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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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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When it's railroading time, you get railroads.

When the railroads turn into the personal satrapies of rail-barons, you get trustbusters.

A couple decades ago, it was online service time. We had the users, the telcoms systems, the computers, the modems, so we got platforms.

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We had that, but we lacked something important: effective antimonopoly enforcement. Lax merger laws allowed companies with access to capital markets to buy out or neutralize all their competitors, so we got monopolies.

Right on schedule, we're getting digital trustbusters.

2/

Now, some people aren't technically sophisticated, but they do understand a lot about competition law. That's how you get meat-and-taters antitrust proposals like @amyklobuchar's #CALERA, which address the structural problems with antitrust

Klobuchar's bill is hugely important. The reason we have monopolies is that we stopped enforcing anti-monopoly law 40 years ago. Monopoly isn't a tech problem, it's everywhere from sneakers to glass bottles to pro wrestling to candy to

Klobuchar's CALERA doesn't just seek to apply antitrust law to tech - it also explicitly restores the pre-Reagan basis for fighting monopolies: we fight monopolies because they concentrate power and corrupt our politics. All monopolies are guilty unless proven otherwise.

5/

It's been twenty years, to the day, since I published my first blog-post.

I'm a blogger.

Blogging - publicly breaking down the things that seem significant, then synthesizing them in longer pieces - is the defining activity of my days.

https://t.co/lksXhU6HKM

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Over the years, I've been lauded, threatened, sued (more than once). I've met many people who read my work and have made connections with many more whose work I wrote about. Combing through my old posts every morning is a journey through my intellectual development.

2/

It's been almost exactly a year I left Boing Boing, after 19 years. It wasn't planned, and it wasn't fun, but it was definitely time. I still own a chunk of the business and wish them well. But after 19 years, it was time for a change.

3/

A few weeks after I quit Boing Boing, I started a solo project. It's called Pluralistic: it's a blog that is published simultaneously on Twitter, Mastodon, Tumblr, a newsletter and the web. It's got no tracking or ads. Here's the very first

I don't often do "process posts" but this merits it. Here's how I built Pluralistic and here's how it works today, after nearly a year.

I get up at 5AM and make coffee. Then I sit down on the sofa and open a huge tab-group, and scroll through my RSS feeds using @newsblur.

5/

Ring - Amazon's surveillance doorbell division - has 4,000 "partnerships" with US police ("public safety") orgs. The company has lied about how these work for years, but the basic deal is that they give cops free stuff to buzz-market their products.

https://t.co/716iaXUXWR

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Ring tells its customers that they get to choose whether to share the footage from their street-facing cameras with cops, but that's a lie, too. If you say no, the cops still get to look through your

That's why cops debase themselves to serve as buzz-marketers for Ring - in exchange, they get an off-the-books, free-to-use, warrantless, city-scale, video surveillance grid.

3/

It's an investment that pays off. Back in July, @EFF documented how the @SFPD was able to commandeer 200 Ring cameras to produce surveillance data on #BLM

And in a new blockbuster report, EFF's @maassive and @mguariglia document how @LAPDHQ did the same thing, fraudulently using "unusual occurance" protocols to gain access to last summer's BLM

We are in an extraordinary moment, but not an entirely unprecedented one. Since the earliest days, societies have had to cope with disasters that wiped out the ability of everyday people to service their debts and thus threatened to destroy their societies.

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If you've read U Missouri econ prof Michael Hudson's writings on the subject, you know that for millennia, rulers in these circumstances simply wiped out the debts, declaring a "jubilee" that allowed people to rebuild after disasters rather than being trapped in debt spirals.

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In a new essay on @NakedCapitalism called "How an 'Act of God' Pandemic Is Destroying the West: The US Is Saving the Financial Sector, Not the Economy," Hudson reveals the abyss on whose brink we are balanced, and what we must do to pull back from

Hudson's oft-repeated golden rule is "Debts that can't be paid, won't be paid." That is to say: making it harder to declare bankruptcy, or binding debtors over to arbitration or wage-garnishing won't actually get them to pay debts they cannot afford.

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This is a very sharp observation in the US context. The 2008 crisis was "solved" by bailing out finance, not people - and so the finance sector was able to lend to consumers to buy things again, while consumer debt mounted to spectacular levels.

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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.

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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

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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__

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20 machine learning questions that will make you think.

(Cool questions. Not the regular, introductory stuff that you find everywhere.)

🧵👇

1. Why is it important to introduce non-linearities in a neural network?

2. What are the differences between a multi-class classification problem and a multi-label classification problem?

3. Why does the use of Dropout work as a regularizer?

👇

4. Why you shouldn't use a softmax output activation function in a multi-label classification problem when using a one-hot-encoded target?

5. Does the use of Dropout in your model slow down or speed up the training process? Why?

👇

6. In a Linear or Logistic Regression problem, do all Gradient Descent algorithms lead to the same model, provided you let them run long enough?

7. Explain the difference between Batch Gradient Descent, Stochastic Gradient Descent, and Mini-batch Gradient Descent.

👇

8. What are the advantages of Convolution Neural Networks (CNN) over a fully connected network for image classification?

9. What are the advantages of Recurrent Neural Networks (RNN) over a fully connected network when working with text data?

👇

The 8-step quick-start guide to learn Machine Learning.

🧵👇

1⃣ Start with Python 🐍

Yes, you can do other languages, but Python is by far the most straightforward option.

👇

2⃣ Get familiar with numpy, pandas, and matplotlib

These three libraries are probably the most common Python libraries you'll have to use every day.

(Even if you don't end up doing machine learning, these libraries are awesome and useful.)

👇

3⃣ Start using notebooks

Look into Jupyter or Google Colab.

Notebooks are essential for data scientists and machine learning practitioners. Most of the code you'll read and write will be in notebooks.

👇

4⃣ Find a problem (already solved)

In my opinion, the best way to start is by working through a problem —especially when you can learn from its solution.

Start with something simple. I usually recommend "Titanic" from Kaggle.

👇