Geoffrey Hinton, the father of deep learning, said a few things at the ReWork Deep Learning Summit in Toronto last week. Hinton often looks to biology as a source for inspiration. I’ll share and expand in this post. Hinton started off with an analogy. A caterpillar is rally a leaf eating machine. It’s optimized to eat leaves. Then it turns itself into goo and becomes something else, a butterfly, to serve a different purpose. Similarly, the planet has minerals. Humans build an infrastructure to transform earth into paydirt. And then a different set of chemical reactions are applied to paydirt to yield gold, which has some purpose. This is much the same way that training data is converted into a set[…]

My contemporaneous notes from a particular INFORMS Marketing Science Conference six years ago feature the letters W, T, and F scrawled in the margins a few times. I learned of a deeper problem lurking in the way we were using the crosstab to identify segmentation. In this post, I’ll unpack a heap of jargon and lay the concern bare. To the twenty or so marketing scientists in the room at the time, I read concern on the faces of about a dozen. It was a atypical because typically that community doesn’t get concerned about too much. One leader remarked that most in industry were not even executing basic segmentation on their users, so it wasn’t a huge industrial concern, but[…]

Here are some notes from a Canadian on visiting Lisbon. We visited Lisbon Sept 20 to Sept 28, 2018. The flight I booked an Air Transat flight. I weighed the option against TAP and Air Canada, and I still chose Air Transat. A few things to report about the Thursday flight. It departs from a remote gate at Pearson’s Terminal 3, in a concourse for discount airlines. Plan extra time for the walk out as it’s around 90 meters to the tunnel, 230 meters through the tunnel, and another 90 meters to Gate 2. I was amused by it because I had planned plenty of time to grab water. We flew in an Airbus 332, with a 3-3-3 configuration. The[…]

Teams, in software engineering, form because of success. Without success, the firm wouldn’t be cursed with the problem of having so much talent to have to organize in some way. A founder can easily reduce the complexity in their human organization, and their lives, by simply not hiring any more than seven technologists to work with them on their mission. For some, this is viable. For others, this is not. Teams emerge in response to scale. They are either formed as by product of centralized hierarchical command structure, or they emerge as a product of network cohesion/polarization. To the extent that either formation is aligned with the vision, goal, mission, or purpose of the organizational chrome is a function of[…]

Imagine with me: what if novels were written like software. Sometimes it’s useful to approach absurdity and look inside. There might be treasure there. I’ll define software as an executable, a set of instructions, that are interpreted by a machine for some reason. As a data scientist, I think of software as a product, and I think, constantly, of turning data into product. I think of data as inertia and all the code around it as flexible. I worry a lot about the people that use the software (if anybody) and think of them as heterogenous segments. I think of a novel as an executable, a set of instructions, that are interpreted by a human brain for some reason. As[…]

Ikigai represents a pretty basic segmentation. So of course I love it. I love it so much I want to share. And you might love it too! The word comes from Japan. It means a reason for being. The segmentation recognizes four attributes of activities and jobs you could do – What You Love, What The World Needs, What You Be Paid For, and What You Are Good At. Combinations of two represent Mission, Vocation, Profession and Passion. Combinations of three segments represents Delight/Poorness, Excitement/Imposter, Comfortable/Empty, and Satisfaction/Uselessness. Doing something at the intersection of all four is called Ikigai. It’s a very elegant segmentation. It can also represents a surface. Assume a 2 dimensional plane representing all the activities and[…]

W1A is so much fun because the main character, Ian Fletcher, tries. And he fails. But he keeps on trying. And even though Ian isn’t aware of the character flaws that cause him to fail, he persists in trying. Ian Fletcher’s tragic character flaw, the source of so much of his pain and anguish throughout the series, is his optimism. That’s what makes it funny. I hope you’re finding this blog, and the twitter feed, funny. Because like Ian, I’m struggling. Like you, I’m composed of a couple thousand hours of meetings, deckage, talks, seminars, code, charts, stories, bullet points, facilitation, deliberation, analysis, email, papers, and pure rage. My stance as a scientist has informed the tools that I use,[…]

This is a dense post. Feldman and March, in 1981, wrote “Information in Organizations as Signal and Symbol”. And it makes good predictions about what a management scientist type would say about the purpose of information in an organization. Indeed, just last month, I hyped Carl Anderson’s 2015 original position yet again, in the framing of information as assisting learning. Feldman and March are cited by another piece that’s been weighing heavily since February. Alvesson and Spicer’s 2012 hit “A Stupidity-Based Theory of Organizations” explains why seemingly intelligent people pretend to be dumber than they are. Please don’t misinterpret this passage. It’s not the case that everybody is stupid. Sometimes people act dumber because they have to go-along-to-get-along. Are you[…]

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[…]

It’s easier to link to this text than it is to repeat the intuition every time. Those who learn fastest win One of the core reasons why, as I write this in mid-2018, Silicon Civilization has the world in their teeth is because they figured out that it wasn’t just about learning. It was about learning quickly. Look at it from their perspective. A startup is a hypothesis looking for validation. Those startups that are able to learn fastest have a greatest chance of pulling up before the runway runs out. Those that learned survived takeoff. Those that really thrived never stopped learning. They win because they got really good at learning. It isn’t purely about data, it’s about how[…]