I realized I never uploaded the final version of my dissertation, ‘Weakening memories by half-remembering them‘.
Preface: I wrote this to a friend asking me for advice about whether to embark on a science PhD. At the time of writing I still had more than a year to go – so I could see the summit in the distance, but I was feeling grim about the steep icewall I had to climb to get there.
In retrospect, I think a lot about Jeff Bezos’ advice: don’t be proud of your talents – be proud of the things you really worked hard to achieve. For this reason, I’m more proud of (and glad about) my PhD than anything else I’ve yet done.
It’s hard for me to summarize my thoughts on grad school, perhaps because it varied so much in so many ways. Grad school was wonderful when I was excited about it – for the first few years, there was literally nothing I wanted to do more than talk and think and write and program lab stuff. Every week was filled with new ideas, a sense of progress and discovery, and I bounded into the lab every morning.
I don’t know what changed exactly, but at some point, I started to really lose enthusiasm. I’m perenially stymied by an inability to understand the source of my own motivations, and to make sense of my own emotions. So I don’t really feel like I understand why the joy started to fade. Perhaps because I worked for years on ambitious experiments that didn’t work out. Because I’d been in one place for years. Because I’m a little flighty. Because I thrive in a more competitive or fast-moving jobs. Because really I love AI and computers a little more than brains. Because I wanted to be my own boss. Because I lost confidence. Because I need to feel part of a team working towards a common goal. Because I needed more inter-personal contact with a range of different people. Because I’m not temperamentally suited to be a scientist. Because I need to be in a city. Because I felt obliged to finish it, after investing so much into it, long after I would have left a normal job. Because the specialization necessary can come to seem like a straitjacket. Because I got obsessed with new ideas. I don’t know.
It seems to me that a PhD is the right move if one loves what one’s doing, and one wants to be an academic. Of course, you can’t know for sure in advance that both of those are true. But if you think they might be, then go for it! While I think we have some things in common, I don’t expect the idiosyncracies of my experiences to apply closely to anyone else, so don’t look too closely for parallels to yourself in my issues above.
Right now, starting a company feels like the job I’ve been looking for my whole life, but I wouldn’t have the wherewithal to do it unless I’d been through the last few years.
I don’t know where your path will lead. Like me, I think you get excited about a lot of things, and could happily set off in many different directions, including becoming a great and happy scientist.
This email doesn’t really answer any of your questions. I’m sorry about that – I just don’t want to give advice one way or the other, because I think you’ll make the right choices without my advice, and because you’ll make whatever choices you make into the right ones. You are a lucky guy, in this (technical) sense – http://gregdetre.blogspot.com/2009/10/i-dont-believe-in-luck.html
Keep me posted.
Try and picture the scene: you’re in a narrow tube in almost complete darkness, there’s a loud thumping noise surrounding you and you’re watching episodes of the 90s sitcom, ‘Home Improvement’, with Tim The Tool Man Taylor and his family. There’s a panic button in case you feel claustrophobic, but it’s all over in less than an hour. It sounds a little surreal, but that’s what it would have been like to be a subject whose functional magnetic resonance imaging (fMRI) brain data was used in last year’s Pittsburgh Brain Analysis Competition.
After you’ve watched three episodes, kindly folk in glasses and white coats would take you out of the scanner bore, give you a glass of water and then over the next few days, they’d ask you to watch those same three episodes again over and over. On the second viewing, they’d ask you ‘How amused are you?’ every couple of seconds. On the third viewing, they’d keep wanting to know how aroused you are on a moment-by-moment basis. Then, ‘Can you see anyone’s face on the screen?’, ‘Is there music playing?’, ‘Are people speaking?’ and so on, until you’ve watched every moment of every episode thirteen times, each time being asked something different about your experience.
Our job, as a team entering the competition, was to try and understand the mapping between your brain data and the subjective experiences you reported. For two of the episodes, we were given your brain data along with the thirteen numbers for every corresponding moment that described your arousal, amusement, whether there were faces on the screen, music playing, people speaking etc. Our team, comprising psychologists, neuroscientists, physicists and engineers, put together a pipeline of algorithms and techniques to whittle down your brain to just the areas we needed and smooth away as much of the noise and complexity as possible. Think of these first two episodes as the ‘training’ data. Then, we were given only the brain data for the third episode, the ‘test’ episode, from which we had to predict the reported experience ratings.
Our predictions were then correlated with the subjects’ actual reports, and we were given a score. We ended up coming second in the whole competition, and we’re hoping for the top spot in 2007. Much of this effort has had a direct payoff for our day-to-day research. We now routinely incorporate a lot of these machine learning techniques when trying to understand the representations used by different neural systems, and how they relate to behavior.
Members of the team: David Blei, Eugene Brevdo, Ronald Bryan, Melissa Carroll, Denis Chigirev, Greg Detre, Andrew Engell, Shannon Hughes, Christopher Moore, Ehren Newman, Ken Norman, Vaidehi Natu, Susan Robison, Greg Stephens, Matt Weber, and David Weiss