A sci-fi UFO landscape, styled after Van Gogh? Ever felt like adding some Van Gogh-style swirls to your photos? A Russian project called Ostagram allows Internet users to combine photos and works of art to create fantastical images that wouldn't be out of place in the world of Alice in Wonderland or in a sci-fi space opera. The Ostagram project, created by user Sergey Morugin, is a web service that uses a computer algorithm to combine the content of one image with the style of another image using convolutional neural networks. This means you can get a photo of your dog to look like a Monet painting, if you pick the dog pic as the source for content, and the Monet artwork as a source for style.

An year or so ago, a chatbot named Eugene Goostman made it to the mainstream news, after having been reported as the first computer program to have passed the famed Turing Test in an event organized at the University of Reading. While the organizers hailed it as a historical achievement, most of the scientific community wasn't impressed. This leads us to the question: Is the Turing Test, in its original form, a suitable test for AI in the modern day? In the last couple of years, a number of papers (like this paper from JHU/Brown, and this one from MPI) have suggested that the task of Visual Question Answering (VQA, for short) can be used as an alternative Turing Test. The task involves answering an open-ended question (or a series of questions) about an image.

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Facebook is now using artificial intelligence to automatically generate captions for photos in the News Feed of people who can't see them. The tool is called Automatic Alternative Text, and it dovetails with text-to-speech engines that allow blind people to use Facebook in other ways. Using deep neural networks, the system can identify particular objects in a photo, from cars and boats to ice cream and pizza. It can pick out particular characteristics of the people in the photo, including smiles and beards and eyeglasses. And it can analyze a photo in a more general sense, determining that a photo depicts sun or ocean waves or snow.

Few days ago, I was asked by the dean of our faculty to present what is behind recent success of artificial intelligence when AlphaGo defeated legendary player Lee Sedol in the ancient game of Go. I do not know anything about playing Go so decided to focus on Artificial intelligence in general and talk about recent advances, background, state of the art and applications. It looks like many people understand the importance of the topic and are willing to come. The capacity of the lecture room is however limited so I decided to share the story line of my talk here on Medium. This is the outline of the talk.

The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses'value networks' to evaluate board positions and'policy networks' to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.

Salesforce.com's automation efforts got a boost Monday with the news it has acquired AI startup MetaMind. Salesforce will integrate MetaMind's technology into its own services for new marketing-automation and personalization capabilities, according to a blog post from MetaMind founder Richard Socher. "We'll extend Salesforce's data science capabilities by embedding deep learning within the Salesforce platform," Socher wrote. Socher's personal Web page now lists his title as chief scientist at the customer relationship management giant. MetaMind's products will be discontinued on May 4 for users of its free versions, and on June 4 for paid users.

The first step to before being able to do Image or audio analyses would be to extract relevant frames from the video streams in real time. This is crucial to a smart interactive device and requires extensive down sizing of the data to run the models on the features identified most relevant. One also needs the device to identify and react to certain events (owner coming home, break-in etc) through a frame by frame comparative analysis. Let us start with the event that there is a disturbance and the image frames and audio data is fed into the trained model to classify the event into pre-defined classes (simplest cast being intrusion vs non intrusion). Given a frame, let us start with the features that we would extract from it to first look for faces within the scenario and then if we find one, to match it with the available "registered" face repository. Given pictures of the home-owner/family, we will have an extensively pre trained model.

Salesforce.com's automation efforts got a boost Monday with the news it has acquired AI startup MetaMind. Salesforce will integrate MetaMind's technology into its own services for new marketing-automation and personalization capabilities, according to a blog post from MetaMind founder Richard Socher. "We'll extend Salesforce's data science capabilities by embedding deep learning within the Salesforce platform," Socher wrote. Socher's personal Web page now lists his title as chief scientist at the customer relationship management giant. MetaMind's products will be discontinued on May 4 for users of its free versions, and on June 4 for paid users.

Feature extraction has gained increasing attention in the field of machine learning, as in order to detect patterns, extract information, or predict future observations from big data, the urge of informative features is crucial. The process of extracting features is highly linked to dimensionality reduction as it implies the transformation of the data from a sparse high-dimensional space, to higher level meaningful abstractions. This dissertation employs Neural Networks for distributed paragraph representations, and Latent Dirichlet Allocation to capture higher level features of paragraph vectors. Although Neural Networks for distributed paragraph representations are considered the state of the art for extracting paragraph vectors, we show that a quick topic analysis model such as Latent Dirichlet Allocation can provide meaningful features too. We evaluate the two methods on the CMU Movie Summary Corpus, a collection of 25,203 movie plot summaries extracted from Wikipedia. Finally, for both approaches, we use K-Nearest Neighbors to discover similar movies, and plot the projected representations using T-Distributed Stochastic Neighbor Embedding to depict the context similarities. These similarities, expressed as movie distances, can be used for movies recommendation. The recommended movies of this approach are compared with the recommended movies from IMDB, which use a collaborative filtering recommendation approach, to show that our two models could constitute either an alternative or a supplementary recommendation approach.