data science

or "From One Black Box to Another" tips for students and educators from learning machines.
a talk by Tracy Mehoke

http://www.techyizu.org/recap-shanghai-barcamp-fall-2013/

Organization Is Knowledge: some basic principles of Machine Learning for application in humans

algorithmic composition created with a Markov chain written in Cinder (c++).  The algorithm learns midi in realtime as input which generates the markov model.  I can then control the output generating phrases at various speeds to create compositions. This composition is built from the relationships found in  Piano Phase by Steve Reich, Scary Monsters and Nice Sprites by Skrillex and Midnight City by M83.

Machine Learning Music - Markov Chain Performance Experiment 1

اللقاء الأسبوعي الثامن و الثلاثون, في هذا الأسبوع يحدثنا المهندس طارق عمرو عن الMachine Learning.

Slides for the presentation
http://tarekamr.com/slides/datascience

EP31 - Ask Developer Hangout - Week 38 - Machine Learning With Tarek Amr

Max Shron and Jake Porway talk with Jon Bruner about frameworks for making reasoned arguments with data. Max's recent book, <a href="http://shop.oreilly.com/product/0636920029182.do"><i>Thinking with Data</i></a>, outlines the crucial process of developing good questions and creating a plan to answer them. Jake's nonprofit, <a href="http://www.datakind.org/">DataKind</a>, connects data scientists with worthy causes where they can apply their skills.

For more on this episode, visit the <a href="http://radar.oreilly.com/2014/03/podcast-thinking-with-data.html">O'Reilly Radar blog</a>.

<i>This episode is brought to you by <a href="http://solidcon.com/">Solid</a>, O'Reilly's new conference about the intersection between software and the physical world--including hardware, manufacturing, design, and the code that makes sense of it all. For more information on Solid, and to reserve tickets, visit http://solidcon.com.</i>

Thinking with data

Social Science, Small worlds and Big Data

In this episode, I talk with Liza Kindred, founder of Third Wave Fashion and author of the new free report “Fashioning Data: How fashion industry leaders innovate with data and what you can learn from what they know.” Kindred addresses the evolving role data and analytics are playing in the fashion industry, and the emerging connections between technology and fashion companies. 

In the second segment, I chat with Max Firtman, founder of ITMaster, about the state of the HTML5 space and the future of mobile. He stresses that the change the Internet of Things will bring to the space will be significant and companies need to prepare: "All we know for sure right now is that everything is going to change."

For more, visit O'Reilly Radar: http://oreil.ly/101zhb5

What happens when fashion meets data?

When people get sick, they often start looking online for information about their symptoms and illness. Sara Del Valle, a scientist and applied mathematician at Los Alamos National Laboratory, says that by training a computer model to analyze the specific health-related pages that users access on Wikipedia, public health experts may be able to identify—and even forecast—outbreaks of diseases such as influenza and Dengue fever. An article about the approach was published this week in the journal PLOS Computational Biology.

More at http://scifri.me/0yobpx

Mining Wikipedia Data to Track Disease

05.11.2014
http://www.bbc.co.uk/programmes/p029kjb5
How do web science, technology & open data intersect? In front of an audience at the British Film Institute in London, Click explores the evolution of open data. Gareth Mitchell chairs a panel including: Dame Wendy Hall, professor of Computer Science at the University of Southampton in southern England; Ruth Mackenzie, the director of the digital arts service, The Space; digital artist, Julie Freeman and Jude Kelly, the artistic director of London’s Southbank Centre. The experts discuss the place of open data in the 21st century. The debate is also part of the Open Data Summit, hosting events about open innovation – the future of the Web, digital business models and even how shared data benefits weather forecasting.
http://www.thespace.org/artist/view/odi

BBC Click: Open Data Revolution

O’Reilly’s big data guru Ben Lorica sits down with prominent data scientist DJ Patil to talk about his new ebook (written with Hilary Mason) and other topics in data science and big data.

A Brief Look At Data Science's Past And Future

There are a couple of seemingly contradictory memes rolling around the deep learning field. One is that you need a truly epic amount of data to do interesting work. The other is that in many subject areas there is a ton of data but it's not just laying around for data scientists to snarf up.

On this week's Structure Show podcast, Enlitic Founder and CEO Jeremy Howard and Senior Data Scientist Ahna Girshick address those topics and more.

Girshick, who is our first guest who's worked with Philip Glass and Björk on creating music visualizations, said while there may be scads of MRIs, CAT scans, x-rays created -- they're typically used for their primary purpose -- to diagnose your bum knee, and are then squirreled away in some PACS system never to see the light of day again.

All of that data is useful for machine learning algorithms, or would be, if it were accessible, she said.

Girshick and  Howard agreed that while deep learning -- the process of a computer teaching itself how to solve a problem -- gets better with more data, there's no reason to hold off working with it for data become available.

"While more data can be better I think this is stopping people from trying to use big data inappropriately," Howard said. He cited a recent Kaggle competition on facial key point recognition that's  using 7,000 images and "the top algorithms are nearly perfectly accurate."

The reason companies like Baidu and Google say you need mountains of data is because they have mountains of data available, he said.  "I don't think people should be put off trying to use deep learing just because they don't have a lot of data."

Enlitic is using deep learning to provide medical diagnoses faster and help provide better medical and outcomes for millions of underserved people.

It's a fascinating discussion so please check it out -- Girshick will speak more on what Enlitic is doing at Structure Data next month.

And, if you want to hear what's going on with Pivotal's big data portfolio, Derrick Harris has the latest. Oh and Microsoft makes a bold play for startups by ponying up $500K in Azure cloud credits starting with the Y Combinator Winter 2015 class. That ups the ante pretty significantly compared to what Amazon Web Services, Google and IBM offer. Your move boys.

No, you don't need a ton of data to do deep learning

O’Reilly’s Ben Lorica chats with Kira Radinsky, CTO and co-founder of SalesPredict, about predicting events using machine learning, NLP, and semantic analysis.

Forecasting events, from disease outbreaks to sales to cancer research

O'Reilly's Ben Lorica talks with Angie Ma, co-founder and president of ASI, about the concept of the ASI, the structure of their training programs, and the data and startup scene in the UK.

Turning Ph.D.s Into Industrial Data Scientists and Data Engineers

Let’s talk money. As a “hot” career right now, data science can pay pretty well. But for an individual person matched with a specific job or industry, how much should someone expect to make? 

Since Katie was on the job market lately, this was something she’s been researching, and it turns out that data science itself (in particular linear regressions) has some answers. 

In this episode, we go through a survey of hundreds of data scientists, who report on their job duties, industry, skills, education, location, etc. along with their salaries, and then talk about how this data was fed into a linear regression so that you (yes, you!) can use the patterns in the data to know what kind of salary any particular kind of data scientist might expect.

Careers in Data Science

O'Reilly's Ben Lorica chats with Gary Kazantsev, who runs the R&D Machine Learning group at Bloomberg LP, about how big data and data science are making a difference in finance.

Data Science Makes an Impact on Wall Street

More at http://scifri.me/718syx

This week scientists unveiled a robot that can sustain injury to one of its six legs, think for a few minutes, and devise a more efficient way to walk—by essentially “limping” away as fast as possible. Jeff Clune and his colleagues accomplished the feat by endowing the robot with what they call a "simulated childhood" of possible motions, and letting the robot figure out the rest.
 
How far can this sort of robotic thought go? According to computer scientist Ashok Goel, if robots have unlimited capability to learn, "why would there be a limit to emotional intelligence?" After all, he says, humans aren’t born with a full set of emotional and ethical intelligence—children learn it by observing adults.

The Limits of Artificial Intelligence

Here at DataSift, we live and breathe all things social data. We are constantly amazed by the pioneers who are manipulating that data in extraordinary ways that promote innovations we never thought possible and get especially excited about those who use big data for the greater good of humanity.
That's why we enjoyed our interview with Jake Porway, Founder and Executive Director of DataKind, a non-profit organization that brings together data scientists and social organizations to correlate big data and provide solutions about global social issues.

To learn more about DataSift visit us at http://datasift.com

data science

Wee Kiang Yeo