rcheu
The reality is that there’s a lot of (what is essentially) matrix factorization, and not much of anything nefarious or very interesting to most people who are not recommendation system engineers.
Another thing is that these systems are intentionally hard to game even if you know the weights. The systems optimize for things like P(comment|personalized features), so even if you know there’s a high weight in the scoring function for comments, just commenting a lot on your content isn’t sufficient. You need the system to predict that other people will also comment.
At these speeds, the WiFi adapters are more of a bottleneck than the ISP. I can only get close to full speed on ethernet.
https://www.instagram.com/nancyzhou_violin/ https://www.instagram.com/joshuabrownviolinist/ https://www.instagram.com/kersonleong/ https://www.instagram.com/violincase/ https://www.instagram.com/augustinhadelich/ https://www.instagram.com/weatherclef/
I also enjoy just browsing the discover tab on instagram. If you mostly click on these instrumentalist accounts, it's pretty good about finding more.
This means looking for ways to improve the things you do and finding shortcuts so you have to think less. Most commonly, I do this by looking back at how I did a task and writing down things to help me complete it faster next time. Examples:
- write down the common mistakes I've made in the past and use it as a checklist to check before I push code
- create run-books for debugging issues. This is both for specific areas and general process things such as "Make sure you check all the relevant grafana dashes."
- add questions and answers on StackExchange/Quora when I run into problems/questions that take me awhile to resolve.
It's not free, but I think the functionality is a bit better than this. It handles fast passages and can change temperament, show how off you were, etc.
Anyways, this matches my expectations--people tend to be overly negative and only remember the good part. The mobile web as a whole has gotten faster due to network speeds+cpu improvements.
It is worth noting that pages are doing more after loading now than they used to be though. This won't show up in onload or first meaningful paint, etc. So the first paint is fast, but then if you try to scroll immediately afterwards you'll probably hit some jankyness while the rest of the page loads asynchronously (but only kind of asynchronously since there's a single main thread).
Some other things that could cause the regression are that more people own a budget Android phone now than before. People may not realize how slow these phones are. The single core performance of the top budget phone, the Samsung A50, is comparable to an iPhone 6 which came out in 2015.
I've been confused how they're able to do this and not charge more than they do (only $200 a year).
The train/test data being imbalanced in the same way does give the model an advantage, but I don't think that making the test set 50% would solve the issue completely either. Doctors have been "trained" on the true distribution, while which is not 50% (I'd guess that the true distribution is actually extremely unbalanced).
The model isn't simply learning to predict no 80% of the time, it is learning the distribution of the data with respect to the input features. For example, let's say that we have a simple model with only 3 binary features. It may learn that when features X_0, X_1 and X_2 are 1, the probability of cancer is 70%. This isn't a simple multiplication of the true probability by the upscaling factor though--it depends on the percent of negative samples with this feature vector and the percent of positive samples with this feature vector.
If we are to change the test set to be 50% positive and keep the same train distribution, the model no longer has the correct information about cancer rates with respect to feature distributions, but neither does the dermatologist. The specificity and sensitivity continue to not be interpretable as predicted specificity and sensitivity in the real world.
There is no issue with reporting specificity/sensitivity if they had used the true distribution of cases. Yes, the curves/AUCs will look better than the precision/recall rates, but they do not mis-represent what the doctors are interested in (what percent of people will be missed, and what percent of healthy people will be subjected to unnecessary procedures).
Anyways, the classifier doesn't actually seem to be that good, there's actually doctors that were better than the classifiers if you check the paper.
That said, I'm not sure I agree that it was winning mainly due to better decision making. For context, I've been ranked in the top 0.1% of players and beaten pros in Starcraft 2, and also work as a machine learning engineer.
The stalker micro in particular looked to be above what's physically possible, especially in the game against Mana where they were fighting in many places at once on the map. Human players have attempted the mass stalker strategy against immortals before, but haven't been able to make it work. The decisions in these fights aren't "interesting"--human players know what they're supposed to do, but can't physically make the actions to do it.
While they have similar APM to SC2 pros, it's probably far more efficient and accurate so I don't think that alone is enough. For example, human players have difficulty macroing while they attack because it takes valuable time to switch context, but the AI didn't appear to suffer from that and was extremely aggressive in many games.
Kids don’t reliably make good long term decisions for themselves. It’s more fun to play fortnite than to practice for a math/piano competition. They can’t see the long term impact of not working out and eating poorly.
I got pushed pretty hard as a kid. It wasn’t fun at the time, but now I appreciate it. There’s a widening gap between the rich and poor in America, and it’s a lot nicer to be on the rich side.
Do you all really think that if this contributed a measureable portion of ads that it wouldn’t leak? There’d also have to be a large amount of hardware dedicated to speech transcription—running constant speech to text on every google chrome browser would need so much compute.
Not to mention that it’d be possible to actually show that the company was lying by modifying microphone drivers and showing that they activate in the background. I’m pretty sure you could also just do it the same way they make game hacks—get access to chrome memory and show that there’s sections that match the recorded speech.
Indisputable proof that this happens would be a PR disaster for either company and cause legal action too.
I’m really in dismay at how long this myth has persisted.
Methodology is very shakey for a sample of young people. Results are also just weird—I’m a young person (24) and very few people I know have deleted FB.
The “security consultant” claims to know that there’s clips of audio being sent back to servers, but not knowing what that audio is since it’s encrypted. First off, if the content is encrypted, you don’t know that it’s audio. Secondly, if you had proof of a major app unexpectedly sending back recorded audio clips, it would be huge news, so I’d assume he doesn’t have that.
Apps do not have access to always on microphone access by default as insinuated by the article. On Android, until P comes out, apps can access the mic in the background if you give microphone access to the app. On iOS, app microphone access triggers the microphone symbol in the top left. These are the apps explicitly asking to take microphone access though, not some sort of listener pattern where apps can attach to the OS’s always on microphone behavior.
Experiment is clearly flawed for numerous reasons (no starting recorded state, didn’t record behavior on FB/other sites that could have impacted results during expt, etc.)
Things I like:
* typing feels much nicer, no lag in character input
* phone runs much cooler, does not make hand sweaty to hold
* far fewer random freezes or lag
* password management much better by default, every app with important information has faceid which is quite nice
* I prefer the photos taken by the iPhone camera
* I find tethering on iPhone works more consistently
Things I don’t like:
* no back button
* some Google apps for iOS don’t seem as good as their android counterparts. YouTube app keeps getting buggy updates, Inbox still doesn’t support full iPhone X screen.
* Prefer Android’s notification system
* A bunch of apps have video playback issues, I suspect there’s an OS level bug
* Print pascal's triangle up to the n-th level, with proper spacing (so that it looks like a triangle).
* Make the game Go for 2 players. I got a sub problem of this during my Google interview (determine whether a piece is captured).
While there's definitely good people at non-elite universities, selection bias is at play here too.
For example, in the senior year case, you're just looking at people who were concerned enough about interviews to go to interviewing.io during their senior year. Most people I knew at MIT had a full time offer they were happy with from their Junior year internship by their senior year.
You can actually see the impact of this in your graphs too, notice that the % of people with a 1 or a 2 in elite schools increases for senior year vs junior year. People who have been doing well in technical interviews and have good return offers won't spend as much time preparing.
There’s been a myth created by crypto currency activists that gold is just like crypto currency in that it’s valued simply as a value store, which is incorrect. Its use as a value store increases the value, but at its core the price is still dependent on the uses of gold. Over half of gold mined is used for jewelry or industry[1]. Yes, some is kept as investment, but that investment is based on the idea that people will continue to want gold jewelry.