If you were asked to draw a picture of several people in ski gear, standing in the snow, chances are you'd start with an outline of three or four people reasonably positioned in the center of the canvas, then sketch in the skis under their feet. Though it was not specified, you might decide to add a backpack to each of the skiers to jibe with expectations of what skiers would be sporting. Finally, you'd carefully fill in the details, perhaps painting their clothes blue, scarves pink, all against a white background, rendering these people more realistic and ensuring that their surroundings match the description. Finally, to make the scene more vivid, you might even sketch in some brown stones protruding through the snow to suggest that these skiers are in the mountains. Now there's a bot that can do all that. New AI technology being developed at Microsoft Research AI can understand a natural language description, sketch a layout of the image, synthesize the image, and then refine details based on the layout and the individual words provided.

An artificial intelligence project capable of writing fake news that was deemed'too dangerous' to release to the public has been recreated by two university students. Open AI, a project founded with the support of Elon Musk, is able to generate news stories from a headline or first line of text. In February, the firm released a limited version of its software for other developers to use, to explore its potential. The firm, which Musk is no longer involved in, has since launched an updated version of the software with half of the power of the full AI. Now, computer science master's students Aaron Gokaslan and Vanya Cohen from Brown University have shared code for what they say is the full version.

In addition, as these tools learn from users and more advanced musicians, it will start to identify patterns that produce successful music and potential hits in the music market. At that point, these systems might create entire albums and musical productions without human assistance of any sort. Popular music has already explored manufactured music, from K-Pop to Boy Bands and has figured out many of the elements of what makes popular music tick. With the addition of AI will humans even be needed at all for music creation? Furthermore, as more people use AI tools to augment their natural ability, you need to ask if these systems are exhibiting real creativity or simply mimicking the creative ability of humans?

In February, an artificial intelligence lab cofounded by Elon Musk informed the world that its latest breakthrough was too risky to release to the public. OpenAI claimed it had made language software so fluent at generating text that it might be adapted to crank out fake news or spam. On Thursday, two recent master's graduates in computer science released what they say is a re-creation of OpenAI's withheld software onto the internet for anyone to download and use. Aaron Gokaslan, 23, and Vanya Cohen, 24, say they aren't out to cause havoc and don't believe such software poses much risk to society yet. The pair say their release was intended to show that you don't have to be an elite lab rich in dollars and PhDs to create this kind of software: They used an estimated $50,000 worth of free cloud computing from Google, which hands out credits to academic institutions.

I was working on creating embeddings for medical concepts. These terms/phrases are used for annotating biomedical documents. Now usually the method of creating a co-occurrence matrix and then factorizing it to obtain dense, lower-dimensional vectors is termed as unsupervised learning since annotated data is not involved. I am using the same process but for the annotations themselves. Does this qualify as supervised learning since I need annotated data or does this qualify as unsupervised learning since the method of obtaining the embeddings is unsupervised?

I have similar experience to you (very new to computer vision), but here's a few ideas off the top of my head: Some form of 3D mapping for autonomous vehicles like seen in this video. It uses a cool technique called SLAM. This is probably way beyond either of our scope but if you want a large project to work towards, this is an idea. An objectively easier idea may be implementing already established techniques such as object recognition and/or tracking (traffic signs or people in a picture/video), nothing is wrong with working on already solved problems imo. Also consider checking out the AWS DeepRacer scholarship challenge, maybe by participating you can figure something out.

"Better Than Us," Netflix's first original streaming series from Russia, transports audiences to a city only 10 years in the future, where robots serve the population in a variety of positions, some have even replaced humans for certain jobs. They look like the humans they serve, though there is a detached air to their presence and slightly stilted way of moving that make them visibly different. Netflix describes the series as "cyberpunk." Popular culture entertainment is often a way for us to imagine a scenario or event that may never occur in our individual lifetimes. Imagining a future, say, where artificial intelligence (AI) has been integrated into society not simply as a complex web of computational frameworks as we acknowledge and accept it currently, but in the form of robotics that look, and act similar to humans.

Existing computational methods for the analysis of corpora of text in natural language are still far from approaching a human level of understanding. We attempt to advance the state of the art by introducing a model and algorithmic framework to transform text into recursively structured data. We apply this to the analysis of news titles extracted from a social news aggregation website. We show that a recursive ordered hypergraph is a sufficiently generic structure to represent significant number of fundamental natural language constructs, with advantages over conventional approaches such as semantic graphs. We present a pipeline of transformations from the output of conventional NLP algorithms to such hypergraphs, which we denote as semantic hypergraphs. The features of these transformations include the creation of new concepts from existing ones, the organisation of statements into regular structures of predicates followed by an arbitrary number of entities and the ability to represent statements about other statements. We demonstrate knowledge inference from the hypergraph, identifying claims and expressions of conflicts, along with their participating actors and topics. We show how this enables the actor-centric summarization of conflicts, comparison of topics of claims between actors and networks of conflicts between actors in the context of a given topic. On the whole, we propose a hypergraphic knowledge representation model that can be used to provide effective overviews of a large corpus of text in natural language.

Progressive Face Super-Resolution via Attention to Facial Landmark arxiv.org is a machine learning model trained to reconstruct face images from tiny 16 16 pixel input images, scaling them up to 128 128 with nearly photo-realistic results. I tried running emojis, Twitch emotes, and a few game sprites through it. I did have to do quite a fit of cherry picking, and I also iteratively ran the output back into the inputs to encourage the model the add human features. I also created a (very sloppy) Colab Version of the paper's github demo if you want to try this yourself.