In this post I am exploring a new way of doing sentiment analysis. This model takes as input a large corpus of documents like tweets or news articles and generates a vector space of typically several hundred dimensions. Each word in the corpus is being assigned a unique vector in the vector space. The powerful concept behind word2vec is that word vectors that are close to each other in the vector space represent words that are not only of the same meaning but of the same context as well. What I find interesting about the vector representation of words is that it automatically embeds several features that we would normally have to handcraft ourselves. Since word2vec relies on Deep Neural Nets to detect patterns, we can rely on it to detect multiple features on different levels of abstractions.

Reading the news you'd imagine Artificial Intelligence technologies as almighty and unstoppable: after all, they beat human players in ancient Chinese board games, make self-driving cars smarter, under one form or another could soon replace bankers, lawyers and who knows what next. One of the current constraints of artificial intelligence is called "catastrophic forgetting", and researchers have been struggling with it for a while. In other words, to add a single object or a single task, while keeping the same overall amount of information, a neural network would have to be retrained on all of the objects, which is usually done using powerful servers located in the cloud. According to sources from the company, Neurala's breakthrough solves the "catastrophic forgetting" problem for deep learning neural networks instantly at the computing device, without the need of being connected to a server.

Monday, May 8th, London, UK - The Chief Science Officer of the Biogerontology Research Foundation (BGRF) will present new research on artificial intelligence for drug discovery at the NVIDIA Graphics Technology Conference (GTC) at the San Jose Convention Center, on Wednesday, May 10, 1:00 PM - 1:50 PM alongside two AI scientists from the BGRF and Insilico Medicine, where they will deliver a presentation titled "Applications of Generative Adversarial Networks to Drug Discovery in Oncology and Infectious Diseases". NVIDIA is the leader in computational hardware optimized for deep learning-based applications, and they are on the forefront of supporting research companies and institutions applying deep learning to grand unsolved problems, with the application of deep learning to drug discovery and development being no exception, as they described in detail in their article "Creating New Drugs, Faster: How AI Promises to Speed Drug Development". "The application of deep learning to ageing research is poised to make rapid progress on many fronts in the years to come. Foremost among these are the application of deep learning to the characterization of quantifiable and practically-measurable biomarkers of ageing (a necessity for the eventual regulatory evaluation and approval of healthspan-extending therapies) and the acceleration of drug discovery and development timelines by using deep learning to characterize drug candidates according to likely efficacy and safety prior to preclinical and clinical trials. AI, machine learning and deep learning have disrupted many industries and areas of activity that were previously the exclusive arena of human cognition, and in the coming years it seems likely that the pharmaceutical industry and the process of drug discovery and development will come to be radically disrupted by AI and deep learning-based approaches as well" said Franco Cortese, Deputy Director & Trustee of the Biogerontology Research Foundation.

At Search Party we are in the business of creating intelligent recruitment software. One of the problems we deal with is matching candidates and vacancies in order to create a recommendation engine. This usually requires parsing, interpreting and normalising messy, semi-/unstructured, textual data from résumés and vacancies, which is where the following come in: conditional random fields, bag-of-words, TF-IDFs, WordNet, statistical analysis, but also a lot of manual work done by linguists and domain experts for the creation of synonym lists, skill taxonomies, job title hierarchies, knowledge bases or ontologies. While these concepts are valuable for the problem we try to solve, they also require a certain amount of manual feature engineering and human expertise. This expertise is certainly a factor that makes these techniques valuable, but the question remains whether more automated approaches can be used to extract knowledge about the job space to complement these more traditional approaches.

Is there anything AI can't make better? Artificial intelligence can recognize musical genres better than humans, improve our running performance and may soon become standard issue for the mobile devices in our pockets. Facebook, in fact, has found some stunning results in new research using convolutional neural networks (CNN), a type of artificial intelligence that uses the benefit of parallel processing to compete complex tasks. The social networking company's AI research team revealed research that shows these systems can outperform traditional language translation software by a factor of nine. In addition, the source code and trained systems are available under an open source license, making it easy for other researchers to verify and replicate the gains in their own work.

Christopher Manning, a Stanford University professor who specialized in machine translation and has reviewed the paper, calls it an "impressive achievement," particularly because it can train translation models more quickly than existing systems. This past fall, Google unveiled a new translation system driven entirely by neural networks that topped existing models, and many other companies and researchers are pushing in the same direction, most notably Microsoft and Chinese web giant Baidu. "We've seen more improvements over the past two years than we have seen in the past decade," says John Tinsley, the CEO of Iconic Translation Machines, a translation technology company based in Dublin. And others have explored such networks as a basic technique for machine translation, including researchers at DeepMind, a Google AI lab based in London.

TwitterLinkedInGoogle Published on Dec 5, 2015, y que nos presentan así: In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They've mastered the ancient game of Go and thrashed the best human players. "The pace of progress in artificial general intelligence is incredible fast" (Elon Musk – CEO Tesla & SpaceX) leading to an AI that "would be either the best or the worst thing ever to happen to humanity" (Stephen Hawking – Physicist).

H2O.ai and Nvidia today announced that they have partnered to take machine learning and deep learning algorithms to the enterprise through deals with Nvidia's graphics processing units (GPUs). Mountain View, Calif.-based H20.ai has created AI software that enables customers to train machine learning and deep learning models up to 75 times faster than conventional central processing unit (CPU) solutions. The company made the announcement at Nvidia's GPU Tech event in San Jose, Calif. H2O.ai will offer its machine learning algorithms in a newly minted GPU-edition and its Deep Water product on Nvidia GPUs. In addition, H2O.ai's platform will now be optimized for the Nvidia's DGX-1 AI processor.

When a language you don't understand appears in your Facebook News Feed, you can touch a button and quickly translate it. Facebook offers a way of communicating not just with the millions of people who speak your language, but with millions of others who speak something else. Or at least, it almost does. Like so many other online translation services, Facebook comes with a caveat: Its translations don't always make sense. But like several other giants of the internet, Facebook is working to eliminate that rather significant caveat.

In a city of the future, it would be nice to know quickly if there's a fire burning out of control, a crime in progress at a certain location, or a traffic snarl at a particular corner. Nvidia hopes to detect such problems in smart cities using Nvidia Metropolis, which the company said could pave the way for the creation of smart artificial intelligence cities. Nvidia announced the tech ahead of its GPU Technology conference this week in San Jose, California. Metropolis is a video analytics platform that applies deep learning AI to video streams for applications such as public safety, traffic management, and resource optimization. Nvidia said that Metropolis could make cities safer, and more than 50 partner companies are already providing products and applications for AI city uses based on graphics processing units (GPUs) made by Nvidia. "Deep learning is enabling powerful intelligent video analytics that turn anonymized video into real-time valuable insights, enhancing safety and improving lives," said Deepu Talla, vice president and general manager of the Tegra business at Nvidia, in a statement.