Named Entity Recognition is a process where an algorithm takes a string of text (sentence or paragraph) as input and identifies relevant nouns (people, places, and organizations) that are mentioned in that string. In our previous blog, we gave you a glimpse of how our Named Entity Recognition API works under the hood. In this post, we list some scenarios and use cases of Named Entity Recognition technology. News and publishing houses generate large amounts of online content on a daily basis and managing them correctly is very important to get the most use of each article. Named Entity Recognition can automatically scan entire articles and reveal which are the major people, organizations, and places discussed in them.

I've first heard of deepfakes a good week ago. There's something called Deepfakes on internet, and it's the most cyberpunk shit you can imagine. Machine learning used to swap porn actresses faces with hollywood stars. You have been warned) consists of people using an app created by user "deepfakes" to create fake celebrity porn. This has caused a shitstorm on the Internet, media discussing the legality of it all, websites taking down the deepfake creations, and people panicking as they realise AI is going to screw us all up (newsflash: it's already been happening in much less obvious ways).

The UAE Minister of Artificial Intelligence (AI) Omar bin Sultan Al Olama on Saturday announced that the UAE will focus on adopting AI in three lead sectors, including natural resources, tourism and logistics. He said the UAE will "lead the world" with fast-developing technologies in AI-Ready sectors.

One of the major drawbacks of modularized task-completion dialogue systems is that each module is trained individually, which presents several challenges. For example, downstream modules are affected by earlier modules, and the performance of the entire system is not robust to the accumulated errors. This paper presents a novel end-to-end learning framework for task-completion dialogue systems to tackle such issues. Our neural dialogue system can directly interact with a structured database to assist users in accessing information and accomplishing certain tasks. The reinforcement learning based dialogue manager offers robust capabilities to handle noises caused by other components of the dialogue system. Our experiments in a movie-ticket booking domain show that our end-to-end system not only outperforms modularized dialogue system baselines for both objective and subjective evaluation, but also is robust to noises as demonstrated by several systematic experiments with different error granularity and rates specific to the language understanding module.

This article was posted by SmileJet on Dev Battles. She paints and writes poetry. She's also an artificial intelligence from the movie Her, which imagines how a juiced-up Siri will change our lives. Now, tech companies large and small are racing to make this a reality. You've heard the jargon: AI, machine learning, deep learning, neural networks, natural language processing.

Artificial intelligence (AI) is a theme that's been frequently dramatized by the entertainment industry. We see it in the form of humanoid figures with superhuman intelligence. These AI marvels work 24/7, nearly at the speed of light, yet they never get tired or complain. What's more, they can remember every detail and learn from every experience, so they constantly get smarter. This understanding of AI won't actually help businesses leverage its full potential but it's certainly effective in creating the fear that AI is about to replace us in the job market.

We have reported on the use of artificial intelligence (AI) in music before, but things are about to change. And the change appears to be positive. To be honest a lot of the AI generated music that we've heard so far has been far from good or innovative. It has either been computer generated attempts to create classical dance or pop tunes, or far out space sounding things that tends to be more experimental than listenable. Now a new generation of bands and acts are active in the AI space, but with the difference that they have a more deliberate strategy in how to incorporate AI into their music creation process. So this new generation of musicians are creatively engaging with algorithmic processes to make some of the most futuristic and genre-bending music coming out right now.

Welcome to the first entry in our "Innovations for Educators" series, spotlighting interesting technologies that have the potential to amplify and complement the work done by educators. Artificial intelligence (AI) is all around us. From self-driving cars to voice and facial recognition technologies to computers that can compose music, AI stands to offer unprecedented convenience in our personal lives. At the same time, AI is also transforming the world of work. From helping lawyers scan hundreds of documents and predicting which are the most useful to a case, to helping doctors analyze massive amounts of data to develop treatment plans for patients, AI can perform in seconds tasks that would normally take hours of human effort.

If an algorithm is set to learning some policy about how to interact with the world to achieve a specific task, what is there to be learned from the algorithm's solution? For example, have new physical or biological principles governing the robustness of - or tradeoffs in locomotion strategies been learned from analysis of the learned movement patterns of the Deepmind walkers? I'm a biology PhD student and I've been wondering how my field could take advantage of advances in machine learning to move biology forward. It's one thing to be able to make predictions, but it seems to me that reinforcement learning approaches offer the potential for machines to act as scientists themselves.