Are surveillance, learning, and persuasion different jobs?

Posted on Posted in Artificial Intelligence, Business, Data Science, Decision Neuroscience, SaaS, Technology

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 […]

The Fast Follow

Posted on Posted in Business, Data Science, Machine Intelligence, Technology

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 […]

Who do you trust to manage your attention?

Posted on Posted in Data Science, Marketing Science, Social Analytics, Social Media Measurement, Strategic Analytics, Technology

Who do you trust to manage your attention? Because now that the news cycle has surfaced Cambridge Analytica issue¬†– that’s the real thesis question. Let me explain. How the Newsfeed manages your attention I really can’t understate just how powerful amplified engagement really is. When you overlay the like/share verbs on top of a network […]

Engineering Quality, Engineering Trust

Posted on Posted in Data Driven Culture, Data Science, Decision Neuroscience, Machine Learning, Technology

There’s a quote from The Office (US) [Season 6, episodes 5/6, “Launch Party”]: Michael: Okay, okay, what’s better? A medium amount of good pizza? Or all you can eat of pretty good pizza? All: Medium amount of good pizza. Kevin: Oh no, it’s bad. It’s real bad. It’s like eating a hot circle of garbage. […]

Unsupervised Learning at the Rework Deep Learning Summit

Posted on Posted in Artificial Intelligence, Data Science, Decision Neuroscience, Machine Intelligence, Machine Learning, Technology

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 […]

The Assembly Line And The Artificial Intelligence Line

Posted on Posted in Artificial Intelligence, Big Data Science, Data Driven Culture, Data Science, Machine Intelligence, Machine Learning, Marketing Science, Technology

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 […]

What if Total Addressable Market can’t be estimated accurately?

Posted on Posted in Data Science, Economics, Machine Intelligence, Marketing Science

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 […]

Goal Formation

Posted on Posted in Business, Data Driven Culture, Data Science, Decision Neuroscience, Design Thinking, Economics, Machine Intelligence, Technology

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 […]