machine learning
Using Themes for Enhanced Problem Solving
Thematic Analysis is a powerful qualitative approach used by many consultants. It involves identifying patterns and themes to better understand how and why something happened, providing context for other quantitative analyses. It can also be used when developing strategies and tactics because of its “cause and effect” nature.
Typical analysis tends to be event-based. Something happened that was unexpected. Some type of triggering or compelling event is sought to either stop something from happening or to make something happen. With enough of the right data, you may be able to identify patterns that help predict what will happen next based on past events. This data-based understanding may be simplistic or incomplete, but often it is sufficient.
But people are creatures of habit. If you can identify and understand those habits and place them within the context of a specific environment that includes interactions with others, you may be able to identify patterns within the patterns. Those themes can be much better indicators of what may or may not happen than the data itself. They become better predictors of things to come and can help identify more effective strategies and tactics to achieve your goals.
This approach requires that a person view an event (desired or historical) from various perspectives to help understand:
- Things that are accidental but predictable because of human nature.
- Things that are predictable based on other events and interactions.
- Things that are the logical consequence of a series of events and outcomes.
Aside from the practical implications of this approach, I find it fascinating relative to AI and Predictive Analysis.
For example, you can proactively monitor data, activities, and patterns by understanding recurring themes and triggers. That provides actionable intelligence that can be automated and incorporated into a larger system. Machine Learning and Deep Learning can analyze tremendous volumes of data from various sources in real-time.
Combine that with Semantic Analysis (and products like AllegroGraph) to harness the power of language through taxonomies and ontologies. The monitoring system would better understand what is happening (not just be aware of it), possibly determine how and why it happened, and result in even more focused and accurate predictions. Finally, include spatial and temporal data such as IoT, metadata from photographs, etc., and you should be able to view something as though you were very high up – providing the ability to “see” what is on the path ahead. It is obviously not that simple, but it is exciting.
This approach provides a multi-dimensional view of events and their causality, giving you the means to identify and prevent complex problems before they negatively affect a business.
Keeping these thoughts in mind will help you see details others have missed. Better tools can make for better analysis, better strategies, and better outcomes. Who wouldn’t want that?
New Perspectives on Changing Business Ecosystems
One of the many changes resulting from the COVID-19 pandemic has been a sea change in thoughts and goals around Supply Chain Management (SCM). Existing SCM systems were upended in mere months as procuring raw materials and components became challenging, manufacturing shifted to meet new, unanticipated needs, and logistics challenges arose from health-related staffing issues, safe working distances, and limited shipping options and availability. In short, things are a mess!
Foundational business changes will require modern approaches to Change Management. Change is not easy – especially at scale – so having ongoing support from the top down and providing incentives to motivate the right behaviors, actions, and outcomes will be especially critical to the success of those initiatives. And remember, “What gets measured gets managed,” so focusing on the aspects of business and change that matter will become more important.
Business Intelligence systems will be especially important for Descriptive Analysis. Machine Learning will likely play a larger role as organizations seek a more comprehensive understanding of patterns and work toward accurate Predictive Analysis. And, of course, Artificial Intelligence / Deep Learning / Neural Networks capabilities will accelerate, filling several gaps, including the need for enhanced Prescriptive Analysis. Technology will provide many of the insights business leaders need to make the best decisions in the shortest time, while accounting for the complexity of cost/benefits/risk/competing efforts.
This is also the right time to consider upgrading to a collaborative, agile business ecosystem that can expand and adapt quickly and cost-effectively to whatever comes next. Click on this link to see more of the benefits of this type of model.
Whether you like it or not, change is coming. So, why not take a proactive posture to help ensure this change has a positive impact and meets your company’s or organization’s objectives?
Changes like this are all-encompassing, so it helps to start with the mindset, “Win together, lose together.” In general, having all areas of an organization moving in lockstep toward a common goal is ideal. Critical junctures like these are what require change and drive success.
Good Article on Why AI Projects Fail
Today I came across this very good article focused on lessons learned, which could help anyone interested in these topics. It included a good mix of non-technical problems.
This is the link to the article, along with my commentary on the Top 3 items listed: https://www.cio.com/article/3429177/6-reasons-why-ai-projects-fail.html
Item #1:
The article discusses how the “problem” being evaluated was misstated using technical terms. At least some of these efforts are conducted “in a vacuum.” Given the cost and strategic importance of getting these early-adopter AI projects right, that surprised me.
In Sales and Marketing, you start the question, “What problem are we trying to solve?” and evolve that to, “How would customers or prospects describe this problem in their own words?” Without that understanding, you can neither vet the solution initially nor quickly qualify the need for it when speaking with customers or prospects. That leaves room for error when transitioning from strategy to execution.
More collaboration with Business likely would have helped. This was touched on at the end of the article under “Cultural challenges,” but the importance seemed to be downplayed. Lessons learned are valuable – especially when you are able to learn from the mistakes of others. This should have been called out early as a major lesson learned.
Item #2:
This second area had to do with the perspective of the data, whether that was the angle of the subject in photographs (overhead from a drone vs horizontal from the shoreline) or the type of customer data evaluated (such as from a single source) used to train the ML algorithm.
That was interesting because assumptions may have played a role in overlooking other aspects of the problem, or the teams may have been overly confident they could get the right results with the data available. In the examples cited, those teams identified the problems and took corrective action. A follow-up article describing the process used to determine the root cause in each case would be very interesting.
As an aside, from my perspective, this is why Explainable AI is so important. Sometimes, you just don’t know what you don’t know (the unknown unknowns). Understanding why and on what the AI is basing its decisions should help provide better-quality curated data up front, as well as identify potential drifts in the wrong direction while it is still early enough to make corrections without impacting deadlines or deliverables.
Item #3:
This didn’t surprise me, but it should be a cause for concern as advances are made at faster rates and organizations race to be first to market with an AI-based competitive advantage, potentially with less validation than ideal. The last paragraph under ‘Training data bias’ stated that based on a PWC survey, “only 25 percent of respondents said they would prioritize the ethical implications of an AI solution before implementing it.“
Bonus Item:
The discussion about the value of unstructured data was very interesting, especially when you consider:
- The potential for NLU (natural language understanding) products in conjunction with ML and AI.
- This is a great NLU-pipeline diagram from North Side Inc. in Canada, one of the pioneers in this space.
- The importance of semantic data analysis relative to any ML effort.
- The incredible value that products like MarkLogic’s database or Franz’s AllegroGraph provide over standard Analytics Database products.
- I personally believe the biggest exception to this assertion will be GPU databases (like OmniSci) that easily handle streaming data, can accomplish extreme computational feats well beyond traditional CPU-based products, and have geospatial capabilities that add an extra dimension of insight to the problem being solved.
Update: This is a link to a related article that discusses trends in areas of implementation, important considerations, and the potential ROI of AI projects: https://www.fastcompany.com/90387050/reduce-the-hype-and-find-a-plan-how-to-adopt-an-ai-strategy
This is an exciting space that will grow significantly over the next 3-5 years. The more information, experiences, and lessons learned are shared, the better it will be for everyone.
The Downside of Easy (or, the Upside of a Good Challenge)
As a young boy, I was “that kid” who would take everything apart, often leaving a formerly functional alarm clock in a hundred pieces in a shoebox. I loved figuring out how things worked and how components worked together as a system. When I was 10, I spent one winter completely disassembling and reassembling my Suzuki TM-75 motorcycle in my bedroom (my parents must have had so much more patience and understanding than I do as a parent). I rebuilt it by spring, and it ran like a champ. Beginner’s luck?
By then, I was hooked – I enjoyed working with my hands and fixing things. That was a valuable skill to have while growing up, as it provided income and led to the first company I started at 18. Learning always involved a fair degree of trial and error, but experience and experimentation led to simplification and standardization. That became the hallmark of the programs I wrote, and later, the application systems I designed and developed. It is a trait that has served me well over the years.
Today, I still enjoy doing many things myself, especially if I can spend a little time and save hundreds of dollars (which I usually invest in more tools). Finding examples and tutorials on YouTube is usually easy, and after watching a few reference videos, the task is generally manageable. There is also a sense of satisfaction that comes with a job well done. And most of all, it is a great distraction from everything else that keeps your mind racing at 100 mph.
My wife’s 2011 Nissan Maxima needed a Cabin Air Filter, and instead of paying $80 again to have this done, I decided to do it myself. I purchased the filter for $15 and was ready to go. This shouldn’t take more than 5 or 10 minutes. I went to YouTube to find a video, but no luck. Then, I started searching various forums for guidance. There were plenty of posts complaining about the cost of replacement, but not much about how to do the work. I finally found a post that showed where the filter door was. I could already begin to feel that sense of accomplishment I was expecting in the next few minutes.
But fate and apparently a few sadistic Nissan Engineers had other plans. First, you needed to be a contortionist in order to reach the filter once the door was removed. Then, the old filter was nearly impossible to remove. Then, once the old filter was removed, I realized the width of the filter entry slot was about 50% of the filter’s width. Man, what a horrible design!
A few fruitless Google searches later, I was more determined than ever to make this work. I tried several things and ultimately found a way to fold the filter small enough to get through the door, and it would fully open once released. A few minutes later, I was finally savoring my victory over that hellish filter change.
This experience brought back memories of “the old days.” In 1989, I was working for a marketing company as a Systems Analyst and was assigned the project to create the “Mitsubishi Bucks” salesperson incentive program. Salespeople earned points for sales and could later redeem those points for Mitsubishi Electronics products. It was a very popular and successful incentive program.
Creating the forms and reports was straightforward, but tracking the points (including generating past reports and adjusting activity from previous periods) was a problem. I finally considered how a banking system would work (remember, there were no books on building banking systems readily available before the Internet, so this was essentially reinventing the wheel) and designed my own. It was very exciting and rock solid. Statements could be accurately reproduced at any time, and an audit trail was maintained for all activity.
Next, I needed to create validation processes and a fraud detection system for incoming data. This was rock solid, but instead of being a good thing, it became a real headache and source of frustration.
Salespeople would not always provide complete information, might have sloppy penmanship, or engage in other legitimate but unusual practices (such as bundling and adjusting prices among items in the bundle). Despite that, they expected immediate rewards, and having their submissions rejected apparently created more frustration than incentive.
So, I was instructed to turn the fraud detection dial way back. I let everyone know that while this would minimize rejections, it would increase the potential for fraud and the volume of rewards. I created a few reports to identify potentially fraudulent activity. It was amazing how creative people could be when trying to cheat the system, and how quickly you could identify patterns based on similar activities. By the third month, the system was trouble-free.
It was a great learning experience from beginning to end. It ran for several years after I left – something I know because I was still receiving the sample mailing with new sales promotions and “Spiffs” (sales incentives) every month. Later, I wondered how many things aren’t being created or improved today because it is easier and less risky to follow an existing template.
We used to align fields and columns in byte order to minimize record size, overload operators, and other optimizations to maximize space utilization and performance. Our code was optimized for maximum efficiency because memory was scarce and processors were slow. Profiling and benchmarking programs brought you to the next level of performance. In a nutshell, you were forced to understand and become proficient with the technology used out of necessity. Today, these concepts have become somewhat of a lost art.
There are many upsides to being easy.
- My team sells more and closes deals faster because we make it easy for our customers to buy, implement, and start receiving value from the software we sell.
- Hobbyists like me can accomplish many tasks after watching just a short video or two.
- People are willing to try things they may not have tried before if getting started were not so easy.
However, there may also be downsides for innovation and continuous improvement, simply because ‘easy’ is often considered ‘good enough‘ so people do the minimum required and move on.
What will the impact be on human behavior once Artificial Intelligence (AI) becomes a reality and is in everyday use? It would be great to look ahead 25, 50, or 100 years and see the full impact of emerging technologies, but I think I will see many of the effects in my own lifetime.
What do you think will happen?
Discussions that Seed the Roots of Creativity
A few months ago I purchased Fitbit watches for my children and myself. My goals were twofold. First, I was hoping that they would motivate all of us to be more active. Second, I wanted to foster a sense of competition (including fair play and winning) within my children. Much of their pre-High School experiences focused on “participation,” as many schools feel that competition is bad. Unfortunately, competition is everywhere in life, so if don’t play to win you may not get the opportunity to play at all.
It is fun seeing them push to be the high achiever for the day, and to continually push themselves to do better week-by-week and month-by-month. I believe this creates a wonderful mindset that makes you want to do more, learn more, achieve more, and make an overall greater impact with everything they do. People who do that are also more interesting to spend time with, so that is a bonus.
Recently my 14 year-old son and I went for a long walk at night. It was a cold, windy, and fairly dark night. We live in fairly rural area so it is not uncommon to see and hear various wild animals on a 3-4 mile walk. I’m always looking for opportunities to teach my kids things in a way that is fun and memorable, and in a way that they don’t realize they are being taught. Retention of the concepts is very high when I am able to make it relevant to something we are doing.
That night we started talking about the wind. It was steady with occasional gusts, and at times it changed direction slightly. I pointed out the movement on bushes and taller grass on the side of the road. We discussed direction, and I told him to think about the wind like an invisible arrow, and then explained how those arrows traveled in straight lines or vectors until they met some other object. We discussed which object would “win,” and how the force of one object could impact another object. My plan was to discuss Newton’s three laws of motion.
My son asked if that is why airplanes sometimes appear to be flying at an angle but are going straight. He seemed to be grasping the concept. He then asked me if drones would be smart enough to make those adjustments, which quickly led to me discussing the use potential future of “intelligent” AI-based drones by the military. When he was 9 he wanted to be a Navy SEAL, but once he saw how much work that was he decided that he would rather be Transformer (which I explained was not a real thing). My plan was to use this example to discuss robotics and how you might program a robot to do various tasks, and then move to how it could learn from the past tasks and outcomes. I wanted him to logically break down the actions and think about managing complexity. But, no such luck that night.
His mind jumped to “Terminator” and “I, Robot.” I pointed out that Science Fiction does occasionally become Science Fact, which makes this type of discussion even more interesting. I also pointed out that there is spectrum between the best possible outcome – Utopia, and the worst possible outcome – Dystopia, and asked him what he thought could happen if machines could learn and become smarter on their own.
His response was that things would probably fall somewhere in the middle, but that there would be people at each end trying to pull the technology in their direction. That seemed like a very enlightened estimation. He asked me what I thought and I replied that I agreed with him. I then noted how some really intelligent guys like Stephen Hawking and Elon Musk are worried about the dystopian future and recently published a letter to express their concerns about potential pitfalls of AI (artificial intelligence). This is where the discussion became really interesting…
We discussed why you would want a program or a robot to learn and improve – so that it could continue to become better and more efficient, just like a person. We discussed good and bad, and how difficult it could be to control something that doesn’t have morals or understand social mores (which he felt if this robot was smart enough to learn on its own that it would also learn those things based on observations and interactions). That was an interesting perspective.
I told him about my discussions with his older sister, who wants to become a Physician, about how I believe that robotics, nanotechnology, and pharmacology will become the future of medicine. He and I took the logical next step and thought about a generic but intelligent medicine that identified and fixed problems independently, and then sent the data and lessons learned for others to learn from.
I’m sure that we will have an Internet of Things (IoT) discussion later, but for now I will tie this back to our discussion and Fitbit wearable technology.
After the walk I was thinking about what just happened, and was pleased because it seemed to spark some genuine interest in him. I’m always looking for that perfect recipe for innovation, but it is elusive and so far lacks repeatability. It may be possible to list many of the “ingredients” (intelligence, creativity, curiosity, confidence (to try and accept and learn from failure), multi-disciplinary experiences and expertise) and “measurements” (such as a mix of complementary skills, a mix of roles, and a special environment (i.e., strives to learn and improve, rewards both learning and success but doesn’t penalize failure, and creates a competitive environment that understands that in most cases the team is more important than any one individual)).
That type of environment is magical when you can create it, but it takes so much more than just having people and a place that seem to match the recipe. A critical “activation” component or two is missing. Things like curiosity, creativity, ingenuity, and a bit of fearlessness.
I tend to visualize things, so while I was thinking about this I pictured a tree with multiple “brains” (my mental image looked somewhat like broccoli) that had visible roots. Those roots were creative ideas that went off in various directions. Trees with more roots that were bigger and went deeper would stand out in a forest of regular trees.
Each major branch (brain/person) would have a certain degree of independence, but ultimately everything on the tree worked as a system. To me, this description makes so much more sense than the idea of a recipe, but it still doesn’t bring me closer to being able map the DNA of this imaginary tree.
At the end of our long walk it seemed that I probably learned as much as my son did. We made a connection that will likely lead to more walks and more discussions.
And in a strange way, I can thank the purchase of these Fitbit watches for being the motivation for an activity that led to this amazing discussion. From that perspective alone this was money well spent.
Where Ideas, Experiences, and Lessons Learned Intersect 