We hear a lot about Facebook as a platform for manipulation - using machine learning to bypass our critical faculties and trick us into believing things that are bad for us - but the real show is in Facebook's ability to target, not manipulate.

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When they write the history of this era, one of the strangest chapters will be devoted to Uber, a company that was never, ever going to be profitable, which existed solely to launder billions for the Saudi royals.

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From the start, Uber's "blitzscaling" strategy involved breaking local taxi laws (incurring potentially unlimited civil liability) while losing (lots of) money on every ride. They flushed billions and billions and billions of dollars down the drain.

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But they had billions to burn. Mohammed bin Salman, the murdering Crown Prince of the Saudi royal family, funded Softbank - a Japanese pump-and-dump investment scheme behind Wework and other grifts - with $80B as part of his "Vision 2030"

Vision 2030 is a scheme to diversify Saudi wealth away from hydrocarbons by attempting to establish monopolies that will allow the family to control entire sectors of the global economy.

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These schemes are longshots, and the fallback position is to unload failed monopolies - with staggering debt-overhangs - on investors who've been suckered with the promise that really big piles of shit surely have a pony buried underneath them somewhere.

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One of the arguments for permitting monopolies is that they are "efficient." That's the logic under which Universal was allowed to acquire Comcast and NBC - the "vertical integration" would make all three companies better and we'd all reap the benefit.

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It turns out that there are DISeconomies of scale, what Brandeis called "the curse of bigness" and really, the Universal-NBC-Comcast octopus is a poster child for that

Comcast has just informed its subscribers that they are at risk of losing access to "Bravo, CNBC, E!, Golf Channel, MSNBC, Olympic Channel, Oxygen, Syfy, Telemundo, Universal Kids, NBC Universo, USA Network and NBC Sports

That is, Comcast has warned its customers that Comcast might not license its channels to Comcast anymore.

This is a "carriage dispute" - a dispute over how much the cable operator will pay the broadcaster. It's a common dispute to have.

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But in this case, Comcast's dispute is over the accounting fiction of how much one division of a vertical monopolist will nominally charge another to access its products.

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The remix culture of the early 2000s left an indelible impression on me, an enduring delight in the power of whimsy, juxtaposition, virtuosity and ingenuity - and the ability of strangers all over the world to collaborate without any explicit coordination.

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The recent Bernie-mittens meme was a sterling example of this, tickling me right to my core, and I just happened on an especially delightful apex example of the form: @ToastedShoes's video of Sanders incorporated into eight AAA

Toasted Shoes got @JoeMashups to create a 3D model of Sanders, then tapped a bunch of different mashup artists to turn the Sanders sprite into playable characters in the games, showing them off in a narrated video.

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I'm not much of a fan of video-game streamers' narration, and I confess that I found the video more entertaining without the narration, but to each their own - Shoes was clearly enjoying himself, as were the commenters on the video.

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They had a lot to be pleased by.

Sanders as a Sith Lord in Star Wars Battlefront 2:

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The "polarization is the problem" narrative to me is bad because it implies the status quo was a stable and acceptable set of trade-offs. It's the equivalent of a pollyanna "why can't we all get along" but the we in question is white supremacy and its targets.

like i understand that conflict mediation requires mutual concessions (i did my MA in conflict studies) and while it's important to meet people's deep needs across spectrums of difference, certain things are just not up for negotiation.

Also while peacebuilding work is good. It's not just "seeing each other as human" it's literally about transforming structural issues. (hence Galtung's distinction between positive and negative peace wherin negative peace is like a ceasefire and positive is a transformed society.

And I think that to whatever extent that kind of peacebuilding work is applicable, it's not between fascists and people who think fascists are bad. Fascists are not invited. They are only invited if they seize territories and can't be crushed. Then secret negotiations.

The US is always like "we don't negotiate with terrorists" but literally every president negotiates with who they consider to be terrorists. It's all just down low. But that's just because counter-insurgency failed.

Ever heard of Autoencoders?

The first time I saw a Neural Network with more output neurons than in the hidden layers, I couldn't figure how it would work?!

#DeepLearning #MachineLearning
Here's a little something about them: 🧵👇

Autoencoders are unsupervised neural networks whose architecture you can picture as two funnels connect from the narrow ends.

These networks are primary focus for compression tasks of data in Machine Learning.

We feed them the data so that they can learn the most important features, a smaller representation while keep the integrity of the data.

Later when someone needs, can just take that small representation and recreate the original, just like a zip file.📥

Being unsupervised, they require no labels.
Our inputs and outputs are same and a simple euclidean distance can be used as a loss function for measuring the reconstruction.

Of course, we wouldn't expect a perfect reconstruction.

We can think of an autoencoder having two components, encoder and decoder, represented by the below equations:

We are just trying to minimize the L here. All the backpropagation rules still hold.

Here's how I got started with my first machine learning project and you can too.
Let's take a look.

(This thread will take from zero to being a hero in machine learning, trust me)

(1 / 22)
🧵👇

Getting started with your first machine learning project might actually much easier than it seems, if I can do it, certainly anyone can.

I did not use:
- Any Math
- An expensive computer
- Complex programming concepts

(2 / 22)

Here's what I did use:

- A free GPU on Google Colab
- Python
- TensorFlow
- Numpy
- Pandas
- Kaggle
- Scikit-Learn
- Google
- StackOverFlow

(3 / 22)

This project was actually a Kaggle challenge based on the MNIST dataset which is a collection images of 70,000 hand written digits.

You can find the dataset here👇
🔗//kaggle.com/c/digit-recognizer

(4 / 22)

Before we go over the code of this project, it is highly reccomended that you complete this free course on YouTube👇

Machine Learning foundations course
🔗//youtu.be/_Z9TRANg4c0

Code👇
🔗//colab.research.google.com/github/PrasoonPratham/Kaggle/blob/main/MNIST.ipynb

(5 / 22)

1 There's a chasm between an NLP technology that works well in the research lab and something that works for applications that real people use. This was eye-opening when I started my career, and every time I talk to an NLP engineer at @textio, it continues to strike me even now.

2 Research conditions are theoretical and/or idealized. A huge problem for so-called NLP or AI startups with highly credentialed academic founders is that they bring limited knowledge of what it takes to build real products outside the lab.

3 A product is ultimately a thing that people pay for - not just cool technology or user experience. But I’m not even talking about knowledge gaps in go-to-market work. I'm talking purely technical gaps: how you go from science project to performant + delightful user experience.

4 Most commoditized NLP packages solve well-understood problems in standard ways that sacrifice either precision or performance. In a research lab, this is not usually a hard trade-off; in general, no one is using what you make, so performance is less important than precision.

5 In software, when you’re making something for real people to use, these tradeoffs are a big deal. Especially if you’re asking those people to pay for what you’ve made (can’t get away from that pesky GTM thinking). They expect quality, which includes precision AND performance.

I've applied for a total of 10 development jobs in my life:

- 7 lead to interviews
- Of those 7, 4 was a good fit for me
- Of those 4, 3 offered me the job
- In all 3 cases, I negotiated a better deal

🧵 on what worked for me and what YOU can do to get similar results 👇

1. Apply for the RIGHT jobs. Look for midsize to large companies, not necessarily tech related. These tend to get way fewer applications than traditional tech companies, so it's easier to get your foot in the door - and they also tend to be great environments for learning.

2. Spend time on your application. Make sure your resumé and written application is tailored to the exact position. Recruiters or hiring managers can smell a copy/pasted resumé from miles away. Use as many words from the job spec in your application as possible.

3. Have a portfolio. Show off your skills and some personality on your website. This is a nobrainer for front-end devs, but it's true for other areas too. It's a great way to stand out over other candidates by actually *showing* the value you can create and your experience.

4. Establish personal contact. Send a quick message or make a phone call before applying. Just introduce yourself to the person in charge of hiring and ask a simple question. It's the best way I find to make them remember your application when you submit it.