r/MachineLearning - [D] The gradient descent renaissance

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The field of machine learning underwent massive changes in the 2010's. At the beginning, the field saw diverse approaches applied to a variety of topics and data structures. Then Alexnet blew away the competition for the Imagenet challenge with his CNN, and the field was forever changed. However, there was a warming up phase. Caffe's first release was in 2013.


Automatic Real-Time Music Generation for Games

AAAI Conferences

Music composition can be a challenge for many small- to medium-sized game companies, largely due to the expense and difficulty in creating original music for each level of a game. To address this, we developed a tool that automatically generates original music, by training a music generator on pieces whose style the game designer wishes to imitate. The generator then creates original music in that style in real-time, and switches between styles when signaled by the game. This software has been refined to produce music that is coherent and imitates a composer’s larger music structure.


What's the Difference Between Machine Learning and Deep Learning?

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Understanding how today's AI works might seem overwhelming, but it really boils down to two concepts you probably have heard of before: "machine learning" and "deep learning". Neither are brand new ideas, but the way they're used seems to constantly evolve. Machine learning and deep learning are how Netflix knows what you might want to watch next, or how Facebook can recognize your friends' face in a photo, or how a support agent can figure out if you'll be satisfied with your customer service. So what are these buzzwords that still dominate the conversations about AI, and how exactly are they different? And what do they mean for customer service?


What's the difference between machine learning and deep learning? - Zendesk

#artificialintelligence

Understanding how today's AI works might seem overwhelming, but it really boils down to two concepts you probably have heard of before: "machine learning" and "deep learning". Neither are brand new ideas, but the way they're used seems to constantly evolve. Machine learning and deep learning are how Netflix knows what you might want to watch next, or how Facebook can recognize your friends' face in a photo, or how a support agent can figure out if you'll be satisfied with your customer service. So what are these buzzwords that still dominate the conversations about AI, and how exactly are they different? And what do they mean for customer service?


The Women Making AI Less Scary And More Accessible

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NEW YORK, NY - APRIL 12: Model, philanthropist, and investor Natalia Vodianova, Epytom founder and CEO Anastasia Sartan, and MSNBC'Your Business' host JJ Ramberg speak onstage during Vanity Fair's Founders Fair at Spring Studios on April 12, 2018 in New York City. "I'm close to artificial intelligence (AI) and it scares the hell out of me," said Elon Musk during HBO's Westworld panel at South by Southwest this year. "It's capable of vastly more than anyone knows, and the improvement is exponential." Musk cited the example of AlphaGo, Google DeepMind's artificial-intelligence program best known as the first computer program to defeat a professional human player at the boardgame Go. The AI had been trained to tackle the Chinese game "Go," which is a 2,000-plus year old abstract war simulation.