lifebeyondfife

If one day we can map exactly an animal brain's architecture, how new ideas are learned etc. (is it backwards propagation or something else?) then we can replicate that behaviour using logic gates and floating point numbers.

Mapping a brain to a digital neural network architecture is such a prohibitively expensive problem I wouldn't know where to begin. Perhaps a biologist would have a better idea than a computer scientist. But it's enough to say that intelligence can theoretically exist on electronic circuits.

EDIT: After going back for a further look, saying that the original link is more detailed than mine doesn't do justice to the amount of content (images, animations, code, explanations) there actually is.

But in the cities and as an adult, I really don't see it as an issue anymore. I work for one of the companies sponsoring the event and I see probably about as many foreign employees (English and other European and world nationalities) as there are Scots.

I think most countries will always have some pockets of racism because some people will always be ignorant. So I'm not absolving my country entirely, nor saying the anti-English sentiment no longer exists, but I don't see it as the big problem it used to be.

The problem I see is that we treat computer science as something you eventually get round to learning once you've already taken some preliminary steps with coding, and even then it's something to be looked at late into high school or at university. Code is the awesome tool that motivates learning, but I wonder if the process is taught the wrong way round. Could it be done a little compsci first, then code?

I started a series of blog posts trying to introduce the non-technical reader to the fundamentals of computer science in plain english: http://lifebeyondfife.com/tag/compsci-in-plain-english/. Still a work in progress, I'm trying to keep it interesting and explain why computer science is important at the same time.

But for coders who've never seen problem classification theory before (not Big O notation), the subject can appear opaque and irrelevant. I'm all for simplified introductions that help those less familiar with formal computer science become more aware of the theoretical side.

The section headings 'Understanding NP-complete completely useless?' and 'Recognising NP-complete problems' are the most relevant. http://lifebeyondfife.com/helping/

Disclaimer: He's a friend and former bandmate from uni. His music is awesome.

Even though other algorithms such as Mergesort also have O(nlogn), Quicksort is normally the preferred implementation because it's relatively easy to do in-place and generally is the most efficient[1] of all the sorting algorithms.

[1]Not my field of expertise though, happy to be told I'm wrong.

There are so many different complexity classes, P and NP are just the start.

Sounds like that advice applies more to the latter route than the former.

Great learning experience though, and this post is a reminder to me to make sure I don't repeat the same mistake.

I could talk about how I love the land of Hyrule, but I'd also like to play a game based in the city streets and countryside of my own country too.

So I blogged about it http://lifebeyondfife.com/81-real-games-html/ (I'm not a game developer and had other things to work on). Excited to see others are getting closer.

Incorrect apostrophe, Aaaaaaargh!

I suppose the problem is, do we need to vet everyone for sharing the same humour and standards of acceptability as us? If we're joining the same football team or going to a comedy club and some beers, I wouldn't particularly enjoy hanging out with someone as dull/safe as I am at work. But is it ok to say to a woman, "We'd love to welcome you onboard so long as you don't mind the dick jokes constantly flying around the office."? (Whether explicitly or implicitly, I don't think it is ok).

If we really want to solve the clique-problem in tech, we need to either change the boundaries of acceptable humour and behaviour. No-one wants to spend 8 hours a day in a "sterile humorless environment". I know I had the most fun coding in my early 20s in an office where 90% of us were just out of university and male.

There are going to be more tradeoffs towards inclusiveness as we go forward. Even it reduces the fun sometimes, I think it's the right thing to do.

If you want a definition of the workplace, it includes hacker meetups, youtube videos, presentations etc. where people of all social groups come together to discuss or create code. I have a very twisted sense of humour that many would find offensive. At work though I keep that side of myself under wraps, not out of a sense of fear but of respect.

The bottom line is this: by collectively changing our behaviour to be less offensive (from the point of view of gender, sexuality, race, religion etc.) then people from those groups feel more safe and secure in that environment. If women truly felt comfortable to enter into the currently male dominated tech industry we get a bigger talent pool to choose from - the competition and skillset increases. Now re-read that sentence substituting 'women' and 'male dominated' with any minority/majority pairing and we start to see how much bigger we can make the number of available programmers.

It may seem dull to have a workplace that's so sterile and inoffensively humourless, but it's work, not a bar.

With regard to the "natural vs supplement" argument, some people are very pale and susceptible to skin cancer. It may not be safe for everyone to get enough Vitamin D through natural means alone.

However, tools improve and you're denying yourself productivity benefits. One of the advantages of a statically typed language is that the computer knows the type of the arguments. I love environments that allow me to 'mouse over' a function or variable etc. and tell me exactly what it is (and allow me to navigate to its definition). You don't have to use a mouse but what's the disadvantage in having alternatives to keyboard shortcuts where appropriate?

IBM stuff, however... well, I've mentioned my feelings on their software on HN before. They're still selling Lotus Notes and Clearcase - I'd be very careful before taking anything they've built/bought.

I think having a starting point of reproduceability would lead to more reimplementations.

There's a risk that the VM platform decision could date and, ultimately, render useless an experiment sandbox.

However, two points. Firstly, any attempt to wholly encapsulate an experiment will be susceptible to problems concerning the technology choices made. By choosing the abstraction at a VM level, you're more likely to get a better number of years out of your choice than picking specific programming languages (which I contend would fail to gain adoption as researchers wouldn't change their obscure toolkit if they felt it was the right one for their research).

Secondly, even a VM for an obsolete architecture is better than what we have today - the original paper and perhaps an email address for the lead author.

In making the argument for recomputation to aid its adoption, it's important not to add too many constraints on the researcher e.g. "Your results should be reproduceable and written in C because it should still be around ten years from now."

Even an obsolete language in a working VM sandbox can be edited and inspected to make changes, verify correctness etc. If you want to extend the results of that research you may have a porting project to update the code to more a current language, but that's leaps and bounds ahead of how things are now: read and re-read the paper until it makes sense and implement the algorithms from scratch.

Agree with your hypervisor/architecture point but I suppose there has to be some broad architectural choice made.

(The author of this article was my PhD supervisor. I've always found him to be an inspiring, forward thinking researcher so as much as I love the recomputation idea on merit, I'm also a little biased.)

For loosely constrained problems, yes, constraint programming will enumerate exponential combinations and that's not a good fit for this methodology. The point I was trying to guard against is that the solver isn't smart and therefore when asking, "Is it smart enought to know X?", I'd say it depends.

If the solver in question has some decent bounds propagation on the six or so binary arithmetic constraints in the problem, then no, it won't enumerate all 1001^6 combinations (assuming domains of size 1001).

A more helpful answer from me would have been, "It's unknown if it's smart enough, but the more constrained i.e. the more combinations your constraints reject, the better".

Seeing as there were four solutions out of 1.8 million states (in the domains of size 11 example), that's very constrained.

I don't mind fielding questions over the basic theory if you want to email me (iain at my domain).