The problem with investing based on pattern recognition

A famous story in artificial intelligence is how the US military developed algorithms to determine whether an image had a tank in it. They used a standard machine learning method: feed the computer a “training set” of photos, some of which had tanks in them and some of which didn’t, and let algorithms identify which features in the photos correlated to tanks being shown.

This method worked for a while but then mysteriously stopped working. Since the features the computer identified were embedded in complicated mathematical equations, no one could figure out what it was really doing and therefore why it stopped working. Eventually someone realized that in the training set, all of the images with tanks were taken on a cloudy day, and all the images without tanks were taken on a sunny day. The algorithms had fixated on the most obvious pattern – the color of the sky. When the algorithm was tested on new photos where the weather varied, it was completely flummoxed.

It is commonly said that good startup investors develop “pattern recognition” that allows them to identify great entrepreneurs and companies. If you look at the hugely successful startups of the last decade, the founders have many similarities that are easy to observe. When they started, many were male, young, unmarried, computer programmers, dropouts of elite universities, etc. As a result, a lot of investors look for founders with these characteristics. But without an understanding of the deeper reasons these founders succeeded, these observable characteristics could just as well be the color of the sky and not the tanks.

At the level of individual investors, pattern recognition can lead to bad investments and missed opportunities. In the context of markets, it can cause companies and sectors with the “right patterns” to be overvalued, and ones with the “wrong patterns” to be undervalued. In the broader cultural context, it can cause large groups of talented entrepreneurs to be denied access to capital.

The classic scientific method provides a better model for investing. Scientists observe data, notice patterns, develop hypotheses, and then test those hypotheses. Pattern recognition is only a step along the way to developing hypotheses about the underlying cause.

Perhaps dropping out of college shows a strong level of commitment. Knowing computer science was probably a necessary condition for starting a tech company in the past, but no longer is. Being young could mean you are inexperienced enough to pursue bold ideas that more experienced people would consider crazy. I am just speculating – I don’t know why these characteristics are common among past successful founders. But the mere repetition of patterns shouldn’t be satisfactory to anyone who wants to understand and predict the success of startups.

97 thoughts on “The problem with investing based on pattern recognition

  1. Great post Chris. Eric Ries loves to say that another word for “Pattern Recognition” is “Bias”. One thing that’s interesting about stereotyping is that it basically becomes a self-fulfilling prophecy. If everyone believes a person with X trait is likely to be successful, they give that person the resources to do so. When teachers believe a certain kid in the classroom is smarter than the rest, they use language and encouragement for that individual that makes their biases come into reality.

  2. GabrielMtn says:

    This is a fascinating line of thinking. Not that your speculation isn’t very interesting, but I’d love to see research resources which relate. Are you familiar with any?

  3. I’ve seen some attempts at academic studies on features that predict startup success, but I find them unconvincing, mostly because much of the required data isn’t public or neatly collected.

  4. Isn’t it very similar to the way insurance companies or banks evaluate the risk/return of potential customers?
    If the recognition model is built correctly (i.e. by someone with domain knowledge, not as a black box), I would expect that, on average, pattern recognition will provide some useful info. Yes there are missed opportunities and bad loans (and bad investments), but their goal is profit, not equal opportunity.

  5. @Benoit: Actually the financial risk modelling is much more similar to science: based on large amount of data which is statistically analysed. Quite often these models are build by ‘real’ scientists i.e. physicists who know the methodology of making and testing hypotheses. That’s not something what happens when people are interviewing the founders. I call the problem “esoteric recruitment” and observe it quite often. Many people use easy to observe (and often very popular but unverified) traits/attributes/behaviors and use them as indicators of required competences. Actually that’s what has to be done: trying to predict future performance by competence assessment based on currently available data. But the problem is that many people use astrology-like “tools” – sterotypes, IPAs, projudices etc. It resembles the issues descibed by Eric Ries (vanity metrics, product development astrology) – making decisions on really weak data.

  6. I think insurance companies & banks probably do it right in the sense that they don’t just find correlations, they find correlations with plausible underlying theories supporting them. For example, there are plausible underlying theories as to why someone who missed a loan payment in the past might miss on in the future.

    In startup investing, I rarely here people talk about the underlying reasons behind certain correlations, and certain things like gender just seem to me to have no real predictive power. For whatever historical reason, men started more tech companies in the past, but once more women enter tech startups I don’t see why they shouldn’t have the same chance to succeed.

  7. I am not so sure that insurance companies pick only plausible features.  In principle, a “motivated” model is more robust, but the idea of machine learning is also to let the data teach you something new.  In the end there is no way one can be sure that a correlation is not just a fluke in the data.

    It seems to me that by using “pattern recognition” you may get a statistical edge over your fellow VCs, if you don’t care about “equal opportunity”.

  8. Another argument for why VC is different: the environment changes much more rapidly than in say insurance. For example, if you look at the founders of the current crop of top internet startups, they tend to be much more design/product focus that then last generation.

    Also, fwiw, I worked at a quantitative hedge fund after college (a very successful one -not due to me though) and they had strict rules about not using ML algorithms unless there was a sound economic argument underlying why it worked. The reasoning was very similar to what I outlined in the post. I think suspicion of pure pattern recognition is much more common in hedge funds than in venture capital.

  9. When we all just come together and accept we do not know WTF we are doing .. lets face it Chris .. we have no clue. We guess our asses off.. 

  10. well, that is definitely true. but top VCs do seem to succeed pretty consistently so there does seem to be some talent component (along with a lot of guessing).

  11. I am sure you are well versed with what happens in the background..so am I .. we just invested in a major round .. we have our excel sheets and investment thesis but in the end of the day two things drive us – ego and revenue…

  12. A fast-changing environment is a big problem indeed, and the analogy with hedge funds is very apt.  But I am always suspicious of arguments against AI that go like “this is different, a machine could not possibly do this better than a human”.  I know that’s not your argument, but you get my drift…

  13. great IRR, but as you know brand recognition tends to lead to access which helps IRR, so they tend to be related. (but there are VCs with great IRR that not many entrepreneurs know about).

  14. If you had great data in VC (which is very hard to come by) I definitely think that would be an interesting input for an investor. I just would want to try to explain each prediction and then perhaps adjust them according to how the environment has changed.

  15. I guess I took “pattern recognition” as the scientific discipline.  Yes, there is nothing worse than anecdotal evidence masquerading as science.

  16. Deal flow is really a joke. Look at your inbox – how many companies did you not invest in that you wished you invest in ? Dropbox ?.. :D.. anyways man I am happy we get so much attention as investors..ppl think we are smart .. ah only if they knew..

  17. U should write an article on how luck plays a huge role in investing .,. I shall co-author it with you :) may even give you some nice spicy info on some big VC’s :) aka my smart drink buddies

  18. GabrielMtn says:

    @ArkadiuszDymalski:disqus I’m not familiar with any quant VC firms, are you? Is anyone else? Finding VC firms who use quantitative analysis seems… unlikely. I personally doubt that there are any, but would love to be proven wrong.

  19. Gabriel, I’m afraid that my post might be confusing, as I joined 2 comments into one. I’m not advocating using such quantitative methodology for recruitment (it’s impossible due to lack of data). However there’s enormous area for improvement in the spectrum ranging between statistical prediction and esoteric prediction: building and verifying competence models for entrepreneurship/leadership as well as building and verifying the tools to asses these competences.

  20. Cory says:

    I agree that pattern recognition is dangerous as you described. I just don’t see the scientific method being the answer. The data/experiments will just leave you with similarly objective “patterns.”

    The problem is largely subjective though–not objective. The question you are trying to solve is not “Do young male CS dropouts make better founders?” but rather “What character traits will make a better founder? That sort of question can’t provide reliable conclusions using a purely scientific method because the data is too messy, there are too many variables and thus no room to isolate controls necessary for true experimentation.

    I think questions like “What makes a good founder?” rely much more upon the art side than the science side of investing. As Buffet would ask, is management “honest and able”? Patterns and evidence displaying the key underlying traits of a founder can help (e.g. proven hard worker, focus, problem-solving, intelligence), but you figure all that out through experience and fine-tuning your ability to judge those things about someone. Attempting to apply a purely scientific approach to this kind of decision making just be a waste of time, practically speaking.

    In matters related to people and our judgment of competence/character, I agree with good old Plato: don’t let mathematics get in the way of good reasoning.

    Now, if we’re talking about other areas of investing (trend analysis, market sizing, competitive landscape, business model, valuation, etc.), that’s another matter entirely.

  21. Can you share examples of VCs you know (with or without naming them) who find a good balance (or not!) between some pattern recognition, some theorizing, some quant and some judgments? 

  22. So, in general terms (without naming if you wish) what do VCs who find a good balance do and what VC who don’t do. Maybe illustrate with examples?

    In the end these are complex choices and only the ones that are “adapting” (in the most generic sense of the word) can make good decisions over the long term.

  23. VC’s make dumb decisions. There is no “pattern” recognition. The Wall street makes great decisions on patterns and have complex systems to do it and still Warren Buffet wins everytime. VC’s for the most part make emotional decisions. So my advice to you is to not waste your valuable time trying to understand this and focus on your company and revenue there i.e focus on understanding what photographers want.. unless you are trying to “network” and kiss Cdixon’s here..

  24. Guest says:

    As an outsider, venture seems ripe for disruption.

    From the investor’s point of view, the asset class has been (on the whole) underperforming, and it’s hard for an interested (and qualified) investor to put together a diversified venture portfolio without very deep pockets and some difficult research. 

    From the entrepreneur’s point of view, fundraising is an enormous distraction.  It’s not bad for a founder to be forced to articulate a clear, concise explanation of the business, but you can watch companies grind to a halt for two months while they scramble around the circuit.

    And as you implied, if the entrepreneur doesn’t fit certain patterns, they’re essentially wasting their time.  Nobody ever got fired for investing in young, male, San Francisco tech geeks, right?

    And it seems like from the VC point of view, there’s a ton of time spent doing road shows,and spent vetting opportunities that doesn’t really add a lot of value, but is done because there isn’t a better way to do these things.

    If I were tasked with redesigning VC, I’d love to come up with a data-driven approach, but I think there’s an intermediate step that needs to be taken.  The creation of venture markets.  That’d provide a more reliable price signal than the current system, facilitating other types of analyses, and hopefully creating a more efficient capital allocation mechanism.

    I know VCs aren’t just a check, but surely provisions could be made to handle board seats and advisory roles, which seems like the best opportunity for VCs to add value, anyway.

    That said, this could all be a terrible idea.  I slept about an hour last night, and haven’t thought this through in the least.

  25. Cindy Gallop says:

    When you believe in people, they will rise to that belief. 

    Which is why it’s so important to believe in the people whom pattern recognition excludes. Like, just for example, in this case, women. :)

    #changetheratio

  26. I don’t think that pattern matching in investing is any different from other sociological behaviour patterns that exist in the real world. People like the familiar – what looks and smells like them, as well as what is consistent with previous success. We are not creatures of change, we are creatures of habit, even when changing our ways might be to our benefit.

    Perhaps VCs should consider that technical competence is not the only important aspect to master when it comes to creating a successful brand. Being able to create sustainable relationships, building stories that tap into the emotional drivers of behaviour – these skills are typically downplayed (read: ‘female’ skills), but are critical to creating brands that rise above the noise and the copycats. It’s great to be able to sell your cool technology to a VC in a pitch, but if you don’t have the team in place with the passion and the experience to execute, you’ll crash and burn.

  27. Scientific method from institutional investors? I think you know how far from that it really is. Even pattern recognition would be a step up. 

  28. Agree 100%

    If you drop by my blog you will see m using fractals. I used the Mandlebrot as the image for my original blog too — as a dig against a classic VC delusion, the deep-seated belief that we (venture capitalists) can predict the success or failure of startups and rely on the experience and so-called “pattern recognition” abilities that we have developed over the years to identify winners and losers.

    The more it goes the more I am amused by how unpredictable success is and by how truly chaotic conditions under which startups grow can be.

    So, if we live in a universe of chaos, fractals are our unit of measure. They are particularly relevant because they appear simple but the closer you get the more they reveal their complexity.

    Good of you to remind all of us of the complexity of the systems we operate in and the need to think non-linearly (and no too normatively) about the problems that we face.

  29. Well, I meant a pretty simplified version of the scientific method – with the emphasis on “don’t forget the theory formation” – and didn’t mean to suggest that startup investing could ever be made as scientific as, say, physics.

  30. I think that’s one of the parts of the process that works. E.g. if you can’t get a warm intro to an investor you probably can’t do a lot of other difficult things that it takes to build a company.

  31. I meant more “theory formation” as opposed to just pattern recognition. I think the best investors do lots of theory formation. At least they talk as if they do and it sounds pretty convincing to me.

  32. Troll_VC is off in the trees.  We need a strong emotional bond to an entrerpreneur … than we try to rationalize ourselves back hard.  That’s called discipline.  Without the strong emotional link we won’t be a good partner for 5-10 years.

  33. Aaron Goodman says:

    We are actually doing even worse than the tank-finding algorithm you described. In the tank finding algorithm you have a positive and negative training set, with a major confounding variable (color of the sky).

    In the startup world, we only are looking at the successes and not the failures. You write “The founders have many similarities … many were male, young, unmarried, computer programmers, dropouts of elite universities, etc ” The problem is we are only looking at the similarities of successful startups. It could very well be that failed startups of the last decade are also founded male, young, unmarried, computer programmers, and that these factors are not predictive of success in any way. 

    In order to make conclusions about what drives success, you need to look  not only at things in common of successful companies, but what makes the successful companies different from the failed companies. 

  34. The classic causation/causality fallacy. Because these factors were present in successful companies doesn’t mean these factors caused their success. 

    The factors that did cause companies to be successful- offering a product that solves problems or offers incremental benefits, and effectively marketing it- can be created by founders not having any of those characteristics.

    Of course, looking back to find answers is always easier than asking simple questions about the future like “Is there a market for this product?” You’d think that professional investors would actually look at the business plan rather than who is presenting it.

  35. Your 2nd paragraph should probably read your 1st paragraph more closely.  Because the 2nd makes what appears to be the same mistake you decry in the 1st (pending evidence, of course).

  36. The real world is always more complicated than it seems. It took humans thousands of years to develop the most rudimentary of recognition techniques. It’s no wonder we can’t make a computer do it with just a couple of years.

    To suggest that entrepreneurs can only be successful if they fit a specific easily identifiable superficial model is ridiculous. 

    I would LOVE to find out more about this tank recognition story. Off to research…

  37. If people use or buy a product, that is directly responsible for its success. I would say that is pretty definitive evidence that making a product people want and persuading them to use it is the driver of business success.

  38. Which is fine, but not what you said the first time around.

    What you said was, “…offering a product that solves problems or offers incremental benefits, and effectively marketing it…”

    It’s entirely possible for people to buy products that solve no problems and offer no benefits.  It’s possible for them to do this without heeding marketing at all.

    Or, as someone once put it, “The classic causation/causality fallacy. Because these factors were present in (some) successful companies doesn’t mean these factors caused their success.”

  39. ” It’s entirely possible for people to buy products that solve no problems and offer no benefits. ”

    No, it’s not. And it’s not possible to use a product you haven;t heard about, either.

  40. Pattern recognition implicitly assumes steady state, i.e. repeatable experiments and as you showed plays tricks when “sky changes”. Both Dropbox and Pinterest had a hard time raising capital for that reason – both were addressing markets that most investors thought played out or lacking potential. Therefore it seems that even for the most experienced investors it is hardly trivial to predict “…and suddenly it works” moment. 

  41. StartupGazette says:

    Chris, if it were that easy somebody would have made a website to automate the finding of these super entrepreneur individuals.
    There is inherently no value in any idea or mechanism. Value is a perceived quality, and those who succeed can make people believe something is valuable to them. Bill Gates did it, Steve Jobs did it, ect….
    In the end nobody NEEDS your product, no matter what it is.
    You have to create enough peer pressure to get them to buy it.
    It’s true that some people are better salespeople than others, but in the end that’s what matters and you don’t need an algorithm to determine who is good at it.
    Chris – startupgazette.com

  42. StartupGazette says:

    I have talked to a few VCs including one notable one in Europe. One thing is common, most VCs don’t mind losing money, or should I say the fund contributors money on somebody they personally like.
    After years of studies I find that the algorithm most used by VCs boils down to this:
    A. Do I like this person?
    B. Do they have a plausible and semi-defensible business plan?
    C. Will this person back me up and not embarrass me?
    (Is this person willing to say anything in defense of me publicly if I give them the cash)
    Another factor that can come into play is ethnic similarity.
    The more you are personally like the VC you are trying to get money from the more you have climbed up the bubble sort.

    Lots of people say lots of things to sound interesting on blogs but not a lot of people are about to tell it like it really is. (people don’t want to know how the sausage gets made)

  43. WinkieBoy says:

    Chris, you are talking about a type of investor that only invests in a group of entrepreneurs (tech consumer hype + blog / media world) that represent less 1% of all the tech entrepreneurs in US. There is no pattern recognition over <1%.

  44. * “Hey, here’s this thing I’ve never heard of before, and that SOB I hate is bidding for it in the auction.  I don’t think I need it at all, but it’ll cheese him off…”  One could argue that the benefit is then the schadenfruede the purchase gives an opportunity for, but that’s hardly the product making that offer, is it?

    Variations on this theme are numerous: Conspicuous consumption in general, trying to show up someone specific in a social light, “fashion” as a category, etc.

    * To what degree was The Catcher In the Rye “marketed”? Think back on the all-maroon cover of the paperback.  How likely do you think it is that 100% of people bought it solely on word-of-mouth?

    * Neophilia in general.  I can recall a wedding reception where the choices were salmon, chicken, and kangaroo.  I warned the bride that, given the nature of our friends, the kangaroo was almost certain to be the runaway choice. (“Hey!  I’ve never had that!”)  Sure enough, it was.  To what degree do you think the marketing efforts of the Kangaroo Meat Ranching Assoc. of Australia (or whatever their trade group may be called) were responsible for that?

    Honestly… The only thing I can infer from such a closed-minded set of premises that you’re advancing is that you don’t have much real-world experience with the teeming diversity of human beings.

  45. Scott Berkun says:

    There are a few possible fallacies lurking in your interesting post. For example: 

    > I don’t know why these characteristics are 
    > common among past successful founders.

    They may also be common in failed founders.   

    And even if there are shared traits among successful founders, its hard to separate the meaningful traits from the coincidental. 

    Lastly, there is a great deal of luck in any venture. Part of what makes one startup work is who they are competing against, and how many, and how good, the competitors turn out to be. This has less to do with picking a particular startup, but picking a particular startup that has the best hand of cards dealt of factors you can’t control. 

  46. Nice article.  There are several variables obviously when posed with evaluating a prospective start-up.  The entrepreneur and his or her qualities and characteristics are certainly relevant, you’re right.  Sometimes pattern stereo-typing is useful and accurate, but it should not be written in stone as a Yay or Nay criteria.  There are always exceptions, unknowns and other factors. 

    Furthermore the above is notwithstanding market trends and conditions, competition analyses, etc.  

    Correctly informed objectivity with all relevant aspects is the key, and is easier said than done in many instances!  

  47. dkural says:

    The problem is, many other people can also manage to get warm intros,  who are socially adept / smooth talkers etc.,  with no capacity for either innovation or execution in the trenches. Let’s call this group “well adjusted”.  College kids with fresh ideas coming from non-white collar backgrounds / abroad  don’t have that sort of “warm intro” network…  VCs would do well to discover talent instead of sitting around talking with people & ideas they already know, which only ensures optimizing for founders who care more about social engineering than genetic engineering.

  48. Actually, it does.  If you can’t convince potential buyers that it is in their interest to buy your product, they won’t buy it. Same is true for services. 

    So this pattern is actually meant much broader than it has been understood here.  Whatever you are trying to achieve (raise money, sell a product, even getting an education), you need to have other people (VCs, costumers, admission officers) help you, and they will only help you if they think it is in their own interest (VC: make money; customer: own valuable product; admission officer: increase reputation of their school; etc. etc.). 

  49. Enjoyed this post, and from my limited experience, I agree.

    Also, I’m glad to hear that I don’t have to follow the exact Bill Gates/Mark Zuckerberg pattern. Otherwise I’d have to go back in time and drop out of college before graduating…

    On an unrelated note, I like how the spell-check suggestion for “Zuckerberg” is “Rubbernecker.”

  50. Drakep11 says:

    Chris,

    An interesting discussion.

    While I agree that hard-coded pattern-matching is ineffective (“the tank problem”), I believe this is a data problem and not conclusive that prediction will not produce extraordinary returns.

    Startup Genome and others with limited data sets have produced highly correlated findings already, even without an investment algorithm. Further, the data only gets larger, thanks to services like Angelist among others.

    This same debate prevailed in the late 80’s in public equity. Back then, it was a “don’t-have-enough-processors problem”‘ Now look where we are.

    Drake Pruitt
    Crown Group Advisors

  51. Well this thread went off course, but in any case the real reason you are out of sync is that “offering a product that … marketing it” is merely indicative of a successful company – in fact many failed companies have done exactly that, and many successful companies have not done exactly that.

    Jim Collins’ list of “great” companies includes some companies whose initial product design and marketing efforts were a failure (3M, for example), which is why he argues that the people involved are more important than the product and marketing.  He even does some kind of semi-scientific comparative study to back this up.

    So, the debate over what is “THE driver of business success” isn’t over with your comments, unfortunately.

  52. Well the actual flaw in his second paragraph back then was that he wasn’t considering that good product design and marketing is present in many companies that fail as well.  So it’s not the single cause of success or failure, just a factor.

  53. I note how you cleverly- or disingenuously- ignored the point about making products that people want and focused only on marketing. The two go hand in hand. Are there other factors that drive business success? Of course- luck, serendipity, chaos theory, social contagion, etc. But those are outside the control of the entrepreneur. Let’s not forget what this article was about. investors cannot select entrepreneurs for good luck, but they can select for great products with a market, and a good plan for getting the word out. These are the controllable factors behind success.

  54. JamesHRH says:

    Holy Toledo what a great post Chris.

    Any cursory analysis of ultra successful investors is that they white run hot and then they blow up.

    Any cursory analysis of very successful investors really warm forever.

    I think that reflect the pattern recognition (theme too?) only investors from the broader view investor.

  55. It is really sort of amazing, and a little scarey, to imagine investors using such pattern matching.

    On the flip side, some level of intuition and pattern matching must be good to avoid endless study and insurmountble burdens of proof being piled onto an entrepreneur – especially before any investment can take place.

  56. tedstockwell says:

    The pursuit of hypotheses to select startups today that will be winners in the future, seems fraught with survivorship bias, confirmation bias, lack of data, and inability to construct experiments.

    On the other hand, I can imagine a more durable set of hypotheses to prune out losers, and that those might generalize over time and across market conditions.

    Maintaining a scientific discipline protects nicely against bias and overgeneralization.  For example, if the data is lacking on female founders, then a hypothesis favoring male founders is unsupported, even though the pattern is conspicuous. 

  57. It seems like one of the benefits of being a high volume investor (YCombinator, 500startups, etc), is that you would be one of few people who had enough good data on a statistically meaningful number of startups to start doing meaningful predictive analysis. The data still won’t be public, but these firms may be the 1st people with a large enough private data set that it could be a game changer for the way they evaluate startups.

  58. Great post Chris.  I think sometimes you have to remove the labels – and realize that success comes in many different shapes and sizes.  From Sarah Blakely to Richard Branson to Jeff Bezos there’s more than one way to skin the entrepreneurial cat.  I think what is a more important measure is a person’s character.  That is the integrity and strength of values that a person holds.  As Buffet said – he make his business decisions based on 3 criteria: 1) trust 2) respect and 3) likeability.  If one is not their its a deal killer.

  59. The argument cuts both ways…
    An older guy who has succeeded in many startups and other business before could be more courageous in the bets he takes than than a younger entrepreneur with less experience with overwhelming pressure to get it right the first time at the first shot.

    Risk is a perception based on all of the information you have.
    Prejudice and biases are not information, they can hardly predict anything, in any case they get you the perfect chance to craft perfect self-defeating scenarios.

  60. I think pattern repetition alone will negate the opportunities for serendipitous discovery.  It’s only part of the equation.  You also need the conviction/gut feel piece of it.

    Interestingly, I also think this same analysis applies to entrepreneurs too:  http://blog.aweissman.com/2010/09/maximizing-serendipity.html 

  61. What we’re talking about here, of course, is the skill of dealing with complexity.  “Oversimplification is the scourge of the 21st century” I always say, because it’s a knee-jerk reaction to the expanding complexity of the world.  We have to both “go fast and slow” — using our amazing intuition, along with due diligence and analysis.  

    Looking through the comments, many don’t see they’re still caught in the “either/or” pattern, when it’s almost always some of both — another oversimplifying pattern.

    I’m 59 and starting a new big venture that tackles complexity, and I only work with those who understand and can deal with complexity, and leave the oversimplifiers behind.  

    The people I work with also understand that we’re human beings, not machines.  In addition, they must have the skill at looking into someone’s head, past what age their body is, and assess what age their mind is.  There’s a sweet spot somewhere between being a green kid and being an old fuddy-duddy which is independent of someone’s physical age.

    One thing we all know is that it’s easy to be a jerk to get things done, but it takes someone who can deal with complexity to manage things with finesse.  The list goes on and on.

    So for me, one of the first red flags is someone who oversimplifies.  But of course, it’s more complicated than that, and I know I can handle the complexity, and if I can’t, I know enough to involve someone who can fill in that gap in building an organization that can deal with the right things in the best ways.

    Also in the comments is the assumption that this is about a search for one single person who has all the right characteristics, missing the possibility that a successful startup is run by a tuned group of people, such as those on the bridge of a ship.

    Word!  You’ve been schooled!  Yeah, humor is essential as well.

    It’s complicated.   Those who love that get to talk to me further.

  62. Pattern recognition algorithms generally aim to provide a reasonable answer for all possible inputs and to do “fuzzy” matching of inputs. This is opposed to pattern matching algorithms, which look for exact matches in the input with pre-existing patterns. A common example of a pattern-matching algorithm is regular expression matching, which looks for patterns of a given sort in textual data and is included in the search capabilities of many text editors and word processors.

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