Category: Decision Neuroscience

Factoring Familiarity Bias

Posted on by Christopher Berry

It took a lot of effort reconcile two forms of order: letters and numbers. Numbers had identities because they had names. Just as I was picking up the sound of names fourteen, sixteen, seventeen, eighteen, nineteen, and the mysteries of numbers that began with twenty, I became quite frustrated with eleven and twelve. Here were two sets of two symbols with distinct names that didn’t fit. They broke the pattern, the beautiful symmetry, between place and place value. And how I ever despised thirteen as a concept because there was not only was there no firteen nor seconteen. Where was thirteen anyway? It wasn’t on the clock. It wasn’t on any packaging. Good things never came in packages of thirteen.[…]

Are surveillance, learning, and persuasion different jobs?

Posted on by Christopher Berry

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[…]

Engineering Quality, Engineering Trust

Posted on by Christopher Berry

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. The launch in that episode was the ill fated “Dunder Mifflin Infinity”, and while the reference in the passage is to the pizza that Michael Scott had ordered, it may as well been referring to the website. For many reasons, people tend to build all you can eat hot circles of garbage, instead of a medium amount of pretty good pizza. Minimum Viable Product and[…]

Unsupervised Learning at the Rework Deep Learning Summit

Posted on by Christopher Berry

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.[…]

Goal Formation

Posted on by Christopher Berry

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[…]

Still in the Trough: Holiday VR Figures

Posted on by Christopher Berry

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[…]