I recently realized something that made me truly realize how powerful service workers can be. When I have internet, it feels like there are infinitely many things competing for my attention. But when I am on a plane for example, and there is no internet connection, the competition for my attention is much less fierce. The 3 things I can usually do is look through my photos, watch a downloaded movie or read an ebook. With service-workers, if you are able to deliver an offline web experience for your users, you are able to get their attention in one of those few moments when the competition for it is least fierce.
After reading this article you will learn, the theory of how service workers work, a short tutorial to apply that theory to make a website that runs without the internet and finally, what this means for the you and future of the internet.
A few days ago I was on LinkedIn and read an article about the world’s youngest self-made billionaire, 27 year old John Collison from Stripe. In the article he mentioned that his success was a result of “intense application and hard work…But I also think that luck was required too… There are [others] who are smarter and harder-working than us who just didn’t get the same good fortune.”
As usual, in the comments section people pitched their ideological camps and started arguing. Some said that his success was purely a result of the luck of being a middle-class white male. Others said “Luck does not exist, you can do anything with hard work”. However, like most things in life the truth is a lot more nuanced and is a balance of both sides.
The luck debate is one that has been brought up many times in all walks of life and while I see people passionately argue both sides, I rarely find a perspective that properly contrasts the strengths and flaws of both arguments. For a long time, I intuitively knew that the short answer was “both”, but I had a hard time articulating my thoughts in a clear, cohesive way. Fortunately, as I write this at 2:53 am in the morning, in a flash of inspiration, I think I have a neat analogy for understanding how luck impacts our life.
Coming of Age
Shortly after my 19th birthday, I realized that I would be graduating from university soon with a software engineering degree and would be entering the “real world”. I spent a lot of time thinking about what this “real world” would look like when I graduated and what places would provide the most opportunities for a better future. I have always been very curious about things happening around me and what the world will look like in the next 5, 25, 50 years.
In the summer after 3rd year, I decided that I wanted to work at a top tech company such as Google or Bridgewater. There was problem though. I didn’t go to a target school, my grades were just okay and my work experience consisted of being a janitor and IT Tech support. I knew if I wanted to get a chance at these top companies I would have to get creative.
At Atila, we’re trying to provide all students with access to funding for a quality education. This is a big problem with many moving parts and the challenges can be very complex. This article will give you an idea of the software stack we use to solve this problem and some advice on how to choose a software stack for your company as well.
Broadly speaking, I generally spend most of my time thinking about two things, technology and investing. More specifically, I often ask myself what is something useful I can build with software (or occasionally hardware) and what is something useful which I should invest in. Algorithmic trading is a nice integration of these two schools and I have been spending some time understanding this field. This is an intriguing field and I learnt some interesting things which I decided to share.
For my summer internship, my project involves using machine learning to help small businesses with funding. I learned a lot about machine learning in the process, so I gave a talk about it to some of my co-workers and shared the slides online:
I also shared the code on my Github. The following is an essay version of the talk.
Before I say anything, I want to show you a video from the 2017 WWDC Apple conference, WWDC is the annual conference which Apple hosts and is one of the most important events in the tech calendar for showcasing the top technology applications that will be used in the near future.
Apple wants you to know that they're really focused on machine learning pic.twitter.com/ZCJvBPGQpi
— VICE News (@vicenews) June 6, 2017
So the machine learning supercut gives some context to how I think society generally views machine learning. On the one hand, it’s a technology which has a lot of potential and will drastically change aspects of our society. Conversely, because it has so much potential people have a tendency to over promise and over-advertise the things which machine learning is capable of doing and often turn it into a marketing gimmick and annoying buzzword. For a high level, non-technical summary of what machine learning is about and what the future of technology in general, I recommend Homo Sapiens by Yuval Noah Harari.
I take a more middle-ground approach and say that you should judge it on the merits of what you can actually build with machine learning, but first, you have to understand what machine learning is.
(Guage audience level) How many of you: has never coded before… used ML in a small side project … Studied ML at a Master’s or Ph.D. Level, written or helped write a paper about ML etc.)I’ve tried to structure my talk in such a way that non-technical people will find it interesting and the more technical, ML-experienced people may some new, interesting concepts.
The reason my talk is called practical machine learning is because I consider myself a very pragmatic, practical person and whenever I learn something, the first thing I ask myself is how can I apply what I’ve learned and put it into practice. Hopefully, after today’s talk, you will hopefully be able to apply what you’ve learned and build actual ML projects. Alright, so let’s get started
What is Machine Learning?
My talk was greatly inspired by 2 tutorials which I did, the website is awesome and the guy who runs it Harrison Kinley is a very good teacher.
- Also, speaks to a pattern of whenever people say “Computers will never be able to do X because they need a certain skill that only humans have”. It’s usually just a matter of time before computers acquire those skills.
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|1.||↑||I forget the exact number, but I’m just repeating what Scott Galloway said|
When I think about the next five, 25, 50 years I often ask myself, “What things will be better in the future? What things will be worse? And how do I best prepare?” I tend to oscillate between excitement-hopefulness and mild paranoia. I have now decided to split the difference between optimism and pessimism and say that I will be Optimistic but Hedged.