Written by Alexander Raginsky MIT researchers are developing a computer system that uses genetic, demographic, and clinical data to help predict the effects of disease on brain anatomy. In experiments, they trained a machine-learning system on MRI data from patients with neurodegenerative diseases and found that supplementing that training with other patient information improved the system’s predictions. In the
Written by Alexander Raginsky Who here remembers taking computer programming in school? Whether you learned programming by punching holes in a never ending series of cards, or by writing simple DOS or other computer language commands, the fact remained that computers needed an incredibly precise set of instructions to accomplish a task. The more complicated the task, the more
Written by Alexander Raginsky We are pleased to announce availability of a limited number of Student Grants for attending FIRE 2015. This includes Student Travel Grants and Lower Registration rates for Student participants, details of which are mentioned at http://fire.irsi.res.in/fire/student_grants. The deadline for application is 25th November. Applicants shall be notified about the decision by 28th November. Priority will
Written by Alexander Raginsky The fear that robots will destroy jobs and leave a great mass of people languishing in unemployment is almost as old as automation itself. And yet, from the Luddites onwards, the fears have been eventually proved wrong, and the economy has ended up stronger than before. Robots threaten 15m UK jobs, says Bank of England’s
Written by Alexander Raginsky Deep learning and neural networks are increasingly important concepts in computer science with great strides being made by large companies like Google and startups like DeepMind. By Nikhil Buduma (Musings of an MIT student). Deep learning. Neural networks. Backpropagation. Over the past year or two, I’ve heard these buzz words being tossed around a lot,
Written by Alexander Raginsky I read the question raised recently by the American Statistical Society (ASA) – As a statistician, what is your super power – and after reading the numerous answers, all of them very geeky and not a single one mentioning producing yield, ROI, or added value, I could not resist to post this question on DSC,
Written by Alexander Raginsky In this article, we discuss various strategies used to generate exponential traffic growth, while preserving traffic quality, and user loyalty. Our growth hacking engine is a combination of Raw data science: getting the right data sets, leveraging them, Playing with various tools and API’s: designing an automated machine-to-machine communication service between Hootsuite and Twitter /
Written by Alexander Raginsky Natural language processing, or NLP, is the machine handling of written and spoken human communications. Methods draw on linguistics and statistics, coupled with machine learning, to model language in the service of automation. OK, that was a dry definition. Fact is, NLP is at, or near, the core of just about every information-intensive process out
Written by Alexander Raginsky What I love about working at Google is the opportunity to harness cutting-edge machine intelligence for users’ benefit. Two recent Research Blog posts talked about how we’ve used machine learning in the form of deep neural networks to improve voice search and YouTube thumbnails. Today we can share something even wilder — Smart Reply, a
Written by Alexander Raginsky Not long ago, the term “data science” meant nothing to most people—even to those who worked with data. A likely response to the term was: “Isn’t that just statistics?” These days, data science is hot. The Harvard Business Review called data scientist the “Sexiest Job of the 21st Century.” So what changed? Why did data
