629 karma · joined June 1, 2016
Akira is a fast growing Toronto-based startup in the medical technology space. We operate a next-generation telemedicine service that lets Canadians access primary healthcare through an app.
We're relatively mature, but still operating very much like a startup. We've intentionally kept the dev team small (about 10 of us now) and are expanding very carefully.
Our backend is primarily Ruby-based (though NOT Rails-based!), but it's expected that you'll bring other technologies into the stack as needed.
More info here -> https://angel.co/akira-2/jobs/106786-sr-full-stack-ruby-engi... ... or email matt at akira dot md
What I'm arguing is that dismissing these philosophical problems as just nonesense rooted in the vagueness of language is missing the point. If nothing else, these paradoxes lay bare the vagueness of language, but moreover they beg the question of why language is so vague. What's more, it turns out that making language more precise — as we've tried to do with computers and mathematics — doesn't seem to solve the problems at all. In fact, it seems to lead to even more trouble, as, for example, the designers of Subversion or Mercurial eventually discovered.
Different systems have chosen to take very different stances on this. As it turns out, taking a stance on what makes a file a file (or a ship a ship) — being explicit rather than vague — does not solve the problem. Not even close. In fact, one of the many little things that make Git a brilliant piece of software is that it refuses to take a stance on identity. Git is intentionally vague about what makes a file a file. It doesn't actually track it at all — file identity is determined retroactively. It's a question for which Git intentionally doesn't have a straight answer.
What I think is really fascinating about all of this is that computers are forcing us to deal with age-old philosophical problems head on. These are not just interesting riddles anymore. They're real problems that need concrete solutions. Event more interestingly, there is something in this that establishes computing as categorically different from just pure mathematics. Computing forces us to bridge abstraction with the real world — to answer questions like what is the relationship between an abstract model and the thing that is being modelled. This to me is really exciting, and what follows from it can't be easily dismissed with a comment like "most philosophical problems only exist because language is vague".
An important addendum to this is that the objective has to be closely tied to the work — the feedback loop for learning and then seeing some results from that learning has to be tight.
I think this may be why in school grades were not a good objective, for me anyway. They were just too distant a reward from the work.
As is often the case in philosophy, Marx was brilliant in critique, but lacking in his prescription. Philosophy tends to be great at asking questions, but not so much at answering them.
I suspect many intellectuals who call themselves Marxists identify with Marx's critique — which arguably still stands and a brilliant and revolutionary body of thought — but not with some of its eventual realizations.
A nice, 500 page overview: https://press.princeton.edu/titles/8973.html
This is maybe comparable to the way high-performance applications are still usually written in C instead of higher-level languages. You'd think that at this point we could write compilers smart enough to generate more performant code than something written by a human hand, but in practice that often doesn't turn out to be true. Not yet anyway. And even when this is possible, due to their inevitably complexity, those abstractions tend to misbehave in unexpected ways, with unpredictable performance hiccups.
I personally wasn't disappointed — I'm really glad I did this as my undergrad. AI research however tends to go through periods of hype followed by disillusionment. There's a history of promising developments that hit a wall or fizzle out in the long run. That's not to say there hasn't been progress (there's been tons!), but based on track record alone, it's prudent to be skeptical of overly optimistic pronouncements — we're probably much further from the "singularity" than one would think, based on current wave of ML hype anyway.
Alexithymia can overlap with ASD, but it can certainly occur in individuals who do not otherwise meet diagnostic criteria for ASD.