jfaucett
That was my impression as well after doing some research in this area a while back. Although I looked specifically at longevity and cardiovascular studies there was a peak benefit at about 2hrs per week after which virtually no more benefits were observed.
1. Start with the intuition. (The big concepts i.e. for BigO trying to figure out the best, worst and average number of steps it takes to run a function as the input size grows). Make sure everyone gets the big picture, core concepts, and intuition before moving on.
2. Use as many graphics and visual aids as possible.
3. Let people work step-wise up to getting the full solution i.e. get them to just sort one element in a list. Then change the algorithm to sort two elements, etc..
4. Start very easy, I mean very easy (like sorting a list of 1,2 and then 3 elements), then let people slowly step up in difficulty until they implement full algorithms and data structures. Also make sure everyone can at least describe, draw, and sketch out solutions that may not necessarily be precise but cover the "gist" of what the algorithm is about.
5. Use Stories: If you can tell them stories with algorithms / data structures its even better. (For stacks I like using the example of the professor grading essays on his desk, or a person browsing the internet, for binary search I like looking for a book in the library, etc)
6. Use Lots of Examples: When explaining the mathematics be sure to have exercises/examples which let them play a lot with the parameters (RStudio or jupyter is nice for this), make sure each expression is clearly broken down and clarified. Also having positive examples (for how it should work) and negative examples (for common mistakes and when things are incorrect) can help improve understanding too.
hope some of these tips help. Good luck!
This is not the case in my experience. I've upgraded pretty decent sized apps (hundreds of models,lines of routes, etc) and in my experience it would take a couple hours a day spread out over a few days a month and then I was done (for versions: 3-4 and 4-5, never done 3-5).
I would say most of the problem is making sure everyone on the team just keeps all functionality as-is. It can be tempting for team members to refactor as they go through but this then becomes a huge time-sync. Anyways, thats my exp on rails but I have no other frameworks to compare it to.
Has anyone migrated a massive app from some PHP Framework like Symfony or from a java framework like Play, or any framework with a large code base?
I have had to upgrade massive systems that were not done with any framework and full of one-off solutions with in-house developed libraries and it was an absolute nightmare, but I'm sure this depends on the language and team. However, in general I think an open-source library used by millions or even hundreds of people is going to have better documentation, bug coverage, support, etc. than something done in house, just IMHO.
So I guess my question would be, what does the alternative look like?
Its hard to discuss any of the things you mention since it all depends on what particular system you are looking at. If we constrain ourselves to the US system we're still going to have massive differences even within states and within counties within states. That doesn't mean many of the subjects you mention aren't issues, but you're confusing a state with a cause IMHO.
We could make ourselves all almost perfectly equal tomorrow by destroying all wealth and living as hunter-gatherers again. That wouldn't solve any of the problems you mention though. So clearly, inequality is not a fundamental issue underlying all these problems.
Personally, I think the major factors will vary depending on the context and problem domain. For access to healthy affordable foods, it could be something as complex as subsidizing or taxing various agricultural products, or as simple as removing all subsidies and tariffs and letting cheaper foreign made produce flood the markets.
With your third point, that certain people attempt to use power - which is a function of many factors not just wealth - to manipulate the laws and the system to their own advantage is something that is remarkably well documented in the political science and sociological literature. I'm not sure what the best approaches are towards mitigating this. Certainly, high degrees of transparency and accountability as well as a swift and well functioning judicial system would be my first guesses at the largest factors, based on my experiences with South American and European economic data. Personally I think the more checks and balances i.e. decentralization of the concentration of power mechanisms (i.e. ability to tax, ability to imprison, etc.) the system allows for is another key factor.
Rich and poor are always tied to purchasing power which itself is tied to productivity, efficiency and innovation within an economy. (as well as the traditional price theory, demand/supply, etc.) I used rich/poor because those are simple terms everyone can understand more easily than PPP or Genuine Savings, etc. (however, wealth is a better term I should have probably used in its stead)
For instance, the phone you can buy and hold in your hand today for $200 is vastly more powerful than the most expensive IBM mainframes of just 20 years ago - A CEO of those times could not even afford the simple phone many refuges have in their Rucksacks these days. Are they rich because of this? It depends on what particular measurement for "rich" you are using.
To answer your question. Yes, ceteris peribus if everyone gets more money instantly (i.e. money supply increases) all prices will rise accordingly and there is a net null effect, however, and this is key, if that increase is in wealth i.e. capital assets, productivity, everyone is better off and prices will increase according to traditional demand-pull.
I agree with you. First, historically speaking, large disparities have led to revolutions and countless atrocities. Also our neural cortex isn't so highly evolved that it doesn't bow to the will of our chimp brain most of the time anyway, which is just to say we have a plethora of evolved intuitions and feelings. However, just because we have some innate biological sense of right or wrong or good or bad, that doesn't necessarily mean we should advocate for that in a normative way.
That was kind of my point. If we are going to build a better society I think it is important what normative values we strive for since we are not completely controlled by our impulses and those values will certainly have an effect on the systems we design and the relative effectiveness or lack thereof.
Our goal should be to increase wealth in general and ensure that the median wealth per capita increases or steadily increases for various segments of the population. Ideally, you would have faster median expansions in lower income segments of the population but in my mind, even if the top %1 had an increased wealth per annum of say 0.05% and the lowest had a lesser increase of 0.02%, I would still prefer that (assuming we've controlled for PPP, inflation, etc) than a system where the poorest increased at 2% a year, the richest dropped -2% and our overall economic productivity declined or stayed roughly the same year after year.
Overall, though I agree with the author in that too much emphasis (in the news at least), is put on GDP. Its still a useful indicator of productivity in an economy and from an investment perspective, but there are as he rightly mentions many qualitative properties left out of this one measurement. Which is kind of always the case is it not? Every time we measure something it measures a specific thing which we decide to interpret in a particular way, its funny how often we generalize that act to areas for which the original measurement was never designed.
Just thought I'd provide some counter data here. I've built small services in Sinatra, which did not warrant the usage of Rails and never had to change anything, having them in fully functional operation for years.
I could have built these little apps in Rails, but I did not need it, never did, and my code/deployment/etc. is cleaner and smaller because of it.
Also these services have always been quite small (a few endpoints or functions), which is what I think Sinatra is best for.
Its been clear to me for some time now, and I'm not alone in this, that as the properties of Bitcoin have moved further away from its primary function as a useful unit of exchange that its usage would decline.
There are other crypto-currencies which are now much better suited as a medium of exchange, which ones people will prefer as time progresses remain to be seen, but unless Bitcoin takes a major corrective path, I would be quite surprised to find Bitcoin in that final set of widely utilized currencies.
1. Nature of Life (we are still nowhere near understanding how it arose or how living organisms work in all their intricate detail).
2. Nature of Consciousness - what is consciousness, how can living matter instantiate it, how can we quantify it, etc?
3. Origin of the Universe
4. Why do we seem so alone in the Universe?
5. Resolving Quantum Mechanics / Gravity
6. Understanding Time
Made me think there's also a big difference in what nobody prefers reading vs. what nobody prefers writing.
No:
Body:
Wants:
To:
Write:
- YAML
<No>
<Body>
<Wants>
<To>
<Write>
<item>YAML</item>
</Write>
</To>
</Wants>
</Body>
</No>
{
"no" : {
"body" : {
"wants" : {
"to" : {
"write" : [ "YAML" ]
}
}
}
}
}
I think that is at best fragile as a general statement. I would argue that increases in local support are dependent on other factors besides heavy-handedness or harshness, and that these vary on a case by case basis. For a plethora of counter-examples see:
The history of the Jewish people, Native Americans during colonialism, the anabaptists or the calvinists in European history, the Muslim populations after the conquest of Granada.
What I would add is you should feel a little bit of stress from the difficulty of the task. As an example, recently, I did research and in an unknown field both for the first time. I was a little tense, it forced me to focus every minute. If I didn't know what a formula or term meant in a paper I had to figure it out, because I had to be able to evaluate and possibly implement it.
I had to look at papers and evaluate them on their quality b/c I was using those to mold my own. I made many,many mistakes and failed hard many times. It was often very unpleasant (like running when you're fit but still not quite getting enough air on the last leg). But I kept pushing through. At the end, I learned more on this project than I had in a very long time.
Anyways, hope my experience can help someone else to push through and see the light on their project.
Anyone working on chip architecture care to give their opinion on the next 10-20 years in chip design? It would really interest me to know if chip designers think Moore's law will continue, since that is probably going to be a big factor in the timeline for AGI.
With "we" you mean the "united states"? There are millions of people the world over in slavery right now, so "we" in terms of humanity have still not overcome this problem. [1,2]
1. https://www.usatoday.com/story/money/2014/11/23/247-wall-st-...
well, in semantics/pragmatics these discourse particles are often not deficiencies at all. They are signals with practical semantic purpose. "hmms" and "uhs" can signal attentiveness, turn-taking (turn holding, turn yielding, etc), agreement - just to name a few.
For any machine system to be able to pass as human, it will have to be able to control these nuances or people will pick up on something being wrong, though they might not be able to articulate precisely what.
No we don't. Some of us do and some of us do not. You are self-admittedly in the former group, I am not.
Also, just because something cost big companies money on one front does not mean it doesn't increase the monopolistic power of said companies and even increase revenues on another. Let me use your own example as a hypothesis we will be able to observationally falsify or not in the coming years. By eliminating roaming charges many smaller companies in the space will have to compensate for the loss of funds and will therefore either have to reduce their current plans, drop service offerings outside of the current country, or eventually collapse entirely. Regardless of the outcome, the total market competition has decreased and ultimately the mega corporations stand to win through decreased overall competition in the space. Additionally, due to lack of monetary incentives, I would expect the rate of innovation in large-scale roaming technology and infrastructure to decrease compared to countries which do not have such legislation.
Socio-economical systems are complex and nonlinear in nature, unfortunately, we i.e. humans have not evolved to think well about nonlinearities neither have we built ourselves sufficient tooling to augment our prediction capabilities for such systems. IMHO, this is the well-spring for the difference between intentions and outcomes in regulatory policy.
If you look into it I think parent is most likely correct with his predictions since they are easily verifiable i.e. big coorps do have massive teams and monetary funds to deal with this legislation, startups and one-man shops do not. This is completely ignoring the deontological question of what should be the case, where I think most would be in agreement.
I disagree about stratification to a certain extent, that is, I only see 2 groups emerging. Here's my thinking.
Highly paid service jobs will remain unscathed only in so far as they have regulatory barriers to entry i.e. someone has to have gone to medical school, be licensed by a community of medical professionals, etc, in order to practice medicine. This applies to lawyers, doctors, psychiatrist, teachers, professors, architects but notably not to software engineers, carpenters, plumbers, etc (note: this split depends on country specific laws). Those fields that can simply block out or control the rate of progress and labor supply will see the least disruption in terms of wages. Also note, that most of these "future stable" fields are occupations which are tied closest to governmental law and regulation and are occupied by the upper echelon of society.
Occupations such as mail carrier, brick mason, railroad operator, gas station operator, steel worker, etc. which are currently well-paying middle class jobs, will evaporate over the coming decades in the face of innovation and automation due in large part to the absence of the ability to set arbitrary barriers to entry.
Within this "unprotected" category of jobs, i.e. those which cannot write regulations in law to protect themselves, those will persist for which it is simply more economical to maintain the status quo. Perhaps, something like a barista, or a part-time McDonalds worker or something of that sort. For these job categories, even though it may be feasible to have an android pour peoples coffee and bring it to their table and ask them about their day, people may dislike the service and/or the cost of producing such a product may simply be too high for that particular occupation.
This, I think, is how we should look at which job categories are more future / AI secure or not. Its at least the prediction metric I would use.
Agree. Basically all current progress in "AI" should really just be called statistics and I think there would be much less confusion. If you say statistics meets Big Data and faster computers then you basically have all current progress. Real "AI" i.e. thinking machines which are behaviourally intelligent without resorting to cheap tricks has made very limited progress.
We might be able to fool ourselves into thinking a machine can think. For example, given a naive Bayes classifier for all X situations there is a probability that a real person responds with Y so therefore the machine responds with Y and then a human observing this system perceives the system to "be intelligent". But for me this is just statistics and nothing more, there is no abstract thought or intelligence going on within that machine system - at least as it relates to all other living creatures on the planet.
see: https://jserd.springeropen.com/, https://research.google.com/pubs/SoftwareEngineering.html, and https://arxiv.org/list/cs.SE/recent for some recent SE research topics.
Software Engineering encompasses, broadly speaking, the following topics:
Software Design => Implementation => Testing & QA => Automation => Project Management
Implementation, Testing/QA, and Automation are largely very quantitative, you can create models and algorithms and analyze them and measure most things even easier than in the physical sciences. On the other hand, due to the nature of Software Design and Project Management you are largely left to tools of the social sciences i.e. surveys, difficult to control experiments, observations, etc.
1. Github got First Mover momentum
2. Git preference over mercurial back when Bitbucket burst on the scene.
3. Github has many users which means its easier if you want to reach a large audience with your FOSS project.
4. Due to #3 large companies hosting all their big projects on GH, leads to a self perpetuating cycle.
this. I've had a fantastic experience as well. Its one of those communities and projects that has been so good I have to leave a comment about it :)
Thank you GitLab team and contributors!
The first two years at university are basically a rehash of stuff you probably should have already learned in high school IMHO (Calculus, English Composition, humanities electives) and junior colleges are a great way to take those courses at affordable costs.
One thing I would recommend - even over community college - is taking CLEP tests. I found out about them a little late but was still able to clep 2 years worth of spanish and french which meant I got credit for around 8 courses for a grand total of $160 bucks. I also clep'd most of my humanities electives like history and sociology. It requires some personal effort i.e. find a textbook at your local library and read and study it - but is definitely worth it from a time/costs perspective.
Anyways, I wanted to mention it because a lot of people don't know about CLEP tests and they saved me literally thousands of dollars (in junior college classes costs, probably 50k if you compared it to a top tier university).
Just thinking out loud here but what's the alternative? Random allocation of goods/power or random promotion of individuals/companies based on nothing?
At least in a meritocracy advancement of an individual/organization is based on some performance measurement - we can of course argue about different performance measures and sets of tasks - but we have some means of guiding society in a normative way.
This is the crux of the of the problem IMHO - at least for the fields I study (AI/ML). Replicating the results in papers I read, is way harder than it needs be, i.e. for these fields it should just be fire up a jupyter notebook and download the actual dataset they used (much harder than it seems to actually get your hands on). Very few papers actually contain links to all of this in a final polished manner so that it's #1 understandable and #2 repeatable.
Honestly, I'd much rather have your actual code and data that you used to get your results than read through the research paper if I had to choose (assuming the paper is not pure theory) - but instead there is a disproportionate focus on paper quality over "project quality" at least IMHO.
I don't really know what the solution is since apparently most academics have been perfectly fine with the status quo. I feel like we could build a much better system if we redefined our goals, since I don't think the current system is optimal for disseminating knowledge or finding and fixing mistakes in research or even generally working in a fast iterative process.
Honestly when you look at the data, its hard to believe there has been no major backlash on a national level until this point, especially when you consider a majority believe there is discrimination against them [3]. One reason could perhaps be the fact that Asians outperform other groups on a variety of positive societal metrics (income, wealth, education). [1,2]
Anyway, whether or not you think we should or should not do anything about it is up to your political tastes, but I for one am at least happy its getting some national attention - given my own personal experience and just knowing what the data says. Admittedly, if the data looked a lot different I would be more inclined to rule this out as bogus.
1. https://en.wikipedia.org/wiki/List_of_ethnic_groups_in_the_U...
2. https://www.census.gov/content/dam/Census/library/publicatio...
3. https://www.npr.org/assets/news/2017/12/discriminationpoll-a...
4. http://asianamericanforeducation.org/en/issue/discrimination...