Education


Carnegie Mellon Solidifies Leadership Role in Artificial Intelligence

@machinelearnbot

Carnegie Mellon University's School of Computer Science (SCS) has launched a new initiative, CMU AI, that marshals the school's work in artificial intelligence (AI) across departments and disciplines, creating one of the largest and most experienced AI research groups in the world. Moore is directing the initiative with Jaime Carbonell, the Newell University Professor of Computer Science and director of the Language Technologies Institute; Martial Hebert, director of the Robotics Institute; Computer Science Professor Tuomas Sandholm; and Manuela Veloso, the Herbert A. Simon University Professor of Computer Science and head of the Machine Learning Department. It created the first and only machine learning department, studying how software can make discoveries and learn with experience. That expertise, spread across several departments, has enabled CMU to develop such technologies as self-driving cars; question-answering systems, including components of IBM's Jeopardy-playing Watson; world-champion robot soccer players; 3-D sports replay technology; and even an AI smart enough to beat four of the world's top poker players.


Sorting Lego sucks, so here's an AI that does it for you

#artificialintelligence

You see, Mattheij decided he wanted in on the profitable cottage industry of online Lego reselling, and after placing a bunch of bids for the colorful little blocks on eBay, he came into possession of 2 tons (4,400 pounds) of Lego -- enough to fill his entire garage. As Mattheij explains in his blog post, resellers can make up to €40 ($45) per kilogram for Lego sets, and rare parts and Lego Technic can fetch up to €100 ($112) per kg. Instead of spending an eternity sifting through his own, intimidatingly large collection, Mattheij set to work on building an automated Lego sorter powered by a neural network that could classify the little building blocks. "By the end of two weeks I had a training data set of 20,000 correctly labeled images."


Deep Learning with R Keras

@machinelearnbot

For R users, there hasn't been a production grade solution for deep learning (sorry MXNET). The post ends by providing some code snippets that show Keras is intuitive and powerful. So if you are still with me, let me show you how to build deep learning models using R, Keras, and Tensorflow together. The R interface to Keras truly makes it easy to build deep learning models in R. I believe the Keras for R interface will make it much easier for R users and the R community to build and refine deep learning models with R. This means you don't have to force everyone to use Python to build, refine, and test your models.


Carnegie Mellon Launches Artificial Intelligence Initiative

#artificialintelligence

Carnegie Mellon University's School of Computer Science (SCS) has launched a new initiative, CMU AI, that marshals the school's work in artificial intelligence (AI) across departments and disciplines, creating one of the largest and most experienced AI research groups in the world. Moore is directing the initiative with Jaime Carbonell, the Newell University Professor of Computer Science and director of the Language Technologies Institute;Martial Hebert, director of the Robotics Institute; Computer Science Professor Tuomas Sandholm; and Manuela Veloso, the Herbert A. Simon University Professor of Computer Science and head of the Machine Learning Department. It created the first and only Machine Learning Department, studying how software can make discoveries and learn with experience. That expertise, spread across several departments, has enabled CMU to develop such technologies as self-driving cars; question-answering systems, including components of IBM's Jeopardy-playing Watson; world-champion robot soccer players; 3-D sports replay technology; and even an AI smart enough to beat four of the world's top poker players.


I learned how to break bad news to patients and loved ones more from business school than medical school

Los Angeles Times

Like most doctors, I spent four years in medical school learning to treat hundreds of illnesses and help patients manage their health. I spent very little of this time learning how to work with patients when modern medicine runs out of miracles -- and only a few hours, spread over four years, learning to lead end-of-life conversations and deliver bad news. A recent study of medical curricula, published last year in the American Journal of Hospice and Palliative Medicine, found that the average time dedicated to end-of-life care is 13 hours spread across multiple courses over four years. Medical schools need to teach doctors to do the same.


Salesforce's chief scientist says AI winters are over

#artificialintelligence

While there's concerning hype about artificial intelligence in the market today, Salesforce's chief scientist says researchers don't have to fear that interest in the field will disappear again. Socher was referencing a set of historical periods when funding in artificial intelligence research cratered after a particularly significant hype cycle. However, the current state of machine learning technology is such that we don't need computers that surpass human intelligence in order to get good results, in Socher's view. While investments in machine learning research are coming from for-profit companies like Salesforce, Google, Microsoft, Facebook, and Amazon, Socher said that the benefits are being distributed broadly because of the social norms in the AI space.


Getting Started with Predictive Maintenance Models - Silicon Valley Data Science

@machinelearnbot

We are also provided with a training set of full run-to-failure data for a number of engines and a test set with truncated engine data and their corresponding RUL values. One way of addressing this is to look at the distribution of sensor values in "healthy" engines, and compare it to a similar set of measurements when the engines are close to failure. The figure above shows the distribution of the values of a particular sensor (sensor 2) for each engine in the training set, where healthy values (in blue) are those taken from the first 20 cycles of the engine's lifetime and failing values are from the last 20 cycles. In blue are the values of a particular sensor (sensor 2 in this case) plotted against the true RUL value at each time cycle for the engines in the training set.


Data Literacy Will Make You Almost Invincible - Shelly Palmer

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They might even know that a simple linear regression is the proper mathematical technique to accomplish the task. They would quickly and clearly understand that a simple linear regression was the appropriate technique to accomplish the task, but someone fluent would consider specific methodologies in ways that literate executives might not. Turning data literacy into invincibility will evolve from your understanding of how best to combine 1st-, 2nd- and 3rd-party data, data-scientific research and your business knowledge to turn information (data) into action. We offer a bunch of different training programs that may work for you or your company, including Executive Data Literacy Training Courses, Data-Driven Media Sales Training Courses and bespoke Data Activation Forums for the C-Suite.



CMU 'mind reading' program breakthrough

Daily Mail

Researchers at Carnegie Mellon University have developed brain imaging technology that can identify complex thoughts, such as'The witness shouted during the trial.' Predicted (top) and observed (bottom) fMRI brain activation patterns for the sentence'The witness shouted during the trial.' The study, led by Carnegie Mellon University Professor of Psychology Dr Marcel Just, revealed that to process sentences such as'The witness shouted during the trial,' the brain uses an alphabet of 42 'meaning components' or'semantic features' consisting of features such as person, setting size, social interaction and physical action. The study, led by Carnegie Mellon University Professor of Psychology Dr Marcel Just, revealed that to process sentences such as'The witness shouted during the trial,' the brain uses an alphabet of 42 'meaning components' or'semantic features' consisting of features such as person, setting size, social interaction and physical action.