Category: Machine Learning

The Generative Era

Posted on by Christopher Berry

I want to believe that the current generation of applications powered by Large Language Model (LLM) don’t represent the height of the state of the art for prediction machines and that no single firm will reach 80% market share and go onto dominate the generative era. I want to believe that the future is quite open, and that these early returns we’ve made in applied machine learning can compound. It’s because I want to believe so much that it’s worth questioning the assumptions. What might cause the future to become closed? Does OpenAI scan its API logs for good ideas? In 2023, a surge of founders developed skins for OpenAI’s ChatGPT. Some based their startup on enabling a user to[…]

Deep Learning: Evolution or Revolution?

Posted on by Christopher Berry

Is what is happening in analytics, in industry, an evolution or a revolution? What is Analytics is the science of data analysis. Those who practice analytics self-identify as analyst, digital analyst, marketing scientist, data engineer, researcher, among many others. Tukey (1962, The Future of Data Analysis, The Annals of Mathematical Statistics, (33), 1) called them all practitioners. The goal of the practitioner depends on their context. That context largely, but not always, depends on the state of knowledge, state of the culture, or sometimes, normatively, the state of maturity, of the group they belong to. Large organizations can have a large amount of difference within them. It’s not uncommon for an operations department to be extremely mature and for its[…]

Prioritizing Knowledge

Posted on by Christopher Berry

The inspiration for this post is John Cutler‘s excellent twitter thread on prioritization. It’s well worth the read. This post builds on that inspiration using Roger Martin’s concept of the The Knowledge Funnel. One big takeaway of John Cutler’s thread is when deciding the sequence of what to do in product management, consider the big picture and think of the impact of what you will do next on what you will know next. What I like about Roger Martin’s concept on knowledge funnels: consider the big picture and think of what you know about value. Product management and data science is all about managing the knowledge funnel. Your ability to manage this funnel is predictive your ability, and those you[…]

Hinton’s Biological Insight

Posted on by Christopher Berry

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[…]

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[…]

Response to After the end of the startup era

Posted on by Christopher Berry

Jon Evans wrote a piece for Techcrunch entitled: After the end of the startup era. In it, Evans writes: We live in a new world now, and it favors the big, not the small. The pendulum has already begun to swing back. Big businesses and executives, rather than startups and entrepreneurs, will own the next decade; today’s graduates are much more likely to work for Mark Zuckerberg than follow in his footsteps. And, Because we’ve all lived through back-to-back massive worldwide hardware revolutions — the growth of the Internet, and the adoption of smartphones — we erroneously assume another one is around the corner, and once again, a few kids in a garage can write a little software to take[…]

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.[…]

Hinton on Back Propagation: I don’t think it’s how the brain works

Posted on by Christopher Berry

Hinton is quoted as saying, with respect to back propagation, “I don’t think it’s how the brain works”. You can read the full article here. Back Propagation To oversimplify, in Back Propagation, the influence of each neuron is rewarded based on how well it predicts something. Accurate predictions are rewarded with more influence. Bad predictions are punished with less. This is how the machine learns. And there’s a lot of optimism about Back Propagation. It’s really useful and generates fairly predictable machines. As data scientists, we like this. And as data scientists, we should also like what Hinton is hinting at. Kuhn It’s much more likely than not that we’re approaching a local maxima on this thread of research. I’m[…]

The Assembly Line And The Artificial Intelligence Line

Posted on by Christopher Berry

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[…]

The Marketing Science of Data Science of Marketing Science

Posted on by Christopher Berry

Earlier in the month, I dined under the space shuttle Endeavour with some of the best minds in marketing science. One mind remarked: “That’s why I bring a glossary with me, oh, you want to do supervised learning? Oh you mean regression? Oh, okay, now we can talk… We’ve been talking to managers about these methods for decades, but it’s just suddenly sexy because it’s all machine learning and deep learning and reinforcement learning.” A lot of the math that underlies much of machine intelligence and artificial intelligence is indeed remarketed marketing science. And, hipsterism aside, the annoyance is understandable. Marketing science started out a bit of a revolt against the Mad Men. Some of the early stories feature post-war[…]