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The wonderful and terrifying implications of computers that can learn | Jeremy Howard | TEDxBrussels

TEDx Talks · Youtube · 10 HN points · 1 HN comments
HN Theater has aggregated all Hacker News stories and comments that mention TEDx Talks's video "The wonderful and terrifying implications of computers that can learn | Jeremy Howard | TEDxBrussels".
Youtube Summary
This talk was given at a local TEDx event, produced independently of the TED Conferences. The extraordinary, wonderful, and terrifying implications of computers that can learn

Jeremy is the CEO of Enlitic, which uses recent advances in machine learning to make medical diagnostics faster, more accurate, and more accessible. The company's mission is to provide the tools that allow physicians to fully utilize the vast stores of medical data collected today, regardless of what form they are in - such as medical images, doctors' notes, and structured lab tests.

He is a serial entrepreneur, business strategist, developer, and educator. He is also the youngest faculty member at Singularity University, where he teaches data science, and is a Young Global Leader with the World Economic Forum. He advised Khosla Ventures as their Data Strategist, identifying the biggest opportunities for investing in data driven startups, and helping their portfolio companies build data driven businesses. Previously he was the President and Chief Scientist of Kaggle, a community and competition platform for over 150,000 data scientists. Before working at Kaggle, he was the top ranked participant in data science competitions globally, in 2010 and 2011. He founded two successful Australian startups (the email provider FastMail, and the insurance pricing algorithm company Optimal Decisions Group), both of which grew internationally and were sold to large international companies. He started his career in management consulting, working at the world’s most exclusive firms, including McKinsey & Co, and AT Kearney (becoming the youngest engagement manager world-wide, and building a new global practice in what is now called “Big Data”). He is also a keen student, for example developing a new system for learning Chinese, which he used to develop usable Chinese language skills in just one year. Jeremy has mentored and advised many startups, and is also an angel investor. He has contributed to a range of open source projects as a developer, and was a regular expert guest on Australia's most popular TV morning news program "Sunrise".

About TEDx, x = independently organized event In the spirit of ideas worth spreading, TEDx is a program of local, self-organized events that bring people together to share a TED-like experience. At a TEDx event, TEDTalks video and live speakers combine to spark deep discussion and connection in a small group. These local, self-organized events are branded TEDx, where x = independently organized TED event. The TED Conference provides general guidance for the TEDx program, but individual TEDx events are self-organized.* (*Subject to certain rules and regulations)
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All the comments and stories posted to Hacker News that reference this video.
The progress here is getting spooky, including the success of such networks for captioning arbitrary images and translating between natural languages. I read that one researcher predicts live narration of video within 5 years.

A cold-splash-in-the-face intro for laypeople can be found in the TEDx talk of Jeremy Howard (founder of Kaggle):

The wonderful and terrifying implications of computers that can learn – https://www.youtube.com/watch?v=xx310zM3tLs

For cutting-edge research, it seems the "NIPS" conference each December is where many of the new results appear:

http://nips.cc/

Dec 08, 2014 · 1 points, 0 comments · submitted by rbdn
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Dec 06, 2014 · 9 points, 4 comments · submitted by jph00
⬐ gradschool
Thanks for this interesting talk and best wishes for the success of your company. Here are a few questions it raised for me. What references would you recommend for someone who wants to know more about machine learning algorithms from a technical point of view? Does the coming economic upheaval you envision with its exponential growth depend on the assumption that machine learning will be applied to the design of computers themselves? The reason I ask is that the applications you demonstrated concentrate on pattern classification, which doesn't seem to me like it would be much help for the work of electrical engineers and computer scientists (albeit a game changer in other fields). Are you aware of any evidence to the contrary?
⬐ jph00
I would start with the Intro to Machine Learning Coursera course, and then the Intro to Neural Networks course. That will get you to a point that you can understand current papers.

The demo I showed actually showed that it's possible to design an algorithm in minutes that previously would have taken years, but combining machines and people. So I do strongly believe that machine learning allows us to create new, better, programs more quickly than before!

⬐ jph00
I'm the speaker from this TEDx talk (and Enlitic CEO / Kaggle Past President) - happy to answer any questions or respond to any comments about it here. Sorry for some rather extreme simplifications in the talk... I only had 18 mins to cover a lot of territory!
⬐ akashtndn
My query deals with the social implications you mentioned at the end of your talk. Are we, as a society, equipped to deal with the changes the surge in computational efficiency deep learning is about to bring when a large portion, including the policy-makers, aren't even able to properly acknowledge them? Can you expand on your last slide's point where you mention that "better education" won't help in dealing with the upcoming upheaval?
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