How else can you have a standard benchmark for testing applicants against? The issue with just looking at background experience, in my belief, is that there’s too much noise, not enough data, to properly gauge someone.
Don’t we already know how to do this? For example, the concept of companion planting — growing plants which behave symbiotically together — is a centuries old idea. The issue is whether we have an economic incentive to.
Oh no, this is setting a bad precedent. If founders can launch a startup within 24 hours, then sometime soon a sadistic tech company will replace their coding challenges with making a startup. \s
If you can't change the incentives, change the game: either cap salaries and stock grants, or provide a universal basic income. The point being is, you want to make it easier for people to make the ethical choice.
Two years ago, I used to spend a lot of my free time browsing Medium, reading articles I otherwise would’ve never discovered. Now? Not so much the case—maybe it’s the degradation in quality or the number of badly written articles.
Maybe what we need for blogs isn’t a platform, where authors come on and content is hosted centrally; but an aggregator, where each author self hosts but content is still discoverable by a wider audience.
IIRC, Google can try to dictate a BGP policy that says not to accept any routes that goes through China. However, without any verification checks (via cryptography), an entity can lie about the path that they are advertising.
I did this recently as well! It kind of reminds me of a Black Mirror episode, “Hang the DJ”, where a dating app used ‘cookies’ to arrange matchings between people. I actually wished someone could invent this for the recruitment process—a stand-in AI that deals with all the boring parts of life.
Isn’t there a misalignment between IPv6 adoption and scarcity of IP addresses? We rely on ISPs to adopt IPv6, but if they also own IP space, why would they lower the value of their holdings.
The issue with Leetcode and HackerRank as I see it, is that you're reproving to various companies each time and each interview that you know how to code. However, the only alternative to this seems be an SAT for programmers, which isn't better.
In reflection, it’s a good idea. Consumer preferences are hard to gauge; there isn’t much of a feedback loop right now with restaurants beyond you either have business and are sustainable, or you don’t and lose it all. This is comparable to YouTube giving you different advertisements, but that these ads (restaurants) are really expensive to run and experiment with. Virtual restaurants essentially bring A/B testing from the digital realm to the physical one. They provide and shorten the feedback loop, lowering the cost of exploration by centralizing and by economies of scale; and resulting in greater exploitation (not in the economic sense but in the multi armed bandits framework).
Why is modeling required? Can’t we just put a sensor on every bus, and just return the empirical expected time it takes for the next bus to drive to your station given the time of day and day of week?
The comparison of AI to the Internet doesn’t really make sense: “nerds” are still the ones building out applications; only difference is that those applications can be used by a wider audience. The comparison could be that “nerds” build out AI systems, and that they’re used by wider audiences—this is what is already happening; look at assistants like Siri or Alexa, recommendation systems like YouTube’s, etc.
I think what this article is getting at, is the notion that AI will become “professionalized”: the role of “AI specialist” will be as common place as software engineers; the salary premium will close. All of this is banking on, however, the idea that AI systems will needed and be implemented by most companies—which I disagree with.
Two notes:
1. This article is not talking about how, for neural networks, you can just have pretrained networks — where the cost of compute and data is incurred there — and then use them to classify images or what not on your decades old computer. Correct?
2. Often times, some problems are “solved” in the sense that they become irrelevant. Is that also the case here. It seems compute and data were seemingly constraints, but technology (algorithms) just got more efficient. Should we not reframe this and say that algorithms are the constraint, then, and that’s what we should aspire to improve? Usually throwing compute and data marginally improves gains anyhow...
Is most of consumer hardware built on top of open source or on proprietary tools? I would love to see a movement where companies build products and then give away implementation details, but the cynic in me says repair costs is the analog of advertising revenue for these companies.
If 61A taught me anything, SICP is bad introductory textbook for people who are not directly CS majors: it is not that accessible, and most people just want to learn Python.
I’m not saying SICP overall is a bad textbook; but it is something that can only be appreciated after realizing CS is not only tool for manipulating data.
Not a necessarily a supporter of free markets, but I’d argue that more information and transparency is crucial for market efficiency. Take for example arbitrage; it disappears when enough people have access to “private information”, that in game theoretic setting, drive market price to equal the “true” price.
It’s not a zero sum game — just because a company makes an unethical decision to gain a competitive advantage doesn’t mean you have to. And it doesn’t mean you can prosper by acting ethical either.