Alexa Stain Detective

Have been exploring alternative skills and their distribution by type on various voice driven assistants.   Found  the 'Tide Stain Remover',  as an Alexa Skill.   Based originally in part on some content in the book:   'Clean it fast, Clean it Right',  by Jeff Bredenberg.   Written by our AI team, long ago.

On a historical note this was originally written in a form that was delivered via a CD Rom in pre common Internet days, then converted to an App that ran under popular smartphone OS's.   It now runs on the Amazon Alexa system via voice interaction.   I also found that the IOS app version has stopped working after about IOS version 9.0, says it needs to be upgraded, which is not good  publicity.    Either fix it or remove it.

The skill idea was used as an early model for Constructing query -> Subtasks-> Cautions->Tasks  -> Solution Models.    By our AI team and later  P&G Productions.

If anyone has more knowledge of its more recent history, contact me and I will be glad to update its history in the Service Skills domain.

How P&G and American Express Are Approaching AI

I am quoted in the Harvard Business Review about how P&G successfully used AI in the past to improve systems, including estimates of actual value.     This HBR article has just been reposted, and the complete article is for sale if you don't have a subscription .... Ask me for more about these efforts.  Much supporting information has also been posted here.   More details were also published in the Cognitive Systems Institute archives.

How P&G and American Express Are Approaching AI
By Thomas H. Davenport, Randy Bean

Published March 31, 2017

There is a tendency with any new technology to believe that it requires new management approaches, new organizational structures, and entirely new personnel. That impression is widespread with cognitive technologies — which comprises a range of approaches in artificial intelligence (AI), machine learning, and deep learning. Some have argued for the creation of “chief cognitive officer” roles, and certainly many firms are rushing to hire experts with deep learning expertise. “New and different” is the ethos of the day. .... 

Two good examples of combining well-established practices with cognitive technology to achieve business success are American Express and Procter & Gamble. Both firms are actively undertaking cognitive technology initiatives.  Both are well into their second centuries; they wouldn’t still be here if they weren’t able to accommodate change well and introduce new technology effectively. We spoke with top executives at each of these firms about the rise of cognitive in their organizations. Ash Gupta is President of Global Credit Risk and Information Management at American Express, and Guy Peri is Chief Data Officer and Vice President of Information Technology at P&G. Both executives have longstanding track records of success at their respective organizations, having seen business and technology change come and go for 20 years or more.

How it will impact business, industry, and society.

Both organizations have a considerable history with artificial intelligence. Gupta at American Express reminded us of the Authorizer’s Assistant, which was one of the more successful rule-based expert systems of the late 1980s. As described in a popular Harvard Business Review article on that generation of technology, the system made recommendations to human authorizers whether to approve large purchase transactions by cardholders.

P&G also built and employed a number of rule-based expert systems. In addition to Peri, the current CDO, we also spoke with Franz Dill, a retired P&G IT manager who focused on AI during the 80s and 90s. He said that the most well-known expert system they developed was one that blended Folgers coffee (no longer a P&G brand). This system, Dill noted, saved P&G in excess of $20 million dollars a year in green coffee costs. The company also built an expert system that helped advertisers at P&G to use, modify, and reuse the company’s advertising assets.

Both American Express and P&G are companies that have explored artificial intelligence over the years, and while the technology may have changed, the established yet innovative approaches that these firms take to incorporating new technologies and capabilities continues to evolve. Their fundamentally sound innovation practices provide a foundation for evolution. The attributes of their respective approaches to cognitive technology include .... " 

Articles about Decision Trees

Good list from DSC of articles about decision trees, provided by Vincent Granville.    We found much value in the enterprise of these methods because their output was explainable to decision makers.   In addition we were able to use these results directly plugged into rule based expert systems to implement AI.  I still believe there is value in such rule bases to implement knowledge in simple logic directly.  Such systems still exist, for example, Visirule, recently updated, which we examined as early as 2009.

Introduction to Robotic Process Automation (RPA)

Shades of expert systems, but with the ability to learn.   The integration of rule-based application of logic and analytic and deep learning methodologies.  Explicit and transparent expression of rules in play.  Including the ability to integrate a clear model of process.   Following.

Introduction to Robotic Process Automation (RPA)   35 page PDF

A Primer Developed and written by the Institute for Robotic Process Automation in association with Carnegie Mellon University

A Message from IRPA Founder Frank Casale: Hard Facts and Hype.

The Data and the Drama Behind Robotic Process Automation

Imagine a world in which the meaning of “work” has been redefined for millions of people. Where our service economy can actually focus on providing services, delivered by an engaged talent pool that is innovating on such service. In this new world, work would no longer be a “four-letter word” associated with functioning within repeatable systems and mundane transactional processes. 

Instead, this other world would have workers who rethink end-to-end processes on a more holistic level with the goal of simultaneously impacting several factors: quality, compliance, functionality, best practices, regulatory functions, customer satisfaction, human error, and the all-important bottom line – all while continuing to create “the next” in the form of remarkable products and services.  If you’ve come to this book, it’s probably because someone you know has told you that, in fact, this is the world we are already living in – that 2015 is to robotic process automation (RPA) what 1994 was to the Internet – an auspicious start, but we ain’t seen nothing yet! Thanks to RPA, we are well on our way to doing the business of the future. However, none of us can really predict just how revolutionary it will be. .... " 

P&G and Amex, Building AI Past and Present

Tom Davenport mentioned some of our past AI work at P&G in his recent HBR article.  Below an introductory excerpt, much more detail at the link.   While considerable improvements have been made in available technology since then, some of the same key challenges exist.

How can advanced technical methods (Logic-based Expert Reasoning back then,  Deep Learning neural nets now)  be integrated with business process and applied to provide intelligent, but adaptive reasoning to real business systems?  Then how can these systems be effectively tested, maintained and reapplied in new contexts.  How can decision makers and the consumer understand their implications and risks? Many of us are working on that now.

How P&G and American Express Are Approaching AI
Thomas H. Davenport and Randy Bean

" ... P&G also built and employed a number of rule-based expert systems. In addition to Peri, the current CDO, we also spoke with Franz Dill, a retired P&G IT manager who focused on AI during the 80s and 90s. He said that the most well-known expert system they developed was one that blended Folgers coffee (no longer a P&G brand). This system, Dill noted, saved P&G in excess of $20 million dollars a year in green coffee costs. The company also built an expert system that helped advertisers at P&G to use, modify, and reuse the company’s advertising assets.

Both American Express and P&G are companies that have explored artificial intelligence over the years, and while the technology may have changed, the established yet innovative approaches that these firms take to incorporating new technologies and capabilities continues to evolve. Their fundamentally sound innovation practices provide a foundation for evolution. ... " 

Stanford 100 Year AI Study

Just received.  We did a great deal of work both with Stanford and with companies that came out of that program in the late 80s.  We were among the first enterprises that got lasting value from the 'intelligent' systems of the time.  Similar studies were done then to project AI's future. .   Considerable depth in this work, which I have just started to examine.   Thoughts?

Stanford : One Hundred Year Study on Artificial Intelligence (AI100)

Stanford University has invited leading thinkers from several institutions to begin a 100-year effort to study and anticipate how the effects of artificial intelligence will ripple through every aspect of how people work, live and play.

This effort, called the One Hundred Year Study on Artificial Intelligence, or AI100, is the brainchild of computer scientist and Stanford alumnus Eric Horvitz who, among other credits, is a former president of the Association for the Advancement of Artificial Intelligence.

In that capacity Horvitz convened a conference in 2009 at which top researchers considered advances in artificial intelligence and its influences on people and society, a discussion that illuminated the need for continuing study of AI’s long-term implications.

Now, together with Russ Altman, a professor of bioengineering and computer science at Stanford, Horvitz has formed a committee that will select a panel to begin a series of periodic studies on how AI will affect automation, national security, psychology, ethics, law, privacy, democracy and other issues.

"Artificial intelligence is one of the most profound undertakings in science, and one that will affect every aspect of human life," said Stanford President John Hennessy, who helped initiate the project. "Given's Stanford’s pioneering role in AI and our interdisciplinary mindset, we feel obliged and qualified to host a conversation about how artificial intelligence will affect our children and our children’s children."  ..... ' 

VisiRule for Building Intelligent Applications

Brought to my attention, a considerable update to VisiRule,  Which I have followed for years.  Back to making rule based expert systems easier?   Links to business process modeling?   Part of cognitive is logic.

Build Intelligent Applications without Programmers
Do you have business processes, captured using flowcharts and decision trees, that you want to automate?
Do you want to offer expert professional advice and guidance in a consistent and timely manner?
Do you want to enable your employees, customers and consumers to behave in an informed and compliant way?
Do you want to replicate your own specialist knowledge using a low-cost, self-help version available 24x7?
Do you want to reduce the burden on your own valuable product engineers and precious service experts?

If you answered Yes to any of the above, then you can use VisiRule to build intelligent decision support solutions.

VisiRule is a graphical software tool that lets business professionals capture, explore and model their expert know-how quickly and easily. Using VisiRule, the author simply drops boxes onto the canvas and links them together. VisiRule is an intelligent tool and knows what to do with your questions and logic, how to create question and answer sessions and how to generate executable rules from diagrams. .... " 

AI for High Frequency Trading

Via the Financial Revolutionist:

Note the statement that this simulates the insights of experienced traders.   So more of an expertise based system rather than a machine learning approach?  Likely some combination of the two. Which brings together business process knowledge and deep analytics.   An ideal mixture!

Securities Houses turn to AI for High-frequency trading
 In the age of ultra-high-frequency trading, financial institutions are turning to artificial intelligence to improve their stock trading performance and boost profit.

     One such company is Japan's leading brokerage house Nomura Securities. The company has been pursuing one goal: to simulate the insights of experienced stock traders with the help of computers. After years of research, Nomura is set to introduce a new stock trading system for institutional investors in May. ... " 

Rulex: Modeling for Rule Based Decision Making

Had spent some time looking at rule based approaches to doing predictive decisions.  Brought to my attention:  Rulex:

 " ... Understand, Forecast, Decide.
New generation advanced analytics tools for proactive decision makers. ... 

Big Data, Predictive and Prescriptive Analytics, Modelling, Forecasting and Data Challenges Beyond all the keywords, you are fighting Complexity.  Rulex gives you the tools to dominate it. ... " 

Looking at Workplace Automation

A topic covered here for some time.  We mean to augment and scale expertise in the form of tasks and jobs.   How and why does that occur, and can we predict when jobs will be replaced by capabilities like AI and Cognitive services?  We can base some of this on how jobs have already been replaced, or redefined.  Excellent,  detailed piece in McKinsey, with some useful statistics:

Four fundamentals of workplace automation
As the automation of physical and knowledge work advances, many jobs will be redefined rather than eliminated—at least in the short term. ... " 

Forgetting Organizations

An executive once told us as we sought to create expert systems: " ... You don't want to record and save my expertise, you want to forget it to make room for new creativity ... " .   Related, from K@W: Digital Transformation: Becoming a ‘Forgetting Organization 

XTRAN for Development

Always interested in the implementation of expertise.   Developed a number of them myself.  This has been around for some time.

Just brought to my attention:  XTRAN.
" ..... In 1984, we introduced XTRAN, our software development meta-tool, which marries compiler and expert system technologies to automate manipulation of computer languages, data, and text.  XTRAN's powerful rules language can automate the analysis, improvement, re-engineering, and translation of any computer language, including assemblers, 3GLs, 4GLs, XML, HTML, and proprietary, Web, scripting, data base, and special-purpose languages.  ... "

Big Data in Recommendation Systems

A good overview in KDNuggets.  Example with Amazon books. When you think of it, much of our purchase interaction online is influenced by such systems.  It is important to make the premise for the reco clear to the user.  Our early pump recommendation expert system was a form of this method, but with much less data, and a clear reason for selection.  Also a way to map markets of need.  Look also at Procurement systems.

Domain Scoping for Subject Matter Experts

The capture, sharing and augmentation of expertise is a key topic for Cognitive.   How do we do it well?  By first understanding and then leveraging the language of the domain.

 Elham Khabiri, from IBM Watson Research, presented “Domain Scoping for Subject Matter Experts.”  

Slides here.   Access to recording.
Further Discussion in Linkedin.

Thinking an Architecture of Advice

Working thoughts:  There should be a compendium of 'Questions to ask first'!   For all sorts of contexts.  Is it the same as an FAQ?   Not exactly.  But is it enough different?  In the enterprise when we did AI, we developed a one page list of questions that an expert would send to some one with a problem, this was a kind of entry ticket to see the expert.  Sometimes the expert might just respond with 'the answer is 42'.   Or feed the answers to a bot to answer it,  ....  but lastly pick up the phone to talk to the person or see them.   This worked, mostly but had its own issues. Makes me think further about the problem.

Rule Based Expert Systems

It is still possible to effectively build rule based expert systems.   I see that LPA has just established a new information and Download page.  Includes some good examples in a number of business contexts. Note how this relates to concept mapping.  Nicely done.  They write:

Rules without Programmers Self-service Expert Systems

Build your own expert applications without programming from simple decision trees to complex interconnected flowcharts.

VisiRule lets you capture your knowledge graphically, test and validate the logic and publish it so that others can benefit from it.

You can even present the VisiRule chart at run-time, as a map to help guide users through your decision logic. ...  "

(Update)   The comments contain a discussion about what knowledge can be implemented using such systems.   We discovered soon that maintenance difficulty was a key issue for updated knowledge in these systems.

Challenge of Cognitive Assistants

Via the Cognitive Systems Institute. Some excellent points are made in this slideshare.  Also good background and examples of the present state of these systems.  Even the definition of cognitive assistants is covered. Separately a group of us is examining how such advice links to particular tasks and jobs.   More on that will follow in this space.

 Advisory systems are probably the most important approach for using AI for typical business use. We addressed  this in much detail during the expert systems era.   Slides by Hamid R Motahari of IBM Almaden.

What Does the Term Cognitive Assistance Mean?

A Linkedin Discussion started in the CSG group by Frank Stein, Director of Analytics Solution Center at IBM.  I have added some additional examples and comments

What Does the Term "Cognitive Assistance" mean? .... 

A US Government representative asked one of our team, Chuck Howell, "What does the term 'Cognitive Assistance' mean"? What is in scope for cognitive assistance systems? Chuck stitched together this working definition, after a quick back and forth, by combining excerpts from two important documents ....  "

Chuck Howell, Mitre, Scott Kordella, Mitre,
Frank Stein, IBM

A Phase Advisor for Industrial Chemical Engineering

A recent conversation with a chemical engineer at MIT led me to remember an expert system a member of our AI group, Dan T. Davis wrote in the late 1980s.  It was meant to provide advisory consulting in the surfactant chemistry space. And was based on the expertise of  employee Bob Laughlin, author of "The Acqueous Phase Behavior of Surfactants".   It was based on methods of knowledge engineering and rule based expert systems.  I am doing some additional digging on the methods and history of this system and will post them here.    Anyone have specific examples of use and methods, contact me.

Bayesian Networks as Expert Systems

Modeling causal reasoning is a favorite topic for me. Bayesia has posted another easy to understand demo of how a Bayesian network can be used to model a system of expertise.  Nicely done.  My former enterprise uses this methodology.  If how this fits into other expertise capture work is still confusing to you, this is a good place to start:

" ... Using a Bayesian Network as an Expert System ... 
The well-known "Visit Asia" example in this Quick Start Demo dates back to a seminal paper by Lauritzen and Spiegelhalter (1988). It describes how a Bayesian network can be used as an expert system for medical diagnosis. Visit Asia has been widely used in the literature to illustrate observational and causal inference in Bayesian networks. ... "