Data Science News – handpicked articles, news, and stories from Data Science world – June.
Experts Predict When Artificial Intelligence Will Exceed Human Performance – Artificial intelligence is changing the world and doing it at breakneck speed. The promise is that intelligent machines will be able to do every task better and more cheaply than humans. Rightly or wrongly, one industry after another is falling under its spell, even though few have benefited significantly so far.
This app uses artificial intelligence to turn design mockups into source code – While traditionally it has been the task of front-end developers to transform the work of designers from raw graphical user interface mockups to the actual source code, this trend might soon be a thing of the past – courtesy of artificial intelligence.
Applying deep learning to real-world problems – The rise of artificial intelligence in recent years is grounded in the success of deep learning. Three major drivers caused the breakthrough of (deep) neural networks: the availability of huge amounts of training data, powerful computational infrastructure, and advances in academia.
How AI Can Keep Accelerating After Moore’s Law – Google CEO Sundar Pichai was obviously excited when he spoke to developers about a blockbuster result from his machine-learning lab earlier this month. Researchers had figured out how to automate some of the work of crafting machine-learning software, something that could make it much easier to deploy the technology in new situations and industries.
Bayesian GAN – Generative adversarial networks (GANs) can implicitly learn rich distributions over images, audio, and data which are hard to model with an explicit likelihood. We present a practical Bayesian formulation for unsupervised and semi-supervised learning with GANs. Within this framework, we use stochastic gradient Hamiltonian Monte Carlo to marginalize the weights of the generator and discriminator networks.
The $1700 great Deep Learning box. – After years of using a thin client in the form of increasingly thinner MacBooks, I had gotten used to it. So when I got into Deep Learning (DL), I went straight for the brand new at the time Amazon P2 cloud servers. No upfront cost, the ability to train many models simultaneously and the general coolness of having a machine learning model out there slowly teaching itself.
Exploring LSTMs – The first time I learned about LSTMs, my eyes glazed over. Not in a good, jelly donut kind of way. It turns out LSTMs are a fairly simple extension to neural networks, and they’re behind a lot of the amazing achievements deep learning has made in the past few years. So I’ll try to present them as intuitively as possible – in such a way that you could have discovered them yourself.
An Algorithm Summarizes Lengthy Text Surprisingly Well – ho has time to read every article they see shared on Twitter or Facebook, or every document that’s relevant to their job? As information overload grows ever worse, computers may become our only hope for handling a growing deluge of documents. And it may become routine to rely on a machine to analyze and paraphrase articles, research papers, and other text for you.
Divide and Conquer: How Microsoft researchers used AI to master Ms. Pac-Man – Microsoft researchers have created an artificial intelligence-based system that learned how to get the maximum score on the addictive 1980s video game Ms. Pac-Man, using a divide-and-conquer method that could have broad implications for teaching AI agents to do complex tasks that augment human capabilities.
Open Source Datasets – A large-scale, high-quality dataset of URL links to approximately 300,000 video clips that cover 400 human action classes, including human-object interactions such as playing instruments, as well as human-human interactions such as shaking hands and hugging. Each action class has at least 400 video clips. Each clip is human annotated with a single action class and lasts around 10s.
One Model To Learn Them All – Deep learning yields great results across many fields, from speech recognition, image classification, to translation. But for each problem, getting a deep model to work well involves research into the architecture and a long period of tuning. We present a single model that yields good results on a number of problems spanning multiple domains. In particular, this single model is trained concurrently on ImageNet, multiple translation tasks, image captioning (COCO dataset), a speech recognition corpus, and an English parsing task. Our model architecture incorporates building blocks from multiple domains.
THE POWER OF GTC – GTC is the largest and most important event of the year for GPU developers. GTC and the global GTC event series offer valuable training and a showcase of the most vital work in the computing industry today – including artificial intelligence and deep learning, healthcare, virtual reality, accelerated analytics, and self-driving cars.
Artificial intelligence can now predict suicide with remarkable accuracy – When someone commits suicide, their family and friends can be left with the heartbreaking and answerless question of what they could have done differently. Colin Walsh, data scientist at Vanderbilt University Medical Center, hopes his work in predicting suicide risk will give people the opportunity to ask “what can I do?” while there’s still a chance to intervene.
Learning Path: TensorFlow: The Road to TensorFlow – Discover deep learning and machine learning with Python and TensorFlow
Python for Data Structures, Algorithms, and Interviews! – Get a kick start on your career and ace your coding interviews!
Python for Data Science by UC San DiegoX – Learn to use powerful, open-source, Python tools, including Pandas, Git and Matplotlib, to manipulate, analyze, and visualize complex datasets.
High-Dimensional Data Analysis by HarvardX – A focus on several techniques that are widely used in the analysis of high-dimensional data.
A developer’s guide to the Internet of Things (IoT) – The Internet of Things (IoT) is an area of rapid growth and opportunity. Technical innovations in networks, sensors and applications, coupled with the advent of ‘smart machines’ have resulted in a huge diversity of devices generating all kinds of structured and unstructured data that needs to be processed somewhere.
Neural Networks for Machine Learning – Learn about artificial neural networks and how they’re being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We’ll emphasize both the basic algorithms and the practical tricks needed to get them to work well.
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