This post describes a fast follow startup and the implication for how that startup learns. Define Startup A startup is a market hypothesis looking for validation. It’s an organization in search of a business. If they’ve accepted funding, then it’s a group of people looking for a liquidity event. Define Follow Follow means imitation. It means that an entrepreneur or a herd entrepreneurs have been observed pursuing a particular product-solution-market fit, or a hypothesis, and some founder wants to join the herd. Define Fast Fast means that the organization is imitating fast enough to nip at the heals of the lead innovator. It is imitating fast enough to be contention of overtaking the leader, or close enough to experience a[…]

I was 28 and sleepless when I encountered a marketing version of the logistic function. It was beautiful. It’s one of those things you’re taught about in one context, and when you’re shown it from another angle, it expands your mind. It was like discovering Pi for the first time. I could use it to check the assumptions of a market penetration forecast, and substitute my own estimates for others. I felt empowered and delirious from being able to produce a solid forecast. It became a tool as useful as btau or the crosstab. There’s a part of that math, a variable called saturation, that worried me from the outset. Saturation is the maximum percentage of adoption that a market[…]

It was a treat to see these three – Yoshua Bengio, Yann Lecun, and Geoffrey Hinton – for an afternoon. Easily the best three consecutive hours I’ve ever seen at a conference. They remarked that Canada continues to invest in primary research. And this is a strength. Much of the exploratory work these three executed in the 80’s, 90’s and naughties was foundational to industrial applications which came after. Much of reinforcement and deep learning has moved on into industrial application. For the three grandfathers of deep learning, all of these algorithms and methods move into the realm of solved problems. For those of us in industry, there remains a lot of work to realize the benefits of deep learning.[…]

Hinton is quoted as saying, with respect to back propagation, “I don’t think it’s how the brain works”. You can read the full article here. Back Propagation To oversimplify, in Back Propagation, the influence of each neuron is rewarded based on how well it predicts something. Accurate predictions are rewarded with more influence. Bad predictions are punished with less. This is how the machine learns. And there’s a lot of optimism about Back Propagation. It’s really useful and generates fairly predictable machines. As data scientists, we like this. And as data scientists, we should also like what Hinton is hinting at. Kuhn It’s much more likely than not that we’re approaching a local maxima on this thread of research. I’m[…]

The other I likened the process for taking apart a Job To Be Done to taking a part a lobster. There’s a very effective way to decompose any problem with enough energy. And then I watched The Founder on Netflix and admired the McDonald brothers using a classic technique in management science to refine a system on a tennis court. And I loved it. They really refined hamburger and frenched fry delivery. And then this morning I read that Andrew Ng in working on a new coursera course for AI. And I’m thankful for his initiative and optimism. Out of those three threads, this one post. The Assembly Line The assembly line was an American invention for Americans. It could[…]

What if Total Addressable Market can’t be estimated accurately? What then? What is Total Addressable Market (TAM)? Total Addressable Market, or TAM, is the number of buyers who are Willing To Pay (WTP) for a solution to a problem they have now, or are Willing To Pay (WTP) your firm instead of the firm they’re currently paying to solve a problem. Why is TAM important? TAM determines the life and death of a firm. The leading cause of startup failure, and perhaps all business failure in general, is the failure to penetrate and/or retain TAM (Including bureaucratic capture and rent-seeking). In this context, I’m concerned about the introduction of a new product into the market in an effort to generate both[…]

Bart Gajderowicz delivered a great talk at Machine Intelligence Toronto about how people go through stages in accomplishing a goal [1]. The talk was about homelessness and AI approaches to public policy. I instantly saw a connection to all sorts of tensions that people endure when they set out on a goal. To distill the concept, let’s start off with the idea that people have goals, people have emotions, and that time moves forward. As people make progress towards their goals, their emotions change over time. They start off in a good mood, in a state of uninformed optimism. Then, as negative information overwhelms their ignorance, they enter into a state of informed pessimism. So much negative information builds up[…]

“A study at Ball State University’s Center for Business and Economic Research last year found that trade accounted for just 13 percent of America’s lost factory jobs. The vast majority of the lost jobs — 88 percent — were taken by robots and other homegrown factors that reduce factories’ need for human labor.” – AP Canada’s labour force is around 19.6 million people, of which 18.2 million people are employed. Together, they worked something like 2.4 billion hours that month. In December 2016, something like 1.7 million Canadians worked about 240 million hours in manufacturing.  Roughly. Because of seasonal adjustments and different data at different times. And error. In terms of our working lives in Canada, collectively, manufacturing is about 10% of[…]

A great mind in public policy told me, just this last September, that people are really bad at judging the rate of technological change and when it’ll affect them. It’s like standing on a railway. You can see the train out there. Some people assume that the train is going to hit them very soon. They get off the tracks. Then, when the train is getting very close, others misjudge the speed and assume that it’s still a far way. And then they get hit. It’s a great analogy because it combines prediction with decision. The rate of technological change is actually quite difficult to predict. If it was easy there’d be a lot more successful startups. One Heuristic Start[…]

You’re going to hear a lot more about Artificial Intelligence (AI) more generally, and Machine Intelligence more specifically. Valuation is the core causal factor. Here’s why: We’ve gotten pretty good at training a machine on niche problems. They can be trained to a point to replace a median-skilled/low-motivated human in many industries. Sometimes they can make predictions that agree with a human’s judgement 85 to 90% of the time, and sometimes, it’s the human that’s causing the bulk of the error to disagree with the machine. We’re confident that we can train a machine to learn a very specific domain. And these days we’re in the midst of that great automation revolution. Most of the organization that build those machines can[…]