In December 2017, a user named "DeepFakes" posted realistic looking explicit videos of famous celebrities on Reddit. He generated these fake videos using deep learning, the latest in AI, to insert celebrities' faces into adult movies. In the following weeks, the internet exploded with articles about the dangers of face swapping technology: harassing innocents, propagating fake news, and hurting the credibility of video evidence forever. In this post, I explore the capabilities of this tech, describe how it works, and discuss potential applications. DeepFakes offers the ability to swap one face for another in an image or a video.
Interestingly enough you might want to look into some RL research for game specific applications. It's usually done using a value iteration methodology to track rewards over time in a game. A successful RL agent will have to have some predictive ability about the environment/opponent's strategies, so even though it's not exactly what you are looking for, with some manipulation to the ideas in that literature you might be able to come up with something that is of use to you. As an aside, LSTMs while incredibly powerful, may not be a good starting point. Perhaps it would be more functional to start off simpler and ramp into an LSTM if you feel that it is necessary.
Few industries have been hit as hard by the technological changes of recent times as the media industry. The same trends that have improved the lives of billions -- the growth of the internet, the spread of social media, and the proliferation of smartphones -- have instead disrupted the business models of every major media company, diluting their ability to sustainably fund their core operations. Most media companies have identified a'shift to video' as a critical pathway out of this digital dilemma. Digital video content is five times more engaging for consumers and four times more valuable for advertisers than text content alone. However, despite huge investments by media companies in increased video production, these shifts to video have so far failed to deliver meaningful bottom-line results.
My background is in academic research applying deep learning to a variety of problems in engineering, healthcare, fintech, etc. I've always wished for there to be an easy way for me to a) share my machine learning approaches with other individuals / experts interested in the problem domain, and b) publish result benchmarks (AUC metrics, etc.) for more transparency on state-of-the-art. The best alternatives I've found are caffe model zoo and Arxiv, both of which I haven't found to easily facilitate sharing, discussion, and experimentation. I think a buffed up "DL model zoo", with options for serving models, publishing results, and facilitating discussion could be useful. If this existed, I'm sure I would also constantly be scrolling through to read about new DL applications on interesting problems.
The newsroom is unrecognisable from those of 10 years ago. Machine learning and other forms of technology falling within the artificial intelligence (AI) umbrella are being used by news organisations to increase efficiency, as well as profitability. How the LA Times used machine learning to interrogate statistics In a report found on Medium.com, Freia Nahser, innovation reporter and editor of Global Editors Network, explains how the Los Angeles Times used machine learning algorithms to show how the city's police department misclassified 14 000 serious assaults as minor offences between 2005 and 2012. This statistical change effectively lowered the city's crime rate, when the reality was somewhat different.
Hi, I am a noob in reinforcement learning, but I want to try and dabble in it. As I have understood, experiments in RL may take a long time to converge compared to regular deep learning methods. Therefore I am looking for to increase my effectiveness when working with these models on AWS. My current workflow in deep learning is to open a notebook on the server, run the model, tune hyperparameters, run the model etc. So my question is, how do you setup many experiments to run in parallell on AWS?
For more than a decade, the Chinese government has been working to push the Chinese manufacturing sector up the value chain. More recently, the push from the central government has become more formalized, resulting in the 2015 issuance of the State Council manufacturing modernization manifesto: Made in China 2025《中国制造2025》(State Council, July 7, 2015). Made in China 2025 focuses less on the types of products to be manufactured and more on the methods of manufacturing. It is okay to continue making rubber duckies, so long as the process for doing so is modernized. That is, massive automated factories churning out thousands of identical items with minimal human intervention.
Every day, there are proclamations about how AI is going to change the world as we currently know it. The reality though is that AI is already transforming industries in meaningful ways from predicting weather to detecting cancer. We are no longer in the realm of future-speak, but in the early stages of vast transformation, and there are many innovators who are paving the way. These innovators are the focus of the "I am AI" docuseries, an original video series that showcases how businesses, from startups to the enterprise, are using AI to achieve what was once thought impossible. The first episode looks at the entertainment industry, specifically music, and introduces Aiva, a French AI startup that's developed an algorithm that composes original music of various styles.
In 1959, Arthur Samuel, a pioneer in machine learning, defined it as the'field of study that gives computers the ability to learn without being explicitly programmed'. Machine learning can translate to using algorithms to parse through data, recognise patterns, and then make predictions and assessments based on what the algorithms have learnt. Machine learning can be used for fact checking and it can make archiving less of a tedious task for journalists. It can let voice assistants like Alexa or Google Assistant know you're pissed off based on the tone of your voice on a Monday morning and then play a song to cheer you up. It can also be used to explore scenes in Wes Anderson films and help uncover hidden spy planes.