I work in a company in the climate tech space, and this all rings very true. Our customer is utilities and I have stayed from fairly early stage through acquisition. It's frustratingly slow moving at times, but we've also been able to have a meaningful impact. Each person in the company can honestly say that they've contributed to emissions reductions to the point that really nothing they do could ever make them carbon positive again. Most of this is due to the scalability of software.
It's also been really interesting to see how regulations play into this. Getting utilities to value reducing their topline revenue through user energy efficiency requires regulation, and our customer base mostly reflects which utilities are under such regulations, though we also have a customer experience play for when that's not a primary driver.
Like layoric, I would also recommend working in this space for similar reasons. I would also add my experience is that the people are great. It's not get rich quick, so the people here are driven mostly by mission and interesting problems, which leads to a generally high level of positivity.
I grew up around hunting and farms and also don't find it surprising that animals are fairly intelligent.
While your statement about pasturing being good for the environment is partially true in that there are some forms that do some good, by and large meat production is actually one of the largest causes of GHG emissions.[1] A lot of this land would actually be used more efficiently for production of non-animal proteins like legumes, especially considering the land that is used to supplement pasturing through production of animal feed in most modern agriculture.
Agree. It's naive to assume your use case is the basic one when using these libraries or that you know the underlying implementation and all of the parameters, since in ML the implementations vary enough to affect the outcome and will have different controls.
To the point of exploratory analysis in some of the parents ,I prefer statsmodels for that purpose. It's not quite up to where the similarly purposed tools are in other languages, but for most of my work where I care about interpretation, it hits the right spot between usability and providing the standard statistical outputs.
We already ban lots of personal choices that are viewed to be harmful to the general population. Smoking in public for example.Given that the impact of private schools is arguably that everyone who can't go gets a worse education, wouldn't it prevent real harm to most to limit the choice of a few or that the impact has to be otherwise mitigated by those that make that choice?
I know this is meant to show some level of not knowing stuff being somehow correlated to a rise in level, but couldn't it be an example of successful use of abstraction. For example, if a manager leads a group of 5 people with diverse expertise, you would need that manager to know 5x as many things as each employee if they're really expected to be as deep as each. It seems that it would be hard to find such people. A more accomplishable strategy would be to learn who knows what and what are the top level fundamentals of each field so that the right questions can be asked.
My biggest concern right now with China trade, aside from their abysmal environmental standards is that they are actively committing ethnic cleansing[1]. It baffles me how little attention this gets.
To the point of exploitation, why not use tariffs as a way to enforce standards AND protect economic interests? Instead of targeting specific countries, it could be a points based system where "Long hours without overtime?" +5%, "Poor safety standards" +10%, and so on. Not to say the US is necessarily perfect here, but as far as I can tell, it's a long way ahead of the typical places it outsources production to. Enforcement might be hard, but any resistance to inspection etc. could be met with automatic application of the suspected tariff.
This seems like it would satisfy the goal of advancing human rights, but also give a more incentive to just hire workers in the developed countries.
No doubt the first 4 hours are intense and draining, which I definitely experience. But curious if any on this thread notice any correlation with what you put into your body? If I heavily caffeinate, I'll usually want to collapse right after work, which isn't helpful since I really would rather spend at least some time on personal projects each night (personal preference, not prescribing or judging to those that don't). Also, a bigger/carbohydrate heavy lunch usually knocks me out whereas something lighter or more fat+protein based won't as much.
Maybe, maybe not. And if there is, they may or may not have enough bandwidth/power to really flesh out the full product vision. Part of the attraction to startups for some, myself included, is the agency that technical people can gain with respect to what they ultimately work on in an environment where anyone with an idea might get "airtime".
I'm not sure there implementation of inverse in stock strategies, but assuming the basis is returns proportional to market rate x, that logic doesn't really follow for all cases I think. If the random strategy has gains of 1x and the non-random strategy has a loss of -0.5x, then wouldn't the inverse just be a gain of 0.5x?