One thing about Canada is that its service industry moves very slowly.
The ^ site being down for the last 4+ hours is a good example.
666 karma · joined August 20, 2015
The ^ site being down for the last 4+ hours is a good example.
I liked SF and Boston. Things moved a lot quicker. The cities were more populated, people were more educated (I was near Harvard / MIT / UC Berkeley ) and unassuming.
I went to college near Toronto, and almost all my professors were American. They liked Canada for all the reasons I didn't like it. Things seemed to progress slower than their American counterparts, and people were less wound up. The downside is that the service quality in the private sector is not comparable that of the US.
I currently reside in Vancouver. It's beautiful here, but you get lots of rain during winter seasons. People are friendly. It's expensive if you live near the city, but where isn't?
Pay is definitely not as good if you're an engineer... Maybe 70 cents on the dollar. If you're trying to build a software startup, definitely consider moving your HQ to Canada (or outside of SF!), and keeping a corporate office in SV for fundraising. There's less competition for engineers, and you'll make 30% more progress per dollar invested.
Technology-wise, I'd suggest Waterloo. Silicon Valley hires graduates from U of Waterloo only second to UC Berkeley. Perimeter Institute for Theoretical Physics chaired by Stephen Hawkings is also there. The city is also home to Institute of Quantum Computing, started by the founder of Blackberry.
D-Wave, one of the major players in quantum computing is based in British Columbia.
In terms of AI, Canada is a bit of a talent drain. Most of the lead researchers and professors have been lured to Oxford or Google or wherever.
Venture Capital presence is limited here. Vancouver has only got a handful of major venture firms. Though, I met a couple of investors from Silicon Valley investors trying to start something up near UBC. I can't say much about Toronto.
Quality of living is pretty good here. I can go out for walks during midnight and not have to worry about getting robbed - something I was never able to do in LA. Many parks.
Overall, you'd like Canada if you're willing to trade slower pace, liberal views, colder weather for population size, and American imperialism.
Every American I've met in Canada really enjoyed being here, just as every Canadian in America I've met really enjoyed being there.
I'm no exception. There's something I love about the narrow Cobblestone streets in Cambridge, the golden hills in SF Bay Area, the weather in LA. And oddly enough, I've found that people I've met in the US are more personable than those in Canada. I suppose that comes with the more unified sense of national identity that is in someways a fascade.
It's funny that selling the company was their priority, and that they had to hire an investment bank to look for funders.
I mostly built this to get for tutorial/lecture videos because some of them are long-winded and contain unnecessary parts.
Maybe this will be useful to you guys.
Also, I'd love to hear what you guys think. Critiques, suggestions, anything is appreciated.
Although when you have a deal this large, the law firms are bound to be well connected and will be pulling every string they have to push the deal through.
Would be interesting to see you quantify this (in output maybe?)?
Traffic through Twitter are mostly spam in my experience (i.e. 0 seconds spent on page). Although if you can get a viral topic going, that is generally a little better.
AI is a bit of a counter-intuitive market: it's a high growth segment, but the structure of the market top heavy, and several things make it an unattractive investment:
1. Democratization: much of the technology is the public domain, meaning anyone with a CS degree can muddle together their own (and many have). That means if you invest, you investing in pure AI, which means you're investing in cutting-edge technology - i.e. Research, which very difficult to assess.
2. Dependencies & Go-To-Market: intelligence (i.e. pattern predictions) that's actually useful on a market wide basis require massive amounts of training data. There are a couple of resources, like ImageNet & Yahoo's data dump, but in the interim - AI needs to be domain specific. As such, the dependency is a widely used application (i.e. Facebook, Instagram, Google). You see the dilemma. Therefore, what seems the likely outcome is acquisition.
3. Unit of Economics: For this sector, it's the accuracy of its predictions. This is almost too one dimensional to assess as the basis for investment. Acquisitions decisions are made almost exclusively by the R&D department. Therefore, if you invest, you need to know what makes one AI different/better than the next.
IMO, it's too early to put money in. The structure of the market is still nascent, heavily favoring incumbents (Google & Amazon offering AI predictions as a service). It's difficult to see how an AI startup can break in, and how it can be profitable.
Take the above with a grain of salt, as it's tailored to my risk appetite.
I had a similar issue - I had to ask one our cofounders to resign and he was one of my best friends. (He started another company that raised conflicts of interest) It was very difficult. Here's what I took away:
1. Be honest with your reasons for leaving: If the fit is not there, it's not there - you can't force these things. It is in the long-term interest of the company to have people that believe in the vision. Anything less will only drag down morale.
2. Be grateful that you had the opportunity to work and learn with them: There's generally two ways to spin these things, "you wasted my time" or "this will be a cherished memory." It's always better to lean towards the latter.
3. Be resolute: If you're bent on leaving, know that this is the outcome walking out of that meeting. Startups are kind of like a band of brothers, it's easy to get guilted into a compromise.
An addendum to (3). Lack of communication is a common killer of startups, and it's usually built up over time, eventually leading to an ultimatum (like this). I'm not sure what the relationship between you guys are, but this can be a point of reflection / growth. If you have thoroughly thought this through, and there are no scenarios where you would be willing to stay, then that's fine. But more often then not, the points you bring up are invaluable to your peers - if they are receptive.
If it were me, I would bring up these concerns prior to your resignation and see what their reaction is. If you guys don't see eye to eye on these threats, then it's time move on.
(x)^n
x being # of people you're able to reach (sorry for the nondescript language), and n being how many people each of those persons will share that tool.
Obviously, whatever endeavor you undertake should seek to maximize n. And it would also be good to aim for low cycle time.
Anyway. Google Scholar is good, I loove Quora, TED to name a few.
Most social network feeds are organized using machine learning algorithms. The ideal case is to see the latest non-trash content, but that requires the machine to interpret natural language / photos in the post. We're not quite there yet, so instead, algorithms rely on meta-data. I.e. views / shares, and match them to similar character models to yours.
This is one layer removed from the actual content, but in order to assess its entertainment value, it needs context, which takes time to collect. The results in delay in reaching your feed.
He was a very good friend of mine, but my negligence definitely took its toll.
Not sure if you got a chance to checkout the video tutorial, but the idea is the video segments are parsed by keywords made by users, rather than video/audio recognition technology. Each segment of video is like a document with keywords, meta-data, etc. so we just run our search algorithm through that, and it's pretty standard from then on.
My name is Dan. This is my first submission so forgive me if I'm awkward. A couple of friends and I are building an app for students (and other awesome people) so they can save segments of any video they find on the internet.
We're really excited about the implications for search because instead of searching for whole videos, people can search for parts of videos that other people have personally curated. If I may, it's sort of like Google's crawler being able to read the text on the webpage rather than just the meta-tags. It's a much more powerful way to find interesting content.
Anyway, it's in private beta at the moment, but there is a video tutorial. Any feedback is super appreciated!