After hearing about all the major websites getting hacked every few months, I decided to be proactive about improving online security.
Over time I began switching to a password manager, and I discovered that it had a feature to review the security of all your passwords for weak, blank, and reused ones. I tried running this and was daunted by how many issues it found and never got around to fixing it.
Then I came up with a solution: just change one password per day. It usually takes less than 5 minutes. The only annoying part is when your login info doesn't even work and then you need to go through a reset process of some sort (and maybe where the account got migrated to some other website where you have to sign up fresh).
Why do this at all, especially for sites you may no longer actively use? So many sites keep our personal info in their databases, even if we aren't actively using them. And to minimize the chance of that info being hacked, it's good to have as strong security as possible.
So along with changing reused or weak/blank passwords, I also took the time for each site to turn on 2FA if it was available for that site. You could argue that I could just turn on 2FA and leave the password alone, but I figured two levels of protection are better than one.
All in all, it took me about 3 months to get through the sites/passwords I cared about to make them all strong, turn on 2FA, etc. (and there were many days I skipped it if I was busy).
Here are the detailed steps in case you want to take on a similar such "daily password change" habit for yourself:
I’m really excited to have been able to invest in Visual Labs. Based in Menlo Park, Visual Labs is the body camera company that does not make body cameras. With technology designed by Stanford University computer science graduates, Visual Labs provides “body worn computer” solutions that are hardware-agnostic for police, private security, and sports teams. Their focus is on superior software with unique, critical functionality such as live streaming, real-time location tracking, and analytics.
The company was started by a founder who went to my high school (and college) and who has pursued this vision since his senior project. He has built out a complex product with multiple interworking components and interfaces, and it’s actually being used in production in many police departments and even in this year’s Super Bowl. Having a smartphone (which also happens to have a camera) provides many advantages over a more clunky/dedicated/proprietary hardware device and allows the Visual Labs software to keep improving over time and providing more critically useful functionality to police and security teams worldwide.
I’m personally excited about the company because I believe this sort of focus on software is much more scalable as a business and effective for the end-user over the long run. I love seeing a team work tirelessly over years to get a product out and adopted in the field (especially in difficult, demanding use cases like police/security). I also think the company has a strong positive impact on the safety and effectiveness of the officers who serve our community.
Here is recent press coverage on the company:
CBS, ABC, Fox, Ars Technica, Police Mag
What I love:
A good friend of mine who's an engineer and entrepreneur and really into AI recommended that I check out Superintelligence: Paths, Dangers, Strategies by Nick Bostrom. I just finished reading it and thought it was pretty eye-opening and scary.
I enjoyed the historical overview of the field of AI, and the many examples of current programs and how they rank against humans in various domains.
My biggest takeaways:
-McCarthy’s dictum: When something works, it’s no longer called AI.
-Quote from Knuth: computers can now do most of the things that require thinking but very few of the things that we or animals can do without thinking.
-If/when general AI is solved, the transition to superintelligence (above-human level intelligence) will happen too fast to respond to it at the time, so we should think and plan ahead.
-It's really hard to design objective functions/values for AI. Most strategies that on first order seem ok are really bad when considering second and third order effects.
-The most likely scenario is that we will get something wrong and basically be screwed. This is quite scary.
-Approaches like whole brain emulation seem interesting but really difficult to pull off in practice.
-The indirect value loading approach ("the AI should try to maximize whatever most of us would want it to maximize had we thought about it long and hard") seems interesting and compelling (and was new to me).
I'm personally skeptical we will ever achieve general AI. I think we'll just get better and better at domain-specific applications, but I don't think we'll ever figure out how to artificially make a machine think in the way we do (or for that matter understand how we truly think). I think it's just one of those mysteries that will never be fully solved.
I kind of lost steam about 2/3 of the way through the book when the level of detailed analysis of very futuristic scenarios seemed kind of overboard to me. I thought that it was hard to really effectively reason about situations which will in all likelihood be very different from anything we can imagine right now. It's good to be cautious and try to plan ahead, but I just thought it got too much into the weeds and too fine-grained for the immense uncertainty in question.
My full notes on the book are below.