Pandora appears to be challenging Apple Music with the launch of its new Premium Family plan. The music streaming and automated music recommendation internet radio service introduced a new plan on Monday, and it is one that directly rivals Apple Music's plan in terms of cost and the number of users it allows to share the service. The Pandora Premium Family plan is a $14.99/month subscription service ($164.89 As what its name implies, the service is intended for families. Hence, family members can enjoy full access to Pandora Premium offers if they are subscribed to this plan.

I am currently going into my 2nd semester of my junior year, majoring in mathematical science. Last semester I took numerical linear algebra, and while difficult, I found it very interesting. We didn't do any machine learning but did talk about it a lot and I got to know python quite well. Since this class I have been juggling the idea of possibly going to grad school for machine learning/AI. I need some advice on what I should do.

We propose an adversarial learning approach to the generation of multi-turn dialogue responses. Our proposed framework, hredGAN, is based on conditional generative adversarial networks (GANs). The GAN's generator is a modified hierarchical recurrent encoder-decoder network (HRED) and the discriminator is a word-level bidirectional RNN that shares context and word embedding with the generator. During inference, noise samples conditioned on the dialogue history are used to perturb the generator's latent space to generate several possible responses. The final response is the one ranked best by the discriminator. The hredGAN shows major advantages over existing methods: (1) it generalizes better than networks trained using only the log-likelihood criterion, and (2) it generates longer, more informative and more diverse responses with high utterance and topic relevance even with limited training data. This superiority is demonstrated on the Movie triples and Ubuntu dialogue datasets in terms of perplexity, BLEU, ROUGE and Distinct n-gram scores.

As making money with drone photography gets tougher, pilots are flying into new uncharted territories. What goes up must come down. This rule applies to both flying drones, and now unfortunately, to making a buck off them. With more affordable models entering the mass market, any enthusiast with a few thousand ringgit to spare can try his hand at aerial photography. While the lower barrier to entry may be good news for newbies, the increased competition is driving down remuneration.

Artificial Intelligence (AI) is basically intelligence demonstrated outside the human mind, essentially by machines. Machine Learning (ML) is a way of achieving AI and can be defined as the ability of computers to learn using statistical techniques without being specially programmed. Both the terms are symbiotic but also mutually exclusive in their own right with different definitions. We are not unfamiliar with the concept of AI, which has been time and again explored and exploited by popular media. Movies have gone as far as to show us a world dominated by AI-enabled machines and robots, and these movies, more often than not, have ended up portraying negative repercussions of an AI-enabled society. This has more or less shaped up the general feeling revolving around AI in the society.

DURHAM – Google Brain's Magenta project, which is exploring the creative potential of machine learning (ML) and artificial intelligence (AI), has developed considerably since Google announced it at Moogfest three years ago. And, Magenta makes many of its ongoing developments available publicly online and collects feedback from musicians, artists and other users to advance the project. Adam Roberts, senior software engineer and ML researcher discussed the nuts and bolts of Magenta at Moogfest over the weekend. Roberts, who did undergraduate work at the University of North Carolina at Chapel Hill, earned his PhD at Berkley, California, where he studied machine learning applied to genomics. Google is developing both hardware and software to explore the potential of machine learning via its Magenta research, Roberts said.

We offer an opportunity for companies and individuals working on interesting problems in AI to introduce their work to the community through the IAMA. Please contact /u/dejormo for more information. We would love to hear from you!

I tried to show that in my post. One problem is the current inputs lose a lot of the information that human mental representations draw on. But NNs only work on vectors represented as numbers.

I wouldn't recommended starting with a set solution in mind for ML problems, let alone a specific NN shape. From my experience the effectiveness of ML solutions are loosely correlated with your intuition on what will work (every problem is unique). If you're trying out an NN solution, I find it's best to start with a wide-NN with lots of redundancy, so that that you can search through the solution space from multiple locations quickly. You can then start iteratively trimming neurons that don't contribute to the best output and retraining until left with an locally-optimal solution. If it happens to be an hourglass then so be it, but in terms of general effectiveness, the thinner a NN is then the more specialized (and limited) it will be.

Scientists in China working on a 10-year-old project believe they can -- one day.