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.[…]

Some people want just one number. Some people want all the numbers. For best results, seek balance. One Number It is very possible to summarize the performance of a business or an organization in a single number. There are two main ways to do so. One is selection. One is indexing. In selection, you pick the most important metric, and you focus on it. It requires discipline and comes at the cost of myopia. In indexing, you pick the most salient metrics and you combine them into a single number. It requires no discipline and comes at the cost of boiling the ocean to the point that all the rocks bleed their salts into the atmosphere. When it comes to[…]

An orthodox Software as a Service (SaaS) business is, in part, math that an organization tries its best to manage. Think about all the math that goes into the construction of a typical SaaS firm. At the core there’s some customer with a job: a goal against which the customer wants to make progress. They can have a mathematical representation in a database somewhere. A bunch of technologists write some code, which is all math, and a bunch of creatives take a few photographs, which expresses itself a mathematical representation, and some data is Created Read Updated and Destroyed in a database somewhere, which is all just more math. And it’s all abstracted by yet more math at the processor[…]

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[…]

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[…]

Ben Thompson calls culture the accumulation of decisions. Assume that it’s true. How do decisions at a tech startup come into being in the first place? A startup can be instantiated with the business plan. And if you take a Beinhocker (2006, The Origin of Wealth) approach to it, you may believe that there’s a Library of Smith which contains every single business plan that’s possible. There are trillions upon trillions of potential business plans. And management is pretty much reduced to a machine that is able to execute the plan to generate wealth. Everything that has potential is possible at the beginning and assume competent management. (Image related – a bit esoteric*). In the context of a startup, a[…]

Previously, I argued that you should look at the Q4-2016 VR sales figures closely and then make decisions about whether to jump in. Some figures are in. SuperData Research, a technology research firm, estimated that Oculus had sold 360,000 headsets and HTC 450,000 since their products went on sale in March and June, respectively. Both of those headsets require high-end PCs with powerful processors. The firm estimated that Sony, which began selling a virtual reality headset in October, has sold about 750,000. — NYtimes Jan 8/2017 Those aren’t encouraging install bases. Obligatory Gartner Hype Cycle image: Consolidation is a long ways off. Facebook, has deep pockets and can sustain a long chasm crossing. The legal issues with Zenimax are a distraction. This is[…]

“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[…]