Human beings have an uneasy relationship with robots. We're fascinated by the prospect of intelligent machines. At the same time, we're wary of the existential threat they pose, one emboldened by decades of Hollywood tropes. In the near-term, robots are supposed to pose a threat to our livelihood, with automation promising to replace human workers while the steady march of artificial intelligence puts a machine behind every fast food counter, toll booth, and steering wheel. The palm-sized robot, from San Francisco-based company Anki, is both a harmless toy and a bold refutation of that uneasy relationship so loved by film and television.

Feature learning and deep learning have drawn great attention in recent years as a way of transforming input data into more effective representations using learning algorithms. Such interest has grown in the area of music information retrieval (MIR) as well, particularly in music audio classification tasks such as auto-tagging. In this paper, we present a two-stage learning model to effectively predict multiple labels from music audio. The first stage learns to project local spectral patterns of an audio track onto a high-dimensional sparse space in an unsupervised manner and summarizes the audio track as a bag-of-features. The second stage successively performs the unsupervised learning on the bag-of-features in a layer-by-layer manner to initialize a deep neural network and finally fine-tunes it with the tag labels. Through the experiment, we rigorously examine training choices and tuning parameters, and show that the model achieves high performance on Magnatagatune, a popularly used dataset in music auto-tagging.

Amazon launched a new music streaming service today into an already crowded field. SYDELL: To make this work, Amazon's Steve Boom says the company has been collecting data from millions of Alexa users about how they talk to it about music, and they've been programming Alexa to respond. SYDELL: Responding in a way that caters more to the way humans actually speak is a great way to open people up to the vastly expanding universe of artificial intelligence, says James McQuivey, an analyst with Forrester Research. Still, that's less than the leading standalone streaming service Spotify.

And a more advanced book, by the same authors: The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2009).

I find Keras a very useful and well done tool. It is perfect to start using Theano and it is really easily understandable and usable. Now, I realised a Keras model (using Theano interface), which works perfectly well and I would like to replicate it using only Theano code. Since Keras is actually using Theano code, I should be able, in principle, to do this. The neural net is a convolutional neural network for a one output regression task, with the following layers: conv2d, maxpool2d, conv2d, maxpool2d, dense, dense, output and using Adam optimizer.

If you have and you want to learn the science behind them, you have come to the right place. In this course, I will show you how these companies use Recommender systems or Machine Learning to influence your purchasing decisions. This course is timely and extremely relevant now as almost all major service-oriented companies function on recommender systems. You will understand how these systems work and learn how to build and use your own recommender systems, just like these big companies do. Learn how to build the recommender systems that are being used by almost every big service-oriented company in today's world with this introductory course for beginners.

The society of intelligent agents now has a new member. His name is Ozlo, from the Palo Alto, California-based company of the same name. According to co-founder and CEO Charles Jolley, he's the only independent intelligent agent left, now that Samsung has scooped up Viv. But the key differentiator, Jolley told me, is that Ozlo is "the only assistant that can link together competing sources of information." As an example, Jolley recalled that he wanted "some steak and live music" while on a recent trip to Las Vegas.

I am unable to train a network using my pretrained wording embeddings as weights for input layer to LSTM. My word2vec embedding is trained on a larger corpus and the training corpus is a subset of it. My vocabulary for the task consists of some word2vec_vocab additional words in corpus. Test data: one hot vector with 1 at position w.r.t corpus(I am not using full dict for mapping, thus position indices differ.) Problem: Model overfitting on training data with increasing loss on validation set.

With the increasing volume of interactions with customers over social media channels, KLM Royal Dutch Airline is the first airline to test how artificial intelligence could assist customer service agents. "We have 100,000 mentions a week on social media," says Tjalling Smit, senior vice president of Digital at KLM Royal Dutch Airlines. "We handle around 15,000 customer service cases a week and we answer our customers 24/7 in 10 different languages." As social channels proliferate, KLM makes sure it is present where its customers live online. "We were the first airline to allow customers to get their boarding passes and flight confirmation through Facebook Messenger," says Smit. KLM is piloting DigitalGenius' GPU-accelerated AI system that is integrated directly into KLM's Customer Relationship Management tool, and provides a layer of deep learning and artificial intelligence to service agents in real-time.

This story was delivered to BI Intelligence Apps and Platforms Briefing subscribers. To learn more and subscribe, please click here. Google will sunset photo-sharing site Panoramio as it focuses on building out features in Google Maps, Digital Trends reports. For context, Panoramio allows users to upload and share their photos by location on Google Maps and Google Earth. However, Google would like users to upload their photos using its inbuilt tool instead.