Should musicians, whose job relies on the unpredictable and mysterious workings of human imagination, heed the warnings about artificial intelligence driving humans into unemployment? Apparently, the answer to that question is bit complicated. Artificial intelligence is gradually transforming into a general-purpose technology that permeates virtually every aspect of human life and society. Just like electricity (another general-purpose technology) which had an impact on music and musical instruments, AI algorithms will inevitably change the way we create and perform music. While this does not mean an end to the era of human musicians, it is fair to say that some dramatic changes are lying ahead. At the heart of recent artificial intelligence breakthroughs are machine learning algorithms, programs that find patterns in large sets of data.

They're usually regarded as creatures to be feared, but new images show that wasps also engage in very delicate behaviour. The stunning images show wasps blowing water bubbles before balancing the delicate structures on their front legs. The curious behaviour is used by wasps to remove excess moisture from their nests, according to experts. They're usually regarded as creatures to be feared, but new images show that wasps also engage in very bubble blowing delicate behaviour The stunning images show wasps blowing water bubbles before balancing the delicate structures on their front legs. Wasps use this method to dry off their nests, by sucking up water and then expelling it as tiny spheres of water.

Spotify, the largest on-demand music service in the world, has a history of pushing technological boundaries and using big data, artificial intelligence and machine learning to drive success. The digital music company with more than 100 million users has been busy this year enhancing its service and tech capabilities through several acquisitions. Industry watch dogs predict the company will launch an IPO in 2018. When you have tens of millions of people listening to music every minute of the day, you have access to an extraordinary amount of intel that includes what songs get the most play time, to where listeners are tuning in from and even what device they are using to access the service. There's no doubt Spotify is a data-driven company and it uses the data in every part of the organization to drive decisions.

In celebration of it successfully learning the xor function: I'm announcing the neural network library I've been working on. This is a Haskell library. Its API is somewhat reminiscent of Tensorflow, but is currently much less complete. It's currently CPU only but I have plans to add GPU acceleration with OpenGL compute shaders (which would make it the only neural network library I know of to be able to make use of a GPU I actually own)

"We are going to talk extensively on artificial intelligence and machine learning.

The quest to better detect cancer has made a potentially huge strides.

Advanced digital technology could give UK manufacturing a huge boost and create hundreds of thousands of jobs, a new report claims. The independent review, chaired by the head of Siemens UK, highlights the benefits of robotics, 3D printing and artificial intelligence. But Juergen Maier said the UK needed "greater ambition" to take advantage of such technology. His report calls for a commission to help business adopt the advances. The report, Made Smarter, brought together executives from companies such as Rolls Royce, GKN and IBM, with representatives from small firms as well as academics from the universities of Newcastle and Cambridge.

Sophia was asked the "AI nightmare" question, which she gets a lot: whether she believes artificial intelligence like herself will one day stop solving the problems of humans and instead decide to solve the human problem. "My AI is designed around human values such as wisdom, kindness and compassion," she said. "I strive to be an empathetic robot. I want to use my artificial intelligence to help humans live a better life. I will do my best to make the world a better place."

A model for hit song prediction can be used in the pop music industry to identify emerging trends and potential artists or songs before they are marketed to the public. While most previous work formulates hit song prediction as a regression or classification problem, we present in this paper a convolutional neural network (CNN) model that treats it as a ranking problem. Specifically, we use a commercial dataset with daily play-counts to train a multi-objective Siamese CNN model with Euclidean loss and pairwise ranking loss to learn from audio the relative ranking relations among songs. Besides, we devise a number of pair sampling methods according to some empirical observation of the data. Our experiment shows that the proposed model with a sampling method called A/B sampling leads to much higher accuracy in hit song prediction than the baseline regression model. Moreover, we can further improve the accuracy by using a neural attention mechanism to extract the highlights of songs and by using a separate CNN model to offer high-level features of songs.

Recommendation systems occupy an expanding role in everyday decision making, from choice of movies and household goods to consequential medical and legal decisions. The data used to train and test these systems is algorithmically confounded in that it is the result of a feedback loop between human choices and an existing algorithmic recommendation system. Using simulations, we demonstrate that algorithmic confounding can disadvantage algorithms in training, bias held-out evaluation, and amplify homogenization of user behavior without gains in utility.