Author: Aaron Smiles

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MIT Sloan—Managed by Q (A Case Study RE: Strategy)

Managed by Q—acquired by WeWork 2019, sold to Eden beginning [2020] this year (raises some eyebrows, but I wasn’t surprised)! Managed by Q = excellent venture/founders, WeWork = not so much! [caption id="attachment_3101" align="aligncenter" width="1920"] Aaron Smiles strategy slide[/caption] Presentation notes: **REDACTED DUE TO THIRD-PARTY PLAGIARISM OF MY ORIGINAL WORK!** RBV slide 1:   [redact] Community culture: Q’s office space had a lounge for everybody to use. When on-call cleaners did not have an assignment, they spent time at the office. Others were welcome to stop by any time.    Allen Erickson, a cleaning operator explained: “Walking into Q’s office is like walking into Cheers. Everybody knows your name.” He considered Q his extended family [/redact]   RBV slide 2:   [redact] Technology was a key part of Q’s offering, as the firm sought to differentiate itself by providing fully transparent services. Q gave customers an iPad with a customized dashboard which listed the tasks requested by the office manager.   Through the iPad, office managers could request maintenance services if, for instance, they needed a desk assembled, furniture moved, or shelves mounted. If a customer needed a job done that fell outside the scope of maintenance—say, installing a new security system—Q would refer it to a vendor. Customers could also use the iPad to order office supplies such as printer cartridges, pens, and paper and could add items to the basic list that Q provided.   For the cleaners, Q developed an app that provided a customized task list to check off during each cleaning as well as the cleaner’s schedule and maps to all locations. The app enabled cleaners to communicate directly both with Q and with the customer’s office manager if any problems or questions came up during a job.   Q had a clear separation of headquarters functions and city functions. HQ functions were responsible for developing applications, acquiring customers, setting salaries and benefits, and standardizing procedures and policies.   This operational autonomy ensured the flexibility to align with local needs (a form of Adaptation on a national scale).   Q Knowledge included a section called “The Info Session Playbook,” which provided step-by-step instructions for how to run an information session. [/redact]   PEST slide 1: [redact] Different state regulations and real estate that could be troublesome to navigate — FOR EXAMPLE: Chicago had more consolidated commercial landownership, which meant more managed properties and fewer of the commercial buildings that were the best fit [/redact] PEST slide 2:   [redact] States can differ wildly on…

OOM-killer Aircraft Analogy

A very amusing analogy by Andries Brouwer about the Linux OOM-killer! When available memory is exhausted, Linux invokes the Out Of Memory (OOM) killer, which has to make a decision on which process or processes should be terminated to free memory. This is not a precise science! The heuristic algorithm cannot satisfy everyone. Enter Andries' take on this sufficient killer; "An aircraft company discovered that it was cheaper to fly its planes with less fuel on board. The planes would be lighter and use less fuel and money was saved. On rare occasions however the amount of fuel was insufficient, and the plane would crash. This problem was solved by the engineers of the company by the development of a special OOF (out-of-fuel) mechanism. In emergency cases a passenger was selected and thrown out of the plane. (When necessary, the procedure was repeated.) A large body of theory was developed and many publications were devoted to the problem of properly selecting the victim to be ejected. Should the victim be chosen at random? Or should one choose the heaviest person? Or the oldest? Should passengers pay in order not to be ejected, so that the victim would be the poorest on board? And if for example the heaviest person was chosen, should there be a special exception in case that was the pilot? Should first class passengers be exempted? Now that the OOF mechanism existed, it would be activated every now and then, and eject passengers even when there was no fuel shortage. The engineers are still studying precisely how this malfunction is caused."

KubeCon Highlight: Saving the Planet from Zombies!

Highlight of #kubecon #cloudnativecon 2020 so far!! Climate Keynote by Holly Cummins; "One of the problems we need to think about in 2020 is zombies destroying the climate"! 🧟‍♂️ 🌍 Very important topic and so great to see it addressed at a tech conference keynote! De-zombify your K8s clusters, everyone! Also worth viewing:

The Battle of the Neighborfoods

1. Introduction 1.1 Background "UK restaurant market facing fastest decline in seven years" A headline from last year[i] prior to the coronavirus. MCA’s UK Restaurant Market Report 2019[ii] indicated that "large falls in the sales value and outlet volumes of independent restaurants is the cause of the overall decline of the UK restaurant market. It attributes this to a “perfect storm” of rising costs, over-supply, and weakening consumer demand." London's restaurant scene changes week on week, with openings and closures happening on a regular basis; it must be hard to keep up. The hyper-competitiveness of London's restaurant scene make it one of the toughest cities in the world to launch a new venture. "With business rates up and footfall down, a winning formula is worth its weight in gold and although first-rate food is inevitably the focus, other factors can also affect a restaurant's success. Atmosphere is frequently cited in customer surveys as second only to food in an enjoyable restaurant visit and getting the vibe right is crucial."[iii] Due to the coronavirus most businesses have suffered even greater losses. As restrictions lift businesses will be looking for ways to make up for lost time and earnings. Reopening a restaurant once lockdown is over is one thing, but knowing what to put on the menu if you haven't been in contact with a punter in months is another. "Are there any grounds for hope? A wild optimist might point to some encouraging data about the overperformance of small chains while everyone else loses their shirts; a realist might make coughing noises about small sample sizes and growth from a low base. The queues snaking out of Soho’s recently opened Pastaio suggest one genuinely viable route to salvation – concepts may need to follow its lead and amp up the comfort food factor while dialling down prices. And while home delivery is a source of confidence for some parties (Deliveroo, for instance, recently listed its shares on the stock market) it may well end up a false friend: the increased volume of so-called “dark kitchens” presage a sinister vision of the future, where restaurants don’t exist to serve customers onsite at all, but just pump out takeaway meals for us to consume on our sofas. A little far-fetched, perhaps, but with lights going out at a faster rate than many can remember, it can’t be too long before whole tranches of…

Max Welling on the Future of Machine Learning (TDS Podcast)

Max Welling, former physicist, current VP Technologies at Qualcomm. Max is also a ML researcher affiliated with UC Irvine, CIFAR and the University of Amsterdam. Max has just shared some great insights about the current state of research in ML, and the future direction of the field: “Computations cost energy, and drain phone batteries quickly, so machine learning engineers and chipmakers need to come up with clever ways to reduce the computational cost of running deep learning algorithms. One way this is achieved is by compressing neural networks, or identifying neurons that can be removed with minimal consequences for performance, and another is to reduce the number of bits used to represent each network parameter (sometimes all the way down to one bit!). These strategies tend to be used together, and they’re related in some fairly profound ways.” “Currently, machine learning models are trained on very specific problems (like classifying images into a few hundred categories, or translating from one language to another), and they immediately fail if they’re applied even slightly outside of the domain they were trained for. A computer vision model trained to recognize facial expressions on a dataset featuring people with darker skin will underperform when tested on a different dataset featuring people with lighter skin, for example. Life experience teaches humans that skin tone shouldn’t affect interpretations of facial features, yet this minor difference is enough to throw off even cutting-edge algorithms today.” “So the real challenge is generalizability — something that humans still do much better than machines. But how can we train machine learning algorithms to generalize? Max believes that the answer has to do with the way humans learn: unlike machines, our brains seem to focus on learning physical principles, like “when I take one thing and throw it at another thing, those things bounce off each other.” This reasoning is somewhat independent of what those two things are. By contrast, machines tend to learn in the other direction, reasoning not in terms of universal patterns or laws, but rather in terms of patterns that hold for a very particular problem class.” “For that reason, Max feels that the most promising future areas of progress in machine learning will concentrate on learning logical and physical laws, rather than specific applications of those laws or principles.”Jeremy Harris, Towards Data Science, Jun 3 2020 ( Hear the full topic discussion on Spotify:

Robin Chase, Zipcar, and an Inconvenient Discovery

As part of my MBA we were tasked with reading the linked case study developed by MIT Sloan about Robin Chase, the founder of Zipcar, and the dilemma she faced when she realized the company’s revenue was half what she needed in order to break even. Then write a 3-page reflection on her leadership. As you’re reading, think about Chase’s decisions as a leader in forming this company. How did she develop her mission, team, and pricing model? What do you think led to her miscalculation? Evaluate Chase’s strengths and weaknesses as a leader, focusing on how they relate to the development of her mission, team, and pricing model. What do you think led to her miscalculation? Then, put yourself in Chase’s position and discuss how you would have acted as CEO. How would your approach have differed, and why? Here’s my reflection... **REDACTED DUE TO THIRD-PARTY PLAGIARISM OF MY ORIGINAL WORK!** [redact] Chase had a clear, achievable vision for Zipcar. With hindsight I might be inclined to say that she should not of considered the environmental benefits of Zipcar as a secondary part of her vision, but rather a main motivator and promoter of it. However, I must be conscious when conducting my analysis that we are discussing events that occurred in 2000, when environmental impacts were not as mainstream as they are today. It would also be too easy to point out where she went wrong from a technology standpoint, with current day technology as an argument, but this would not be accurate, since the technology she had access to in 2000 was far more crude than it is today.   My initial thoughts when reading the case study, were that Chase might have been slightly premature in terms of her vision and where the technology was at the point when she decided to launch, but I could be wrong. It is true that perfection is the killer of progress, and it’s often better to get something out there [that isn’t perfect] and update iteratively with feedback from users. Chase’s reason for starting up Zipcar was sound — based on a personal problem she experienced — however, did she conduct proper market analysis and get impartial customer insight? Chase maintained close contact with Zipcar members, but was she guilty of confirmation bias, because she seen this as a “nice-to-have” in her life?   It is also easy…

Word of the Month: Heteroscedasticity (and Homoscedasticity)

In Linear Regression Residual Analysis heteroscedastic results mean that the variance in errors is not consistent (see: Graph 1 and 2), which is what a good linear regression model should show — a good random scattering, showing no particular pattern. This is called, homoscedasticity (see: Graph 3). Graph 1Graph 2Graph 3 If your residual analysis results look like this then the model is not a good fit. To fix this, one could perform a data transform, or add a variable to the model to help account for what is the cause between the relationship of errors and input values. In the example above for Graph 1 and 2, this could be the number of people at a table or the time of day — since larger groups sometimes tip less because they assume everyone else will tip, or people are more generous later in day after some vino in the evening! But remember, "Essentially, all models are wrong, but some are useful." Now that’s what I call statistical bombasticity!

Spinout of the Month: Conplx, an XR IDE to promote STEM inclusion

This month I’ve decided to select the best spinout concept and treat it with an imaginative MisVis Statement. The chosen spinout is, Conplx — domain: — which was conceived when trying to find a solution to STEM inclusion and getting more girls interested in coding. Conplx Not just a new way to code. A new way to STEM! Conplx is an abstraction of the Latin word conplexio, meaning abstract(ion). The concept of Conplx is to take the first principals of STEM, notably Abstraction, and present them at the forefront of an extended reality integrated development environment (XRIDE) to the user as a customisable tool.  The Conplx Mission The mission of Conplx is to get more people interested in STEM, especially girls, by removing the two main identified obstacles; “too hard” and “too boring” — this will be achieved by applying new solutions — AI and XR technologies — to an already tried and somewhat failed paradigm; Visual Programming Language (VPL). The issues identified with VPL attempts thus far are that they still act like code and don’t detach from coding practice enough to remove the “too hard”, “too boring” obstacles (referred to hereafter as TooHB). The Conplx Vision Think of a more abstracted and fun version of Matlab, using XR technologies with AI support.  Matlab is one of the most established versatile and visual tools in STEM, yet according to Stack Overflow’s 2018 Developer Survey it is one of the most dreaded environments among coders. Both Mathematical and Computer Sciences share the same first principal; Abstraction. This first principal is what Conplx is built on.  The best example of what Conplx aims to achieve is that of the video game controller. The video game controller is an abstraction that simplifies all the complex and beautiful code under the hood in order to make playing games enjoyable, exciting, and easy (as compared to operating a game without the game controller abstract). Another example would be an animated movie and how that is an abstraction of the written story — here the story being traditional code. Conplx could be viewed as an animation of code objects for OOP. The power of abstraction/level of animation is in the user’s control — for example, the higher the level of abstraction the more generalised and encompassing an object would be and less objects would exist in the XRIDE, whereas, the lower the abstraction the more objects would…