Discovering Information with a Little Help from Tweet Assistant

If you noticed a spike in my Twitter posts today, there is a reason.  I have been searching #machinelearning using Tweet Assistant (more on Twitter Assistant in a future post)

These tweets, hashtags, mentions and popular posts provide a gold mine of information.  However, there is more to analyzing tweets than getting news and opinions. Within minutes, I was following some cool people and started discovering awesome tweets and visualizations.

Machine Learning – Top 10 Mentions

 

Machine Learning – Top 5 Hashtags

I started retweeting some of the posts I found but felt that there are far too many posts and a discussion of these may fill a couple of blog posts. So that is what I am going to do.

 

 

Applications of ML – Improving Depth detection, Night Sight, Video Play and Notifications

Here are a few fascinating applications of ML. I mostly track business applications of ML so I was pleasantly surprised to see how Unsupervised Learning and Reinforcement Learning (two ML techniques that do not get much coverage) was being used by two of the biggies in the AI and ML space.

The first two – Depth detection and Nightsight are posts from Google AI Blog.  The following concepts were covered in these posts.

  • PSL Positive shutter lag
  • Motion metering
  • Exposure stacking
  • Astrophotography
  • Super-resolution
  • Auto white balancing and Learning based AWB algorithm
  • Ultra long shot photography
  • Optical Image stabilization

    Facebook Horizon article covered how they were improving user experience with Reinforcement Learning.

  • Improving video playing using Reinforcement Learning (RL)
  • Improving user notifications using Reinforcement Learning

Links to articles:

  1. Under the hood of the Pixel 2: How AI is supercharging hardware – Google AI 
  2. Google AI Blog: Night Sight: Seeing in the Dark on Pixel Phones
  3. Horizon: An open-source reinforcement learning platform – Facebook Code 

Tim O’Reilly: It is up to us

Listening to this conversation with Tim O’Reilly, was one of the most rewarding experiences. In this conversation, Tim and Byron discuss several topics that make you think. I listed a few here.

  1. Fitness functions (see the quote below)
  2. On Amazon’s use of robots for business
  3. About Cognitively Augmented workers
  4. The law of conservation of attractive profits
  5. On Intelligence (human and artificial)
  6. How to pair humans with machines
  7. Step changes and their impact
  8. On anticipating  and countering the worst fears
  9. The Robustness Principle
  10. Agreement Protocols
  11. On Platforms and Eco-systems
  12. On doing meaningful work

I will pick a few of the topics (1-5) and take the liberty of quoting from the transcript. My goal is to kindle your interest enough to read the article and then the book.

On Fitness functions and how they focus companies on delivering value with technology (including AI).

If you look at Google; their fitness function on both the search and the advertising side is relevance. You look at Facebook; loosely it could be described as engagement.

On Amazon’s use of robots in their business:

an analysis of Amazon. In the same 3 years which they added 45,000 robots to their factories, they’ve added hundreds of thousands of human workers.

About Cognitively Augmented workers.

Then, Sidecar and Lyft figured out the other piece of the equation, because Uber was just black cars. They figured out that in order to have enough drivers to really fill out the marketplace, other than a small segment of well-off people, you’d get ordinary people to supply their cars. And you could do that because those drivers are cognitively augmented. It used to be that you had to be a professional driver, because [when] somebody says, “I want to go to such and such an address,” you’d need to really know the city. You [would] need to have a lot of experience to know the best routes. Well, guess what, with these apps [like] Google Maps and Waze, anybody can do it. So I started looking at that and [saw that] we have a marketplace of small businesses managed by algorithms to help them match up with customers.

Law of conservation of attractive profits:

Clay Christensen, back in 2004  talked about  “the law of conservation of attractive profits,” and that’s what helped me get from open source to web 2.0—[is] when one thing becomes a commodity, something else becomes valuable. So if self-driving cars commoditize driving, you have to ask yourself, what becomes valuable. And I think it’s going to be new kinds of augmentation for humans, new kinds of services that you’ll put on top of driving.

On Intelligence (artificial):

And so what would be something that we have today that would qualify or come close to qualifying as that in your mind?

You mean, in terms of machines?

Yes.

Nothing.

And why do you think that?

I’m with Gary Marcus on this, you know. He kind of talked about how the frontier of AI right now is deep learning, and it’s great, but you still have to train it by showing it a gazillion examples of something, and after you show it a gazillion examples, it can figure stuff out. That’s great, but it can’t figure that out without being exposed to those examples. So, we’re a long way from kind of just flicking the switch, having a machine take in its experience of the world, and basically come to conclusions about it.

Tim ends with this appeal and an inspiring message.

“Hey, we have a lot of things to worry about, we have enormous new powers, let’s put them to work, in the right, way, tackling the hard problems.”

I have been a big fan of Tim’s “Work on stuff that matters” and quoted him. It framed some of my decisions on what to spend time on.

Meta:

I have been following Tim for a couple of decades. More about that in a later post. I recently started listening to Byron’s One Minute AI podcast and the GigaOm AI podcast.

Data Science – A Few Tweets and Links

What is Data Science?

What is Data Science from Wikipedia Talks a bit of the history as well.

What is data science? – O’Reilly Radar

Data Science Courses and Recipes

Coursera Introduction to Data Science Course

RT @radar: Want to be a data wrangler? School of Data offers free online data science  courses

Applications, Tools

If you are wondering about the applications of Data Science, please watch the first couple of videos from this course

RT @StartupYou: DIY Data Science – when will this happen and think of how big it will be

Data Science Tools: Tools slowly democratize many data science tasks

“Deep Learning – The Biggest Data Science Breakthrough of the Decade” – Free webcast from O’Reilly

Tim O’Reilly – “Data science is transformative. The first wave was marketing analytics, before that financial arbitrage.”

Mapping Twitter’s Python and Data Science Communities

Data science and the analytic lifecycle  by @bigdata #strataconf

Other Resources

A bitty bundle of data science blogs Collected by @hmason. via @mikeloukides Call for more http://t.co/2iFABfIl2q (look at the comments in the blog for more resources links)

What’s A ‘Data Scientist’ Anyway? Real-Time With m6d’s Claudia Perlich”

Machine Learning – A Few Links and Tweets

On Machine Learning from A free book on ML – A First Encounter of Machine Learning by Max Welling

The first reason for the recent successes of machine learning and the growth of the field as a whole is rooted in its multidisciplinary character. Machine learning emerged from AI but quickly incorporated ideas from fields as diverse as statistics, probability, computer science, information theory, convex optimization, control theory, cognitive science, theoretical neuroscience, physics and more.
The second, perhaps more important reason for the growth of m
achine learning is the exponential growth of both available data and computer power. While the field is build on theory and tools developed statistics machine learning recognizes that the most exiting progress can be made to leverage the enormous flood of data that is generated each year by satellites, sky observatories, particle accelerators, the human genome project, banks, the stock market, the army, seismic measurements, the internet, video, scanned text and so on.

On why this book was written

Much of machine learning is built upon concepts from mathematics such as partial derivatives, eigenvalue decompositions, multivariate probability densities and so on. I quickly found that these concepts could not be taken for granted at an undergraduate level.

Machine learning will be one of the most important tech trends over the next three to five years for innovation” http://t.co/kBFPHlANHa

Startups making machine learning an elementary affair http://t.co/FkF7TSy45R

Use Cases Machine Learning on Big Data for Predictive Analytics http://t.co/1AvQHXkgr4 #ml usecases

A startup journey, the improvement in Python’s data science capabilities and hosted machine learning http://t.co/Vx4g7lIM1X #techtrends

RT @woycheck: Zico Kolter wants to use machine learning to analyze electrical current behavior and provide details about your power bill (@…

Microsoft Research Machine Learning Summit: April 22-24, 2013 http://t.co/x9YxylgMeX

RT @siah: A free ebook by Max Welling “A First Encounter with Machine Learning” http://t.co/5KjCCylL3Y

Google Hires Brains that Helped Supercharge Machine Learning | Wired Enterprise | http://t.co/cVgZpNri4c http://t.co/2mJ7ggZE2n

RT @siah: PyMADlib: A Python wrapper for MADlib – an open source library for scalable in-database machine learning algorithms http://t.c

Peekaboo: Machine Learning Cheat Sheet (for scikit-learn) http://t.co/6UyYWO74

Panels and Discussions

This is a panel from Churchill Club featuring
Peter Norvig, Director of Research, Google ,Gurjeet Singh, Co-founder & CEO, Ayasdi, Jeremy Howard, President and Chief Scientist, Kaggle

Meta

Once in a while, I go and gather my recent tweets and create a Tweet Cloud (a project developed by a student). I find some interesting topics, save the tweets and start a blog. I have written about this Linked Tweet Cloud a couple of times.

tweets_on_machine_learning

App Log: IBM Watson, BIg Data and Treatment for Cancer

It is heartening to see emerging technologies like Big Data Analytics, Natural Language Processing and Artificial Intelligence combined into powerful tools to solve real problems. IBM Watson is an example. From Wikipedia

Watson is an artificial intelligence computer system capable of answering questions posed in natural language, developed in IBM‘s DeepQA project by a research team led by principal investigator David Ferrucci.

From Watson provides cancer treatment options to doctors in seconds

Watson has ingested more than 600,000 pieces of medical evidence, two million pages of text from 42 medical journals and clinical trials in the area of oncology research.   Watson has the power to sift through 1.5 million patient records representing decades of cancer treatment history, such as medical records and patient outcomes, and provide to physicians evidence based treatment options all in a matter of seconds.

Starting with 1,500 lung cancer cases, Memorial Sloan-Kettering clinicians and analysts are training Watson to extract and interpret physician notes, lab results and clinical research, while sharing its profound expertise and experiences in treating hundreds of thousands of patients with cancer.

The core of the technology is available through Apache Foundation’s UIMA Project.

UIMA – a framework for  analyzing  large volumes of unstructured information and discover relevant knowledge  to a user

You can look at sites and services that take advantage of UIMA’s capabilities.

Meta:

Applog is a series of curated links of innovation applications of emerging technologies.