Tag: deep learning

On Synthetic Media, Synthetic Audiences, And Facts

Posted on by Christopher Berry

Generative Pre-trained Transformers (GPTs) have captured the public’s imagination. There’s a lot of fear. The relevance of that fear, as always, depends on who you are. The technology first caused a surge of panic in December 2022 among some communications professionals. They fear a surge of supply, a massive increase in synthetic media, along with all of the misinformation that goes along with it. Because attention is inelastic, the price for content will collapse, and it’ll take their wages down with it. They aren’t the only ones likely to be affected [1]. It’s kind of curious that you aren’t reading too much publicly from developers and their experiences with the technology. GPT’s are a tool. A good GPT is capable[…]

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[…]

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[…]

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.[…]