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Wednesday, July 27, 2016

Encyclopedia of Interaction

An update on this work, which I first examined in 2011.  Still apparently a free resource.   The Encyclopedia of Human-Computer Interaction, 2nd Ed. ...  from the  Interaction Design Foundation. which now also sells courses in the area.  

Defending the Power Grid

Further applications to other kinds of monitored networks, IOT applications.

Detecting Cybersecurity Threats by Taking the Grid's Pulse   By Peter Fairley:    " .. DARPA teams are building grid defenses using phasor measurement units that combine GPS and power sensors ... " 

Future of 3D Printing

Good forward looking article in CACM on the future of consumer 3D printing.

" ... Bill Decker, chairman of the Association of 3D Printing, agrees the consumer market for 3D printers is largely untapped, although he noted, "Best Buy, Barnes & Noble, the big box retailers are betting on it." What is helping to "escalate the market are small, handheld 3D printers sold by Brookstone, such as the 3Doodler, which is like an electric toothbrush, an advanced glue gun." Such products, Decker said, are inexpensive, and because they do not require software like their desktop brethren, are super-easy to use.

Decker adds, "It’s a disruptive technology, and you’re seeing more and more of it in education, on the Internet, and with e-publishing. And when the kids see it and use it in school, they want to have it at home." ... " 

Predix Opening to Development

GE Opening Development.
 " ... General Electric has more than a century of industrial experience, but its five-year-old GE Digital division hopes to leverage outside expertise in its IoT mission. This week the company is hosting the first developer conference for its GE Predix software platform, where it will announce developer kits to help get the ball rolling on new IoT projects.

Predix started as an internal tool for GE to monitor products like jet engines that it had already built and sold. Now the company is offering it to others as a platform for capturing and analyzing data about all kinds of industrial assets. It can collect many kinds of information about those assets and, with customers’ permission, combine inputs from many users to learn things like when a piece of factory equipment is likely to fail. ... " 

Tuesday, July 26, 2016

A Conversation about the State of AI

Link to talk with Peter Norvig who is Director of Research at Google.  We used his text as an intro to AI and it is still very popular.  This is not ot a technical talk.  It is very non hype.

It reminds me of similar discussions in the early 90s on AI.  Notable that 'rule based systems',  is still brought up, just as it was back then. Also the implication that there is no magic as yet,  or really any automatic autonomy.  Just hard coding ahead.   Also a continued nod to open source systems.

Applications of AI technologies: Peter Norvig in conversation with Tim O'Reilly
Exploring the rise of conversational interfaces, how AI will change the way programmers create software, and open source tools for AI and machine learning. ...  "

Video by Tim O'Reilly -  Peter Norvig   July 19, 2016

Putting Digital in Perspective

 In CustomerThink: 

" ... This year, the number of smartphones, tablets and connected devices is expected to cross 7 billion – that will mean more connected devices than there are human beings on earth. With more and more people owning smart gadgets, these devices are going to have an increasingly bigger influence on our communication patterns, online habits and of course, our buying habits, and globally, organisations are trying hard to keep up with this shift by leveraging Digital Business Technology themselves. ... 

Consider what Tesco did in Korea around four years ago. In 2011, Tesco’s affiliate, Homeplus in South Korea, had to deal with a strange situation in order to meet its aspiration of market leadership: While it was a problem that Homeplus did not have enough stores as its main competitor did, the real problem though seemed to be the very lifestyle of their potential customers – given that Koreans were extremely hardworking, they just didn’t have enough time to go shopping. So even if Homeplus was to set up enough stores, it was going to be a challenge to get people to visit the store. ... 

The killer plan Homeplus came up with was to set up virtual grocery stores in strategic locations like subways and metro stations – they erected posters of products and groceries just like they were displayed at their stores – each item displayed in the poster came with a QR Code that provided product details such as pricing, etc, and could be scanned from an app on a smartphone into a virtual shopping cart. What’s more, the delivery of their checked-out items could be timed conveniently. Customers could now use their smartphones to buy grocery as they waited for their trains or whenever they found time as they commuted."

For Better Decisions: Get more Varied input

In Inc.    Big observer of the decision process.  How do you improve the decision process?  Get a diversity of input.     People that fundamentally think about the process in different ways.

Monday, July 25, 2016

Google Data Studio Beta

Brought to my attention. Looks interesting.  Seems a limited trial.   Will report on this.

" ... Beautiful data stories start here

Google Data Studio (beta) provides everything you need to turn your data into beautiful, informative reports that are easy to read, easy to share, and fully customizable. Data Studio lets you create up to 5 custom reports with unlimited editing and sharing. All for free — currently only available in the U.S. .... " 

Nestle Innovation Platform

Previously mentioned,  my former colleague Pete Blackshaw announces Nestle's Open Innovation  Platform. :

" ... I’m extremely excited to unveil our new “Open Innovation” platform, Henri@Nestle.com (http://Henri.Nestle.com). Check it out! If you are an entrepreneur, tech enabler, or partner, sign up and tag your interest areas. Henri facilitates the rapid sharing of ideas and solutions in response to business challenges. Four challenges are now up, including one related to Nespresso and sustainability, micronutrient deficiency, and the future of bottled water. Each brief owner clearly outlines expectations, and we’ve tried to make the platform mobile friendly in every way possible. 

“With the ingenuity and innovative spirit of startups Henri@NestlĂ© can help address some of the world’s biggest nutrition, health and wellness challenges, creating funded opportunities to drive real impact at scale," said my colleague Gerardo Mazzeo, Global Innovation Director. ...  " 

See also Procter's Connect and Develop.

P&G Sells UK Product Innovation Center

Does innovation need location anymore?   It has been debated.   Known as the Egham Center.   In Bizjournals:

" ... Procter & Gamble has agreed to sell a major product innovation center near London that employs more than 600 people engaged in the research and development of the Cincinnati-based company’s beauty brands. ....  intends to lease back the complex of buildings known locally as the Rusham Park Technical Centres from the buyer, Royal Holloway, University of London, for the next three to five years as part of the deal.   ...  " 

Need for Dynamic Machine Learning

In one sense all typical, static machine learning methods are wrong.  That is they are based on past data, and the context that drove that data has changed.   The question is,  are the resulting models 'too wrong' to get useful results from?  Integrating dynamic (time varying) elements to the model can address this.  It does often add considerable complexity to the modeling effort.   A good introduction out of DSC on this topic.  

History of Measuring Time

In Gizmag, a remarkable illustrated look at the history of the stopwatch,  A very favorite topic, the history and process of measurement.  One of my earliest jobs was to wander a factory with a stopwatch to look for opportunities.

Solutions to VR Motion Sickness

Surprisingly, though I have some tendency to have motion sickness, have never seen it in VR demonstrations, though these have usually been short.  Some solutions.  via Columbia University Research.

Cyber Social Learning Systems

Cyber-Social Learning Systems   Via Jim Spohrer

Cyber-social learning systems (CSLS) deeply integrate digital computing with human and social phenomena to drive rapid learning by, and improvements in, the functioning and performance of societal systems at all scales. As such, CSLS promise to transform health care, education, communities, security, and many other sectors. The next major frontier in research and development is the integration of cyber-physical with complex human and social systems and phenomena at scale.  .... " 

Sunday, July 24, 2016

Magic Leap and China Augmented Reality Shopping

Video at the link.  Suggesting further advances in China retail technology.

In Mashable: 
Magic Leap demo in China shows augmented reality shopping
The incredibly secretive augmented reality startup Magic Leap showed off another demonstration of its technology this week, this time using Chinese apps. 

During a discussion with Alibaba CMO Chris Tung in China, Magic Leap's chief marketing officer Brian Wallace gave audience members a look at how the technology works. The early look for Alibaba makes sense given the Chinese company's role as a major investor in Magic Leap. .... "

Autonomous Selection of Mars Laser Targets

An example of autonomy for a complex system.  Here the Curiosity Rover on Mars.   At one level this is similar to closed loop process control, but with more complex sensor analysis being done in that loop.  

Assume this increases accuracy, speed in going through analysis goals ... and even decreases targeting labor required to enact,  thus decreasing cost.     So is  closed loop process control we did in manufacturing, though the adjustments here appear to start to arise to the strategic.   Article and image examples:

From the Jet Propulsion Lab: 
  " .... NASA's Mars rover Curiosity is now selecting rock targets for its laser spectrometer -- the first time autonomous target selection is available for an instrument of this kind on any robotic planetary mission.

Using software developed at NASA's Jet Propulsion Laboratory, Pasadena, California, Curiosity is now frequently choosing multiple targets per week for a laser and a telescopic camera that are parts of the rover's Chemistry and Camera (ChemCam) instrument. Most ChemCam targets are still selected by scientists discussing rocks or soil seen in images the rover has sent to Earth, but the autonomous targeting adds a new capability. ... "

Kinds of Choice in Decision Process

I have been a long time student of decision methods, and the concept of a 'Hobson's Choice' (i.e. take it or leave it)  came up recently.  The formal classification of different kinds of related decisions are described in this WP article.

  Not to say this concept is often brought up when modeling decision trees or processes.  It is much more invoked when describing observed and often bizarre human decisions.  But could we tag a tree describing business process to make it more useful?  Is this inherently what is done with Bayesian methods?    Seems this should also be thought about when automating data science.   Thoughts?

Programming with Objects

A technical piece that won't mean much to you unless you have done professional programming in the last decades.  I did some of that in the early days of what was called OOP, and remember misgivings when the 'sharable' aspects of code broke.  I don't go there much anymore, but have a deep appreciation of the needs addressed,  We all wanted ways to efficiency re-use what others had done,   maintenance could be so much easier.  No?   Yes, if you didn't build things that were by themselves fragile.  Read for insight.    In Medium.

Saturday, July 23, 2016

Singularity University

I was reminded of Singularity University

" ... What is Singularity University?
Our mission is to educate, inspire and empower leaders to apply exponential technologies to address humanity's grand challenges.  .... 

"Singularity University is an opportunity to bring people from every conceivable walk of life with people that share a common aspiration that is to do better, to be better, and to make a positive impact on people's lives."   ...  '

Following Watson Facing Cybercrime

Continuing to follow Watson to determine what the ideal application is.  Is it mostly about big and volatile knowledge, appropriately indexed?   Leveraged with machine learning.  To find appropriate cases for reapplication?  That's what I have seen so far as part of application proposals.   Not AI ... but certainly a form of 'Practical Intelligence'.    And maintaining that knowledge via focused learning.

Example, Recently in  Wired:

" ... IBM announced that Watson is taking its cognitive learning chops to the cloud, where it’ll apply them to analyzing, identifying, and (hopefully) preventing cybersecurity threats. But first, it’s going to have to learn. Fast. .... 

There are already plenty of computer-enhanced approaches to combating cybercrime, most of which involve identifying outliers or abnormalities—like when a user logs a few too many failed password attempts—and determining whether those constitute some sort of threat.

Collecting and analyzing this type of data can and does work. It’s not ideal, though. First, there’s simply too much of it; according to a recent IBM report, the average organization sees over 200,000 pieces of security event data every single day. There’s simply no way to keep up with it all. And while solutions like MIT’s recent AI2 can trim down the number of incidents a human researcher needs to sift through, there’s still the fact that the data points being considered are only a small part of the picture.  ... " 

What is Cognitive Analytics?

Good question.  Larry Smith gens up a good answer:

"  ....  Cognitive analytics focuses on the analytical thinking you do in your mind, just with a cognitive machine. With cognitive computing technology as its backbone, cognitive analytics provides people a thinking partner to process and analyze information in context to their decision-making process. Cognitive analytics creates a human-machine partnership to help people make better, clearer and faster decisions.  .... " 

Read the rest.  So a focused virtual assistant to do the 'math' that people don't like to do?   It does mean we have to link to an interaction or conversation with the cognitive machine.  So we can see how Facebook Messenger's interaction, with their billion users,  is a good place to try.  But will that work, or will the bots just annoyingly get in the way?

Disruption Considered

 Been asked about the concept recently,  not that well defined,  but a case where extreme examples are obvious.    In the consumers mind, or business?    In McKinsey:

" ... Incumbents needn’t be victims of disruption if they recognize the crucial thresholds in their lifecycle, and act in time.  ... " 

Targeting Physical Out of Stocks

Seen a number of related methods,  this PG article looks at methods that combine image recognition and robotic methods.    We also looked at a method that could use streams from security cameras to count shelf items to address planogram compliance and detect out of stocks.    I liked the idea of multi-tasking.    Further in Progressive Grocer. 

Friday, July 22, 2016

Importance of Data Management

Some good thoughts here.  Recently have seen the particular importance of how data is changing over time.    Metadata is important and may differ in quality and nature over time as well.  Good to consider when understanding the nature of the data involved as it supports a particular application.

AI, Deep Learning, and Machine Learning: A Primer

AI, Deep Learning, and Machine Learning: A Primer by Frank Chen
“One person, in a literal garage, building a self-driving car.” That happened in 2015. Now to put that fact in context, compare this to 2004, when DARPA sponsored the very first driverless car Grand Challenge. Of the 20 entries they received then, the winning entry went 7.2 miles; in 2007, in the Urban Challenge, the winning entries went 60 miles under city-like constraints.

Things are clearly progressing rapidly when it comes to machine intelligence. But how did we get here, after not one but multiple “A.I. winters”? What’s the breakthrough? And why is Silicon Valley buzzing about artificial intelligence again?

From types of machine intelligence to a tour of algorithms, a16z Deal and Research team head Frank Chen walks us through the basics (and beyond) of AI and deep learning in this slide presentation. ... " 

Micro Moments During Mobile Shopping

Via Think with Google.  A Guide with statistics.

Shopping Micro-Moments Guide: How to Be There and Be Useful for Shoppers

Mobile has forever changed the way people shop. A shopper's smartphone is there for them anytime, anywhere in countless micro-moments. The question for retailers is: Are you prepared to meet these shoppers in the moments that matter most? Explore this guide—full of new research on consumer trends—to find out how to be there and be useful in this new digital landscape. .... " 

Bringing Intelligence to Data Handling

Linking AI and data, in an efficient and semantic (meaning linked)  manner, then utilizing integrated machine learning, is a huge leveraging point.

In Medium: 
" ... “Why has AI so dramatically changed in the last few years compared to 30, 40, 50 years ago when it was started?” He answered his question, saying, “It’s all the power of data.” He then used the refinement of mass-manufactured clothing processes improving to the point where people can find what they want to wear instead of having it specifically tailored to them as an analogy for the possibilities now open through effective data usage.

“When you have these finished APIs, application development becomes very simple,” he said. “You just grew those applications together, those APIs together, and you get a very powerful application all residing in the cloud, supported with the SLAs of the cloud, and backed by a company like Microsoft. And that creates unreasonable speed in developing intelligent applications. That’s a huge revolution. ... "

InsightETE Seeks Root Cause in Process

Just brought to my attention, and applicable to all kinds of process measuring and management.  Whats more important than determining root cause in a process?    Digging deeper, join me.

Welcome To InsightETE
Founded in 1999, InsightETE is the brain child of founder and systems engineer Bill Johns.

InsightETE is the first and only company in the world to offer a proprietary and patented method (PATENT No.: US 7,577,701 B1) to perform root cause analysis in 15 minutes or less.

InsightETE’s software gives you the ability to measure and troubleshoot IT system performance on a granular level. No other company in the world can truthfully claim the same ability.

InsightETE clients can measure true response time, track service levels, and reduce outages as they root out problems from their verified source. What’s more, they see an increase in their customer service satisfaction by eliminating service level disagreements.  ... " 

Analytics Magazine

Long before data science and machine learning, there was Analytics.  Here is their most recent e-newsletter.    Subscribe.  Always some useful information.

Thursday, July 21, 2016

Exploring Databases: The Indiana Project

I much like the premise here, often we find ourselves spending much time exploring, and even just finding databases.

Letizia Tanca - Exploring Databases: The Indiana Project

Latizia Tanca, Professor at Politecnico di Milano, gave a wonderful presentation today on "Exploring Databases: the Indiana Project" as part of our Cognitive Systems Institute Speaker Series. Please continue the conversation here in this discussion group with Letizia, who's work can be found http://tanca.faculty.polimi.it/.  

Slides here.  Presentation here. Further CSIG discussion here.

" ... Big Data, and its 5 Vs – volume, velocity, variety, veracity, and value – have been talked about a lot. In the latest ACM SIGMOD blog post, Letizia Tanca is writing about the Double V for Big Data: Wisdom. Reciting an extract from her blog: …not only we want to make sense of the data, whether they are big or not, but we can, and should, extract from them a worth that makes us wiser, doubling the Value. Read more at: http://wp.sigmod.org/  .... "

Facebook's AI Chatbots Now Serve a Billion Users

Its claimed that Facebook Messenger now has a billion users,  and those billion users can be served by a swarm of AI chatbots,   Will those billion users be served well enough, to some sort of useful and profitable end, so that the users of Messenger will take the Bot's advice?   Or will they leave for the exits?  Further  Discussed in CWorld.  The AI will have to be good and very adaptive to the users. How much will this handle a conversation rather than just a tailored ad or notification?

Machines Replacing Humans

Good thoughts from the McKinsey Quarterly.  Agree there will be dramatic differences by sector, but do also expect some dramatic speedups.

Where machines could replace humans—and where they can’t (yet)
By Michael Chui, James Manyika, and Mehdi Miremadi

The technical potential for automation differs dramatically across sectors and activities.

As automation technologies such as machine learning and robotics play an increasingly great role in everyday life, their potential effect on the workplace has, unsurprisingly, become a major focus of research and public concern. The discussion tends toward a Manichean guessing game: which jobs will or won’t be replaced by machines?  ... " 

Wearable, IOT Memory

In CWorld: New kinds of very low power memory for Wearables and IoT by Samsung and IBM.

DSC Surveys Data Science Techniques

Index to useful article lists from Data Science Central.  Click through to their site for search details.  Join the the group.  Very nicely done articles from introduction to in-depth.  I have not linked them all, but go to DSC for more.

" ... These techniques cover most of what data scientists and related practitioners are using in their daily activities, whether they use solutions offered by a vendor, or whether they design proprietary tools. When you click on any of the 40 links below, you will find a selection of articles related to the entry in question. Most of these articles are hard to find with a Google search, so in some ways this gives you access to the hidden literature on data science, machine learning, and statistical science. Many of these articles are fundamental to understand the technique in question, and come with further references and source code.

Starred techniques (marked with a *) belong to what I call deep data science, a branch of data science that has little if any overlap with closely related fields such as machine learning, computer science, operations research, mathematics, or statistics. Even classical machine learning and statistical techniques such as clustering, density estimation, or tests of hypotheses, have model-free, data-driven, robust versions designed for automated processing (as in machine-to-machine communications), and thus also belong to deep data science. However, these techniques are not starred here, as the standard versions of these techniques are more well known (and unfortunately used) than the deep data scienceequivalent. To learn more about deep data science,  click here. Note that unlike deep learning, deep data science is not the intersection of data science and artificial intelligence.

Finally, to discover in which contexts and applications the 40 techniques below are used, I invite you to read the following articles:
   40 Data Science Techniques
  1. Linear Regression 
  2. Logistic Regression 
  3. Jackknife Regression *
  4. Density Estimation 
  5. Confidence Interval 
  6. Test of Hypotheses 
  7. Pattern Recognition 
  8. Clustering - (aka Unsupervised Learning)
  9. Supervised Learning 
  10. Time Series 
  11. Decision Trees 
  12. Random Numbers 
  13. Monte-Carlo Simulation 
  14. Bayesian Statistics 
  15. Naive Bayes 
  16. Principal Component Analysis - (PCA)
  17. Ensembles 
  18. Neural Networks 
  19. Support Vector Machine - (SVM)
  20. Nearest Neighbors - (k-NN)
  21. Feature Selection - (aka Variable Reduction)
  22. Indexation / Cataloguing *
  23. (Geo-) Spatial Modeling 
  24. Recommendation Engine *
  25. Search Engine *
  26. Attribution Modeling *
  27. Collaborative Filtering *
  28. Rule System 
  29. Linkage Analysis 
  30. Association Rules 
  31. Scoring Engine 
  32. Segmentation 
  33. Predictive Modeling 
  34. Graphs 
  35. Deep Learning 
  36. Game Theory 
  37. Imputation 
  38. Survival Analysis 
  39. Arbitrage 
  40. Lift Modeling     ..... " 
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