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Aankondiging: The Analytics Lab Hackathon – Escape Room Style!

Het is weer tijd voor onze jaarlijkse The Analytics Lab Hackathon!


Dit jaar nodigen wij je uit voor onze Escape Room, waar je de heer Piet A. Choras kan helpen bevrijden. Dit wiskundige genie zit, ten onrechte, vast voor overfitting en heeft jouw hulp hard nodig om te ontsnappen. Op vrijdag 20 april 2018  (van 14:00 tot 20:00 uur) ontvouwt hij zijn meesterlijke ontsnappingsplan. Daarom doet hij een beroep op jouw rechtvaardigheidsgevoel en analytisch vermogen. Kom jij hem, in teams van maximaal 5 personen, helpen te ontsnappen uit de Atoomclub in Utrecht?


 Schrijf je dan nu in door een mail te sturen naar info@theanalyticslab.nl 


Wat, Wanneer, Waar:
The Analytics Lab Hackathon – Escape Room Style
Vrijdag 20 april, 14:00 – 20:00 uur
Atoomclub Utrecht, Atoomweg 50, Utrecht


Laat Piet A. Choras niet in de steek en kom helpen met zijn meesterlijke ontsnappingsplan!
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It’s time for edition two of our ‘We crunch(ed) Artificial Intelligence’ Meetup!

It’s time for edition two of our ‘We crunch(ed) Artificial Intelligence’ Meetup!

We’re very happy to announce that our first speakers will be Gerard Verbeek & Erwin Konink of Luminis. They’ll show us their experience on training robots to move around and pass obstacles in a room. We’ll get the ins and outs of their unique way of training: in a simulated environment, in a population of other robots.

And also our second speaker, Matthijs van der Kroon of Primed.IO, has a very interesting story. He’ll tell us more about how you can easily get from your (data science) model to the product you need to deliver the results back to the business and/or the customer facing frontends.

It’s going to be an exciting Meetup. We’re happy to welcome you with a free dinner :)
Get to know our enthousiastic team of young analysts and data scientists, and learn exciting stuff about Artificial Intelligence. RSVP to our Meetup! Looking forward to meet you :)

Check out the impression of our previous Meetup

Cryptocurrencies sessie

Op het moment van schrijven zijn er 1465 verschillende Crypto currencies op coinmarketcap. Dagelijks komen hier nieuwe valuta’s bij. Crypto currencies zijn hot en je kan geen week meer doorkomen of verjaardag bijwonen zonder dat iemand het heeft over Crypto currencies en/of blockchain. Desalniettemin kreeg de Crypto markt een behoorlijke klap te verwerken afgelopen week, grote munten zoals Bitcoin, Ethereum en Ripple daalden in een paar dagen tot wel 40%. Sommige experts denken dat deze dip komt door onder andere de realisatie van “wat kunnen we er eigenlijk mee?”. Deze vraag hadden we binnen The Analytics Lab en Cmotions ook. Vanuit ons vakgebied vinden we niet alleen de currencies interessant, maar zien we vooral ook toepassingen voor de blockchain technologie voor data, analytics en data governance.

Afgelopen vrijdag hadden we Daan Maasson van blockchainformation bij ons uitgenodigd om ons bij te praten over dit onderwerp. Door zijn ervaring in de cryptos, en dan voornamelijk in Initial Coin Offerings (ICO; uitgeven nieuwe valuta’s) wist ons uit te leggen hoe de blockchain technologie werkt, en hoe en waarvoor het gebruikt kan worden (naast virtuele valuta’s). Hij stond uitgebreid stil bij het uitgeven van nieuwe munten, waarom mensen daarvoor kiezen, waarom investeerders erin investeren en naar welke instanties men wel en naar welke men niet hoeft te luisteren als het gaat om wetgeving.

Als tegensprestatie lieten wij Daan onze trading bot zien. In de afgelopen weken hebben we bij The Analytics Lab de basis van onze automatische trading bot ontwikkeld. Een robot die op basis van geavanceerde algoritmes handelt in Crypto currencies en ons via text messages op de hoogte houdt van acties en waardeveranderingen in de portfolio. Binnenkort zullen we hier meer over vertellen!

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Hoe we via Kunstmatige Intelligentie klanttevredenheid verhogen en kosten verlagen

Iedereen die wel eens gebeld heeft naar het servicenummer van een overheidsinstantie of een grote dienstverlener zal het ongetwijfeld herkennen: een lange wachttijd, een aantal keer doorverbonden worden en niet meteen het juiste antwoord krijgen. Sinds Youp van het Hek in 2010 een Twitter-storm startte over T-Mobile en de, in zijn ogen, beroerde klantenservice is er veel verbeterd in de meeste klant contact centers. Maar er kan ook nog steeds een hoop beter. Omdat wij wel van een uitdaging houden hebben we bij The Analytics Lab een dashboard gemaakt dat via kunstmatige intelligentie bedrijven helpt om hun klantcontact te verbeteren en tegelijkertijd hun kosten te verlagen. 

Een groot deel van de extra kosten in klant contact centers is te wijten aan onvoorziene proceskosten. Denk hierbij aan extra telefonische vragen of een groter aandeel klachten. Meestal leidt dit niet alleen tot extra kosten, maar ook tot een lagere klanttevredenheid. Vaak lijkt het alsof deze vragen en klachten het callcenter overvallen: de aard van de vragen en het volume zijn onvoorzien. Onvoorziene gebeurtenissen zijn echter wel degelijk goed te voorspellen met de juiste kennis, de bedrijfsprocessen en de combinatie met kunstmatige intelligentie.

Via kunstmatige intelligentie kunnen wij een dashboard opleveren dat gebruikmaakt van voorspellende algoritmes die realtime aanbevelingen kunnen doen. Deze aanbevelingen voor procesverbetering kunnen per uur, dag, week en maand niveau gegeven worden. Op basis van een zelf gecreëerde dataset met onze data sampler hebben we een eerste versie van een dashboard gemaakt om meer grip te krijgen op onverwachte kosten. Daarnaast zijn we van mening dat het realtime omzetten van spraak naar tekst waardevolle informatie genereert. Daardoor is sneller inzichtelijk wat de daadwerkelijke reden is van een telefonische vraag. Via sentiment analyses kunnen we ook inzicht geven over het sentiment van elk gesprek en kan er een psychometrische profiel van een beller worden gemaakt. De eerste versie van ons dashboard staat nu live.

Benieuwd naar ons dashboard? Wilt u weten hoe uw bedrijf via kunstmatige intelligentie meer grip kan krijgen op klantinteracties? Neem dan een kijkje op de pagina Het ongeplande voorspellen of neem contact met ons op.

Dit artikel verscheen eerder op cmotions.nl.

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“We crunch(ed) Artificial Intelligence” Meetup

On December 13 we had our very first The Analytics Lab Meetup at our Headquarters in Amersfoort. Despite the traffic jams around Amersfoort and a broken train that blocked the track between Utrecht and Amersfoort we could welcome around thirty people on this cold Wednesday. Unfortunately one of our expected speakers, Thomas Stalman from welkrestaurant.nl wasn’t one of them, the broken train made it impossible for him to get to Amersfoort on time. But on the bright side, this means we already have an amazing speaker planned for our next meetup!

After some simple but nice dinner, our other three speakers made grateful use of the space that Thomas left for them and brought their story full of enthusiasm to the bedazzled attendees of the meetup. Our first speaker was Klaas Tjepkema who told us about the Advanced Data Sampler and about the plans he has to evolve this. A story about dreaming big and starting small, a perfect example of putting AI to practice. Our second speakers were Joost van der Leegte and Willem van der Geest who told us about their Project Friday Project: AI and Coffee. This project is part of our The Analytics Lab Playground, where we can play around, have fun and learn a lot of new skills! In their presentation Joost and Willem told us more about the coffee machine that was extended with facial recognition. We even got a live demonstration of this coffee machine.

All in all it was a really interesting and fun evening, which gave a nice opportunity to learn more about artificial intelligence in general and data sampling and facial recognition more particular.

Thanks to everybody who was there at our very first meetup, we definitely enjoyed it and hope you did as well!

If you want to know more, or you want to be informed about our next meetup, please sign up at meetup.com and join our The Analytics Lab group. We’d love to see you there!

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Our Advanced Data Sampler Beta version is live!

Great news! Our Advanced Data Sampler is live!

Why great? Well, did you ever find yourself preparing for teaching a course by spending hours on Kaggle (or other websites) to get the right dataset. Which contained data that fitted the business sector of your students as well as the subject of your course and had the right type of data issues? We did and we resented it, such a waste of precious time… Don’t get us wrong, we absolutely love Kaggle! But not if we are looking for just the right dataset to use when teaching.

Or did you ever find yourself wanting to show your colleagues or customers a really fancy and shiny dashboard, maybe by using some new/advanced/cool tool, but you couldn’t use or didn’t have the right data?

After hours on Kaggle and adjusting data by hand we have something that we can classify as “OK”, but not great. Sometimes it get’s even more frustrating, the data is available, but you’re just not allowed to use it due to security or privacy issues.

Here is where our Advanced Data Sampler comes in the picture. This all started with an idea of a tool that could help us mimic a database, but without any of the privacy and security issues and with only the data issues (outliers, missings) that we want or need.

THINK BIG, START SMALL

Because we live by the motto ‘think big, start small’, that is exactly what we did, and here we are with our first beta version of the Advanced Data Sampler. Within this version you, as a user, are capable of creating two datasets. One dataset will contain customers with all the characteristics you defined, the other set will contain all their orders.

This is just our first small step in the path to our vision. We’re really curious how you value this first version and where you think we should put the focus on when further developing our Advanced Data Sampler. We want to invite you to try and test our Advanced Data Sampler!

Please share your opinion, feedback and ideas with us at info@theanalyticslab.nl

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Does your coffee machine know who you are? Ours does!

When we tell  our friends or family we were adding face recognition to a coffee machine, often the first question was ‘Why?’. A valid question, which is actually quite easy to answer: because we want to know whether we can, it’s fun and it’s another, just not your ordinary, reason to drink some beers on a Friday afternoon. This project is part of an ongoing idea, which we call Project Friday: doing stuff we’re excited about, which don’t necessary generate revenue. When we answer our friends and family that we do this just for fun, you can deduct from their facial expressions that the words ‘Nerd-Alert’ are going through their minds. The question that often follows is ‘does it actually work?’, well check out this video.

Credits video: Jan Persoon

So, how does it work. We placed a small camera on top of the coffee machine, which captures everything and sends the images to a small computer (a Raspberry PI). An algorithm looks real-time at these images and tries to detect faces. Once it has detected a face, it tries to recognize the face by comparing it with photos of colleagues. After recognizing the face, the computer can determine the favorite drink by using a database filled with these preferences. It then sends a signal to an Arduino board which we’ve soldered to the motherboard of the coffee machine. When the signal is sent, the coffee machine knows whether to brew coffee, espresso or cappuccino for example. When you want to differ from your preferences, you can just say “Stop”; the computer also has a microphone attached, which is able to recognize some basic commands. And we’ve even made the time you have to wair for your coffee a bit more fun, by making sure the computer plays a part of a song that you like.

Still not convinced? Just drop by (but send a photo beforehand)! You can reach us at info@theanalyticslab.nl

The code is on Github, check it out if you’re interested.

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Read more about what happened before during our friday afternoons we were working on this cool project!

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Create your DIY remote for Philips Hue with Raspberry Pi

You know those Philips Hue lights, which are amazing and really cool gadgets for your house? Well, thanks to Philips it is also easy to create your very own DIY remote control for them. Yes, of course you can get a remote for these lights in the shop as well, but why would you if you can program one yourself?!

To do this I’ve used my Raspberry Pi 2 model B, and from my Arduino Starter Kit I’ve used a breadboard, a remote control and an IR (infrared) receiver. I’ve named my remote control “SpecialForMP3”, since this is the only text I could find on it. But you can use any infrared remote control you want, so if you have some useless remote controls lying around, just give them a purpose again.

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First, we need to setup the Raspberry Pi to be able to receive infrared signals sent by the remote control. Furthermore, we have to make sure the Raspberry Pi can not only receive the signals, but is also capable of deciphering them. In other words, we want to make sure the Raspberry Pi can “hear” the remote control and is also capable of understanding what is said; they need to speak the same language.

The first step towards achieving this goal, is connecting the IR receiver to the Raspberry Pi. After we’ve done this we can start with setting up the LIRC (Linux Infrared Remote Control) package on the Raspberry Pi. LIRC is a package that allows you to decode and send infrared signals of many (but not all) commonly used remote controls. To setup both the IR receiver and LIRC, you can follow the steps as described here. You can find the resulting LIRC configuration file on GitHub, together with the rest of the code needed to finish this project.

When finished, my setup looks like this:
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As a side note, I had some troubles using mode2 -d /dev/lirc0, after pushing a button on my remote instead of seeing something like the example I got the message “Partial read 8 bytes” and then it just stopped. After changing the driver from devinput to default in lirc_options.conf this issue was fixed.

When you find yourself having no permission to change files, look at the chmod 777 option, this makes sure everybody has read-write-execute permissions on the specified files and/or folders.

Now we’re ready for installing Python packages that make it possible to use LIRC and connect to the Philips Hue Bridge which controls the lights:

  • sudo pip install phue
  • sudo apt-get install python-lirc

We get started by connecting the Philips Hue Bridge through a Python script, this can easily be done by using it’s IP address. You can find out what the correct IP address is in multiple ways, an easy one is using the (official) Philips Hue app:

  • Go to the settings menu in the Philips Hue app and go to My Bridge, click on Network settings and switch off the DHCP toggle; the IP address of the bridge will show.

The next thing we need to do is connect to the Philips Hue Bridge and determine the names of the available lights and light groups.

# import the necessary packages
from phue import Bridge
# identify the bridge
b = Bridge(‘192.168.1.128’)
# connect to the bridge (first press button on bridge)
# this only has to be done the first time you setup the connection
b.connect()
# get the names of all the lights
print b.get_light_objects(‘name’)
# get the names of the light groups
print b.get_group()

 In order to change the colour of a light, we need to know the XY code of this colour. Since we’re more familiar with using RGB codes for colour, we can use this function to convert RGB to XY.

Finally, we can connect to the LIRC and create the loop in which we will change the colour of the lights. In my case I have the following lights available: Zithoek, Zithoek bloom, Raamkant, Midden, Keukenkant, Slaapkamer. And I have the following light groups available:  Zithoek, Eetkamer, Slaapkamer. You can find the complete script to use the remote to control the Philips Hue Lights on GitHub as well, this script connects to the Philips Hue Bridge and to LIRC and has ten different scenes for the lights, but you can of course adapt this to your own needs and wishes.

Good luck and enjoy!

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Project Friday 1.5: AI & Coffee – When you flip your coffee machine the finger

Everybody has those mornings when your mood is not as wonderful as always and your energy levels seem to be lagging, especially before you had your coffee. Those mornings when you just want to flip the finger to everything and everyone and tell the world to fuck off. When “working” on our A.I. coffee machine, we recognized this feeling and felt that it’s our duty to take this feeling seriously.

So, here’s what we did, we trained our coffee machine to recognize when someone flips it the finger. Thomas spend the entire afternoon collecting training data, meaning that he was just flipping the finger to his computer and looking for YouTube clips where people flip the finger. Which is why I quoted “working” in the first paragraph. In the picture below you’ll see some of our training data.

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And the results after two days of training:

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When it spots the middle finger, it serves a double shot of espresso. Now, we’re not claiming that it fixes your mood entirely, but sure as hell makes it a bit better!

Last session we’ve had our dr. Frankenstein moment, it was working. It was however on a dry run, we saw the buttons worked, but didn’t connect the coffee machine to the water. This time we did, apparently there’s still a little bug, as it decided to serve a cappuccino every 20 seconds.

 

Read more about what happened before or check out the video we’ve made of this project