The YouTube algorithm that I helped build in 2011 still recommends the flat earth theory by the *hundreds of millions*. This investigation by @RawStory shows some of the real-life consequences of this badly designed AI.
Flat Earth conference attendees explain how they have been brainwashed by YouTube and Infowarshttps://t.co/gqZwGXPOoc
— Raw Story (@RawStory) November 18, 2018
https://t.co/EKed0B1XhD
8/
https://t.co/yZpqdiJgsR
https://t.co/cqYz3SbDO8
https://t.co/M2mVtZRut9
https://t.co/myxPsrhlKa
Flat-earthers are the canaries in the coalmine /18
I think they're missing the point. 19/
With AI in charge of our information, we're facing a brand new, existential problem that concerns all of us. We need to develop tools to understand it better. 20/
From the algorithm's point of view, flat earth is a gold mine.
Full article: https://t.co/LPjCKpbwXj
21/
https://t.co/LPjCKpbwXj 22/
More from Tech
A common misunderstanding about Agile and “Big Design Up Front”:
There’s nothing in the Agile Manifesto or Principles that states you should never have any idea what you’re trying to build.
You’re allowed to think about a desired outcome from the beginning.
It’s not Big Design Up Front if you do in-depth research to understand the user’s problem.
It’s not BDUF if you spend detailed time learning who needs this thing and why they need it.
It’s not BDUF if you help every team member know what success looks like.
Agile is about reducing risk.
It’s not Agile if you increase risk by starting your sprints with complete ignorance.
It’s not Agile if you don’t research.
Don’t make the mistake of shutting down critical understanding by labeling it Bg Design Up Front.
It would be a mistake to assume this research should only be done by designers and researchers.
Product management and developers also need to be out with the team, conducting the research.
Shared Understanding is the key objective
Big Design Up Front is a thing to avoid.
Defining all the functionality before coding is BDUF.
Drawing every screen and every pixel is BDUF.
Promising functionality (or delivery dates) to customers before development starts is BDUF.
These things shouldn’t happen in Agile.
There’s nothing in the Agile Manifesto or Principles that states you should never have any idea what you’re trying to build.
You’re allowed to think about a desired outcome from the beginning.
It’s not Big Design Up Front if you do in-depth research to understand the user’s problem.
It’s not BDUF if you spend detailed time learning who needs this thing and why they need it.
It’s not BDUF if you help every team member know what success looks like.
Agile is about reducing risk.
It’s not Agile if you increase risk by starting your sprints with complete ignorance.
It’s not Agile if you don’t research.
Don’t make the mistake of shutting down critical understanding by labeling it Bg Design Up Front.
It would be a mistake to assume this research should only be done by designers and researchers.
Product management and developers also need to be out with the team, conducting the research.
Shared Understanding is the key objective
I\u2019d recommend that the devs participate directly in the research.
— Jared Spool (@jmspool) November 18, 2018
If the devs go into the first sprint with a thorough understanding of the user\u2019s problems, they are far more likely to solve it well.
Big Design Up Front is a thing to avoid.
Defining all the functionality before coding is BDUF.
Drawing every screen and every pixel is BDUF.
Promising functionality (or delivery dates) to customers before development starts is BDUF.
These things shouldn’t happen in Agile.
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!
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Recently, the @CNIL issued a decision regarding the GDPR compliance of an unknown French adtech company named "Vectaury". It may seem like small fry, but the decision has potential wide-ranging impacts for Google, the IAB framework, and today's adtech. It's thread time! 👇
It's all in French, but if you're up for it you can read:
• Their blog post (lacks the most interesting details): https://t.co/PHkDcOT1hy
• Their high-level legal decision: https://t.co/hwpiEvjodt
• The full notification: https://t.co/QQB7rfynha
I've read it so you needn't!
Vectaury was collecting geolocation data in order to create profiles (eg. people who often go to this or that type of shop) so as to power ad targeting. They operate through embedded SDKs and ad bidding, making them invisible to users.
The @CNIL notes that profiling based off of geolocation presents particular risks since it reveals people's movements and habits. As risky, the processing requires consent — this will be the heart of their assessment.
Interesting point: they justify the decision in part because of how many people COULD be targeted in this way (rather than how many have — though they note that too). Because it's on a phone, and many have phones, it is considered large-scale processing no matter what.
It's all in French, but if you're up for it you can read:
• Their blog post (lacks the most interesting details): https://t.co/PHkDcOT1hy
• Their high-level legal decision: https://t.co/hwpiEvjodt
• The full notification: https://t.co/QQB7rfynha
I've read it so you needn't!
Vectaury was collecting geolocation data in order to create profiles (eg. people who often go to this or that type of shop) so as to power ad targeting. They operate through embedded SDKs and ad bidding, making them invisible to users.
The @CNIL notes that profiling based off of geolocation presents particular risks since it reveals people's movements and habits. As risky, the processing requires consent — this will be the heart of their assessment.
Interesting point: they justify the decision in part because of how many people COULD be targeted in this way (rather than how many have — though they note that too). Because it's on a phone, and many have phones, it is considered large-scale processing no matter what.
