“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[…]
Category: Machine Learning
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[…]
Into the trough of disillusionment with the hyped technologies! The canary in the coal mine for me, with respect to BitCoin, is this post. Look, nobody has enjoyed more popcorn around BitCoin than I have. From Coinye to Dogecoin, crypto-currencies have delivered the lulz. Do I believe there’s a slope of enlightenment for crypto-currency? Absolutely. Do I believe that’s imminent? Nope. Banks are apex ruminants. The lessons from BitCoin have to be fully digested before something really good comes out of it. Machine learning. Huge potential and the best is yet to come. The first wave around machine learning gave us Netflix and Amazon. And then the bloom came off the rose a bit. And now there’s deep learning and we’re[…]
Let’s start with a story. Daan did a traditional fast follow. He calls it Netherflix. His story was: “It’s like Netflix…for The Netherlands!”. At first, he buys rights on the cheap, pays for digital subtitling, and has a successful kickoff. He gets through to 10% household penetration, or roughly 700,000 subscribers, with an annualized gross revenue of about 60 million Euros. The strength of the Euro lets him raid the Anglosphere and he can stock 10,000 hours of content reliably [1]. He gets through the struggle of getting his stack to deliver content and minimize churn. He’s able to host and deliver 10,000 hours reliably, in spite of supporting video players across 11 different front end platforms, and the costs associated with hosting,[…]
A score serves as an ultimate abstraction or summary. That’s especially true in sport. “Who won?” “The Blue Jays. 11 to 5.” The Blue Jays won because they moved men more often across one specific plate more often than the other team. This is all very American. A brief period of action. Collect statistics about that brief period. ???. Profit. And it’s easy. Baseball is nice for the 1 to 1 correspondence of points to a single event. American football and basketball are spicier. Cricket, with all due respect to my antipodean friends, is ridiculous. There’s so much more to the performance of The Blue Jays or the Australian National Cricket Team. But the score is the ultimate summary. There’s[…]
There are varying concerns about what constitutes a causal model, the degree to which data is biased, certainty that the model is predictive about the future, and, that the model itself is a truthful depiction of nature. Over the course of the past two weeks I’ve talked with many people about their perspectives – data scientist, developers, technologies, product managers, brand managers, statisticians, consultants, professors, executive producers, and founders. We’ve talked about everything from why analysts and their customers won’t accept narrow models, why it’s far easier to summarize data than it is to describe the relationships in it, and the intractable differences between what is performance reporting and what constitutes an insight. The verdict is not in. There are varying beliefs[…]