https://t.co/ss6XwqrysZ
2/
In 2003's Pattern Recognition, @greatdismal discusses the role of "apophenia" - finding patterns where none exist - in paranoid thinking. We are a pattern-matching animal, prone to seeing faces in clouds and hearing speech in static.
https://t.co/lKHfKbvzLN
1/
https://t.co/ss6XwqrysZ
2/
3/
https://t.co/Pr8Y6UtEZD
4/
5/
6/
7/
8/
9/
More from Cory Doctorow #BLM
I've just read one of the most lucid, wide-ranging, cross-disciplinary critiques of cryptocurrency and blockchain I've yet to encounter. 1/
It comes from David "DSHR" Rosenthal, a distinguished technologist whose past achievements including helping to develop X11 and the core technologies for Nvidia.
https://t.co/tkAMShno4k 2/
Rosenthal's critique is a transcript of a lecture he gave to Stanford's EE380 class, adapted from a December 2021 talk for an investor conference. 3/
It is a bang-up-to-date synthesis of many of the critical writings on the subject, glued together with Rosenthal's own deep technical expertise. He calls it "Can We Mitigate Cryptocurrencies' Externalities?"
The presence of "externalities" in Rosenthal's title is key. 4/
Rosenthal identifies blockchainism's core ideology as emerging from "the libertarian culture of Silicon Valley and the cypherpunks," and states that "libertarianism's attraction is based on ignoring externalities."
This is an important critique of libertarianism. 5/
It comes from David "DSHR" Rosenthal, a distinguished technologist whose past achievements including helping to develop X11 and the core technologies for Nvidia.
https://t.co/tkAMShno4k 2/
Rosenthal's critique is a transcript of a lecture he gave to Stanford's EE380 class, adapted from a December 2021 talk for an investor conference. 3/
It is a bang-up-to-date synthesis of many of the critical writings on the subject, glued together with Rosenthal's own deep technical expertise. He calls it "Can We Mitigate Cryptocurrencies' Externalities?"
The presence of "externalities" in Rosenthal's title is key. 4/
Rosenthal identifies blockchainism's core ideology as emerging from "the libertarian culture of Silicon Valley and the cypherpunks," and states that "libertarianism's attraction is based on ignoring externalities."
This is an important critique of libertarianism. 5/
More from Tech
On Wednesday, The New York Times published a blockbuster report on the failures of Facebook’s management team during the past three years. It's.... not flattering, to say the least. Here are six follow-up questions that merit more investigation. 1/
1) During the past year, most of the anger at Facebook has been directed at Mark Zuckerberg. The question now is whether Sheryl Sandberg, the executive charged with solving Facebook’s hardest problems, has caused a few too many of her own. 2/ https://t.co/DTsc3g0hQf
2) One of the juiciest sentences in @nytimes’ piece involves a research group called Definers Public Affairs, which Facebook hired to look into the funding of the company’s opposition. What other tech company was paying Definers to smear Apple? 3/ https://t.co/DTsc3g0hQf
3) The leadership of the Democratic Party has, generally, supported Facebook over the years. But as public opinion turns against the company, prominent Democrats have started to turn, too. What will that relationship look like now? 4/
4) According to the @nytimes, Facebook worked to paint its critics as anti-Semitic, while simultaneously working to spread the idea that George Soros was supporting its critics—a classic tactic of anti-Semitic conspiracy theorists. What exactly were they trying to do there? 5/
1) During the past year, most of the anger at Facebook has been directed at Mark Zuckerberg. The question now is whether Sheryl Sandberg, the executive charged with solving Facebook’s hardest problems, has caused a few too many of her own. 2/ https://t.co/DTsc3g0hQf
2) One of the juiciest sentences in @nytimes’ piece involves a research group called Definers Public Affairs, which Facebook hired to look into the funding of the company’s opposition. What other tech company was paying Definers to smear Apple? 3/ https://t.co/DTsc3g0hQf
3) The leadership of the Democratic Party has, generally, supported Facebook over the years. But as public opinion turns against the company, prominent Democrats have started to turn, too. What will that relationship look like now? 4/
4) According to the @nytimes, Facebook worked to paint its critics as anti-Semitic, while simultaneously working to spread the idea that George Soros was supporting its critics—a classic tactic of anti-Semitic conspiracy theorists. What exactly were they trying to do there? 5/
THREAD: How is it possible to train a well-performing, advanced Computer Vision model 𝗼𝗻 𝘁𝗵𝗲 𝗖𝗣𝗨? 🤔
At the heart of this lies the most important technique in modern deep learning - transfer learning.
Let's analyze how it
2/ For starters, let's look at what a neural network (NN for short) does.
An NN is like a stack of pancakes, with computation flowing up when we make predictions.
How does it all work?
3/ We show an image to our model.
An image is a collection of pixels. Each pixel is just a bunch of numbers describing its color.
Here is what it might look like for a black and white image
4/ The picture goes into the layer at the bottom.
Each layer performs computation on the image, transforming it and passing it upwards.
5/ By the time the image reaches the uppermost layer, it has been transformed to the point that it now consists of two numbers only.
The outputs of a layer are called activations, and the outputs of the last layer have a special meaning... they are the predictions!
At the heart of this lies the most important technique in modern deep learning - transfer learning.
Let's analyze how it
THREAD: Can you start learning cutting-edge deep learning without specialized hardware? \U0001f916
— Radek Osmulski (@radekosmulski) February 11, 2021
In this thread, we will train an advanced Computer Vision model on a challenging dataset. \U0001f415\U0001f408 Training completes in 25 minutes on my 3yrs old Ryzen 5 CPU.
Let me show you how...
2/ For starters, let's look at what a neural network (NN for short) does.
An NN is like a stack of pancakes, with computation flowing up when we make predictions.
How does it all work?
3/ We show an image to our model.
An image is a collection of pixels. Each pixel is just a bunch of numbers describing its color.
Here is what it might look like for a black and white image
4/ The picture goes into the layer at the bottom.
Each layer performs computation on the image, transforming it and passing it upwards.
5/ By the time the image reaches the uppermost layer, it has been transformed to the point that it now consists of two numbers only.
The outputs of a layer are called activations, and the outputs of the last layer have a special meaning... they are the predictions!
You May Also Like
The entire discussion around Facebook’s disclosures of what happened in 2016 is very frustrating. No exec stopped any investigations, but there were a lot of heated discussions about what to publish and when.
In the spring and summer of 2016, as reported by the Times, activity we traced to GRU was reported to the FBI. This was the standard model of interaction companies used for nation-state attacks against likely US targeted.
In the Spring of 2017, after a deep dive into the Fake News phenomena, the security team wanted to publish an update that covered what we had learned. At this point, we didn’t have any advertising content or the big IRA cluster, but we did know about the GRU model.
This report when through dozens of edits as different equities were represented. I did not have any meetings with Sheryl on the paper, but I can’t speak to whether she was in the loop with my higher-ups.
In the end, the difficult question of attribution was settled by us pointing to the DNI report instead of saying Russia or GRU directly. In my pre-briefs with members of Congress, I made it clear that we believed this action was GRU.
The story doesn\u2019t say you were told not to... it says you did so without approval and they tried to obfuscate what you found. Is that true?
— Sarah Frier (@sarahfrier) November 15, 2018
In the spring and summer of 2016, as reported by the Times, activity we traced to GRU was reported to the FBI. This was the standard model of interaction companies used for nation-state attacks against likely US targeted.
In the Spring of 2017, after a deep dive into the Fake News phenomena, the security team wanted to publish an update that covered what we had learned. At this point, we didn’t have any advertising content or the big IRA cluster, but we did know about the GRU model.
This report when through dozens of edits as different equities were represented. I did not have any meetings with Sheryl on the paper, but I can’t speak to whether she was in the loop with my higher-ups.
In the end, the difficult question of attribution was settled by us pointing to the DNI report instead of saying Russia or GRU directly. In my pre-briefs with members of Congress, I made it clear that we believed this action was GRU.
