The Ledger
Every statement that passed quotation and attribution checks. Mix any filter with any other: certainty 1/5, debate potential 5/5, or both at once.
why aren't all 7 resolved? a statement only gets an assessment when the public record can support or contradict it. opinions and what-ifs never can, and 0 checkable ones are still open, waiting for their date. predictions held up or didn't; assertions are supported or contradicted. on every card: ▮▮▮▮▮ certainty · ▮▮▮▮▮ debate potential. speakers are clickable
Erb: Existing CI/CD workflows do not fit machine learning pipelines
“Existing CI CD workflows, talking about how those don't really fit anymore. And then making the case that you know, these new pipelines are fundamentally different and require new tools, new workflows, and generally like new paradigms.”
Infrastructure, not algorithms, is the primary barrier to AI adoption
“I'm gonna make the case that the biggest barrier to adoption is an infrastructure and tooling problem, not necessarily an algorithmic problem.”
Erb: AI researchers and infrastructure teams view algorithmic primitives completely differently
“So if you're an AI or machine learning or deep learning developer or researcher, you think of your primitives as, you know, hidden layers and LSTMs and things like that. From an infrastructure perspective, it's actually much higher up”
CI/CD principles address the black-box interpretability problem in deep learning
“You care about it because it adds reliability, reproducibility, determinism. You know, your systems are no longer inscrutable, which is a very big criticism, especially of deep learning architectures, which is that they're black box or they're, you know, they …”
Machine learning systems must trigger pipelines based on data or model drift
“Code drift there, which arguably is the number one, is the only trigger for most web apps. You know, your code drift, someone adds something, you rebuild your model, which is your web application. But actually what you care about in these new systems is data d…”
Lack of standard model repositories forces companies to hack solutions using Dropbox
“There's no good model zoo or model repo, and, you know, almost every company that I've worked with has their own version of that, and it can range from a Dropbox folder to, you know, actually repurposing Travis to actually do that.”
Deep learning teams using standard code branches are light years ahead
“If you find, you know, companies that are doing deep learning that have, you know, staging development and master branches, that's already, like, light years ahead of, I would say, 99% of the companies that will be using this technology in five years.”