joefkelley
I found it to be at least an order of magnitude faster, though, to invert the solution: loop through each word in the dictionary and check whether it exists in the grid! The dictionary is small compared to the number of grid paths, and checking whether a word exists in the grid is very very fast, requiring not much backtracking, and lends itself well to heuristic filtering.
I don't have a problem in general with a ticketing system for the government to request specific kinds of moderation. There do exist valid carve-outs of free speech, and I see no reason why government and industry couldn't collaborate on that. If it were used to censor political speech, then yes, I would have a problem with that. But I haven't seen evidence of that.
The emails they include show that there were meetings between DHS and Twitter and between DHS and Stanford, on the topic of election integrity. And that there was a Signal chat (I guess this is kind of sketchy).
But there's no evidence of censorship or anything politically-motivated that I can discern.
In my experience, Scala does pretty well without it.
I guess is your point that the language should make it impossible to write bad code, not just make it easy to write good code?
This is an interesting one. I totally get your point. But also users are terrible about updating their software if you give them the choice. Automatic updates have very practical security benefits. I've witnessed non-technical folks hit that "remind me later" button for years.
Really? I have youtube premium and I can't recall seeing ads for it. Why would they advertise a product to people that already have that product?
> Google Ads could have a flag that turns it's data collection/sharing off and replaces it with micropayments to any site that user visits that are running Google Ads.
FWIW, this flag already exists, except you don't have to do micropayments: https://adssettings.google.com/
I guess you still see ads with this setting, they're just not personalized. Hypothetically you could imagine a "stronger" setting that doesn't just do away with personalization, it does away with ads altogether by allowing the user to "outbid" any advertiser. But I suspect there will be some surprised users who get a bill for hundreds of dollars by doing some particularly high-value searches like "personal injury lawyer" or "mortgage" or something.
And if it were a flat rate, my intuition is that the fee would have to be much higher than most would expect or be willing to pay.
The answer to both questions is no. If you opt out, your data is not used for modeling or targeting or anything. Perhaps some internal reporting that isn't used for anything other than like PMs wanting to understand user behavior? Even that I'm not sure about.
If you are identified as being in market for something based on activity and then opt out, you will no longer be classified as being in market. That classification will be deleted- though perhaps not immediately but within some reasonable time frame, say 24 hours or so.
There are a few settings for this kind of thing. You can take a look at https://myaccount.google.com/data-and-personalization
The "Ad personalization" bit is probably what the parent comment is referring to. But it sounds like you're interested in the "Web & App Activity" bit, which will turn off the non-ads usage of your data. To a certain extent at least, since there are some grey areas.
For example, I'm on the team that sends Google Shopping emails. If you click a button to track the price of a specific TV, we'll still send you an email if that price drops even if you've opted out of "Web & App Activity". But if you've just been browsing shopping pages for TVs, we won't send you a general email about TV deals if you've opted out. Both of those cases are in some literal sense "web activity" but it's still pretty clear what the user expects.
But you might imagine- if you're tracking the price of a specific TV and opted-out of Web & App Activity, should we send you an email if a near-identical TV drops in price? We probably wouldn't, and we don't have anything like this today, but it's not quite as clear. And Google has so many features across different teams, I can imagine there's probably at least one where some privacy reviewer made a different call than what you would have made.
For instance, I take your approach for most purchases under two thousand dollars or so. If I want to buy myself a new computer or whatever, I'd mention it to her, but ultimately I'm probably going to get it even if she thinks I shouldn't. I know some couples where this isn't the case, even if they have the means. Their price threshold for making the decision together is much lower.
But on career changes we make decisions together. For instance, she recently made a change that will result in her making less money, especially in terms of long-term career trajectory. But her stress level and overall happiness is much better. And she knows that my income was a good amount higher anyway and it ultimately won't affect things like when we retire or our quality of life that much.
But then it would be pretty shitty of me to change to a lower-paying profession down the road without her OK. She has sacrificed her earning potential with this kind of commitment in mind and maybe wouldn't have if she didn't know she could count on me to make future decisions with her collaboration.
I'm not saying either end of the spectrum is necessarily better. Just that there are pros and cons and it's more important to be in agreement.
If Apple deposits $200 billion and the ratio is 1/10, then the bank can loan out $180 billion of that 200; it must keep $20 billion in reserve - 1/10th.
In your scenario, the bank has negative $1.8 trillion in reserve.
I would love it if local governments would stop restricting the rights of local developers and allow the free market to determine what is built where more often.
Each individual wavelength activates all three to some extent: think about this as a point in 3d space. For example, 400nm corresponds to something like (0.1, 0.05, 0.0), from that chart. 500nm might be (0.1, 0.4, 0.3).
But we experience a mix of many different wavelengths at once. So we can not only experience just these points in 3d space, but we can also experience any linear combination of any of these points. For instance, a mix of half 400nm and half 500nm light might be "sensed" by us as (0.1, 0.225, 0.15), even though maybe there's no individual wavelength that corresponds to that point. Any linear mix of any number of any wavelengths covers the entire gamut of what we can perceive.
The question then for someone picking primary colors for an additive display is: if I can only do a linear mix of three wavelengths, what wavelengths should I pick? What covers the biggest subset of the whole perceptible gamut? It just so happens that red, green, and blue do the best.
If you swapped green for yellow, there would be a section in that 3d space that you could no longer create. Specifically, the area where M cones are strongly activated compared to L and S. Unsurprisingly, this would be the greenest greens.
I know Hive can definitely query other datasources like traditional SQL databases, redis, cassandra, hbase, elasticsearch, etc, etc. I thought Impala had some bit of support for this as well, though I'm less familiar with it.
And SparkSQL can be run on a multi-tenant cluster with automatic resource allocation - Mesos, YARN, or Kubernetes.
Drafting from that pool, item and skill builds, last-hitting, creep aggro, laning in general, jungling, item and spell usage, ganking, team-fight positioning, pushing objectives, warding, map control, farm priority, when to retreat vs engage. All of these require an understanding of micro vs macro goals and how they relate.
Surely this qualifies as "a difficult task."
Wondering if anyone is aware of any others.
The logic was we have fewer users over the weekend, so if something goes wrong, fewer people will notice.
Here's my understanding: Google runs real-time bidding ad auctions by sending anonymized profiles to marketers, who bid on those impressions. The anonymous id used in each auction was the same for each bidder, which is in violation of GDPR. If Google were to send different ids for each bidder, it would be ok? Is this correct?
Why would it matter that the bidders are able to match up the IDs with each other, aren't they all receiving the same profile anyway? Wouldn't privacy advocates consider the sending of the profiles at all an issue?
And Google taking the lead by implementing this for free seems like it should put a stop to any worry that carriers will charge for it.
As for the network, it's also not as clear what's profitable. Bidding per click or conversion probably gives them more opportunity to do well with good targeting. But high per-impression bids are also useful for the users the network knows very little about.
X is just the content of the ad and is usually entirely advertiser-defined. Some ad networks will allow you to do some fancy stuff to help determine the best phrasing or arrangement or whatever of your ad.
Z can be in dollars per impression, per click, or per conversion. An impression is one person being shown an ad one time. A click is a person clicking an ad. And a conversion is advertiser-defined but is usually someone buying the product being advertised. Bidding per click is the most common.
Then when a user loads a page with an ad on it, the ad network finds all campaigns that are eligible - meeting the criteria defined in Y. It then runs an "auction". It has to calculate which eligible advertiser is willing to pay the most for that potential impression. But not all advertisers are bidding per impression, so some prediction comes into play. If an advertiser bid per click, then the expected amount they will pay is the probability the user will click multiplied by the price if they do click. Their probability of clicking is often called CTR which stands for "click-through-rate" so that's where the bid * CTR comes from. Usually an ML model takes what is known about the user, the ad, and the page, and predicts CTR. Similarly for conversion-based bids, it's bid * CTR * CVR, where CVR is "conversion rate". Whichever ad comes out with the highest result of this calculation is shown.