nhaliday + πŸ–₯   157

design patterns - What is MVC, really? - Software Engineering Stack Exchange
The model manages fundamental behaviors and data of the application. It can respond to requests for information, respond to instructions to change the state of its information, and even to notify observers in event-driven systems when information changes. This could be a database, or any number of data structures or storage systems. In short, it is the data and data-management of the application.

The view effectively provides the user interface element of the application. It'll render data from the model into a form that is suitable for the user interface.

The controller receives user input and makes calls to model objects and the view to perform appropriate actions.


Though this answer has 21 upvotes, I find the sentence "This could be a database, or any number of data structures or storage systems. (tl;dr : it's the data and data-management of the application)" horrible. The model is the pure business/domain logic. And this can and should be so much more than data management of an application. I also differentiate between domain logic and application logic. A controller should not ever contain business/domain logic or talk to a database directly.
q-n-a  stackex  explanation  concept  conceptual-vocab  structure  composition-decomposition  programming  engineering  best-practices  pragmatic  jargon  thinking  metabuch  working-stiff  tech  πŸ–₯  checklists 
october 2017 by nhaliday
Superintelligence Risk Project Update II

For example, I asked him what he thought of the idea that to we could get AGI with current techniques, primarily deep neural nets and reinforcement learning, without learning anything new about how intelligence works or how to implement it ("Prosaic AGI" [1]). He didn't think this was possible, and believes there are deep conceptual issues we still need to get a handle on. He's also less impressed with deep learning than he was before he started working in it: in his experience it's a much more brittle technology than he had been expecting. Specifically, when trying to replicate results, he's often found that they depend on a bunch of parameters being in just the right range, and without that the systems don't perform nearly as well.

The bottom line, to him, was that since we are still many breakthroughs away from getting to AGI, we can't productively work on reducing superintelligence risk now.

He told me that he worries that the AI risk community is not solving real problems: they're making deductions and inferences that are self-consistent but not being tested or verified in the world. Since we can't tell if that's progress, it probably isn't. I asked if he was referring to MIRI's work here, and he said their work was an example of the kind of approach he's skeptical about, though he wasn't trying to single them out. [2]

Earlier this week I had a conversation with an AI researcher [1] at one of the main industry labs as part of my project of assessing superintelligence risk. Here's what I got from them:

They see progress in ML as almost entirely constrained by hardware and data, to the point that if today's hardware and data had existed in the mid 1950s researchers would have gotten to approximately our current state within ten to twenty years. They gave the example of backprop: we saw how to train multi-layer neural nets decades before we had the computing power to actually train these nets to do useful things.

Similarly, people talk about AlphaGo as a big jump, where Go went from being "ten years away" to "done" within a couple years, but they said it wasn't like that. If Go work had stayed in academia, with academia-level budgets and resources, it probably would have taken nearly that long. What changed was a company seeing promising results, realizing what could be done, and putting way more engineers and hardware on the project than anyone had previously done. AlphaGo couldn't have happened earlier because the hardware wasn't there yet, and was only able to be brought forward by massive application of resources.

Summary: I'm not convinced that AI risk should be highly prioritized, but I'm also not convinced that it shouldn't. Highly qualified researchers in a position to have a good sense the field have massively different views on core questions like how capable ML systems are now, how capable they will be soon, and how we can influence their development. I do think these questions are possible to get a better handle on, but I think this would require much deeper ML knowledge than I have.
ratty  core-rats  ai  risk  ai-control  prediction  expert  machine-learning  deep-learning  speedometer  links  research  research-program  frontier  multi  interview  deepgoog  games  hardware  performance  roots  impetus  chart  big-picture  state-of-art  reinforcement  futurism  πŸ€–  πŸ–₯  expert-experience  singularity  miri-cfar  empirical  evidence-based  speculation  volo-avolo  clever-rats  acmtariat  robust  ideas  crux  atoms  detail-architecture  software  gradient-descent 
july 2017 by nhaliday
Lessons from a year’s worth of hiring data | Aline Lerner's Blog
- typos and grammatical errors matter more than anything else
[I feel like this is probably broadly applicable to other application processes, in the sense that it's more important than you might guess]
- having attended a top computer science school doesn’t matter
- listing side projects on your resume isn’t as advantageous as expected
- GPA doesn’t seem to matter
career  tech  sv  data  analysis  objektbuch  jobs  πŸ–₯  tactics  empirical  recruiting  working-stiff  transitions  progression  interview-prep 
december 2016 by nhaliday
I don't understand Python's Asyncio | Armin Ronacher's Thoughts and Writings
Man that thing is complex and it keeps getting more complex. I do not have the mental capacity to casually work with asyncio. It requires constantly updating the knowledge with all language changes and it has tremendously complicated the language. It's impressive that an ecosystem is evolving around it but I can't help but get the impression that it will take quite a few more years for it to become a particularly enjoyable and stable development experience.

What landed in 3.5 (the actual new coroutine objects) is great. In particular with the changes that will come up there is a sensible base that I wish would have been in earlier versions. The entire mess with overloading generators to be coroutines was a mistake in my mind. With regards to what's in asyncio I'm not sure of anything. It's an incredibly complex thing and super messy internally. It's hard to comprehend how it works in all details. When you can pass a generator, when it has to be a real coroutine, what futures are, what tasks are, how the loop works and that did not even come to the actual IO part.

The worst part is that asyncio is not even particularly fast. David Beazley's live demo hacked up asyncio replacement is twice as fast as it. There is an enormous amount of complexity that's hard to understand and reason about and then it fails on it's main promise. I'm not sure what to think about it but I know at least that I don't understand asyncio enough to feel confident about giving people advice about how to structure code for it.
python  libraries  review  concurrency  programming  pls  rant  πŸ–₯  techtariat  intricacy 
october 2016 by nhaliday
Before the Startup
You can, however, trust your instincts about people. And in fact one of the most common mistakes young founders make is not to do that enough. They get involved with people who seem impressive, but about whom they feel some misgivings personally. Later when things blow up they say "I knew there was something off about him, but I ignored it because he seemed so impressive."

If you're thinking about getting involved with someoneβ€”as a cofounder, an employee, an investor, or an acquirerβ€”and you have misgivings about them, trust your gut. If someone seems slippery, or bogus, or a jerk, don't ignore it.

This is one case where it pays to be self-indulgent. Work with people you genuinely like, and you've known long enough to be sure.
advice  startups  business  paulg  yc  essay  πŸ–₯  instinct  long-term  techtariat  barons  entrepreneurialism 
october 2016 by nhaliday
Colin Percivel's homepage
interesting links to Hacker News highlights
people  security  engineering  programming  links  blog  stream  hn  hacker  πŸ–₯  techtariat 
october 2016 by nhaliday
What You Can't Say
E Pur Si Muove:

Sam Altman and the fear of political correctness: http://marginalrevolution.com/marginalrevolution/2017/12/sam-altman-fear-political-correctness.html
Earlier this year, I noticed something in China that really surprised me. I realized I felt more comfortable discussing controversial ideas in Beijing than in San Francisco. I didn’t feel completely comfortableβ€”this was China, after allβ€”just more comfortable than at home.

That showed me just how bad things have become, and how much things have changed since I first got started here in 2005.

It seems easier to accidentally speak heresies in San Francisco every year. Debating a controversial idea, even if you 95% agree with the consensus side, seems ill-advised.
And so it runs with shadow prices for speech, including rights to say things and to ask questions. Whatever you are free to say in America, you have said many times already, and the marginal value of exercising that freedom yet again doesn’t seem so high. But you show up in China, and wow, your pent-up urges are not forbidden topics any more. Just do be careful with your mentions of Uncle Xi, Taiwan, Tibet, Uighur terrorists, and disappearing generals. That said, in downtown Berkeley you can speculate rather freely on whether China will someday end up as a Christian nation, and hardly anybody will be offended.

For this reason, where we live typically seems especially unfree when it comes to speech. And when I am in China, I usually have so, so many new dishes I want to sample, including chestnuts and pumpkin.

replies: http://www.businessinsider.com/sam-altman-ignites-debate-on-whether-silicon-valley-culture-makes-it-tough-to-innovate-2017-12


Baidu's Robin Li is Helping China Win the 21st Century: http://time.com/5107485/baidus-robin-li-helping-china-win-21st-century/
Therein lies the contradiction at the heart of China’s efforts to forge the future: the country has the world’s most severe restrictions on Internet freedom, according to advocacy group Freedom House. China employs a highly sophisticated censorship apparatus, dubbed the Great Firewall, to snuff out any content deemed critical or inappropriate. Google, Facebook and Twitter, as well as news portals like the New York Times, Bloomberg and TIME, are banned. Manned by an army of 2 million online censors, the Great Firewall gives outsiders the impression of deathly silence within.

But in fact, business thrives inside the firewall’s confines–on its guardians’ terms, of course–and the restrictions have not appeared to stymie progress. β€œIt turns out you don’t need to know the truth of what happened in Tiananmen Square to develop a great smartphone app,” says Kaiser Kuo, formerly Baidu’s head of international communications and a co-host of Sinica, an authoritative podcast on China. β€œThere is a deep hubris in the West about this.” The central government in Beijing has a fearsome capacity to get things done and is willing to back its policy priorities with hard cash. The benefits for companies willing or able to go along with its whims are clear. The question for Baidu–and for Li–is how far it is willing to go.

Silicon Valley would be wise to follow China’s lead: https://www.ft.com/content/42daca9e-facc-11e7-9bfc-052cbba03425
The work ethic in Chinese tech companies far outpaces their US rivals

The declaration by Didi, the Chinese ride-hailing company, that delivery business Meituan’s decision to launch a rival service would spark β€œthe war of the century”, throws the intensive competition between the country’s technology companies into stark relief.

The call to arms will certainly act as a spur for Didi employees, although it is difficult to see how they can work even harder. But what it does reveal is the striking contrast between working life in China’s technology companies and their counterparts in the west.

In California, the blogosphere has been full of chatter about the inequity of life. Some of this, especially for women, is true and for certain individuals their day of reckoning has been long overdue. But many of the soul-sapping discussions seem like unwarranted distractions. In recent months, there have been complaints about the political sensibilities of speakers invited to address a corporate audience; debates over the appropriate length of paternity leave or work-life balances; and grumbling about the need for a space for musical jam sessions. These seem like the concerns of a society that is becoming unhinged.


While male chauvinism is still common in the home, women have an easier time gaining recognition and respect in China’s technology workplaces β€” although they are still seriously under-represented in the senior ranks. Many of these high-flyers only see their children β€” who are often raised by a grandmother or nanny β€” for a few minutes a day. There are even examples of husbands, eager to spend time with their wives, who travel with them on business trips as a way to maintain contact.

What I learned from 5 weeks in Beijing + Shanghai:

- startup creation + velocity dwarfs anything in SF
- no one in China I met is remotely worried about U.S. or possibly even cares
- access to capital is crazy
- scale feels about 20x of SF
- endless energy
- not SV jaded


Western values are freeriding on Western innovation.
Comparatively unimpeded pursuit of curiosity into innovation is a Western value that pays the carriage fare.
True. A lot of values are worthwhile in certain contexts but should never have been scaled.

Diversity, "social mobility", iconoclasm
but due to military and technological victory over its competitors
There's something to be said for Western social trust as well, though that's an institution more than an idea
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october 2016 by nhaliday
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