So, I want to talk about how sexual harassment and fair pay are linked at Google (and beyond), because I think that's an angle that isn't being highlighted enough in the coverage of these walk-outs. I'm going to frame it largely around my personal experience there. Thread.
When Sergey Brin himself is openly having relationships with employees, it creates an environment where exec men, on down to men in senior management, think it's fine to treat the office like their harem.— Kelly Ellis (@justkelly_ok) October 30, 2018
I'll also point out it's forbidden per Google's own training.
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In that spirit, here's some quick Things Many People Find Too Obvious To Have Told You Already.
Your idea is not valuable, at all. All value is in the execution. You think you are an exception; you are not. You should not insist on an NDA to talk about it; nobody serious will engage in contract review over an idea, and this will mark you as clueless.
Technologists tend to severely underestimate the difficulty and expense of creating software, especially at companies which do not have fully staffed industry leading engineering teams ("because software is so easy there, amirite guys?")
Charge more. Charge more still. Go on.
The press is a lossy and biased compression of events in the actual world, and is singularly consumed with its own rituals, status games, and incentives. The news necessarily fails to capture almost everything which happened yesterday. What it says is important usually isn't.
Companies find it incredibly hard to reliably staff positions with hard-working generalists who operate autonomously and have high risk tolerances. This is not the modal employee, including at places which are justifiably proud of the skill/diligence/etc of their employees.
It was pretty simple to do—Apple Time Machine backups let me do it with one click.
That first tweet captures, in two pictures, how badly Apple has “lost the plot” (to quote @wylieprof). On the right is the Apple MagSafe adapter, from 2013. On the left, what I had “upgraded” to.
Thanks, Apple! I really was nostalgic for worrying about yanking my computer off the table.
Oh and I really appreciated not knowing if my computer was charging. What was great was the little whoop sound you used, so that the speaker before me could be informed I was charging my laptop.
Thanks to @chamath for laying this out in Social Capital's 2018 annual letter.
I've always appreciated his outspokenness.
2/ The hardest thing for most startups today is the path to market: first finding product-market fit & a way to reach customers, then building a ruthless machine to acquire, monetize & retain them.
3/ Because of this, when the VC industry invests capital into fast growing startups today, the plurality (if not majority) of invested capital will go into user acquisition and ad spending, for better or worse— usually worse.
4/ Todays massive venture-backed advertising, sales, and user acquisition playbook has morphed into one that champions growth at any cost.
This is creating a big bill that will soon come due...
5/ Ad impressions and click-throughs are bid up to outrageous prices by startups flush with venture money, and prospective users demand more and more subsidized products to gain their initial attention.
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!
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Funny thing is the guy is quite young. He can't be more than 25. How can someone that young have this sort of mentality?
I actually did not think that the guy could have wanted to get away with my car at the time. Now that I think of it though, that's a big possibility. We need to be careful out there. These thieves now have different ways of operating
1. Spend 30% of your effort on skimming all student ML papers (e.g. Stanford NLP CS224n) the past 3 years and prototype your favorites
The idea is everything. Pick an area you are interested in and ideally something that has a visual aspect to it
Most of my 'on the top of my mind' ideas were bad in retrospect. Skimming 100s of student papers will give you an overview of what's interesting.
Student papers are overlooked, easy to understand, and have good compute constraints.
2. Spend 30% on your effort on coding
Create an edge to the project. Apply it to something new and use FastAI or Keras to improve the accuracy with 5-30%.
3. Spend 30% writing an in-depth article
Have a north star article in terms of structure and quality. Find something that stretches you to your utmost capability. I used @copingbear’s Style transfer article:
4. Spend 10% marketing your project
Invest a week in studying the strategies to rank on sites like HN and Reddit, then use them. If you have an interesting result and a great article, you've done the hard work.
And sometimes one’s childhood passion follows them into adulthood, sometimes not, sometimes as an adult one finds a passion they never had as a child and embraces it. For me, that must include getting involved with a puppet show at the age of thirty. /2
So who’s to say whose passion’s legitimate and whose is bogus? Nobody. Someone might indict your passion but they have no ground to stand on and should be dismissed out of hand. /3
Maybe they’re envious that you have a passion, maybe they think that their passion is cooler than yours, or maybe they’re just insecure, who knows. Doesn’t matter. /4
What matters is that if something gives you joy, and in the process doesn’t hurt others, then by all means follow it. It’s how we explore our secret hopes, our shadow sides, our inner character. /5
2/ Black holes are weird. Duh. People like to think of them as something like incredibly dense nuggets of stuff that sucks other stuff in. But they’re not like that.
3/ If you squeeze enough mass into a small enough volume, the gravity gets so intense that it’s impossible to escape them *if you get too close*. From far away, though, gravitationally they more or less act like anything with that much mass.
4/ So if the Sun turns into a black hole we wouldn’t get drawn in. In fact, we’d orbit it almost exactly the same way as we do now, though we’d freeze to death pretty rapidly, which, in the end, would still kinda suck.
5/ Black holes get weird *when you get close*. Anything with mass warps space — we feel that warp as gravity! — but black holes stretch space to its limit.