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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While many Amazon warehouse workers in Europe have unionized, its brutalized, underpaid, routinely maimed US workforce remains tragically unorganized, thanks to the US's weak labor laws that make forming a union far harder than at any time since the Gilded Age.

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But all that may be about to change: 6000 warehouse workers in #BHM1, the massive Amazon warehouse in Bessemer, AL, will vote next month on a push to unionize under the

Amazon, like other tech giants, is absolutely reliant on building and maintaining a competitive advantage by exploiting technology to drive wages ever downward while ducking responsibility for harming workers.

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Big Tech is pants-wettingly afraid of unionization, and that goes double for any solidarity between high-paid tech workers and misclassified/outsourced warehouse workers and other "unskilled" laborers.

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Amazon has gone so far as to hire the Pinkertons, renowned as some of history's most violent anti-worker terrorists, to bust unionization drives in its

It's been eight years since @aaronsw took his own life. Aaron had been charged with 13 felonies under the Computer Fraud and Abuse Act (#CFAA) for violating the terms of service on the @JSTOR database of scholarly articles.

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Prosecutors Stephen Heymann and Carmen Ortiz didn't dispute that Aaron was allowed to access the articles he retrieved. Rather, they said that the WAY he accessed them (using a script instead of clicking on links) was a terms-of-service violation and hence a crime.

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In other words: any business could conjure a felony out of thin air by making you click through an unreadable garbage-novella of legalese proscribing the use of a service they granted you access to. Violate any of those terms and you face a prison sentence.

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This isn't law as we know it, it's Felony Contempt of Business Model, and the most alarming thing was that this interpretation of the CFAA wasn't completely ridiculous, given how badly drafted that law is.

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Ronald Reagan signed CFAA into law. Fed prosecutors had been seeking broad authority to punish "hacking" and had drawn up an absurdly broad definition of cybercrime that would give them latitude to go after anyone they didn't like.

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There is no shortage of takes about what's going on with Gamestop (and other surging stocks), Robinhood and Reddit's r/wallstreetbets, many of them contradictory - at least on the face of them. But I think it's possible for most of these takes to be right. Here's how.

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First you need to understand the underlying mechanics of the story. Stock markets are fundamentally a way of making bets, including bets on the outcome of other peoples' bets, and bets on the outcomes of THOSE bets.

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All this complexity creates lots of exploitable opportunities. Some of these opportunities are considered legitimate and are given respectable names like "arbitrage." Others are considered illegitimate, and are called disreputable things like "stock manipulation."

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A hypothetical Martian observing all this through a telescope could not tell you which kinds of bets were honest and which were dishonest, because the difference isn't about any objective standard, but rather, about power.

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The strategies of powerful people are legit, while the strategies of their would-be dethroners are not legit. Sometimes, even outright frauds are OK if they're done by people with enough power.

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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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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)

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.

A tweetorial on optimal machine settings for B line image acquisition, with a focus on: focal position at pleura, harmonics off, and increased far gain.

Inspired/based on the recent article - https://t.co/vv2xBSZm6N
@cameron_baston @IsaacMatthiasM1

#POCUS #IMPOCUS

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Let's start with some polls
We will stick to low frequency probes here (i.e. curvilinear and phased array - linear has its own purpose for detailed pleural assessment)

Which preset do you prefer for B line assessment?

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What depth do you typically set?

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After selecting preset and selecting depth, do you ever adjust any other machine settings to optimize B line assessment?

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Importantly, there is no right answer to the above questions!

We won't discuss much about preset or probe type. But we will discuss machine settings to optimize B line quality, based on
1) Theory based on physical basis of B lines
2) In vitro study
3) Study in patients

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Here's everything you need to know about the math for machine learning.

(+ free resources )
🧵👇

Before diving into the math, I suggest first having solid programming skills.

For example👇

(2 / 18)

In Python, these are the concepts which you must know:

- Object oriented programming in Python : Classes, Objects, Methods
- List slicing
- String formatting
- Dictionaries & Tuples
- Basic terminal commands
- Exception handling

(3 / 18)

If you want to learn those concepts for python, these courses are freecodecamp could be of help to you.

🔗Basics:youtube∙com/watch?v=rfscVS0vtbw
🔗Intermediate :youtube∙com/watch?v=HGOBQPFzWKo

(4 / 18)

👉You need to have really strong fundamentals in programming, because machine learning involves a lot of it.

It is 100% compulsory.

(5 / 18)

How to learn Python for machine learning. 🐍

The beginner's guide.
(based on my real life experience of mentoring 300+ students)

🧵👇

In this thread we'll look at all the concepts in Python you need to know for machine learning along with all the free resources that you'll need.

All of this is based on my experience of successfully teaching 300+ students how to code using Python.

(2 / 19)

You can use many languages for machine learning, why Python?

Because of 2 reasons:
- Comparatively easier to learn than other languages
- Has the biggest and most mature community

This makes Python a no-brainer to learn for machine learning as a beginner.

(3 / 19)

These are the absolute basics which you must know about:

- Basic terminal commands
- Basic arithmetic (+,-,/,*)
- Accepting user input
- For & While loops
- Exception handling
- If-Else statements
- Functions, modules & Imports

(4 / 19)

Then comes the more tougher concepts which you must know about:

- Object oriented programming in Python:Classes, Objects, Methods
- PIP (Pypi)
- List slicing
- String formatting
- Dictionaries & Tuples
- Managing environments
- Dunder methods like __init__

(5 / 19)