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 (https://towardsdatascience.com/the-future-of-machine-learning-cd5b8b6e43cd) Hear the full topic discussion on Spotify: https://open.spotify.com/episode/20flI9imCj9YhW7HVUL92Z?si=glb6JLwzR86KKc6Yc-LvRQ

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. https://mitsloan.mit.edu/LearningEdge/Leadership/Pages/Zipcar.aspx 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... 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 to judge Chase’s choices based on knowledge available today — a sort…

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: conplx.com — 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…

Steve Blank —Clayton Christensen [REPOST]

Terrible news. But a beautiful post by Steve Blank in tribute of the late Clayton Christensen. #innovatorsdilemma Clayton ChristensenPosted on January 28, 2020 by steveblank Say not in grief he is no more – but live in thankfulness that he wasIf you’re reading my blog, odds are you know who Clayton Christensen was. He passed away this week and it was a loss to us all.Everyone who writes about innovation stood on his shoulders.His insights transformed the language and the practice of innovation.Christensen changed the trajectory of my career and was the guide star for my work on innovation. I never got to say thank you.Eye OpeningI remember the first time I read the Innovator’s Dilemma in 1997. Christensen, writing for a corporate audience, explained that there were two classes of products – sustaining and disruptive. His message was that existing companies are great at sustaining technologies and products but were ignoring the threat of disruption.He explained that companies have a penchant for continually improving sustaining products by adding more features to solve existing customer problems, and while this maximized profit, it was a trap. Often, the sustaining product features exceed the needs of some segments and ignore the needs of others. The focus on sustaining products leaves an opening for new startups with “good enough” products (and willing to initially take lower profits) to enter underserved or unserved markets. These new entrants were the disruptors.By targeting these overlooked segments, the new entrants could attract a broader base of customers, iterate rapidly, and adopt new improvements faster (because they have less invested infrastructure at risk). They eventually crossed a threshold where they were not only cheaper but also better or faster than the incumbent. And then they’d move upmarket into the incumbents’ markets. At that tipping point the legacy industry collapses. (See Kodak, Blockbuster, Nokia, etc.)Christensen explained it wasn’t that existing companies didn’t see the new technologies/ products/ markets. They operated this way because their existing business models didn’t allow them to initially profit from those opportunities – so they ignored them – and continued to chase higher profitability in more-demanding segments.Reading The Innovator’s Dilemma was a revelation. In essence, Christensen was explaining how disruptors with few resources could eat the lunch of incumbents. When I finished, I must have had 25 pages of notes. I had never read something so clear, and more importantly, so immediately applicable to what we were about to undertake.We had just…

Prolixity, grandiloquence, and sesquipedalianism, Oh My! 😛

Interesting article in ACM’s Ubiquity, Communication Corner, How not to be overwhelmed by obvious advice, by Philip Yaffe (DOI: 10.1145/3375552), which [loosely] ties nicely with Ray Dalio’s Principle 4.4J; “Watch out for assertive, fast talkers.” https://www.instagram.com/p/B051n51JGmD/?igshid=u13vaxpfuomy Be dubious of people or advice that is delivered with too much assertion, speed, or verbose language. This is often used with goals related more towards the ego than towards the true goal. If you do not fully understand something being said, ask for clarity. If it’s still not being explained clearly, it is likely not fully understood by those saying it (as said by Einstein no, Feynamann no, Rutherford). Often, questioning someone in this scenario helps the speaker understand their own thoughts more clearly. By being forced to reword their thinking using different language, they have to remap the thoughts in their brain and reconstruct it for verbal delivery. This process can also spark new ideas and new thinking that would otherwise have been lost, along with the original idea that no-one got too! This especially applies to managers. Allow others and opportunity to speak or challenge the leading argument(s) in a discussion or meeting. There may be introverts in the room with excellent ideas that are being suppressed by the “assertive, fast talkers”. Do not mistake quietness for a lack of confidence, or simple language for lack of intelligence. Often, quite the opposite is true! With unprecedented access to information, the internet can be a source of useful material. However many times, so-called experts share specious advice. In this article, Phil Yaffe addresses a widely discussed topic, clear writing, and dives deeper to fix the flaws found in regurgitated writing advice.In the previous installment, we took a close-up look at the functional definition of "concise," i.e. as long as necessary, as short as possible, and saw how useful it is in preparing an expository (non-fiction) text that most people will probably want to read. As you will recall, there are two other functional definitions needed to render an expository text effective: "clear," and "dense." We are going to take a second look at these now (see Communication Corner No. 2 "The Three Acid Tests of Persuasive Writing"). Fixing the Flaws in the Ten "Principles" of Clear Writing Ten Tips and Techniques Keep sentences shortThis is usually interpreted to mean an average sentence length of 15–18 words. Not because readers can't handle longer…

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Happy 2020!! {COMPETITION ANNOUNCEMENT!}

Happy New Year all! We followed the industry closely last decade and recorded all the business startup failures that happened In those last 10 years. Stay tuned, as we will be posting our reviews and analyses of those unfortunate companies and entrepreneurs across the coming months, highlighting reasons they failed and ways to avoid such failures. As part of our commitment to making sure companies have as much chance as possible of success and try to avoid the mistakes of the past decade, we are running a competition. 4 consultancy packages are up for grabs (one of each specialism); 1) Business 2) Finance, 3) Marketing, OR 4) Technology! To enter, Simply complete the form below and include your entry choice 1-4. That’s it!  Winners will be announced January 31 2020 via our social channels. Good luck! All the best! VRA Facebook Twitter Competition Entry Form Complete form to enter competition and make sure to put 1-4 in your message to choose prize! Please note: Enterants must also be followers of our Facebook and Twitter pages!

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