If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The learning rate is one of the most important hyper-parameters to tune for training deep neural networks. In this post, I'm describing a simple and powerful way to find a reasonable learning rate that I learned from fast.ai I'm taking the new version of the course in person at University of San Francisco. It's not available to the general public yet, but will be at the end of the year at course.fast.ai Deep learning models are typically trained by a stochastic gradient descent optimizer.
In Imitation Learning (IL), also known as Learning from Demonstration (LfD), a robot learns a control policy from analyzing demonstrations of the policy performed by an algorithmic or human supervisor. For example, to teach a robot make a bed, a human would tele-operate a robot to perform the task to provide examples. The robot then learns a control policy, mapping from images/states to actions which we hope will generalize to states that were not encountered during training. There are two variants of IL: Off-Policy, or Behavior Cloning, where the demonstrations are given independent of the robot's policy. However, when the robot encounters novel risky states it may not have learned corrective actions.
From doing Sudoku every morning to playing more chess to learning a musical instrument, lots of people try different ways to become smarter and improve their memory. Thirty-five years after a landmark memory training experiment in 1982, have scientists really found any foolproof way to make us more intelligent? In a new paper, researchers have looked through several cognitive training programmes and find they actually don't improve our general cognitive and academic skills. Writing for The Conversation, PhD Candidate Giovanni Sala and Professor Fernand Gobet from the University of Liverpool say the general public should be fully aware of the benefits - and limits - of training the brain. Music instruction does not seem to exert any true effect on skills outside of music.
"Attracting Venture Capital for Dummies" is a best seller. The book states on page one, the Venture Capitalists (VCs) goal in life is to find cybersecurity unicorns. Much like a Cyndaquil Pokemon, unicorns have common traits and in order to attract VCs you must exhibit the commonalities of said unicorns. And for bonus points, require lots of data scientists. This is one of the two prerequisites that venture capitalists use to gauge Unicornness.
ODSC East 2018 is one of the largest applied data science conferences in the world. Our speakers include some of the core contributors to many open source tools, libraries, and languages. Attend ODSC West 2017 and learn the latest AI & data science topics, tools, and languages from some of the best and brightest minds in the field. See schedule for many more.. Core Contributor of scikit learn.
These new applications require a new way of thinking about the development process. Traditional application development has been enhanced by the idea of DevOps, which forces operational considerations into development time, execution, and process. In this tutorial, we outline a "cognitive DevOps" process that refines and adapts the best parts of DevOps for new cognitive applications. Specifically, we cover applying DevOps to the training process of cognitive systems including training data, modeling, and performance evaluation. A cognitive or artificial intelligence (AI) system fundamentally exhibits capabilities such as understanding, reasoning, and learning from data.
Machine Learning (ML) is now a de-facto skill for every quantitative job and almost every industry embraced it, even though fundamentals of the field is not new at all. However, what does it mean to teach to a machine? Unfortunately, for even moderate technical people coming from different backgrounds, answer to this question is not apparent in the first instance. This sounds like a conceptual and jargon issue, but it lies in the very success of supervised learning algorithms. What is a machine in machine learning First of all here, machine does not mean a machine in conventional sense, but computational modules or set of instructions.
Machine Learning (ML) is one of the hot buzzwords these days, but even though EDA deals with big-data types of issues it has not made much progress incorporating ML techniques into EDA tools. Many EDA problems and solutions are statistical in nature, which would suggest a natural fit. So why is it so slow to adopt machine learning technology, while other technology areas such as vision recognition and search have embraced it so easily? "You can smell a machine learning problem," said Jeff Dyck, vice president of technical operation for Solido Design Automation. "We have a ton of data, but which methods can we apply to solve the problems?
In this article, we will learn about autoencoders in deep learning. We will show a practical implementation of using a Denoising Autoencoder on the MNIST handwritten digits dataset as an example. In addition, we are sharing an implementation of the idea in Tensorflow. An autoencoder is an unsupervised machine learning algorithm that takes an image as input and reconstructs it using fewer number of bits. That may sound like image compression, but the biggest difference between an autoencoder and a general purpose image compression algorithms is that in case of autoencoders, the compression is achieved by learning on a training set of data.