical AI generates the cognitive representation of the world through learning that's based on past experiences to predict future decisions.

Artificial intelligence is playing an increasingly important role in many fields of medicine, assisting physicians in most steps of patient management. In nephrology, artificial intelligence can already be used to improve clinical care, hemodialysis prescriptions, and follow-up of transplant recipients. However, many nephrologists are still unfamiliar with the basic principles of medical artificial intelligence. This review seeks to provide an overview of medical artificial intelligence relevant to the practicing nephrologist, in all fields of nephrology. We define the core concepts of artificial intelligence and machine learning and cover the basics of the functioning of neural networks and deep learning. We also discuss the most recent clinical applications of artificial intelligence in nephrology and medicine; as an example, we describe how artificial intelligence can predict the occurrence of progressive immunoglobulin A nephropathy.