Welcome to the bi-weekly letter covering Deep Learning Patterns, Methodology and Strategy. We’ve come up away to organize the topics to appeal to the broadest of audiences. The more general topics always are at the top, while more specialized ones are towards the bottom. We hope that this newsletter we appeal to all those interested in Deep Learning developments.
For this issue, we revisit the Metalearning symposium, introduce courses and tools for AI, look at various ethical viewpoints with regards to the development of AI, see how advances in AI parallel advances in Neurosciences, and explore recent researches and developments on AI.
Several approaches to metalearning have emerged, including those based on Bayesian optimization, gradient descent, reinforcement learning, and evolutionary computation. The symposium presents an overview of these approaches, given by the researchers who developed them.
Tools and Courses
The new open ecosystem for interchangeable AI models
Educational resources for machine learning
Pandas on Ray accelerates Pandas queries by 4x on an 8-core machine, only requiring users to change a single line of code in their notebooks.
GitHub — onnx/onnxmltools: ONNXMLTools enables conversion of models to ONNX. Currently supports CoreML and SciKit
GitHub is where people build software. More than 28 million people use GitHub to discover, fork, and contribute to over 80 million projects.
My notes from the excellent Coursera specialization by Andrew Ng
Alexandria integrates machine reading and reasoning, natural language understanding, computer vision, and crowdsourcing techniques to create a new extensive, foundational common sense knowledge source for future AI systems to build upon.
Using Google Clips to understand how a human-centered design process elevates artificial intelligence
Written in Haskell, Queryparser is Uber Engineering’s open source tool for parsing and analyzing SQL queries that makes it easy to identify foreign-key relationships in large data warehouses.
Don’t worry about supersmart AI eliminating all the jobs. That’s just a distraction from the problems even relatively dumb computers are causing.
The Center for Humane Technology is a world-class team of former tech
insiders and CEOs who are advancing thoughtful solutions to change the
culture, business incentives, design techniques, and organizational
structures driving how technology hijacks our brains.
A robotics researcher afraid of robots, Peter Haas, invites us into his world of understand where the threats of robots and artificial intelligence lie. Befo…
When? This is probably the question futurists, AI experts, and even people with a keen interest in technology dread most. It’s been famously difficult to predict when developments…
AI in our Daily Life
If we can’t understand our own brains, maybe the machines can do it for us.
The gaming company’s Commit Assistant AI tool has been trained to spot when programmers are about to make a mistake
(left:) Test image displayed on computer monitor. (right:) Image captured by EEG and decoded. (credit: Dan Nemrodov et al./eNeuro) A new technique developed
Studying 70,000 mouse neurons could help Andreas Tolias build smarter AI.
Not only is the technique an advancement of holographic technology, but also, the holograms could have fascinating (and practical) medical applications.
Low-precision computation has been gaining a lot of traction in machine learning.
Neuroevolution is a form of artificial intelligence that uses evolutionary algorithms to generate artificial neural networks, parameters, topology and rules.
Algorithms off the convex path.
I challenge you to find a field as interesting and exciting as Deep Learning.
“Whatever you are studying right now if you are not getting up to speed on deep learning, neural networks, etc., you lose,” says Mark Cuban.
“We are going through the process where software will automate software, automation will automate automation.”
Source: Deep Learning on Medium