About 20% beyond core case of government-critical speech in formally public spaces and media I'd say.
There are only 3 real challenges to free speech at the tech level a) the need for CDNs at scale b) distortions of net neutrality c) nation-state firewalls.
All other claims are about social governance on private sites, where applying free speech doctrine is a stretch at best
About 20% beyond core case of government-critical speech in formally public spaces and media I'd say.
That's a genuine de facto increase in practical, informal free speech/expression.
This is reasonable. You should not expect no-strings-attached access to the attention of potentially billions of people under the same terms as ranting from a soapbox in a 19th century town square.
More from Venkatesh Rao
Both this thread and the outraged response threads are... something.
This is why I never wanted kids. Way too much responsibility for another human’s development. Depending on the child, this might either be the day they discovered who they were or the day that traumatized them into a lifelong fuckup. Either way I don’t want to direct the show.
As far as the can opener goes, it wouldn’t even occur to me to try and turn it into a teachable moment. That sounds vaguely quixotic. I’d just show them how immediately. I think my default is to try and instruct clearly but not demonstrate unless the person is truly disoriented.
I think there’s basically a right answer here: show the kid. If the kid has the aptitude they’ll enjoy the mechanism so much they’ll develop the figure-it-out skill with other devices. If not, it’s a training data point that will build remedial levels of intuition more slowly.
I think perseverance is both misframed and over-rated as a virtue. Misframed as in: everybody has potential for it in some areas and lacks it in others. Aptitude is those areas where perseverance comes easily to you. Meta-skill of knowing where/why you persist is more important.
So, yesterday my daughter (9) was hungry and I was doing a jigsaw puzzle so I said over my shoulder \u201cmake some baked beans.\u201d She said, \u201cHow?\u201d like all kids do when they want YOU to do it, so I said, \u201cOpen a can and put it in pot.\u201d She brought me the can and said \u201cOpen it how?\u201d
— john roderick (@johnroderick) January 2, 2021
This is why I never wanted kids. Way too much responsibility for another human’s development. Depending on the child, this might either be the day they discovered who they were or the day that traumatized them into a lifelong fuckup. Either way I don’t want to direct the show.
As far as the can opener goes, it wouldn’t even occur to me to try and turn it into a teachable moment. That sounds vaguely quixotic. I’d just show them how immediately. I think my default is to try and instruct clearly but not demonstrate unless the person is truly disoriented.
I think there’s basically a right answer here: show the kid. If the kid has the aptitude they’ll enjoy the mechanism so much they’ll develop the figure-it-out skill with other devices. If not, it’s a training data point that will build remedial levels of intuition more slowly.
I think perseverance is both misframed and over-rated as a virtue. Misframed as in: everybody has potential for it in some areas and lacks it in others. Aptitude is those areas where perseverance comes easily to you. Meta-skill of knowing where/why you persist is more important.
I’m guessing these responses really reflect people’s weighted averages (age*current average effort fraction) though I kept it simple and asked for just averages.
I suspect a healthy weighted average should be ~ (age-20)/2. So a 30 year old should be at 5, a 40 year old at 10, a 50 year old at 15 etc.
Standard deviation should be ~average/3 maybe, so distribution spreads as you age and accumulate projects and get better at them.
Other things being equal, people get good at starting in their 20s, at follow through in 30s, at finishing in 40s.
No point learning food follow through until you’ve found a few good starts to bet on. No point getting good at finishing until a few projects have aged gracefully.
I’m in the 7+ range myself. Probably 8-9. Slightly less than healthy for my age.
I suspect most self-judgments on being good starters/follow-through-ers/finishers are really flawed because of the non-ergodicity of project management skill learning. You can’t learn good practices for the 3 phases in an arbitrary order. On,y one order actually works.
Poll: where is the temporal center of gravity of all your live projects based on average age of start-dates?
— Venkatesh Rao (@vgr) January 17, 2021
I suspect a healthy weighted average should be ~ (age-20)/2. So a 30 year old should be at 5, a 40 year old at 10, a 50 year old at 15 etc.
Standard deviation should be ~average/3 maybe, so distribution spreads as you age and accumulate projects and get better at them.
Other things being equal, people get good at starting in their 20s, at follow through in 30s, at finishing in 40s.
No point learning food follow through until you’ve found a few good starts to bet on. No point getting good at finishing until a few projects have aged gracefully.
I’m in the 7+ range myself. Probably 8-9. Slightly less than healthy for my age.
I suspect most self-judgments on being good starters/follow-through-ers/finishers are really flawed because of the non-ergodicity of project management skill learning. You can’t learn good practices for the 3 phases in an arbitrary order. On,y one order actually works.
More from Tech
The first area to focus on is diversity. This has become a dogma in the tech world, and despite the fact that tech is one of the most meritocratic industries in the world, there are constant efforts to promote diversity at the expense of fairness, merit and competency. Examples:
USC's Interactive Media & Games Division cancels all-star panel that included top-tier game developers who were invited to share their experiences with students. Why? Because there were no women on the
ElectronConf is a conf which chooses presenters based on blind auditions; the identity, gender, and race of the speaker is not known to the selection team. The results of that merit-based approach was an all-male panel. So they cancelled the conference.
Apple's head of diversity (a black woman) got in trouble for promoting a vision of diversity that is at odds with contemporary progressive dogma. (She left the company shortly after this
Also in the name of diversity, there is unabashed discrimination against men (especially white men) in tech, in both hiring policies and in other arenas. One such example is this, a developer workshop that specifically excluded men: https://t.co/N0SkH4hR35
USC's Interactive Media & Games Division cancels all-star panel that included top-tier game developers who were invited to share their experiences with students. Why? Because there were no women on the
ElectronConf is a conf which chooses presenters based on blind auditions; the identity, gender, and race of the speaker is not known to the selection team. The results of that merit-based approach was an all-male panel. So they cancelled the conference.
Apple's head of diversity (a black woman) got in trouble for promoting a vision of diversity that is at odds with contemporary progressive dogma. (She left the company shortly after this
Also in the name of diversity, there is unabashed discrimination against men (especially white men) in tech, in both hiring policies and in other arenas. One such example is this, a developer workshop that specifically excluded men: https://t.co/N0SkH4hR35
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!