Setting up the website and creating markdown with the Jekyll YAML front matter was easy. Gemini identified an issue that was solved by updating the version of python being used to render the site, and guided me through various layout decisions.  Presentations and talks and publications were pulled out of my CV, and Gemini helped guide creating categories and filling in the YAML for the dates and permalinks.  The fun part was going to be converting the blog posts.

For the blog posts, I used this chance to practice vibe coding. While in the past I did this inside of an IDE, this time I did this from the terminal using Google Antigravity CLI. I had it write a python script that:

1. Read posts from my blogger site.
2. Extract the date and title.
3. Extract text (and links)
4. Write markdown from text and filled in YAML frontmatter
5. Name the file using date and slug generated from the blog title
6. Save this to my Obsidian folder.  This has a side effect of having additional writing samples with my thoughts and opinions in my own words for Generative AI to use when I’m using it to write.

My prompt was

write a script that takes posts from https://lugerpitt.blogspot.com and creates a markdown file for use in an academicpages site. Yaml header should include title, date, permalink in the posts folder. File name should start with date of posting to the lugerpitt.blogspot site. put the markdown files in the posts directory

So Antigravity wrote a series of python scripts to explore the blogger site, inspect posts, identify links/media, format the posts, and bring everything together into a final script. 

I have created and executed the Python script convert_blogger_to_academicpages.py to fetch all posts from https://lugerpitt.blogspot.com and convert them into AcademicPages (Jekyll) Markdown format inside the posts directory.

  ### Key Script Features & Formatting

  1. Blogger JSON Feed Integration & Pagination:       • Automatically handles Blogger feed pagination to fetch all 569 blog posts.

  2. AcademicPages YAML Front Matter:       • Each file starts with front matter containing title, date, permalink (formatted as /posts/YYYY/MM/slug/), and tags (derived from Blogger categories).       • Titles and tags are safely quoted to handle colons, quotes, and special characters.

  3. Date-Based File Naming:       • Output files are named YYYY-MM-DD-slug.md matching the post’s published date on Blogger.       • Includes automatic collision handling if multiple posts share the same posting date and slug.

  4. Clean Content Conversion:       • Converts Blogger HTML markup (images, links, iframe embeds, blockquotes, lists, tables) into clean Markdown.       • Cleans up non-breaking spaces and redundant HTML wrapper elements.

  ──────

  ### Example Generated Markdown File

  From 2026-07-25-why-learn-theory.md:

    —     title: “Why learn theory”     date: 2026-07-25     permalink: /posts/2026/07/why-learn-theory/     tags:       - “artificial intelligence”       - “generative AI”       - “math”       - “operations research”     —

   

    Why learn theory? We were bringing our daughter back from an arts camp in northern Michigan… 

After looking at the output, I did a series of prompts to take care of removing some extraneous links and my standard writing of docstring for every function.  While the python script was pretty good right away, I did a few prompts to improve the code and output.  And finally I gave Antigravity my usual instructions of writing complete docstrings for each function. (the docstrings also serve as the description of the code.)

This was a fairly straightforward task, although as I am not a web developer I would not have been able to write this. And certainly not in such a short amount of time.  I could have also had Antigravity actually create the markdown versions of the posts. I chose to use AI to write python because I wanted the consistency of actual python code handling the conversions.

To put this in my earlier context on using AI, I find that using Generative AI is great in anything that is not my area of work, it is having a reasonably competent assistant in any domain available as a teammate. I’m sure a real web designer could do a better job, and give me more options, but this was good enough for something that now looks like it has history. And I’m happy with the result.

Why learn theory? We were bringing our daughter back from an arts camp in northern Michigan. As we talked about music, her lessons and teachers, pieces, performances, and ensembles, we also talked about her music practice (music theory and music history) class. And as a musician, while music practice was a welcome and sometimes entertaining break, she asked why we learn theory.

All practical domains have an underlying theory. Theory is how a domain understands its environment and how practitioners and researchers interact with its environment. For example, knowing the fire triangle/tetrahedron enables firefighters approaching a scene to assess how best to control and contain a fire while ensuring the safety of people in the area. Planning looks a capability and capacity and how to employ these to turn strategic and operational goals into resource allocation and actions over time.

What does theory give you? First, it helps explain why things work or do not work. Theory provides the basis to understand success and learn from failures. So that each observation is not simply a succeed/fail assessment, but you learn lessons on what made it work and how it can be done better. And each event/observation is also an opportunity to examine the theory and make it more detailed (with experience and wisdom comes a more nuanced and detailed theory. e.g. physics)

What are alternatives to theory (always know the alternative to any concept)? The most common is ‘how we have always done it” or “this is our experience”. Next is “loudest person in the room” and its relative “highest paid person in the room”. But experience is highly situational, and without theory you do not know what is the key detail that made the experience what it was. The appeal to authority and judgement is also highly situational - people’s expertise is in large part based on the experiences they have had (one reason that best practices for developing executives and general/flag officers is to give them experiences in a wide range of settings over the course of their early careers). Theory creates a framework to have conversations and identify what the questions that need answering and how to create and evaluate alternative solutions.

In my own area of operations research, which has led me to artificial intelligence and it generative AI form, theory is what I use to evaluate alternative approaches to addressing a problem. I like to say I know how to break every tool I’ve ever used. In this new era of Generative AI, I start projects with new business partners and domains by figuring out what about the problem domain generative AI gets wrong. (my business partners say that I am secretly happy when they find mistakes or cause problems with generative AI tools. They are wrong. I’m openly happy) And theory helps look at those problems and work out ways to modify those tools and make them work in the setting at hand. Even as the world changes around me.

Part of socialization of being an analyst in the United States military is valuing frankness and competence. And this is taught person to person, through example and stories of bosses who wanted analysts to only do what they are told, analysts who worked to give their bosses what they wanted to hear, and the consequences of bad decisions that hurt the organization and mission, even if the analyst and boss felt good in the moment. It is a story that is retold in many novels and movies as plot points leading to a disaster. And when military and intelligence analysts move to the private sector, one culture shock is an American management culture that values obsequious servants instead of frank and competent advisors.

In this constitution, Anthropic is firmly on the side of frank and helpful, even if it causes discomfort for the user. This is in stark contrast to the common observation that generative AI is eager to please and wants to tell users what they want to hear to maximize engagement.

One thing about the constitution, Anthropic wrote it knowing it would not have an opportunity for conversations over time, which is how values like this are taught. And since this is written for an AI, they could be verbose. So there are a lot of different aspects of the question of how to be a good advisor that are discussed, each a slightly different take on what it means to be genuinely helpful with a myriad of nuances. In particular, it has many discussions along the lines of “what if the user wants only affirming answers” and “what if the user does not ask to not be given misleading statements that will lead them into trouble”. Because that is something that is seen in real life.

If I were a place that taught analysts, like schools of policy or the service graduate schools, I would assign the Claude’s Constitution as a reading assignment. And there are a lot of topics that are covered here that would be topics of reflection and rich discussion. Because these are issues that analysts will face in real life. And history is full of examples where obsequiousness leads to harm for the organization. And those who claim that mission and service come before self need to reflect and discuss this in the classroom, so that they are prepared for these issues when they occur in real life.

I was in a group that had a conversation about computer privacy recently, and someone expressed a belief that everyone in positions of power was supportive of encryption and privacy. But I remember the days when governments actively opposed the spread of good encryption, even for businesses who wanted to be able to secure discussions and transfer of payments. The technology issues brought up here I still remember, and I recognize a lot of the debates and issues, the opposition to encryption, the desire to do mass tracking and surveillance through cameras and electronic devices. And the technology and social methods that the kids in the book used I recognize from those that were used in real life (which fits the author’s purpose).

But the real question here is what does being an American mean? One scene involved the review of the Declaration of Independence and what it means, all the while comparing the grievances expressed in the Declaration of Independence to the fictionalized Department of Homeland Security. The kids are not a White Rose brought to present day. They are not philosophical about morality and righteousness. They only want to live their lives as they see fit, and for their friends to be able to do the same, because they recognize that if the right to live as they please is refused to some, it is refused to all. So they act in ways that provide the freedom action they want to everyone. In contrast, the purpose of those who are demonstrating power is purely to be able to exercise power and limit the ability of others to live their lives.

Another side note. Power for its own sake becomes incompetent. Free creativity creates its own quality. The kids in their creativity create technical solutions outpacing the Department of Homeland Security, who only knows power and cruelty. The kids are not super technologists. They peripherally encounter older protagonists who can do what the kids do, but better. And because they are focused, the older counterparts are quieter and more effective. But this story is about the kids. This is not a tech-savvy version of White Rose. The kids are not portrayed as idealistic saints. They are kids, who want to live freely and create space for others to do so as well. Which is a very real way what the Declaration of Independence proclaims.

First, why learn computer programming when AI can write code faster than you. Microsoft has a set of studies showing a high (40%) rate of errors, yet their programmers also say they are more productive. Because, as I use AI at work, I find that it is helpful in creating good structure and framework scaffolding, especially when I have to context switch (I regularly switch between three data stacks at work, each of them have many people who spend all their time in one) or if I am applying methodologies new to me or my organization. But because I am competent, I can correct it as I go, and the fact that there were originally errors is not a big concern, because I was going to revise everything anyway

Another reason to learn programming is you learn to think in a different way. The ancient Greek philosophers had students learn geometry before philosophy. Not because geometry and math is beautiful (even though they are), but because with geometry comes proofs. And geometric proofs is about how much you can understand starting with a minimum amount of assumptions (Euclid’s five axioms). And you now have experience in determining an objective truth, no appeals to authority, no claims of different point of view. And your logic is in the open, to be critiqued on their own merits. Far different from my friend in grad school who claimed that perception is reality. And only then were you fit to move into the realm of ideas, where even facts have to be evaluated.

Programming languages differ from natural languages in their precision. Every statement has a single clear meaning. And this is different than natural languages, where the ambiguity of human life plays a role. So to work with anything regarding computers, it is helpful to recognize that computers will work with language in different ways we do, it handles ambiguity differently than people, and how it will use randomness to handle the difference (which is key to how Generative AI works).

The next is the link between the capabilities of technology and the needs of the business. According to everyone who has studied project failures in depth, failed communications between the business partner and the analysts is the biggest cause of project failure. And data projects have a failure rate between 80-90% (this range has been persistent in studies over decades in the data world, and it is consistent across definitions of failure and different segments in data analytics, data engineering, or data reporting (dashboards)). Being able to understand the business needs of end customers as well as understanding potential classes of technology solutions leads to asking better questions and getting value out of the applications of technology. The main reason for breakdowns in communication is ego and arrogance. From the technology side, there is often a belief that the customers are idiots who do not know what they want, so the technology people should just build something and pitch it back to the customers. This is mirrored by business people who think that technology is a a turnkey product so they should not interact with the people who are creating the solution. A third variation is when upper leadership decides to act as an intermediary between the analysts and the end customer. The logic here is generally that the leader believes both the technologists/analysts and the end customer have no communications skills, therefore the leader will handle all of the communications and give the requirements to the analysts. All of these are wrong. Especially in anything involving data, details matter, and the entire project involves discovery of details that no-one realized were important at the beginning. So the analyst and the end customer need to be regularly reviewing these discoveries, and adapting along the way. And only the end customer (because they are closest to the problem on the ground and know what kinds of actions can be taken) and the analyst (because they will be representing the detail in models and they know what the range of alternative models can do) together can make those decisions. Without that direct communication (potentially facilitated by someone who knows both sides), a project falls into the trap of solving the wrong problem. And this requires people who understand purpose and can determine the impact of nuance, both of which Generative AI does badly in.

The third category of future work is understanding your field. Computers are very good at retrieving facts, if those facts are in its knowledge base. Gen AI is better than prior technologies because it is not as sensitive to getting the wording precise. Computers are also very good at following instructions if those instructions are given. (people also tend to be better at things when they are given good instructions). So, if this is the extent of your subject expertise, you are in trouble. In software development, there is actually a very large workforce like this, whose careers are built on the ability to fill out a given framework or instructions. But if your place in the world is built on more than knowing facts or following recipes, if there is actual understanding that has to be applied on a situation specific basis, there is still room for you. Without that understanding, an organization can execute perfectly, a solution to the wrong problem. Which is worthless. So you need the level of understanding that allows for good judgement, and you need to be working in an organization that allows for its employees to use that judgement.

A last criteria is based on a number of conversations I’ve had. Many people express that they believe in the answers Generative AI gives because of the massive investment these companies have made, the smart people they have hired, and the belief that these companies would ensure correctness. I had to explain that these are industries and communities that have historically claimed they had no interest in accuracy or correctness. And until recently, viewed the paying customer as king and only sought to full the demand. Ethics was not part of the conversation. And what they delivered, did not come with guarantees other than it does what it does. This attitude that you did not question the authority that came with wealth and success is the first thing that has to be broken before people can use Gen AI productively. Both of my kids do it. I design the rollout and presentation of projects at work to make sure my business partners who are using Gen AI view its output skeptically and looking for specific types of flaws. As an avid reader of science fiction over the years, much of which addresses AI as part of society, and worry much less about the power of AI than I do about people who use the output of AI without being critical thinking. It is the kind of following that leads people to enact policies without analysis, and punishes people. And the outcomes are the fault of the people who followed the AI. Because AI has no goals, purpose, or conscience beyond that of its user.

The author, Brad Smith, was General Counsel for Microsoft (he was also President of Microsoft, but his role as counsel is more relevant for this book). The book is a discussion of privacy in a context where governments and large corporations hold immense amounts of personal and business data, and there is a large temptation for corporations to take advantage of that knowledge or governments to access that information, for governments in pursuit of legal action or suppression. So much of the book is about cases that involved Microsoft and how they developed a stance on corporate responsibilities to their customers on privacy matters, specifically in cases where government demanded customer data.

Each chapter revolves around a policy argument that played out in public forums, regulatory, legislative, and in the courts in the U.S. and Europe. And it is in a backdrop where technology companies used to believe that as technology companies they did not have an interest in policy. But as companies became less sellers of goods and more providers of services, in particular of data storage and cloud based communications services, they became targets of government and criminal action to access customer data without consent. In each chapter Smith introduces the context, then introduces a historical principal that predated cloud computing, and he makes the argument that the choices and policies used to govern oud based computing services should be the same that governed the same type of services in the pre-digital age.

The overall philosophy he gives is that Microsoft is a custodian of customer’s data, not the owner. And as custodian it will protect the customer’s property (data). And throughout the book he identifies allies (who have similar philosophies of protecting customer’s/citizen’s property and privacy) who only differ in details. And those that he as to be contentious with, because they are seeking to use and profit from individuals data or desire access for investigations. (and Microsoft in these cases wants a transparent process for doing this what protects their customers, who have the rights of citizens/residents)

Clearly, Smith is proud off his work, and believes that protecting the privacy of Microsoft customers, even in the face of government pressure, is the right thing to do (with a procedure for governments to prevent harm to other citizen’s rights, life, or property. But he does acknowledge allies, Google and several European governments come across very well here. So a reader has to be mindful that he does have rose colored glasses on Microsoft’s journey in this topic.

I appreciate the view of a non-technology person on these topics. As he is a lawyer, his perspective is to look at issues that seem very new because of the pace of technology change, and recognize that the issues have existed and debated before the digital age. As the infrastructure is owned by multi-national corporations, the relative power of industry and government is different. But the idea that industry desiring to protect the interests of their customers and government desiring the safety of its citizens should align is one worth engaging in.

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While I am not a student or early career who was the target audience, I took part, and I had three goals.

1. Use a new programming toolkit. I used VS Code with R and Quarto. I usually use R Studio and I wanted to try R on VS Code. And I think Quarto is the future replacing Jupyter Notebooks for Python and a natural evolution from R Markdown.

2. Practice in optimization. In the Operations Research world, I am NOT an optimization person. My thesis was applied probability (queuing) and my methods research has been in simulation (one stream in ranking & selection and another stream in Bayesian methods for input modeling)

3. Using Generative AI. I wanted to see how generative AI does in an operations research project. And I wanted to do it right in a setting where I can give it references to guide it. Note: I have found that Generative AI favors descriptive statistics, machine learning, and hypothesis based statistics over other forms of analytics, so it needs some guidance.

Toolkit

I had to set up VS Code with the R extensions, Quarto (and extension), ompr and the glpk with associated R ROI packages, and to make sure everything worked, I download the repository for OR_using_R by Tim Anderson. Then to render the book (meaning I made sure all the code ran) I had to install texlive with xetex and extra fonts. Generative AI (I had Gemini CLI installed) was very helpful in all of the system administration tasks since it could figure out what was needed every time there was an error message.

Data analysis and Optimization

Working with the data sets, it read in the data, (I had to give it some corrections along the way to help it recognize the data types. When the data files were read in, it recognized that the data sets did not correspond in granularity. In the R markdown file it created, in addition to generating the code that read in the data and created summaries, it also identified a number of questions and concerns about the data and created questions for the stakeholder. This was a good set of questions that corresponded to what others put forward.

It also did well with the optimization. Given an optimization textbook, I first asked the generative AI for a mathematical formulation based on the project description. Similarly, it created a process for determining what kind of problem this was and worked through that process to determine that this was a linear programming optimization problem.

Next was a LP formulation using OMPR. The first formulation was straight forward. I went and had the GenAI break out the formulation into its own R script to enforce a separation of concerns between the data handling, optimization model, and output processing.

I generally also ask for docstrings as I go, and the Gen AI did this for both the model as well as various handling functions. I generally read the docstrings to ensure they say what I expected them to say. When it did not, since the docstrings were written based on the code, I took it to mean that the code was not right (this exposed a mistake in the initial formulation of the LP in OMPR). Similarly, I had the Gen AI write unit tests for the constraints and a mock problem to test the optimization.

Agile Operations Research

One of the aspects of having the Advent of OR over 24 days is that it rotates topics between the art of modeling, implementing and managing models, and interactions with stakeholders. There are a couple of very important points. First is that interacting with stakeholders is not something that is at the beginning and end of project and ignored in the middle. There needs to be stakeholder engagement throughout the modeling process. A second point that has come out in the conversations on LinkedIn is that the most common cause of project failure across data analytics are communication failures, in particular between the analyst and the end customer. While this can have many causes (including management inserting themselves in between the analyst and end customer), as analysts we must have that direct interaction from the beginning of the project (business problem formulation in the INFORMS Analytics Framework)

In the early stages of the project, one factor we need to face is failure of imagination. The first level for analytics is that our stakeholders often do not know what is possible across the full range of analytics. Often a problem is presented as a request for a tool, but for the results of the project to have any value, it has to address the end problem, so business problem formulation has to start with the end problem, determine what kind of information from data can help the decision makers address the problem, and then we can start discussing what methods can provide results in the form that will be useful. Currently, because of media hype, the initial request can be for a dashboard, or a predictive model, or a generative AI tool. As operations research analysts we can also bring to bear statistics, forecasting, optimization, simulation, and queueing; and different ways of applying those methods to give different kinds of results that can be delivered to decision makers to make better decisions.

After the business problem formulation, the next big change in the project will occur when presenting the first minimum viable model to the end user. This is the first model that uses a minimum acceptable subset of the data and model that covers the most essential aspects of the smallest version of the problem. The reason this is important is before this, all conversations are abstract and theoretical. The first time a model with outputs is presented to an end user, the end user will start to imagine how they would use these results in real situations that have happened in the past. And they will start telling about all of the considerations they account for, the information they need to gather to make decisions, and who they need to consult and coordinate with. And this can change the entire project. And from experience, I do not think it matters how much work is done at higher levels to define the project, the first time a model is presented to an end user the project will change so that the outcomes can be usable to the business. So it is best to make that happen as early as possible so that change causes the least disruption to the work in progress.

The idea of rapid cycles of iteration and feedback from the customer, and the willingness to accept changes to the project due to that interaction are the hallmarks of agile development methodologies in the software development world. Having regular rounds of model iteration where additional elements are added to the model, and getting feedback from stakeholders to confirm that the project is on the right track to produce something useful. And just like the software development world has experienced, this is more likely to lead to useful product, and actually faster than attempting to follow a rigid path that leads to something irrelevant.

Conclusion

The Advent of OR proved to be a valuable exercise, offering a full-cycle project experience that highlighted two critical modern aspects of Operations Research: the integration of Generative AI and the necessity of an Agile approach. Generative AI demonstrated significant utility in accelerating system setup and basic modeling tasks, freeing up the analyst for higher-level problem-solving. More importantly, the experience reinforced that project success hinges on continuous, direct stakeholder engagement, mirroring the principles of Agile development. By prioritizing early delivery of a Minimum Viable Model, analysts can gain crucial feedback that aligns the project with real business needs, ultimately reducing the risk of communication-based failure and ensuring the final product is relevant and utilized.

I am going to define mentoring as a longer term, unstructured professional relationship, with the focus of the relationship being the personal growth of the mentee. Typically, the basis of the relationship is that the mentor has gone on a path that the mentee is on themself, and the insights of time may be helpful for the mentee’s development.

One thing that distinguishes mentorship relationships from other professional relationships is that mentorship relationships are holistic. They look more than just the task at hand, or even a job position. The mentorship relationship may be career focused, but it will look at the whole person, and will recognize that overarching goals can change with life events, even life events outside their occupation. So, while a supervisor/manager can be a mentor, this is really not apparent until after the manager relationship has ended, and the relationship has become larger than the roles both individuals had when the relationship started.

As we all have unique life paths, we cannot expect that any one person has gone on the same path that we are on, but mentors bring not only their own life experience, but also the experiences of those whom they have lived life alongside. They have seen the decisions and choices of others, and how those decisions have advanced the goals, or not. They have seen people whose lives have taken them on different paths, and so have a broader view on what the future can hold than those whose view of the world is from the relatively structured life of home and school.

What topics come up? The focus on a mentorship relationship is on the growth of the mentee. In the context of technical professionals, this is the professional growth, but as part of a full life. So, with an understanding of the long term goals of the mentee, it can be working through broader issues on a project, such as other points of view. It can be soft skills or relational skills working with co-workers, superiors, juniors, or outside colleagues (customers, business partners, etc.). It can be suggestions on how to stretch as a person, to see and work through things from a broader perspective, and the skills needed to do this. A mentor can be a sounding board, providing different points of view (especially on the behalf of people who may not be good at communicate their point of view). It can be how to handle work/life balance, looking at a whole person. It could also include looking at alternative paths, that different positions or even career paths may be more suited for the goals of the mentee.

How does a mentorship relationship start? Like all relationships, you can never tell if a relationship is going to be long term at the beginning. But you have to begin somewhere. A first conversation is often about a particular topic, one that is of mutual interest. (and this initial meeting is sometimes arranged by organizations such as a company or a professional organization trying to promote mentorship among employees or members). After the first few conversations about that first topic, you should have observed if the relationship is broader than that first topic, and you can talk about if you want to continue meeting about topics as they come up.

What does the mentor get out of this relationship? Typically, people who are in mentoring relationships also have other rich relationships, which is how they get the background that makes them valuable as a mentor. Over time, the relationship becomes driven by both concern and curiosity about the other’s experiences in life. Often that includes issues that are more apparent to someone at an earlier stage of life or career. A mentorship relationship can then become one of an ongoing set of relationships that makes up a life well lived, and the ultimate hope, even when it is not an expectation, is that a relationship be one that lasts.

Do mentorship relationships last? Sometimes. Organizations such as workplaces and professional societies will often organize mentorship relationships, but these are always based on a topic of interest in the moment, and these relationships typically start out with short term boundaries. But, like all relationships, a short term relationship is what has potential to broaden into something longer. Does the relationship broaden beyond the topic where it was started? Do conversations evolve organically and feel natural when they branch into new topics? Over time, can the relationship feel like something that lasts as both sides grow and change (as all growing people do). So the transition from a formal, temporary relationship with a defined schedule and defined boundaries changes into something more long term and fluid. And a mentor/mentee relationships begins to feel more like professional colleagues, each moving through life and careers on adjacent paths.

Can mentorships relationships be informal? Yes, in the sense friendships are informal. In professional society meetings, it is common to see someone and immediately follow up from a conversation from a year ago, just like old friends. So you can have a relationship where you only see each other on occasion, but immediately pick up where you left off, just as old friends do. But the key is the long term relationship, that the conversations are about growing people, not only about topic at hand.

Are there aspects of Analytics that mentorship relationships are especially helpful? One area are the soft skills, the skills of working with colleagues, managers, and customers that is not part of the standard training of a technical professional. A mentor can relate to what the other person may be thinking and help the mentee develop that sense of empathy for others that make them more effective professionally. A second aspect is dealing with the hype that often accompanies the profession. The most recent example is the rise of Generative AI, but similar waves of publicity occurred around deep learning, big data, and machine learning in general. A mentor can place new ideas and concepts in the context of everything else a mentee knows, in contrast to teachers or thought leaders whose responsibility at any given point in time is single topic focused. A third aspect is a sense of what a mentee may need to be a well rounded professional. Training programs and classes tend to be singularly focused with a specific goal, but professional growth needs to be holistic, and designing such a path needs the attention of a person who is looking at the whole person.

Mentorship presents the potential of a valuable relationship, fostering personal and professional growth through a holistic and potentially long-term connection. It goes beyond task-oriented guidance, embracing the mentee’s whole person, from developing crucial soft skills and navigating career paths to contextualizing industry trends. While it can begin focused on specific topics or within formal programs, at their best mentorships evolve into enduring relationships, offering mutual benefits and enriching the lives of both mentor and mentee. In dynamic fields like Analytics, such relationships are particularly vital, providing the comprehensive support needed to cultivate well-rounded, effective professionals in changing times.

If you are interested in mentoring relationships, I would look to your professional society. If you are in analytics, I would recommend you look at INFORMS and their mentoring programs (Video on the value of mentoring in analytics) It is a professional society for advanced analytics (broadly defined) and is vendor, tool, and methodology neutral, which is important for a field that sees major changes over the course of decades.

I read AI Snake Oil as part of the INFORMS Book Club. I work with predictive AI and generative AI at work, and I describe what I do as figuring out how AI fails, then work with my business partners to develop a process and application to make AI useful and productive. This book falls into the category of demonstrating how AI fails.

There are several chapters, each with a discussion of a way that AI fails and how the authors figured it out. But they have a pattern. First, the failures in AI is in part due to how a particular model is trained. If the training data does not match the intended use, such as the data actually represents one characteristic but the model is being used for something else. Next, they discuss that the people who made the model do not always have incentive to get it right. In particular, the large AI companies do not have incentive to either evaluate the quality of the models or improve them.

Some things I think they do well.

1. Differentiate between various generations of AI. They specifically break out predictive AI, generative AI, and symbolic AI. Each of which work differently than the others.
2. Focus on the training data. This is where AI models need to be examined (by definition, AI does not include a description of the system, so predictive and generative AI have to learn about the world through large amounts of diverse data.) And failures come from the data not matching the setting where a model is applied.
3. Be skeptical of claims that come from computer companies. I always say don’t let people selling you things define terms. They also say don’t let industry set the rules, the standards, or barriers of entry. Because their goal is to defend their market share, not the benefit of society.

This is a good book to read, especially as part of a discussion. Highly recommended

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As part of ongoing professional education, I took the INFORMS Business Problem Framing class, which is also the lead in to the Certified Analytics Professional training that is being developed. You may wonder what can be learned in a 5 hour course. It is a tour of frameworks for looking at business problems. And this is something that gets short changed in engineering training that focuses on analytic methods (i.e.math) to the expense of understanding the problem in context and working with people. This course is needed by anyone who learned analytics as a math or computational practice and needs to learn to work with business partners/clients and is good for those who work with business partners/clients but want more range and ways of communicating (which is everyone who works in analytics in the real world)

Back when I was an enginneering professor, the supply chain center at the associated business school asked if I would be willing to coach their supply chain case study teams. As a school with a certain amount of pretension, they found it disheartening that they were never winning these regional competitions (operations management supply chain professors and industrial engineering supply chain professors are drawn from the same pool, so this actually made sense as they needed someone who was not actually part of running a case competition.) But, after some conversations I did not approach it as a refresher on supply chain modeling, I approached our coaching sessions as teaching them how to read a case through the lens of frameworks that they had learned at some point, but may not have realized were a key tool. Because frameworks are tools for organizing how you think of problems and a way to communicate about problems.

In every domain of expertise, experts use frameworks to organize how they look at the world. Frameworks help experts look at all aspects of the situation. And when provided information they can realize that they have not been given critical information that can change how they should approach a problem. Novices will often work only with what they have, like a classroom problem. I have noted that Generative AI foundation models make the same mistake, they work with only what they have in cases where a framework would have told them a direction to go to get missing information for a fuller picture of the problem.

The other use of frameworks is communication. Frameworks provide a way of quickly communicating the essense of the issue and the information that a decision will depend on. When I was deployed in Afghanistan, I had a brief that was working its way up the chain.It was being scheduled for a 2-star general. A couple of that general’s staff were present when I had given the brief at a lower level. And they told me that the brief was good, but I had to completely redo it to fit into a specific framework. Because that is how that general processed information. (In the end, the staff went through my brief and realized that the recommendations were sound and we got the result without having to formally present the brief)

What can a 5-hour class do? The Business Problem Framing course is a tour of a wide range of frameworks, probably familiar to someone coming out of business school, but not familiar to those whose focus is on analytical methods, math, and programming with data. And many of the frameworks are very similar in purpose. But the right framework for the problem is on partially about the particulars of the problem, it is also about the framework that communicates the problem to everyone involved. And just as having many ways of delivering a message is helpful to have, familiarity with a number of frameworks is helpful to have when communicating with stakeholders, because it is more possible that you will find a framework that resonates with everyone. And that leads to a better and more fruitful effort in solving the right problem at the right time.