I was reminded that my early experiences with government and enterprise systems dealt with the optimization of systems. That is, the mathematical means of linking a specific mathematical statement of a problem, with value goals and constraints, to a specific best possible solution. We used the predecessors of ILOG, and CPlex directly for these problems. We saved millions using these methods. Of course optimization does not have the current hype.
Now how is AI, as it currently defined, dissimilar from Optimization? Usually because the Optimization approach is more specifically and numerically defined. If AI uses human-like intelligence, it is usually not precisely mathematical. And unfortunately not as closely tied to specific business process. Not saying that AI cannot use optimization methods, it just usually does not. So there should be a strong consideration towards using more precise and direct and process oriented methods.
Was pointed to this company that works the space, have never worked with them:
Optimization Direct Inc., co-founded by Dr. Robert Ashford, a pioneer in the field of optimization, and Dr. Alkis Vazacopoulos, a leader in the industry, markets IBM® ILOG® CPLEX Optimization Studio®, the world's leading software product for modeling and optimization.
CPLEX Optimization Studio* solves large-scale optimization problems and enables better business decisions and resulting financial benefits in areas such as supply chain management, operations, healthcare, retail, transportation, logistics and asset management. It has been applied in sectors as diverse as manufacturing, processing, distribution, retailing, transport, finance and investment.
CPLEX Optimization Studio is an analytical decision support toolkit for rapid development and deployment of optimization models using mathematical and constraint programming. It combines an integrated development environment (IDE) with the powerful Optimization Programming Language (OPL) and high-performance ILOG CPLEX optimizer solvers. CPLEX Optimization Studio enables clients to:
Optimize business decisions with high-performance optimization engines.
Develop and deploy optimization models quickly by using flexible interfaces and prebuilt deployment scenarios.
Create real-world applications that can significantly improve business outcomes. ...... "
Showing posts with label ILog. Show all posts
Showing posts with label ILog. Show all posts
Wednesday, March 14, 2018
Tuesday, April 11, 2017
Electronic License Plates for Drones
In IEEE Spectrum
Electronic License Plates for Drones
In late 2015, mandatory drone registration went into effect in the United States. Since then, anyone who wants to fly a drone (or model aircraft) weighing over 0.55 pounds (0.25 kilograms) must register with the U.S. Federal Aviation Administration to receive a unique identification number. This number needs to be placed on the drone, but there is no requirement for it to broadcast signals to allow for remote identification. That might change in the future, as the FAA considers different mechanisms to identify drone owners and operators. Last month, DJI, the world's largest commercial drone manufacturer, announced a proposal outlining a general scheme for doing just that. ... "
Electronic License Plates for Drones
In late 2015, mandatory drone registration went into effect in the United States. Since then, anyone who wants to fly a drone (or model aircraft) weighing over 0.55 pounds (0.25 kilograms) must register with the U.S. Federal Aviation Administration to receive a unique identification number. This number needs to be placed on the drone, but there is no requirement for it to broadcast signals to allow for remote identification. That might change in the future, as the FAA considers different mechanisms to identify drone owners and operators. Last month, DJI, the world's largest commercial drone manufacturer, announced a proposal outlining a general scheme for doing just that. ... "
Friday, September 30, 2016
Sentiment Analytics Master Class
Quite a lengthy, complete and interesting view I am looking at. In Linkedin.
Sentiment Analytics Triology - A master class on extracting sentiments or aspects from data
Published on September 28, 2016 Featured in: Customer Experience, India, Technology ... A master class on extracting sentiments or aspects from data by Snehamoy Mukherjee, Data Science & Analytics Leader
Credit goes to my colleagues Sumit Pratap Singh, Prateek Sharma, Kuntal Basu and Subhamoy Ganguly for delving deep into this field and creating an outstanding capability in this much talked about field. The article below is the first of a series of articles, which we intend to publish over the course of the next few days, and is an attempt to articulate the wisdom and share it with a wider audience, so that others can benefit from it. Hence, I have called it a master class as there is a touch of pedagogy in this attempt to carve out the essence of a complex topic.
Introduction
One of the areas of analytics/data science that has generated a huge amount of hype and interest of late, in the industry has been “sentiment analytics”. And yet, interestingly, though a lot of research and investment has gone into it, there haven’t been many takers for it from the point of view of business users. One of the major reasons for it has been that sentiment analytics has to deal with a lot of noise in the data, which the conventional, automated, “plug and play” solutions can’t handle. A lot of folks in the industry tried to come up with tools and products and trivialized “sentiment analytics” into a product play and hence, nipped an emerging industry in the bud.
A well-executed sentiment analysis needs a lot of human intelligence to interpret the results as well as to coach the self-learning algorithms to do the right things. In this series of three papers, which we can call the “Sentiment Analytics Trilogy”, we provide a successful approach to performing sentiment analysis that gives outperforms the droves of so called “sentiment analytics products and tools” in the market, which are mostly unstable and error prone. ... "
Sentiment Analytics Triology - A master class on extracting sentiments or aspects from data
Published on September 28, 2016 Featured in: Customer Experience, India, Technology ... A master class on extracting sentiments or aspects from data by Snehamoy Mukherjee, Data Science & Analytics Leader
Credit goes to my colleagues Sumit Pratap Singh, Prateek Sharma, Kuntal Basu and Subhamoy Ganguly for delving deep into this field and creating an outstanding capability in this much talked about field. The article below is the first of a series of articles, which we intend to publish over the course of the next few days, and is an attempt to articulate the wisdom and share it with a wider audience, so that others can benefit from it. Hence, I have called it a master class as there is a touch of pedagogy in this attempt to carve out the essence of a complex topic.
Introduction
One of the areas of analytics/data science that has generated a huge amount of hype and interest of late, in the industry has been “sentiment analytics”. And yet, interestingly, though a lot of research and investment has gone into it, there haven’t been many takers for it from the point of view of business users. One of the major reasons for it has been that sentiment analytics has to deal with a lot of noise in the data, which the conventional, automated, “plug and play” solutions can’t handle. A lot of folks in the industry tried to come up with tools and products and trivialized “sentiment analytics” into a product play and hence, nipped an emerging industry in the bud.
A well-executed sentiment analysis needs a lot of human intelligence to interpret the results as well as to coach the self-learning algorithms to do the right things. In this series of three papers, which we can call the “Sentiment Analytics Trilogy”, we provide a successful approach to performing sentiment analysis that gives outperforms the droves of so called “sentiment analytics products and tools” in the market, which are mostly unstable and error prone. ... "
Friday, July 17, 2015
Building Your Cognitive Technology
Tom Davenport on building cognitive technologies.
" ... Most organizations that are exploring cognitive technologies—smart machines that automate aspects of decision-making processes—are just putting a toe in the water. They’re doing a pilot or “proof of concept” to explore the technology and how it fits into their business. Rajeev Ronanki, a Deloitte consultant who works with IBM Corp.’s Watson and other cognitive technologies, told me recently that virtually all of his clients want to start with a small step:
'They want to make sure that it’s not science fiction before they really commit to the technology at full scale. So the first project is typically a pilot that takes four to six months to develop' .
The good news is that this technology is not science fiction, but rather something from which organizations can benefit today. Chances are good, then, that organizations will want to create architectures for cognitive technologies that support more than a single application. In fact, I believe that it won’t be long before sophisticated organizations set out to build “cognitive architectures” that interface with, but are distinct from, their regular IT architectures. ... "
Later on he mentions a rule engine (JRules) which is part of IBM owned ILog, and how this should be part of Watson services. A good point, connectivity is not quite what it could be yet. Making it more difficult and time consuming to build prototypes, which are essential for this kind of new tech. Later there is a good list of requirements for easy to use, test and integrate technologies. Would read that carefully before proceeding.
" ... Most organizations that are exploring cognitive technologies—smart machines that automate aspects of decision-making processes—are just putting a toe in the water. They’re doing a pilot or “proof of concept” to explore the technology and how it fits into their business. Rajeev Ronanki, a Deloitte consultant who works with IBM Corp.’s Watson and other cognitive technologies, told me recently that virtually all of his clients want to start with a small step:
'They want to make sure that it’s not science fiction before they really commit to the technology at full scale. So the first project is typically a pilot that takes four to six months to develop' .
The good news is that this technology is not science fiction, but rather something from which organizations can benefit today. Chances are good, then, that organizations will want to create architectures for cognitive technologies that support more than a single application. In fact, I believe that it won’t be long before sophisticated organizations set out to build “cognitive architectures” that interface with, but are distinct from, their regular IT architectures. ... "
Later on he mentions a rule engine (JRules) which is part of IBM owned ILog, and how this should be part of Watson services. A good point, connectivity is not quite what it could be yet. Making it more difficult and time consuming to build prototypes, which are essential for this kind of new tech. Later there is a good list of requirements for easy to use, test and integrate technologies. Would read that carefully before proceeding.
Tuesday, December 02, 2014
Modeling Complex Decisions
Recently had to review the use of rules to model business decisions. Exploring the integration of these methods with Cognitive systems. Integrating these rules with optimization models. We utilized ILog JRules (Now part of IBM) to do simple integration of business rules.
The rules can be used to state very complex decisions. In practice though, the rules are simple. Determining them accurately is often the challenge.
Came upon a company that does this work called Decide. They recently published an example of a complex puzzle type problem implemented with JRules that show the possibilities of using these methods. See also their blog.
" ... Decide is one of the leading companies in the Spanish market in the development of decision support systems. Founded in 2008 by industry experts, decide was set up to be a leading company in designing and building business rules and optimization systems. Our vocation is to improve the operating efficiency of our customers and we bring to the table one of the best and most experienced teams in these technologies in the Spanish market.
With excellence as one of our core values, our structure is divided into Experience Units focused on continuous improvement in each of the assigned disciplines .... "
The rules can be used to state very complex decisions. In practice though, the rules are simple. Determining them accurately is often the challenge.
Came upon a company that does this work called Decide. They recently published an example of a complex puzzle type problem implemented with JRules that show the possibilities of using these methods. See also their blog.
" ... Decide is one of the leading companies in the Spanish market in the development of decision support systems. Founded in 2008 by industry experts, decide was set up to be a leading company in designing and building business rules and optimization systems. Our vocation is to improve the operating efficiency of our customers and we bring to the table one of the best and most experienced teams in these technologies in the Spanish market.
With excellence as one of our core values, our structure is divided into Experience Units focused on continuous improvement in each of the assigned disciplines .... "
Sunday, November 16, 2014
Thinking Causality in Science and Statistics
I have been looking back to understand how AI has changed since the 90s, when we worked with rule based expert systems. One book that addresses some of the changes is Judea Pearl's: Causality: Models, Reasoning and Inference. Now over a decade old, it contains some interesting gems. Dealing with the mixing of knowledge in diagrams and equations, and developing approaches that have evolved to now commonly used Bayesian Networks. More on his site.
There is also a copy of a lecture that Pearl gave at the time: The Art and Science of Cause and Effect, originally an epilogue in the book. Now available free at the link. Deals with the interesting concept of Causality, which is remarkably complex. The idea is essential in working engineered systems, avoided in the physical sciences, and most always warned against in statistics. The article examines why, and poses some remedies. I disagree with some of his early historical views, causation was not discovered at the time of Galileo, but the lecture is still an excellent read.
Consider also how Big Data methods have backed off the need for strict causation requirements.
There is also a copy of a lecture that Pearl gave at the time: The Art and Science of Cause and Effect, originally an epilogue in the book. Now available free at the link. Deals with the interesting concept of Causality, which is remarkably complex. The idea is essential in working engineered systems, avoided in the physical sciences, and most always warned against in statistics. The article examines why, and poses some remedies. I disagree with some of his early historical views, causation was not discovered at the time of Galileo, but the lecture is still an excellent read.
Consider also how Big Data methods have backed off the need for strict causation requirements.
Wednesday, March 12, 2014
Don't Bury Business Rules Management
Back in 2012 I wrote a post on Business Rules Management. This is related to work we had done with expert systems in the enterprise. Based on conversations I have now had, this rebranding of the former Ilog JRules capability has buried its existence. It would appear that the capability would be particularly important to connect to Big Data Analytics to real workflow decision streams. Are there plans to integrate Watson and rule streams? Can I ask someone at IBM to contact me on this? Have real applications.
Wednesday, September 26, 2012
Rules and Decision Management
The large enterprise can think about how to systematize their business processes and the rules that they use to make decisions. This has often been difficult for the midsize to smaller business. The systems have not existed to help these businesses think through how their business should work well to grow and be profitable. These businesses have their own internal expertise, but do not typically have the internal resources for other specialized knowledge.
In the large enterprise we used methods called expert systems. We interviewed dozens of experts and then placed their expertise in sets of rules. These were then combined with analytical methods to calculate and adjust the results of particular choices to optimize results based on the rules . This created a simulation of a specific set of business decisions. When we did not have internal experts available for a set of decision rules, we went outside to interview experts that had additional domain knowledge we could readily insert into the process.
A recent discussion with a Mid Size business led me to take another look at this area. The business had some quantitative methods that calculated results, but did not have a means to implement their business rules. I needed a better way to generate these rules from flowcharts of their processes.
I had previously looked at the Ilog JRules capability for delivering decision rules. Now re-branded as Operational Decision Management. Also to be seen on the IBM Decision Management page on Facebook. Check this out as a method to implement rules within business processes.
This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. More on that here: http://goo.gl/S6P7m
In the large enterprise we used methods called expert systems. We interviewed dozens of experts and then placed their expertise in sets of rules. These were then combined with analytical methods to calculate and adjust the results of particular choices to optimize results based on the rules . This created a simulation of a specific set of business decisions. When we did not have internal experts available for a set of decision rules, we went outside to interview experts that had additional domain knowledge we could readily insert into the process.
A recent discussion with a Mid Size business led me to take another look at this area. The business had some quantitative methods that calculated results, but did not have a means to implement their business rules. I needed a better way to generate these rules from flowcharts of their processes.
I had previously looked at the Ilog JRules capability for delivering decision rules. Now re-branded as Operational Decision Management. Also to be seen on the IBM Decision Management page on Facebook. Check this out as a method to implement rules within business processes.
This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. More on that here: http://goo.gl/S6P7m
Thursday, September 06, 2012
New Supply Chain Book
I was introduced to a new and visually instructive book on optimizing the supply chain: Supply Chain Network Design: Applying Optimization and Analytics to the Global Supply Chain (FT Press Operations Management) by Michael Watson, Sara Lewis, Peter Cacioppi and Jay Jayaraman. This book includes many of the methods which we used for years in the enterprise. Now they are integrated in packages like iLog/Cplex. As I peruse it, this book seems to include many of the approach details you need to solve these kinds of problems effectively. The methods are often too deeply hidden in books and manuals. More information to follow as I seek use it to apply to a specific client interaction.The book also has a site and blog. #IBMSCGS
Tuesday, July 17, 2012
Business Analytics, Past and Future
I have been a long time practitioner of business analytics, the use of quantitative methods to improve business processes. Starting at the Defense Department, where we built military simulation models, and ending up at P&G. My first project there, when I arrived in 1977, was to improve warehouse efficiency using IBM's mathematical programming system: MPSX. That followed with using the same mathematical optimization approaches for scheduling, supply chain siting and executive decision making.
So I was happy to see in the July/August Issue of Analytics Magazine an article by Arnold Greenland on the history and current state of business analytics at IBM. This allows me to reflect on the growth and impact of analytical methods at IBM and elsewhere. Since then the original MPSX package has disappeared, and has been replaced by the acquired Ilog/Cplex, which goes far beyond the original package, adding nonlinear methods as well.
Shortly after being introduced to MPSX, in 1980, we addressed the analysis of unstructured data, typically consumer comments in unstructured text, using recently developed methods called 'Content Analysis'. These permitted the semantic analysis of multiple human languages. An early attempt to look at and understand unstructured 'Big Data'.
During all of this time we also used a number of statistical methods throughout the enterprise to explore and improve systems. SPSS and SAS were in frequent use. A package called Clementine allowed us to use advanced logical methods, like artificial neural nets, to implement what were essentially statistical methods, to store and implement specific decision rules. These methods could then be inserted in both software and hardware processes. Clementine also permitted the structural exploration of the decision process. Ultimately Clementine was acquired by SPSS, and were eventually absorbed into Modeler.
During the 1990's, a heady time for artificial intelligence, we implemented expert systems using a now defunct language called M1. Which were successful for a number of complex industrial management processes. Some of the same capabilities can be seen in JRules. That work has been extended into recent AI explorations like that of IBM's Watson.
What is further interesting now is that many of these methods are now available to the small and medium sized business. We live in a time where the tools are available, just go out and use them.
So I was happy to see in the July/August Issue of Analytics Magazine an article by Arnold Greenland on the history and current state of business analytics at IBM. This allows me to reflect on the growth and impact of analytical methods at IBM and elsewhere. Since then the original MPSX package has disappeared, and has been replaced by the acquired Ilog/Cplex, which goes far beyond the original package, adding nonlinear methods as well.
Shortly after being introduced to MPSX, in 1980, we addressed the analysis of unstructured data, typically consumer comments in unstructured text, using recently developed methods called 'Content Analysis'. These permitted the semantic analysis of multiple human languages. An early attempt to look at and understand unstructured 'Big Data'.
During all of this time we also used a number of statistical methods throughout the enterprise to explore and improve systems. SPSS and SAS were in frequent use. A package called Clementine allowed us to use advanced logical methods, like artificial neural nets, to implement what were essentially statistical methods, to store and implement specific decision rules. These methods could then be inserted in both software and hardware processes. Clementine also permitted the structural exploration of the decision process. Ultimately Clementine was acquired by SPSS, and were eventually absorbed into Modeler.
During the 1990's, a heady time for artificial intelligence, we implemented expert systems using a now defunct language called M1. Which were successful for a number of complex industrial management processes. Some of the same capabilities can be seen in JRules. That work has been extended into recent AI explorations like that of IBM's Watson.
What is further interesting now is that many of these methods are now available to the small and medium sized business. We live in a time where the tools are available, just go out and use them.
Tuesday, November 15, 2011
New Book on P&G
Wednesday, October 05, 2011
Visual Teams Book
See a report on correspondent David Sibbet's new book: Visual Teams: Graphic Tools for Commitment, Innovation, and High Performance. To be released by October 11. Congratulations David, great work as always. Second book in his upcoming trilogy.
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