Million-dollar babies  | The Economist

As Silicon Valley fights for talent, universities struggle to hold on to their stars

THAT a computer program can repeatedly beat the world champion at Go, a complex board game, is a coup for the fast-moving field of artificial intelligence (AI). Another high-stakes game, however, is taking place behind the scenes, as firms compete to hire the smartest AI experts. Technology giants, including Google, Facebook, Microsoft and Baidu, are racing to expand their AI activities. Last year they spent some $8.5 billion on deals, says Quid, a data firm. That was four times more than in 2010.

In the past universities employed the world’s best AI experts. Now tech firms are plundering departments of robotics and machine learning (where computers learn from data themselves) for the highest-flying faculty and students, luring them with big salaries similar to those fetched by professional athletes.

Last year Uber, a taxi-hailing firm, recruited 40 of the 140 staff of the National Robotics Engineering Centre at Carnegie Mellon University, and set up a unit to work on self-driving cars. Read More Here to know how to avail the best car services. You can also look for the best places to visit in Columbia to enjoy your car ride.That drew headlines because Uber had earlier promised to fund research at the centre before deciding instead to peel off its staff. Other firms seek talent more quietly but just as doggedly. The migration to the private sector startles many academics. “I cannot even hold onto my grad students,” says Pedro Domingos, a professor at the University of Washington who specialises in machine learning and has himself had job offers from tech firms. “Companies are trying to hire them away before they graduate.”

Source: Million-dollar babies  | The Economist

 

Busy and distracted? Everybody has been, since at least 1710 | Aeon Essays

Header essay 120406428
The rise of the internet and the widespread availability of digital technology has surrounded us with endless sources of distraction: texts, emails and Instagrams from friends, streaming music and videos, ever-changing stock quotes, news and more news. To get our work done, we could try to turn off the digital stream, but that’s difficult to do when we’re plagued by FOMO, the modern fear of missing out. Some people think that our willpower is so weak because our brains have been damaged by digital noise. But blaming technology for the rise in inattention is misplaced. History shows that the disquiet is fuelled not by the next new thing but by the threat this thing – whatever it might be – poses to the moral authority of the day.
Source: Busy and distracted? Everybody has been, since at least 1710 | Aeon Essays

 

justmarkham/DAT8: General Assembly’s Data Science course in Washington, DC

DAT8 – General Assembly’s Data Science course in Washington, DC

DAT8 Course Repository

Course materials for General Assembly’s Data Science course in Washington, DC (8/18/15 – 10/29/15).

Instructor: Kevin Markham (Data School blog, email newsletter, YouTube channel)

Binder

Tuesday Thursday
8/18: Introduction to Data Science 8/20: Command Line, Version Control
8/25: Data Reading and Cleaning 8/27: Exploratory Data Analysis
9/1: Visualization 9/3: Machine Learning
9/8: Getting Data 9/10: K-Nearest Neighbors
9/15: Basic Model Evaluation 9/17: Linear Regression
9/22: First Project Presentation 9/24: Logistic Regression
9/29: Advanced Model Evaluation 10/1: Naive Bayes and Text Data
10/6: Natural Language Processing 10/8: Kaggle Competition
10/13: Decision Trees 10/15: Ensembling
10/20: Advanced scikit-learn, Clustering 10/22: Regularization, Regex
10/27: Course Review 10/29: Final Project Presentation

Python Resources

Course project

Comparison of machine learning models

Comparison of model evaluation procedures and metrics

Advice for getting better at data science

Additional resources

Source: justmarkham/DAT8: General Assembly’s Data Science course in Washington, DC

 

Getting a Ph.D. Will Turn You Into an Emotional Trainwreck, Like Me

There are no academic jobs; getting a lit Ph.D. will make you a horrible person http://www.slate.com/articles/life/culturebox/2013/04/there_are_no_academic_jobs_and_getting_a_ph_d_will_make_you_into_a_horrible.html



Who wouldn’t want a job where you only have to work five hours a week, you get summers off, your whole job is reading and talking about books, and you …
Getting a Ph.D. Will Turn You Into an Emotional Trainwreck, Like Me

 

BayesDB: Data science is a communication problem – O’Reilly Media


Query languages, like BQL, offer a bridge between domain experts and software experts.

The late Fred Brooks may be most famous for The Mythical Man Month, but his greatest essay, in my opinion, is No Silver Bullet. Brooks recognized that our civilization, in adopting computing, is fascinated with tools that make things go faster. Tools are great. We understand tools. We like to build tools. We get to sell tools. But computing is not a tools problem. Computing is a communications problem. We need to enable communication between those who understand software and those who understand reality.

Data science is currently in a tools rush where we hope that the right graphical environment will eliminate the need for qualified data scientists. However, no graphical environment can overcome the fundamental information asymmetry and complexity inherent in modeling data. Responsibility for modeling data cannot be delegated to a marketing specialist or a clinician. If we try to delegate that responsibility, we will have simply abdicated it.

Source: BayesDB: Data science is a communication problem – O’Reilly Media