Stasis5001

Oops, that's what I get for mathing before coffee-- mixed up the formula for \sum (1+r)^n vs. \sum r^n

Tons of comments already in the direction of 'eat less' and 'exercise more'. Of course. One thing I've found extremely helpful is to cook more! WFH is amazing for this. If you can find 15 minutes of meeting time throughout the day where you aren't an active speaker, you can chop vegetables or do dishes for lunch and dinner.

Back to the primary topic: cooking your own meals makes 'eating less' a matter of dialing in the portion sizes over time. I eat ~110g of pasta, or 0.8 cups of rice, or similar, and about 0.3-0.5 lb of protein, and about 1-1.5 vegetables per meal. Overall, I eat two meals like this, black coffee, a breakfast bar, and a snack, which puts me at 2000-2500 cal.

Now, on to a weird tip: meal planning! What a fun mix of intuition and ideas from operations research.

It's interesting to compare this to the physical-world analog. What if somebody shows up at the US-Canada border, start shooting rockets at Canada, and the US refuses to acknowledge this as a crime or extradite? What if the rockets are aimed at a critical piece of infrastructure near the border and can cause billions in damage? One could argue that if the US condones these attacks, they have effectively already declared war on Canada.

I started looking at the map in the article and realized I had just booked a flight where there was an existing rail line ! So I checked out the Amtrak site, and what's 1h20m by plane is 14h40m by train -- and 8h by car. Maybe by getting a sleeper cabin I could have had an enjoyable trip by train, but as the trip scales things get dramatically worse.

Of course, maybe this is exactly what the future of transportation should look like: more localized travel on modes that can be powered by renewable sources or nuclear.

As an employee at a megacorp, sure, you can see a little bit about how things are going from the inside. That said, you probably spend less time looking at reports than professional analysts. Secondly, sure, maybe you can see how great your company is, but do you have a frame of reference to a million other companies and their projects and cultures? There's a strong bias towards thinking you know more than the market that I would be wary of.

As somebody with some ML background but no expertise in crypto, is the following ELI~20 summary correct?

We take an ML model trained on unencrypted data, use a 'homomorphic evaluation' technique (let's just leave that as magic here) to convert the model operation-by-operation to a model that runs on encrypted data, do a little more crypto magic, and we've solved the business problem described at the beginning of the article.

(In particular, if you train a model on encrypted data you get a really bad model, right?)

Any good references on the art part, or is it just an intuition you develop over time? In my experience, all the ML education will teach you a ton of theory and basics but none of the practical details you're referring to.

Sure, Wiles obviously understands the main thrust of his proof. But one could argue that Wiles' result depends on lots of other results, which in turn depend on other results, and so on through decades and decades of work, ultimately going back to the foundations of mathematics. Neither Wiles nor anybody else can claim to rigorously understand all of it. You can imagine this as a tree, with Wiles' work as root, and his dependencies as ancestors, and so on. An error at a lower level of the tree could, in theory, invalidate the root node.

I do agree with Buzzard that it's hard to be sure. I've definitely read papers where a critical argument isn't well written or what is written seems wrong. However, if there are low-level errors, I suspect that with some work things could be patched up.

Tipping is a form of price discrimination, which generally makes things more efficient and has been a staple of capitalism since forever. A business actively wants to extract more money from customers who can afford more, and still sell to customers who are more price sensitive. Coupons are the more normal way to do this, but tipping serves this exact purpose. This is in theory good for customers (except in monopolies, where the business extracts all the value), but in practice, sure, I agree tipping is obnoxious as a customer and possibly even worse for those whose pay involves tips.

It does change something. DoorDash's old policy, weird as it is, reduces variability of daily pay for the Dashers. It literally put a derivative structure onto the Dasher pay that reduces volatility -- the pay was cash plus a call option on the tip. Now they will get cash plus the tip. Obviously DoorDash will tune their base pay, but they can't meet the prior model's mean and variance at the same time. How Dashers balance those two is unclear, but generally people need a higher mean to accept higher variance.

The problem is that the old policy only works if the customers don't understand it, and maybe even seems generally unethical.

I think you may be missing the point -- the one being named and maybe shamed in this post is actually Airbnb.