The community around Stable Diffusion, the open source AI project for text-to-image generation, has been buzzing. From nonexistent a year ago to thousands of contributors and forks and spinoffs. There’s even a GUI macOS app.
Lexica is a project to index and make prompts and images from Stable Diffusion searchable. Playing around with it, it’s pretty impressive. So much incredible possibility here. This tech will make the volume of content on the internet literally infinite.
This interview was one of the best overviews and deep dives on the current state of AI / machine learning I’ve heard yet. Daniel was at Apple in the early work on machine learning in iOS, and Nat Friedman was CEO of GitHub during their development of the excellent Copilot product.
Nat on the previously-predicted tendency toward centralization in AI:
The centralization/decentralization thing is fascinating because I also bought the narrative that AI was going to be this rare case where this technology breakthrough was not going to diffuse through the industry and would be locked...
Martin Gurri is one of the best minds we have for the current moment. Make sure to subscribe to his essays on the Mercatus Center’s “The Bridge.”
The American people appear to be caught in the grip of a psychotic episode. Most of us are still sheltering in place, obsessed with the risk of viral infection, primly waiting for someone to give us permission to shake hands with our friends again. Meanwhile, online and on...
A discussion among physicians on how oncology is changing and will likely continue to evolve in the wake of the coronavirus. Testing, chemo, and other treatment steps currently considered to be standards of care will change, and things like telemedicine will change what options doctors have in working with patients.
I’ve got a set of scans and a follow up this week, so will see how Mayo Clinic has adapted their approach in response to this crisis.
We’ve been doing some thinking on our team about how to systematically address (and repay) technical debt. With the web of interconnected dependencies and micro packages that exists now through tools like npm and yarn, no single person can track all the versions and relationships between modules. This post proposes a “Dependency Drift” metric to quantify how far out of date a codebase is on the latest updates to its dependencies:
I don’t know what Lex Fridman is doing to recruit the guests he gets on his show (The Artificial Intelligence Podcast), but it’s one of the best technical podcasts out there.
This one is a good introduction to the work of legendary psychologist Daniel Kahneman (of Thinking, Fast and Slow fame).
This is a new notes app from Brett Terpstra (creator of nvALT) and Fletcher Penney (creator of MultiMarkdown). I used nvALT for years for note taking on my Mac. This new version looks like a slick reboot of that with some more power features. In private beta right now, but hopefully dropping soon.
Progress itself is understudied. By “progress,” we mean the combination of economic, technological, scientific, cultural, and organizational advancement that has transformed...
This is an interesting interview with Been Kim from Google Brain on developing systems for seeing how trained machines make decisions. One of the major challenges with neural network-based based deep learning systems is that the decision chain used by the AI is a black box to humans. It’s difficult (or impossible) for even the creators to figure out what factors influenced a decision, and how the AI “weighted” the inputs. What Kim is developing is a “translation” framework for giving operators better insight into the decision chain of AI:
I enjoyed this interview with robotics professor Rodney Brooks on EconTalk. The conversation around AI and automation in the popular conversation is so charged, it’s good to hear a perspective that brings some reason into the discussion. The collective conversation on the subject of AI, driverless vehicles, and other forms of automation leans toward “it’ll be here tomorrow” or “we’ll never have any automation.” I think there’s too much pessimism in the former view, and too little optimism in the latter.
Brooks (who has spent his entire career on robotics and intelligence, currently at MIT) brings some reason to the...
This week was Amazon’s annual re:Invent conference, where they release n + 10 new products for AWS (where n is the number of products launched at last year’s event). It’s mind-boggling how many new things they can ship each year.
SageMaker was launched last year as a platform for automating machine learning pipelines. One of the missing pieces was the ability to build training datasets with your own custom data. That’s the intent with Ground Truth. It supports building your dataset in S3 (like a group of images), creating a labeling task, and distributing it to a team to annotate...
The cascading effect of a world with no human drivers is my favorite “what if” to consider with the boom of electric, autonomous car development. Benedict Evans has a great analysis postulating several tangential effects:
However, it’s also useful, and perhaps more challenging, to think about the second and third order consequences of these two technology changes. Moving to electric means much more than replacing the gas tank with a battery, and moving to autonomy means much more than ending accidents. Quite what those consequences would be is much harder to...
Trying out a new thing here to document 3 links that caught my interest over the past week. Sometimes they might be related, sometimes not. It’ll be an experiment to journal the things I was reading at the time, for posterity.
Good piece from Ben Thompson comparing the current developmental stage of machine learning and AI with the formative years of Claude Shannon and Alan Turing’s initial discoveries of information theory. They figured out how to take mathematical logic concepts (Boolean logic) and merge them with physical circuits — the birth of...
Great post from Benedict Evans on the state of voice computing in 2017. On wider answer domains and creating the uncanny valley:
This tends to point to the conclusion that for most companies, for voice to work really well you need a narrow and predictable domain. You need to know what the user might ask and the user needs to know what they can ask.
This has been the annoyance with voice UIs. For me Siri was really the first commonplace voice interface I ever tried for day to day things. The dissonance between “you can say a...
