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.