Sunday, May 17, 2015

Human Learning Needs in the Age of Machine Learning

Yesterday I experienced quite a coincidence that helped me crystallize my thoughts regarding machine learning; as I was scanning my Twitter and Google+ timelines and my Inoreader feeds each site had a post about machine learning. This post is about 2 common questions I get asked and how these posts helped me reflect on my responses to these questions.

Due to my work with the IBM Watson research team, I often get asked these 2 questions by other physicians:

  •  The first question is if/when computers are going to make physicians redundant.  My response is that at present the vision is to create Artificial Intelligence systems that would help healthcare providers provide more efficient or better quality care and not to replace them.  Clearly, no one has a crystal ball and the expert opinions run the gamut from fear of AI to tremendous optimism.
From Newsweek

Peter Diamandis and Steven Kotler
  • The second question is how to train our current generation of learners to prepare them for the future where they will work closely with artificial intelligence systems.  My response here is that students need to be part of building these systems in their disciplines.  Just like in high schools students take immersive experiences to learn foreign languages, our learners need to start doing electives in computer sciences and  data analytics departments or companies to leverage these technologies to solve problems in their disciplines.  Thus medical students should work with computer science students to use big data from wearable devices to improve health of a population for example.  This will not only help solve problems but they will learn first hand the limitations of these tools and recognize these in the future rather than just blindly relying on their recommendations.  
So what were the three posts?
This is a post about a paper by researchers at Rutgers University who developed machine learning algorithms to help recognize styles and artistss of fine art paintings with a great deal of accuracy.  But the most important lesson was that when the algorithms failed to identify an artist correctly, there was a lesson to be learned.  There was a similarity in the paintings that was evidence of how the two artists (the correct one and the one wrongly identified by the algorithm) were similarly influenced.  Something that an art historian may not even have been aware of.
From: MIT Technology Review (Click image for web page)

2. Image Scaling Using Deep Convolutional Neural Networks

This amazing post describes in an (relatively speaking) easy to understand manner how a neural network was designed to process low resolution images to "fill in the missing pixels" to produce high-res images for Flipboard posts.  The post is a great one for our high school and college math students to see how concepts that they are learning have tremendous practical implications.

Courtesy Normal Tasfi via Flipboard

3.  a16z Podcast: Making Sense of Big Data, Machine Learning, and Deep Learning

This is a terrific 27 minute podcast the quotable quote being,
"Machine learning is to big data as human learning is to life experiences" 
It has a great story of how Larry Page talking with Google employees exhorts them to shorten the latency between entering a search term and getting the results.  When asked if what the final goal should be, whether it should be zero, he responds, "Why should we stop at zero?".  The goal should be that machines should be able to anticipate our needs not just respond to our requests.  Nguyen goes on to discuss why machine learning needs to be part of every app.

Sonal interviewing Christopher Nguyen CEO of Adatao

Given enough data, machine learning can identify patterns that humans cannot and will be able to predict problems before then happen.  Thus humans know that driving a car with poor brakes and wipers, very fast in rain on a curvy road often leads to disasters. 
We have an increasing torrent of data from medical literature, genomic analysis, electronic health records and wearable devices. When we start making this data available to appropriately programmed machines, patterns will emerge that may help predict or prevent heart attacks, strokes and cancers.  
This is what our learners need to get comfortable with so they know when to rely on these predictions and when to spot errors and learn from them.

Tuesday, May 12, 2015

Microsoft Strings Together an Amazing List of Innovations!

This has been an amazing year for Microsoft.  The list of hardware and software innovations that they have come out with is mind boggling.

  1. Surface Pro 3 - probably the best single device I have owned - replaced my laptop and iPad and Android tablet in one fell swoop and added the functionality of the superb Stylus.
  2. Office Mix - the easiest way to create content for flipped classrooms
  3. HoloLens - the mixed reality headset that has huge potential in education
  4. Surface Hub - Large "Smartboard" with video conferencing, OneNote and motion sensing built in.
  5. Microsoft Band - for $200 a fitness tracker with ability to read notifications, tweets, text messages, email, calendar alerts, heart rate monitor, GPS etc.
  6. Windows 10 with Microsoft Edge - a browser that lets you annotate the web
  7. Skype Translator (in Preview) - instant translation between English, Spanish, Mandarin, Italian and other languages coming soon.
On top of this making all of Office apps available on both iOS and Android and also allowing developers to quickly convert their iOS and Android apps for Windows is a big shift in MS philosophy.

Competition is great for consumers and I am glad MS is back in the fray in a big way!

HoloLens for Anatomy Education

Office Mix Video

Surface Hub

Web Note feature of Microsoft Edge Browser

Saturday, April 25, 2015

A Personal Learning Network becomes a Print Journal Issue: Why Academics Really use Twitter

Social Media allows a motivated and engaged learner to build connections that can enhance lifelong learning.  The connections become a learners Personal Learning Network or PLN.  The ability of social media to help a learner find and connect with the right people that might be otherwise impossible to "meet" in real life is one of its huge potential advantages.

This can be a difficult concept to convey to someone who may have a very negative attitude of Twitter.  One can hardly blame them for thinking that Twitter is a time waster, that there is a huge noise to signal ratio with very little tangible benefit.

A famous Nature study showed that very few academics use Twitter compared to sites like Google Scholar.  This led to a spoof by PhD Comics on "Why Academics Really use Twitter".  This is quite funny maybe because it has an element of truth for those who use Twitter.  At the same time infographics like this might unintentionally dissuade people from trying out Twitter as it may reinforce their beliefs about its lack of usefulness.

Now we have a great example of how a PLN created on Twitter led to an entire issue of a print journal.  The credit goes to Margaret Chisholm who was the editor of the special issue of "International Review of Psychiatry" and put together the issue with the help of a group of authors who mostly got to know each other first on Twitter and are part of a large PLN of health care social media users.

Granted, the special issue was regarding the use of Social Media but it could well have been any other topic in biomedical sciences where the scientists engage in social media.  This special issue of a print journal may be an excellent showpiece of the huge potential benefit of social media for academics - to create a PLN for lifelong learning.