And to help manage this porcine horde, the country's farmers are turning to a decidedly un-traditional tool: artificial intelligence.

Artificial intelligence is here to stay. It has proven itself as a valuable and effective way to perform routine tasks in a variety of industries. While AI may not have poked its head around the corner of your office door yet, it doesn't mean that it is not on the horizon as a tool to help unload some of your most …

Mistaken extrapolations, limited imagination, and other common mistakes that distract us from thinking more productively about the future. We are surrounded by hysteria about the future of artificial intelligence and robotics--hysteria about how powerful they will become, how quickly, and what they will do to jobs. I recently saw a story in MarketWatch that said robots will take half of today's jobs in 10 to 20 years. It even had a graphic to prove the numbers. How many robots are currently operational in those jobs? How many realistic demonstrations have there been of robots working in this arena? Similar stories apply to all the other categories where it is suggested that we will see the end of more than 90 percent of jobs that currently require physical presence at some particular site. Mistaken predictions lead to fears of things that are not going to happen, whether it's the wide-scale destruction of jobs, the Singularity, or the advent of AI that has values different from ours and might try to destroy us.

People are misled by artificial intelligence because of a phenomenon known as the Eliza effect, named after a 1966 computer program that responded to people's typed statements in the manner of a psychotherapist. The computer was executing some very simple programming logic.

The task of estimating the fundamental frequency of a monophonic sound recording, also known as pitch tracking, is fundamental to audio processing with multiple applications in speech processing and music information retrieval. To date, the best performing techniques, such as the pYIN algorithm, are based on a combination of DSP pipelines and heuristics. While such techniques perform very well on average, there remain many cases in which they fail to correctly estimate the pitch. In this paper, we propose a data-driven pitch tracking algorithm, CREPE, which is based on a deep convolutional neural network that operates directly on the time-domain waveform. We show that the proposed model produces state-of-the-art results, performing equally or better than pYIN. Furthermore, we evaluate the model's generalizability in terms of noise robustness. A pre-trained version of CREPE is made freely available as an open-source Python module for easy application.

I am about to submit this new paper on using simulations as a way to incorporate expert knowledge into machine learning, via a similarity kernel. Has anyone seen this idea before (instead of feature data - machine learning training, feature data- simulation - kernel capturing similarity between all samples - machine learning training.? I'd like to see the work and cite it if appropriate.

I'm finding it very hard to find online any GAN model source code which comes with pre-trained parameters. Specifically, I'm interested in image generation (from random noise input), trained on a "challenging" data set such as ImageNet (even if downsampled...) (I'm less interested in MNIST...) Also, I'm wondering why is this so? So many papers appear with amazing results, and some of them share their code. Why don't they share their pre-trained models also? It would be very helpful for the community.

Music is a universal language that can bring people together from all over the world. As emerging technologies help us communicate better, artificial intelligence is beginning to overtake our hearts, minds, and even ears. AI is opening up a world that users can automate, personalize, and learn from. The music and education sectors are not exempt from the efficiency of emerging technologies. Smart bots like Amper's A.I. can now compose their own albums, while other intelligent applications like SmartMusic allow users to experiment with composition and production.

Facebook now has 2 billion monthly users… and responsibility.