I interpret it a different way than that. I see application code and testing code as both a part of blue team. It's the code reviews and architectural critiques that are part of red team.
Personally, I've found GitHub's feature of AI PR reviewers exceptionally helpful. I think that's the type of red team LLM app Tao is describing here.
This is really cool! I've seen Jensen's inequality used many times over in my stats/ML classes, but the traffic example here gave me an "aha" moment about how it manifests.
I like the visualizations of the expected value against the individual probabilistic components as well, though I wish there were more non-uniform distributions visualized. Perhaps if we take the traffic example and tweak the distribution to be non-uniform, that might make for a cool interactive viz.
I'm not a huge fan of the color/pitch relationship they seem to be trying to establish.
What I do appreciate is the engineering design in these interactives. The circular metronome in the rhythm apps is very cool, might be good for generating musical ideas once I get past the semi-opaque UX.
It sounds like Google wants to get more edge compute in people's homes so they have a new vector to deploy AI products on, but they're still so far from actually deploying an innovative product that they can't announce anything to actually drive up hype.
Then, the rebranding is only because they've abandoned the original "minimal footprint" ethos of ChromeCast.
At a high level, Bayesian statistics and DL share the same objective of fitting parameters to models.
In particular, variational inference is a family of techniques that makes these kinds of problems computationally tractable. It shows up everywhere from variational autoencoders, to time-series state-space modeling, to reinforcement learning.
I noticed the landing page has a rotating PNG gallery in desktop but not on mobile. I'm sure you must've wanted to put it in the mobile page too, was it hard to UX or just too inconvenient to implement easily?
I don't know. I think the younger 20-somethings all have this same kind of dream, but the older people get, the more comfortable they seem hiding behind NDAs at FAANGs or staying in "stealth mode." They really don't owe anyone anything.
I mean, instead of using a fixed threshold at 500, if you use a live threshold determined by the recent average upvotes, then yeah I'd have no qualms with calling them outliers.
It's just that this method is susceptible to votecount inflation, a la Reddit from 2014 to 2024.
Right. But if you make the notation slightly more explicit, then the integral of L(data, params) over data is 1. This follows from the independence assumption.
So we ARE working with a probability function. Its output can be interpreted as probabilities. It's just that we're maximizing L = P(events | params) with respect to params.
Hi! Some background first: I'm putting together a blog right now using Hugo and D3. I'm a huge fan of D3's infinite flexibility, as seen in some famous scrollytellers [0-1], and I've spent some time experimenting with that format myself [2].
My question is: what does Observable Framework offer for data storytellers who want to blog? Is this meant to go up against Hugo/Jekyll in terms of full-fledged max-efficiency site generation? If not, are there plans to add integrations with other blogging frameworks?
Sea otters: https://www.youtube.com/live/abbR-Ttd-cA
Jellyfish: https://www.youtube.com/live/eQ_foBERmzA