I’m a technical and scientific person. I’ve done some online courses on machine learning, read enough articles about different machine learning projects, I go through the discussions of those projects on Hacker News, and kept a bunch of ideas what would be cool for the machines to actually learn. I’m in the right place to actually do some project, right? Right? 🚨 Wrong, the Universe says no…
This is the story of how I’ve tried one particular project that seemed easy enough, but leading me to go back a few (a bunch of) steps, and rethink my whole approach.
I bet almost everyone in tech (and a lot of people beyond) heard of AlphaGo, Deepmind’s program to play the game of Go beyond what humans can do. That has evolved, and the current state of the art is Alpha Zero, which takes the approach of starting from scratch, just the rules of the game, and applying self-play, can master games like Go to an even higher level than the previous programmatic champion after relatively brief training (and beating AlphaGo and it’s successor AlphaGo Zero), but also apply to other games (such as chess and shogi). AlphaZero’s self-learning (and unsupervised learning in general) fascinates me, and I was excited to see that someone published their open source AlphaZero implementation: alpha-zero-general. That project applies a smaller version of AlphaZero to a number of games, such as Othello, Tic-tac-toe, Connect4, Gobang. My plan was to learn by adding some features and training some models for some of the games (learn by doing). That sounds much easier to say than to do, and unravelled pretty quickly (but probably not as quickly as it should have been).
This is the third time I’m embarking on a quest to decaf. It is usually triggered by observing the effect of all the coffee and tea that I’m drinking: jumpiness, difficulty getting up in the morning, generally being an arse… The signs are pretty unmistakable, that I’ve had a bit too much…
The first time it was a very special experience, many aspects really stayed with me. The most important memory I carry over is the expected way the caffeine withdrawal should play out, based on this first time. The first week is kinda easy. The second is harder. The third was really rough, I reverted to pretty much to be a manchild, (figuratively) banging on the table and shouting “I am a grownup, I can have coffee whenever I want!” Then the last week was very good, much better mood, much more balanced physical functions too. It was bliss. Was almost strange to stay in that state for only a week, and resuming having coffee .
I maintain a couple of ArchLinux user-contributed packages on the Arch User Repository (AUR), and over time I’ve built out a bit of infrastructure around that to make that maintenance easier (and hopefully the results better). The core of it is automated building of packages in Continuous Integration, which catches a number of issues which otherwise would be more difficult.
Last year I’ve moved back to London after living eight years in Asia.
No Man’s Land
(2016 December) I was reading a few volumes of Pinter’s collected plays, and was feeling very envious of New York, that they had Patrick Stewart and Ian McKellen putting a dual production of Waiting for Godot and No Man’s Land on stage there. But then I was lucky enough to catch No Man’s Land in London, and that was a really intense kick-off for my theatre season.
Theatre of the absurd is maybe my favorite, and this one makes a really good watch. The play is not perfect, though, the long monologue towards the end made me switch off, regardless how good the delivery was. But it was superb setup, superb acting (though Sir Ian had the better part), and very memorable. I think it’s a pretty good introduction to British culture as well, I’m definitely drinking much more since (alcoholism is one of the central elements).
The second season of Mr. Robot has just finished last week. While it’s one of the most amazing thing I’ve ever seen, it is also held as the TV show with the closest depiction how computer hacking works for real (see for example Quora or Reddit). Looks like it inspired a lot of people to “try out” the tools the characters use on the show (adding to the popularity of Kali Linux, or the Wickr chat app for example), which does feel a natural way to relate to your favorite characters, in a geek way.
I couldn’t resist either, and tried to dig a bit deeper, learning some geek (ie. not professional) lessons about how hacking works, party from the events in the show directly, partly by following for a few steps what were done in the show, and deconstructing the results. Here’s what I’ve collected so far:
Social engineering is likely a big part, a crucial enabler of most “successful” hacks. The show works with characters so it might be biased towards human actions, but it makes sense that social engineering can open door where the “bits” are closed. All the shows main hacks I remember included social engineering (not going to spoil here anything). This also made me a lot more aware in life, for example when talking with my bank online, or trying to get official things done in a way that it involves trust. Very much seems to me, that social engineering vulnerabilities and “opportunities” are really abound, and that makes me a lot more careful. For example, when calling to the bank, my verification data is three pieces of information that is available online or relatively easy, and I think of how I’ve seen such situation abused (in fiction, mind you), then I get a little jumpy. Not sure how other countries are like, but it feels like so many weak points in Taiwan, that she is just protected by the language barrier from western hackers/scammers – but sure that doesn’t deter another hostile nation. So yeah, my lessons is trying to improve on things, be mindful of trust-based situations encountered, while do be more confident to get things done better (in a non-malicious way of course), as confidence is one of the key ingredients of social engineering as well.