Last weekend happened the second “Machine Learning Prague” conference and I was lucky enough to was one of the attendees. I’d like to recap what I learned and my experiences in this blog post. I hope this is helpful to anyone who couldn’t attend and it helps me to remember.

The first thing which impressed myself was the amazing location. It happened at the Lucerna Cinema in Prague; for whom doesn’t know what I’m talking about just have a look at the pictures.

Overall it was very well organized and they improved things even while the event happened. For instance on the first day there wasn’t enough space to have proper lunch but this was different on the second day by opening an additional room. Well done! Speaking of food: as at most of the developer / computer science conferences there was plenty and good food, coffee and drinks. Yummy.

But surly I wasn’t attending because of food and drinks. Talkwise there was a big variety in terms of topics but sadly there wasn’t a big diversity in terms of gender. There were apparently many more men talking than women. Surprisingly to me especially because I had the feeling that the proportion of women attended the conference was pretty high compared what I’ve seen at developer conferences so far. So I’m convinced to see more women on stage next year.

Since there where too many talks as I can write up in a reasonable time I will only pick some I was particular excited. Please bear with me in case I skipped yours.

Vertical AI: Solving full-stack industry problems that require subject matter expertise, unique data, and a product that uses AI to deliver its core value proposition

By Bradford Cross, DCVC (USA). This talk inspired me a lot. He mentioned that engineers tend to apply the engineering knowledge first to their very own craft. This seems to me somehow true and at the same time it’s an opportunity to change it. Bringing engineering / science mindsets to a variety of industries will help to add another dimension to see things from a totally different angle.

The only problem I see in this path is that in my feeling only tech companies appreciate the value of engineers and scientists enough and therefore a lot of companies don’t have engineers at c-level. My personal assumption is that companies with people having an engineering/science background in the c-level will overcome the competition in the long run because they making use of more advanced technology and science. But this are just my personal thoughts and where not part of the talk.

Data Science at The New York Times

By Chris Wiggins, New York Times (USA) – my personal favorite. He gave good general advice and was inspiring at the same time. For example he mentioned that they integrated their Machine Learning tool as slack bot. With it you get the power of ML to marketies and journalists without friction. Here a selection of tweets which reflect very well what I mean:

Microsoft Cognitive Toolkit: fast and furious

By Willi Richert, Microsoft (DE). MCT seems to be a good tool in anyones belt so give it a try and figure out for yourself if it’s helpful for your particular need or not. The talk itself gave a basic overview what I personally liked.

Predicting short term profit on US stocks by multiple ML methods

By Michal Illich, Wikidi (CZ). This talk had an unexpected twist for a machine learning conference. But I think it was even a good thing to show that there is still more than machine learning what solves problems. He made a good point and hearing the story about the path they went was very worth to listen.

Serving a billion personalized news feeds

By Lars Backstrom, Facebook (USA). Since Facebook has both a big variety of data per person and a lot in total I was looking forward to get some insights what they are doing. He explained briefly the journey they made from decision trees to neuronal networks. I’d liked the talk very much.

And one funny exploration…

Neuron soundware

By Martin Krivanek (Neuron soundware). “We use IoT devices to listen for the sound of industrial machines and use deep learning and power of neural networks to recognize their failures.” Nothing more to add besides that this is really a cool idea.

The road towards chat automation starts with humans

By Janos Szabo (Chatler). “How human chat agents can help chatbots to get smarter, and how ML can help human chat agents to be more productive.” Since chat bots aren’t as good as humans when it comes to customer service yet it’s definitely a good idea to combine the best of both worlds. Not sure if Chatler is the best product doing this but certainty the idea it reasonable.

TensorFlow & Deep Learning

By Yufeng Guo, Google (USA). Tensorflow is so famous lately of course there has to be a slot for it. Good overview, thank you Guo!

Creating adaptive worlds where people experience imagination

By Maria Vircikova, Matsuko (SK).

Towards Good AI

By Roman Yampolskiy, University of Louisville (USA). Clearly an important topic to discuss the ethical aspects of AI and especially of Artificial General Intelligence. I remember he said something like we will have basically two options: a) we get uploaded somehow to such a system or b) we are becoming Cyborgs. In case of (a) we are loosing most of the things a human is today (eating, sex, etc) in case of (b) the system will get rid of us when it’s getting too smart. Who attaches a 486 PC to an iPhone? Nobody does. Why should a super intelligence attach a human in the long run?

Certainly a lot of worth points to be discussed.

DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker

By Martin Schmid, IBM (CZ). Too bad for online Poker: DeepStack beat single Poker games now after years of research. Congratulations for that and thank you for sharing with us in this talk!

Multi-Instance Learning in Security

By Tomas Pevny, Cisco (CZ). How to apply AI in network security could be seen in this talk. And analysing network traffic is in particular harder than normal AI problems because you don’t have just one vector as training set, you have multiple different ones. I have to emphasis that his Q/A session was in particular informative and a little bit funny at the same time. At least I was awake afterwards again.


So far so good. You can find the official schedule with all abstracts at I hope you continue the great conference and see you next year.

In the end some more ML/AI related links: