Nobody refers to Twitter as a "micro-blogging" platform any more but I think it's under-appreciated how much Twitter today fills the same niche that early blogging did.
A lot of early blog posts block-quoted a paragraph of text and then offered 1-3 paragraphs of analysis. Now we screenshot a paragraph from an article and offer 1-3 tweets of analysis.
Early bloggers spent a lot of time responding to other bloggers. Bloggers today (especially professionals) don't do that much because they're trying to maximize the readership of each post. Instead, we do short, blog-style responses here on Twitter.
I think this is a big reason that classic blogging isn't going to come back. The aspect of blogging that early bloggers remember most fondly is still happening, it's just happening on this micro-blogging platform, not on traditional blogs.
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Nvidia has an amazing new technology that essentially uses deepfake AI technology to reduce the bandwidth needs of video calling by 10x. Full explanation of how it works here: arstechnica.com/gadgets/2020/1…
The software sends a single frame of video. Then for subsequent frames it just sends data on the positions of the subject's eyes, nose, mouth, etc—much less data than a whole frame. The receiving computer then uses a neural network to re-create the subject's face.
Our comments have a lot of hand-wringing about how this "doesn't show reality," but I think this is based on a philosophically untenable conception of reality. A conventional video isn't "reality" it's a pixel-by-pixel approximation of reality. Even more so with compression.
People seem to think this is a compelling argument against antitrust enforcement but it's really not. Anyone familiar with economic history knows that new high-tech industries tend to have a lot of competitors in their early years before settling down.
There were dozens of oil companies in the 1860s, dozens of car companies in the 1900s, lots of small-scale experimentation with radio in the 1920s, etc. Then Standard Oil, Ford/GM/Chrsler, and NBC/ABC/CBS emerged and became dominant for decades.
It's relatively easy for new companies to emerge when the industry is still young and growing. New customers who don't yet have established brand loyalties. Untapped innovations for a new company to discover and exploit. It gets harder as the industry matures.
I made a scatterplot comparing the change in coronavirus cases over the last two weeks to cooling degree days—a proxy for air conditioning use. (Thanks @jeremy_gibbs!) The hottest states have suffered the worst outbreaks.
I think this effect explains a lot of the partisan divergence in this chart. The hottest states are all Republican. Still, at any given temperature Democratic states seem to doing modestly better, on average, than Republican states.
Lest you think the recent increase in southern states is an artifact of testing: a scatterplot of the positive coronavirus test rate shows a similar relationship. The hottest states are seeing coronavirus infections outstrip testing capacity.
I made a chart. On average, blue states have seen steadily declining coronavirus infections since mid-April. On average, red states have... not.
People asked to see the same chart with NY, NJ, and CT broken out. If anything I'd say this makes the point more strongly: red and (non-NYC) blue states were on similar trajectories until about 3 weeks ago. Then blue states started falling while red states rose.
People are also asking for similar charts for hospitalizations and deaths. My source, covidtracking.com, doesn't have good enough data on hospitalizations to make a meaningful chart—a number of states aren't reporting data at all and others seem spotty.
It seems like the people who are eager to cut back postal service haven't thought very hard about the economics of network industries.
Take subways. MTA, the agency that runs the New York subway and bus networks, only recovers about 50 percent of its operating costs through fares. Normally, we'd consider a business that only captures half its operating costs as hugely value destroying and shut it down.
But the New York City economy, with its ~$1.3 trillion GDP, couldn't operate without its trains and buses. So obviously the value created by the system vastly exceeds the cash captured through fares. A paradox!
There's been a lot written about the social implications of deepfakes, but less about how they actually work. Here's a thread about that. Read my article here for the full details. arstechnica.com/science/2019/1…
The goal of a deepfake is to start with a video featuring one person's face, and replace it with a different person's face, while preserving the original face's position, expression, illumination, etc.
The core of most deepfake software today is an autoencoder. That's a neural network that's been trained to take in an image and output an identical image. Training an autoencoder is easy because you know exactly what the output should look like (same as the input).