PR stuffs

I joined yesterday evening another Meetup hosted by Bayer in Berlin. A master class about PR in the digital age given by Andreas Winiarski from RCKT & Rocket Internet. 

Why attending this event you may ask? First of all I heard about of the mini burgers legend that are possibly served during the Meetup organized at this location... No burgers this time but mini flammkuchen, so that wasn't too bad. Secondly and more seriously I was curious to hear a PR person talking about his work and experience. I'm not one of them - I'm a data scientist with a liking for computer vision and color science - but in my work, at some point, I may have to communicate with the communicating people.

What I like about the talk was the way - it seems - this profession has changed putting the people more in control by taking control of the available tools: you are the first communicator. You don't need much of infrastructure to start spreading your voice as you are always two clicks away from starting a blog.

I also appreciate the position of the speaker - and I guess the one of the company he is representing - regarding where they want to go, what they want to achieve. The internet is not only reserved to the US or China. Germany and Europe have to be part the game. There is no local market, everything is global from the beginning and the model followed by Rocket Internet is completely going in that direction.

To be short that was a pretty interesting presentation. I can't say I learned many new things, but it's nice to see some of your thoughts formalized by others. It make you feel that you are not completely disconnected from the world.

About not being an expert as a data scientist and other tech stuffs

Last evening I did attend a joined Meetup from the Python User Berlin (PUB) and the Zalando Tech Event hosted by Zalando and offering talks on Natural Langage Processing (NLP). Both talks went well and gave two views on the topic: one on the state of the art of the tools for NLP using Python and a second more applied.

The discussions I add after while enjoying a club mate - la boisson des champions - were equally interesting. First of all I started discussing with a expert of NLP trying to explain why I joined this event and what was my link with NLP. In my very recent job experience at EyeEm I just touched the surface of NLP preparing data for Machine Learning (ML) using nltk together with WordNet, ImageNet. Actually I didn't do much of text analysis but batching word definition. In that experiment the text analysis will have come after this step and that's where semantic is jumping into the discussion. Because working with the word dictionary is one side of the problem: you have one word with its definition and often - at least with scientists or engineers - you are in the inverse configuration which is you having words when actually you want to extract a definition, an idea, an information... And I let you google automatic image tagging, deep learning.

After exchanging ideas and experiences about NLP I did continue seeping the offered mate with one Zalando employee. I was curious - as usual - to understand what it means to be a data scientist here. Because if the definition is very general - a data scientist works with data, we are not expert - it's interesting to see how many fields we - I'm one of those people - cover in our daily work. Using the same language - e.g. Python - we can go from signal processing, computer vision, image retrieval, NLP, how to deal with Databases - a year ago I wrote on the topic Databases and natural Langage graphs en stock - how to present your results to non expert by doing nice visualization and many more... So if we are not expert we need to be pretty fast I acquiring skills from various fields and/or use the appropriate tools.

Meetup for the human machines

I finally managed to attend the Shadow ML - for Machine Learning - meetup in Berlin yesterday evening, hosted by Amazon in their Computer Vision division in Berlin. Two talks were scheduled, one with images and a second with words. I explain.

Before pizza time
Here we learn about soft shadow removing. I liked this talk because it combined computer vision (CV) and machine learning (ML) and it's a problem I'm aware of as I'm regularly facing it when I'm post-processing my spherical panorama pictures taken under the sun - you can see my shadow in the picture.

What I remember from the hard shadow problem description is that a big part of the solution is to be able to isolate in the picture the shadow areas. What is a shadow area you may wonder? It's a part - or parts - of an image where the brightness has been drastically reduced such that they appear almost grey but there is still some color information available. Saying that we almost solve our problem: we need to find the color information in the shadow area and adjust its brightness to match the non shadowed neighbor area. You may have to operate in a different color space than RGB to keep the chromatic information undamaged and to change only the pixel brightness/luminance. A good image segmentation is an inevitable step.

For hard shadow the segmentation is an "easy" task as the transition between shadow/not-shadow areas is fast/brutal, in another word not soft. The problem with the soft transition is that is required a lot of human inputs to mask the image - in the sens of creating a mask that isolate the shadow areas from the others -  and we want to automate this task.

A solution proposed yesterday was to use machine learning in order to make your system learning about the difference image with and without shadow. The speaker talked about the problem of getting data - which is a recurrent part of machine learning problem modelisation and any other scientific problems - and how he did create his data-set: computer generated images with Maya where he could get two sets, one with shadow and another without for the same scene.

After that I got a bit lost of on what the author does when he found out where the shadow areas were. But assuming the areas have been well discriminated you still need to adjust the brightness level. From that two solutions at least: if the area is homogeneous then a simple scaling factor/function should do something, treating the background - or the area - as a texture can be helpful too especially if you plan is to use in-painting techniques. But the chosen solution is of course linked to what you want to do: preserving information in the image - then I will say no in-painting - or tricking the eye/human brain such that the image appears nice without shadows - then go for in-painting.

After pizza time
A complete different topic to follow but not less interesting. It was about text and word analysis. For an introduction you can check WordNet to have a glimpse of what that field is. But back to the second speaker, his problem was to see if we can predict an affiliation to a political party based on text analysis.

As the speaker did mention it this is/was a work in progress where the first task was to establish a usable data-set for building the classifier. The text of each party manifesto was employed for that purpose.

Once you have your classifier what you want is to evaluate it. All the interventions, talks given by the government members, parliament members are the perfect data sources to be used for that as well article from different newspapers could be feed to the system.

This work goes as well into the direction of sentiment analysis and a temporal parameter is something you want to have in such problem. Depending of who is running the country, who has the majority at the parliament the roles, the words play/use by the people representatives evolve. It might be obvious but this kind of tool can tell us how much we perceive the words, talks given by our politicians and how much they or we interpret/dream/hallucinate about different situations.

Building such system wasn't too complicated - if I got it right from the speaker(s) - and the main challenges were/are to get clean data. As for all machine learning you need clean data, in every basic or applied research actually.

Deep learning talk @Zalendo Tech Event

First Zalendo Tech Event at their Tech HQ nearby Alexanderplatz yesterday evening. To open their series of Meetup event Zalendo invited Professor Sepp Hochreiter of Johannes Kepler University in Linz to talk about deep learning. 

attentive crowd

About the talk
The talk was good but not adapted to an academic audience. If you are familiar with the topic you probably wouldn't have learned something new. But the talk did lead to interesting - and often expected - questions around and about deep learning. Sadly - to me - it was more where does it work?, what are the best parameters? than how does it work actually? 

As the speaker did remind to us, neural networks (NNs) aren't new on the market. They were discoveries years ago, it was promising and then nothing, other techniques were used, leaving specialists in their niche. I do remember courses during my master in image processing about 15 years ago [in Pierre et Marie Curie Paris VI] where the person teaching and introducing KNN and NNs sounds both excited and disenchanted. This until computers got faster (thanks to cpu, gpu, many-core, cluster, graphic card programming "et j'en passe") and suddenly it was possible to use NNs, to get results, to reproduce them and to beat classification challenges by far comparing to the expert of the field.

For every new promising technique there is the temptation to use if for everything in a brute force manner. But it doesn't work all the time. One remark given by the speaker is these solutions work when you are overloaded with data, when you immersed into data. It's not a surprise that big players such as Google, Facebook, Amazon and more are heavy on growing their deep learning team.

About automation, AI and drugs
You hear and see more and more presentations about deep learning, artificial intelligence (AI) where people are dreaming of AI being able to put words on a given image in a similar way a human will do. It's kind of working but there is no magic. It made me remember about an experiment where the researchers claimed to be able to produce images/video corresponding to the images we see in our dreams. Often people fear - and they can - about computer taking control over us, making decisions for us until we start working for them.

It is interesting to understand why pharmacy companies - those making drugs - are so big into deep learning. Bio-Informatics offer the perfect environment for developing big data solution. Here I'm not talking about the phase where drug need to be tested and evaluated on human but what happen before. Biology and chemistry (or computer chemistry) can be simulated using pretty accurate models, meaning you don't need to run an actual biological or chemical experiment. You can simulate the experiment, generate a huge amount of data and let your algorithm do the analysis. And guess what, computer vision, machine learning, deep learning - not to mention optimization - are part of the solution. And the faster you get your results, the faster you have a new drug to potentially introduce on the market hopefully before your competitor. I'm not sure "normal" people got a glimpse on that side of research, in that field it's actually the biological/chemical experiment that will validate a virtual experiment (remember to watch Terminator 4 or 5 at leas the last on screen...).

About the big brain project and graphic cards and evolution
Research is cool. It's very interesting to see how connections/links between highly specialized fields are happening to build a new framework for research. The big brain project (not sure about the name but there is the US and the EU version) is the perfect example, different fields from neuroscientists to computer graphics and hardware manufacturers need to collaborate to build this virtual brain model.

One of the last comment from the speaker yesterday had a pertinent echo in my head. This comment illustrates perfectly how technology is evolving and frameworks are crossing their paths. He told us that graphic card manufacturer (such as nvidia to not name them) are now developing hardware dedicated to run deep learning process, once again the hardware architecture helping to fasten a programmed algorithm. But until when and is it a good approach? 

Years ago and not so long time ago when computers were already getting faster, people were designing hardware to run image processing/computer vision algorithms. This because the computers in their at-this-time state weren't fast enough. Like the brain was too small and needed to grow or modify its physical body to evolve. But then computer became faster and those special design weren't adaptable enough, too specialized. I feel that we are living a similar state with deep learning. The question will be is hyper-specialization of computer hardware the solution - momentarily for sure - for deep learning or not?

About the future
We are all doomed. Soon computer will be smart enough to redesign their body when they will reach their limits to overpass them. I haven't any spoiler about how and when, out Mayan friends had a big fail about it three years ago, we have to be patient.