Applied Machine Learning Days

The Applied Machine Learning Days (AMLD) 2020 is around the corner - in about two months, we’ll open our doors for the fourth time to an expected audience of over 2000 people, with 30 hands-on sessions and 29 tracks on machine learning and artificial intelligence with top speakers from around the world.

An industry representative told me the other day “it’s amazing how you managed to create such a high quality event, and such a brand, from nothing, in just three years”. I’m very grateful for these kind words. But it also made me think. Did we succeed? Where could we go? Where should we go? What did we do right, or wrong? And then I also realized that there is no written record of how AMLD came to be, and so I felled compelled to write this post.

In 2016, my lab at EPFL launched crowdAI, an AI challenge platform (today, it’s a spin off with the name AIcrowd). The idea was to run public machine learning challenges, in an affordable and open source way. We had ideas for a few challenges, specifically also around the research we were doing, and we knew how to build a challenge platform - but what could we offer to the community as prizes, with loads of money not being an option? After some thinking, we decided that one cool prize could be to bring people to Switzerland, in the winter (snowy Alps!), to a small workshop where top performers in these challenges could share their approaches, and learn from each other.

Around the same time, EPFL hired to new professors, Martin Jaggi (machine learning) and Bob West (data science). The three of us felt like it would be a cool idea to create something a bit bigger than a small workshop around the topic of applying machine learning to lots of interesting problems, and we created the Applied Machine Learning Days, with the idea to bring together ML practitioners to share do’s and don’t of this exciting technology. Without any resources, we went ahead and started inviting people to speak at AMLD, and managed to attract a great speaker setup, mostly from our own personal networks. We were hopeful that around 100 people would show up (ML was a hot topic, after all). To our great surprise, hundreds of people signed up, and the largest room we could find on campus in the short term had a capacity of 450 people. AMLD 2017, a 2-day event with talks, was a great success, and we were motivated to do more after that.

When putting together the website for AMLD 2017, I added the slogan “2 days of talks and tutorials”. But given that the AMLD 2017 organization was rather rushed, we did not really have the time to organize tutorials. So for AMLD 2018, we created a call for workshops, and to our great delight, numerous high-quality workshops were proposed. Given that AMLD 2017 ended up being much bigger than planned, we felt that AMLD 2018 could be even bigger, and in the summer of 2017, we brought on an event manager to help us coordinate the event full time (hi Sylvain!). AMLD 2018 thus became a 4-day event, with 2 days of workshops, and 2 days of talks. Almost 1000 people ended up coming to the event, with the workshop weekend completely booked out and long waiting lists.

At this point, we realized we had hit a nerve. People really seemed to like the mix of academia and industry. In parallel, many AI events were popping up left and right (and of course we were not the first either), with some of them being very much focused on marketing and sales, while traditional ML conferences were highly technical. We seemed to have found a sweet spot in between these two extremes, where practitioners and enthusiasts from all types of organizations could come together and learn from each other.

AMLD 2018 was great, but we realized that the single track model would not work for much longer. Thus, the idea of domain-specific tracks - AI & your field - was born. For AMLD 2019, we opened a call for tracks, and once again, the community came along and put together awesome tracks! Given the expected increase in size, we asked Sylvain to stay onboard full time :-). Overall, AMLD 2019 ended up being again a 4-day event, with 2 days of workshops, and 2 days of conference with both keynotes and domain-specific parallel tracks that over 1700 people attended. Speakers like Garry Kasparov, Jeff Dean, and Zeynep Tufekci gave the event a very special vibe.

For AMLD 2020, we primarily thought “never change a winning team”. But nonetheless, I became personally frustrated that while we were holding this interesting event, the public discussion was getting increasingly negative and concerned about this technology, and most of the uncertainty - not surprisingly - was about work, jobs, and skills. So we decided to extend AMLD by one day, and to have a third day that more specifically focuses on all things AI & economy: jobs, skills, employment, HR, social policy, startups, etc. which we're organizing with our neighbors and colleagues from the University of Lausanne. Given the growth, we recently also brought on another person to help with the organization (hi Pauline!).

And once again, the community came along and put together absolutely stunning workshops and tracks. Some of the tracks have such a stellar speaker line up that they would very much go through as independent conferences in their own right!

On reflecting what made AMLD work so well, in such a short time, I’ve come to learn a number of insights. The first is to create an event that you would love going to. This is a truism in industry, certainly in the consumer sector - if you are not using your own product or service, why would anyone else? I keep reminding people that we are not organizing AMLD because somebody told us to. We are doing it simply because we want such an event to exist. Indeed, one of the most difficult thing for us as organizers is to not be able to enjoy the event as visitors. Tough life 😉

The second insight is to not do it alone, but together with others. People were often shocked to hear that the event management team was composed of one person, for a conference of the size of AMLD. But of course there were hundreds of volunteers behind the scene, from the volunteers helping, people in the labs of the organizers, and others who came to help during the event. And most of all, of course, the workshop and track organizers who put together the program.

The final insight is to take it easy on the hype, and just stick to quality. The amount of AI bullshit available on the internet and at some events has taken on rather stunning proportions. Personally I have nothing against some long-term thinking and some excitement around it. But at some point, one should put up, or shut up. It’s for that reason that we want AMLDs to always be associated with academic institutions. That is not to say that non-academic institutions wouldn’t be able to put together great events; of course they are. But academic institutions have the benefit that they are full of deeply skeptical scientists that won’t tolerate overselling for too long, and most speakers will naturally focus on serious work when they present at an academic institution.


So, what is the future of AMLD? I can’t say for sure, but it’s worth reflecting on what the ultimate goal of AMLD is. An event is a huge effort, both for organizers and attendees. If you calculate the overall costs, and the energy spent by thousands of people coming together in a particular location, the numbers are absolutely enormous. So there’d better be a very good reason why you do this. For me, the ultimate reason to organize AMLD is to make sure that this technology remains on people’s radar, and becomes accessible to them. Modern machine learning is once-a-lifetime kind of technology, and may even end up being a once-a-century kind of technology. If AMLD can help many more people to understand this technology and use it for their goals, then it will have been worth it. Because I believe very strongly in Feynman’s observation of “what I cannot create, I do not understand.”

That is the ultimate reason I believe that AMLD should grow much more, both in size and in scope. To give you an idea of the importance of machine leaning, PwC believes that by 2030, AI (they mean machine learning) will boost GDP by 13% globally, and up to 26% locally. That’s 15.7 Trillion Dollars, more than today’s GDP of China and India combined. But more than money, machine learning will affect all social systems deeply. Not mastering this technology is simply not an option. Events like AMLD can do their share to ensure a well informed society, from academia to industry to the general public.









Why I am not interacting on LinkedIn

TL;DR: I won't participate in LinkedIn communications, because I have no more trust in LinkedIn. For important matters, please send email instead.

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LinkedIn would have the potential of being useful. Unfortunately, it has in recent times become a master of dark patterns (see https://www.darkpatterns.org/), and I see no indication of this stopping any time soon.

Just a few examples:

  • I've been getting LinkedIn requests from women "to share the passion of love", with LinkedIn apparently being unable / unwilling to filter this as spam.
  • LinkedIn has started prefilling communication fields in ways that I do not like at all - for example, pre-filling responses with "Hi XYZ, thanks for reaching out. I’d like to learn more." - No, I don't like to learn more. It's preposterous to pre-fill communication forms like that, LinkedIn.
  • I've been getting emails saying "You have 1 new message" - but instead of showing the message right there (I am looking at email right now, for goodness' sake), LinkedIn forces me to open the website, or the app, so that it can track me better.

I could of course leave LinkedIn, the same way I left Facebook a few years ago. The problem is that the network itself is very  interesting, and unlike Facebook, I never had any real trust that information on LinkedIn would be private. So rather than abandoning it, I just want to clarify that I am not using it for communication purposes, because a) I cannot assume our communication to remain private, and b) I'd like to stay away from the dark patterns of LinkedIn as much as possible. Like everyone else, I am trying to do my best to mitigate the onslaught of digital overload - my detachment from LinkedIn is a further step in that direction.




The perils of "free" education

Imagine reading one day on a restaurant website the following:

"Come eat with us FOR FREE! That's right - we believe in open food, and that nutrition is a basic human right for everyone! We provide free meals, created for people at any hunger level! Eat as much as you want!"

Sounds ridiculous, doesn't it? Even a place that would offer "all your nutritional needs covered for just $49 / month" sounds incredibly suspicious. Would you really eat there? What could they possibly be putting on those plates to cover their expenses?

It's pretty simple - you know that food has a price, and that the people making and serving it have expenses to cover - so anything extremely cheap is most likely very bad quality, or a scam, and immediately raises red flags.

Yet, when it comes to education, we quickly seem to let our guard down. Free courses to learn anything I want? Bring it on! A full education for $49 / month? Sounds good, count me in! No suspicion is raised - this is normal. After all, most of us didn't pay for school either.

The fundamental problem there is that we easily confuse education with information. Yes, information can be free, and when it comes to knowledge about the world, I'd argue it should be free. But education is not just information - not by a long shot. Education is helping learners make a selection about what is worth learning (at least initially); it's helping learners differentiate good quality from bad quality; it's helping learners when they get stuck; it's reviewing learners' work, and give them guidance on how to improve; it's assessing their knowledge at regular levels, and eventually putting your name to vouch for the level of know-how they have. And it's a million other things as well, as anyone who has ever taught another person anything can readily confirm.

Many of these things cannot be automated yet, and the question is not only if they ever will be, but also if that's what learners really want. But whatever the future may bring: today, when you are getting an education, someone is paying for it. And thus, if it's free, or almost free to you, then someone else is paying for it. Do you know who that is, and why they are doing it? Do you know "the deal"?

Our students at EPFL, who currently pay about 1'200 $ per year - a tiny fraction of the true cost - (hopefully) know that it's the Swiss tax payers who are paying for them. They also hopefully know that the tax payers are paying because they think they're getting more in return in the long run - the until know safe assumption being that a well-educated population will be a wealthy population. Same for all the parents in the country (the vast majority) who send their kids to the excellent public schools, at no direct cost to them - again paid for by the tax payers, for the same reason.

This is not free. In fact, many governments spend multiple percentage points of their GDP on education. The EU, for example, spent 715 Billion Euros on education in 2017. That's right: that's € 715'000'000'000 in a single year. So much for free education.

So that makes you wonder - what are all the people thinking who are signing up for (almost) free online education? That somehow, all of these mechanisms don't apply anymore? Part of the problem, as mentioned above, is that we confuse online education with online information. Online information can be free, yes - although even there, its creation and maintenance costs something.

But the problem with (almost) free online education goes further. In the same way that you are not a Facebook user, but the Facebook product (with advertisers being the customers), free online education means that you're not the only one who is learning something - someone else, with vested financial interests, is also learning something about you. What kind of learner you are, for example. How quickly you grasp new concepts. How well you work with others. How you solve problems. How you search for solutions. How motivated you are. If you go to any job interview, these are exactly the kinds of things companies want to know about potential hires. And there is a huge market developing that sells this information about you to companies that are hiring - directly, or indirectly through recruitment services. It's worth a lot of money - enough to pay for the education.

It may be a deal worth making. But we should be aware that there is a deal in the first place, and most of us simply are not. And we should realize that the education that we hope would advance our career, may actually be putting a break on it.

There is a long term solution, and a short term solution. The long term solution is appropriate legislation - that learners getting the free education deal must be kept totally in the clear about this deal. Perhaps an even better solution would be to prevent such deals in the first place, at least for adult, continued education; I'm not entirely sure yet. The short term solution is to prevent the problem in the first place, and find someone who is truly interested in your education so that they will pay for it. Oftentimes, that will be you; other times, that may be your employer, or perhaps even your government, should you be so lucky to live or work in an environment that supports life long learning and continued education.

At the EPFL Extension School, we think about these issues a lot. We offer courses and programs for digital up-skilling online, and the topics of lifelong learning, online education, and data ownership are parts of our daily discussions. The entire learning experience going through the EPFL Extension School is what we offer as a service, and because of that, we don't even have to think about monetizing any data about our learners to anyone. In fact, we viciously protect our learner's data, far beyond our legal obligations. Being in total control of our learner's data was also a major factor when we decided to build our own learning platform, rather than using someone else's.

I think that's the fairer deal.


1. Meditate

This is one of the brain tools I can't really understand anymore how I managed to do without.

My favorite - and largely only - form of meditation that I practice regularly is mindfulness mediation. I first encountered the concept about 15 years ago when I came across a book called "Wherever you go, there you are" by Jon Kabat Zinn. I was about to become a PhD student at the time and so my natural instinct was to think that this was likely some trivial nonsense. But I was in enough adolescence-related mental pain at the time that I thought I'd give it a try. It changed the way I looked at myself, and at how the mind works. It was the first time when I fully realized, I am not my thoughts, and that thoughts are objects I can study objectively. I've been expanding on this concept for quite some time ever since then.

I most recently came back to regular practice with the Waking Up app, which I very much like (and I can also recommend the book with the same name by the same author, Sam Harris).

Mindfulness meditation has become a key tool for me, and today, as we are in the midst of the attention economy, being able to realize when someone tries to hijack your mind has become extremely valuable. That's of course in addition to all the benefits you get from realizing when your mind gets hijacked by your own thoughts. I now rank the ability to do basic mindful mediation so highly that I will teach my kids to understand it before I teach them how to code (and if you've ever been on the receiving end of one of my sermons about everyone having to learn how to code, you know what that means).

So this is my first advice: Look into mindfulness meditation.



Notes to my younger self

I recently heard someone on a podcast ask a guest, "what advice would you give to your younger self"? The question was rhetorical, of course, as the younger self clearly missed the chance to listen to any advice, but I thought it was a nice way of soliciting condensed advice based on years of life experience. And naturally, I started thinking, what advice would I give? After some reflection, I decided to write it down in small, bit-sized blog posts. It's not going to be useful to me - but in the same way that I occasionally find other people's advice very useful, I hope this may be of use to someone else (hey there!).

Come to think of it, "advice" may be the wrong word here - let's go with "ways to think about the world based on some things I've experienced". I'll keep this list going and growing for a while. Whatever reason brings you here, I hope one of those may change the way you look at certain things in a way that benefits you. That'll already have made it worth it (#payingitforward).

1. Medidate



Facebook

It's impossible to escape the Facebook "scandal" at the moment, and it's important to be fully aware of what is going on. I think this is a defining moment in our digital evolution as a society, so it's worth spending some time reflecting on what is happening.

As you have surely heard, it has been revealed that a rogue researcher by the name of Kogan at Cambridge University has built an app that scrapped a lot of data from Facebook users and their "friends". (Apologies for the many quotes, but so many of the words used in this story have been hijacked to mean different things.) Nothing that Kogan did at that point was illegal by the terms of Facebook. This is, of course, the crux of the story - it may not have been illegal by Facebook's term, but it may have been highly unethical nonetheless. In any case, Kogan then shared the data with a third party, which was illegal, and this is how the company Cambridge Analytica (CA) got hold of the data. It was then used by CA for political purposes. Some people say CA was a decisive factor in the Trump and Brexit victories, but there is at the moment no evidence for that.

The reactions of shock that I've heard so far are of four types:
1. Why does Facebook have so much data on us?
2. Why does Facebook allow others to obtain our personal data?
3. How is this data used to manipulate us?
4. Are all tech companies the same? What about Apple, Google, Amazon, Twitter?

Let's address each of these points briefly.

1. Why does Facebook have so much data on us?
The easy answer is because we give it to them. But there is more to this than meets the eye. Facebook tracks you almost everywhere you go online. Facebook also tricks you into sharing more data than you are probably aware of. As many Android users have found out, Facebook has been scrapping their call and text message data for years - either without permission, or using extremely sleazy tricks to get "permission" from its users. Facebook's value proposition is targeted advertising. Advertisers pay lots of money to Facebook to show their ads specifically to a small target group. This is a highly efficient way to advertise because you know you are advertising to the right audience. It's this lucrative advertising model that has turned Facebook into one of the most highly valued companies on the planet. Yes, Facebook is a surveillance machine, but it has itself no malicious intent - it just wants to know everything about you so it can match you to advertisers. Facebook is not a data seller, it is a matchmaker. The more it knows about you, the better it can match you with those who are willing to pay.

2. Why does Facebook allow others to obtain our personal data?
If data is Facebook's gold, why would it share it with others - such as Kogan, or anyone developing a Facebook app - on its platform? The best answer I can give is that by opening up to app developers, Facebook was hoping to increase engagement on its platform. The more you use the Facebook platform, the more Facebook knows about you, which is good for its matching-making capabilities. Facebook is, of course, aware of this problem and has already some time ago begun to limit data access. Given the current scandal and bad press, Facebook will almost certainly continue to constrain data access to third parties.

3. How is this data used to manipulate us?
As mentioned above, Facebook is in the matchmaking business. It sells this access to anyone willing to pay for it. This is no secret - you can go to Facebook and read in great detail how it works. Facebook writes: "With our powerful audience selection tools, you can target the people who are right for your business." It should come as no surprise that by business, they mean anyone willing to pay, including politicians and organizations with political intent. Advertising is manipulation. 

4. Are all tech companies the same? What about Apple, Google, Amazon, Twitter?
It's easy to engage in the blame game and begin to accuse all tech companies of being "data hungry". Isn't it always good to know more about your users? Yes, but as we're learning, that knowledge is also a huge liability (and this doesn't even factor in direct legal liabilities - hello GDPR). The central question is whether that knowledge is core to your business. This is clearly not the case for Apple. The vast majority of Apple's business is selling hardware with a very high margin. Apple is now actively advertising the fact that it can take privacy very seriously because its business doesn't depend on user data, which is both true and smart. The majority of income for Amazon is also not advertising, but services (retail and web services). For Google and Twitter, the story is different, because their business does indeed depend on knowing their users for better advertisement. Close to 90% of Google's and Twitter's income comes from advertisement. Twitter may be in a better position because it is a micro-blogging platform, and it would be difficult to be outraged by the fact that Twitter data can be used by anyone given that it is de facto public data. In addition, Twitter's size is still very small compared to Facebook. Google may be the closest to Facebook in terms of business models. But importantly, Googles does not run a social communication network - it tried with Google Plus, but failed - and that sets it a bit apart. It is difficult to insert manipulative political content into the discussion unless you are the discussion platform. Still, the concern with Google is that its business currently depends most strongly on knowing users intimately.    

Now what?
These answers can provide us with some insights. The first is that Facebook is never going to change substantially. The more it knows about you, the better it can do its matchmaking, which is of existential importance to its multi-billion dollar business. That is why Mark Zuckerberg has been on a 14-year apology tour - he embodies the idea of asking for forgiveness, not for permission. The second is that Facebook will continue to be used for political manipulation. As historian Niall Ferguson put it so aptly, there are two kinds of politicians: those who understand Facebook advertising, and those who will lose. We have just seen the tip of the iceberg. The third is that regulation will be quintessential to tame the beast, which is not Facebook, but the extreme effectiveness of micro-targeting. I believe you can manipulate absolutely everyone if you know all the details about their lives, their friends, their fears, and their dreams. And it is generally not necessary to manipulate everyone very strongly; by just nudging a fraction of people undecided on an issue, systems can change rather dramatically. Nudging 10% of swing voters will define the victor; nudging 10% of undecided parents to opt out of vaccination will lead to large disease outbreaks, etc. The fourth is that Mark Zuckerberg may have to step down from Facebook, which could spell its end in the long run. He built Facebook, and stands for everything that happened, for better or worse. I fully believe Facebook did not have any malicious intentions - they simply discovered an extremely lucrative business model and ran with it. But this is not just another "oops - we're sorry" story that's going to go away soon. People are waking up to the core of the Facebook business model - and to some extent to the micro-targeting model - and they don't like it. Someone will have to face the consequences. 

CODA
As a final note, I've found it incredibly liberating, a bit more than a year ago, to leave Facebook. I did it because it took more from me than it gave me, and truly valuable interactions I continued to have through other communication channels. I was also getting concerned about its surveillance power, but that was the lesser problem to me, then. But fundamentally, I do believe that the only way to solve the extreme micro-targeting problem is by abandoning those platforms whose business are entirely built on it, and for many of us, this should be easy. I am extremely disturbed to hear some people argue their ability to communicate with friends depends on Facebook. In the end, unless we realize that Facebook's business depends on being our communication platform, and on knowing everything that we communicate through it for efficient micro-targeting, we won't be able to argue we're part of the solution.

Rule 10: Be the best you can be, not the best there is

(This post is part of a bigger list of rules that I have found helpful for thinking about a career, and beyond. See this post for an explainer).

Comparing yourself to others is perhaps the greatest source of self-inflicted unhappiness there is. Unfortunately in academia, it's rampant. But by realizing that this is a major source of stress, you can better recognize when you fall victim to it, and try to ease its negative effects.

No matter how hard you try, there will always be someone who is better than you. It's a mathematical necessity for all but one person. The day you come to terms with this reality is the day you become more relaxed, and being relaxed makes you perform better (as already indicated in rule 9).

That doesn't mean sitting back and drinking mojitos all day long. In fact, becoming the best you can be is hard work. Some even argue (myself included) that it'll take you an entire life, because it's a never-ending task. Trying to consistently improve yourself seems like a smart strategy in general, not just for a career. The important question to ask is not "how can I be as good or better than person X", but "how can I be a bit better today than I was yesterday". It seems like a small difference, but the effect is quite enormous.

I know I'll never be the best scientist in the world. I was never the best evolutionary biologist, never the best network scientist, not even the best digital epidemiologist. I won't be the best writer, the best blogger, the best pianist. And even though my kids tell me I'm the best dad in the world, I know I'll never be, because there are over a billion dads in the world, and surely some of them are better. And that's just fine with me, as long as I'm trying to be the best I can be. And if tomorrow, I'll try to be just a little bit better than I was today, everything will turn out all right in the end.




Rule 9: Have alternatives

(This post is part of a bigger list of rules that I have found helpful for thinking about a career, and beyond. See this post for an explainer).

This rule has carried me through both my academic and non-academic lives for two decades, and it's still going strong.

Having alternatives gives you peace of mind, and in my experience, peace of mind is what allows you to take that occasional extra risk that's necessary to excel at what you do, to innovate at a higher pace than what you'd be comfortable with in the absence of alternatives.

Having alternatives does not mean not being 100% committed to what you currently do. It simply means having that deep trust that tells you "even if things go totally wrong, I'll be fine. There will be something else".

Some people have that trust even when there are no obvious alternatives. I envy those people. Fundamentally, I think they are right. In the end, it'll be alright. In my dreams, I am as cool as that :-) But in my real life, I am not, and I love having a backup plan.

For somewhat random reasons, my backup plan has always involved web technologies. It's something I began playing with as an undergrad, and that I kept getting better at over the years, out of a fascination for the rapidly expanding web and all its implications. The day I realized these skills have serious market value was the day I became a much more relaxed and focused student of biology. I studied biology for the love of plants and animals, and I did my PhD in theoretical biology because I wanted to very deeply understand the most important idea in the world (evolution). I absolutely did what I loved, but it was also absolutely clear that the market for this kind of knowledge was virtually non-existent, and that having an alternative was necessary.

Asking people to reflect on alternative career paths is some kind of taboo - often used as a euphemism to suggest that they're not good enough at what they're doing. This is not at all what I mean when I invite people to reflect on alternatives; quite the opposite. Realizing that you have options is a great relief and brings back a sense of control. And because of that, it will most likely improve your ability to concentrate on what you're currently doing, enabling you to do the best work you possibly can.


Rule 8: Be visible

(This post is part of a bigger list of rules that I have found helpful for thinking about a career, and beyond. See this post for an explainer).

As indicated at the end of the last rule (networks, networks, networks), talking about your work and ideas is very important, and it gets more and more important by the day. 

Some of us have grown up in a culture that is deeply rooted in the exact opposite idea. When I grew up, I learned proverbs like "Reden ist Silber, Schweigen ist Gold" (speech is silver, silence is golden), or "Eigenlob stinkt" (self-praise stinks). I've written before that I think modest chronic under-confidence is much more harmful than modest chronic overconfidence, so here I'll focus exclusively on my belief that being quiet about your own work, in the hope that it'll be discovered because of its own merit, is a bad idea.

Ultimately, in order to be recognized for your work, it needs to be known. You need to be known. The traditional route is to publish in good journals, present at good conferences, and network with the right people. These are still very good ideas, precisely because they help you and your work be visible. But they are by no means the only routes. Today, there is a multitude of options that you can add to that arsenal, and amplify the effects of the traditional route. The most obvious one is public social media - in other words, Twitter. I didn't care too much about Facebook before the CA story, because at the end of the day, I don't need my "friends" to hear about my work - I need to reach everyone else. I strongly advise you to tweet, and tweet regularly; not just about your work, but generally interesting stuff. People follow other people if they think they are a good source of information. Try to be one.

The other extremely good way, and completely underutilized in my opinion, is to do interesting things on the web. There is no science that you could not somehow make more attractive on the web. Most of the work I do these days is fundamentally web-based, which makes things a little easier - it's already online by design. But even if you work in, say, molecular biology, you're only limited by your creativity with respect to what you can do on the web. Why don't you create that amazing website where you list your work, blog about it, blog about other people's work, create interactive visualizations of your models, write short tutorials on certain aspects about your work that you know is relevant to others? When you put in consistent effort into such things, you'll grow your visibility dramatically - often explosively, if something you did on the web goes viral for one reason or another.

Naturally, there is trade-off here, in the sense that you can only invest so much time in such visibility efforts. But when you think about it, the kinds of skills you'll learn doing that - mostly in the form of getting proficient with web technologies - are highly marketable, and will be extremely useful for the rest of your career. For PhD students, I would recommend to spend at least 10% of your time on doing this. It'll be worth it.


Rule 7: Networks, networks, networks

(This post is part of a bigger list of rules that I have found helpful for thinking about a career, and beyond. See this post for an explainer).

To get the job of your dreams, you need two things:

  • Have the right skills
  • Be at the right place, at the right time.

Most people know what is needed to meet the first criterion: education & talent. That one's easy to agree on.

What's harder is to agree on is how much the second point matters, and how you achieve that goal. Even die-hard fans of the idea that "I got here because I'm awesome and hard-working" are coming around to the idea that that's not the entire story. There are always people who are working harder, and are smarter than you, so other factors must be at play, too.

How to be at the right place, at the right time? Luck is one of the things that makes that happen. The problem with luck, of course, is that you can't do anything about it, by definition. "Just be lucky" isn't great advice. 

Better advice can be found by thinking about social networks. The small-world phenomenon - the observation that you are connected to everyone on the planet by just a few hops - is now well understood and described. In other words, there is always the "I know someone who knows someone who knows someone who knows about this fantastic opportunity" situation. But in order to take advantage of this situation, you can improve your position in the network, to be closer than others to such opportunities. 

This is what people usually mean when they say you should network. Honestly, I never understood exactly what they meant. "To network" seems like a verb, but it makes little sense. We are all part of the big human social network, so what exactly does it mean "to network"?

In my experience, to network productively means to try and get closer to interesting opportunities, and to interesting people (because interesting opportunities tend to cluster around interesting people). For that to happen, you need more connections to those people. One advice could therefore be to talk to as many people as possible. But that alone won't cut it - if you spend all your time socializing, and talking to new people, what will you tell them? That you are spending 100% of your time on socializing? Clearly, there is a trade-off between doing novel, interesting things, and talking to others about it. 

Importantly, the other extreme - doing 100% interesting work and 0% networking - is not a good idea either. Unfortunately, it remains some kind of ideal, especially in the academic world, where a lot of people continue to think that eventually, their work will speak for themselves. That is very, very rarely, if ever, the case. If you're doing great things, tell others about it!

The other benefit of networking with interesting people is not just to tell them about what you're doing, but to learn about what they and their contacts are doing. The number of interesting ideas one can get from a good social network is absolutely astounding.

So overall, I would argue you should network as much as possible, i.e. to talk about your work, and to get more ideas, where "as much as possible" means as long as it doesn't negatively impact your work. Coincidentally, this is why I am such a huge fan of Twitter - it's an extremely efficient way to talk about your work and ideas, and to get input from other people you find interesting. But that's something for the next rule.

Closing tidbit 1: My own introduction to social network theory was during a sociology class at Stanford, where the professor asked us to read work by a sociologist named Mark Granovetter on "how people get jobs". Pretty boring, I thought. But as I dug deeper, I came to learn about his fascinating findings that most people seem to get crucial information about job opportunities not from strong ties in the network (good friends and family), but predominantly through weak ties (i.e. acquaintances). This phenomenon has been observed in many other network phenomena. His paper "The strength of weak ties" has been cited over 45,000 times, and he's a strong contender for a Nobel.

Closing tidbit 2: The US Bureau of Labour Statistics says that 70 percent of jobs are found through networking.