The latest in AI @Bol.com

Agenda:
17.30 – 18.30 Welcome, food and drinks
18.30 – 19:00 Daniël Heres – Machine Learning Engineer at bol.com
The Aha Moment of Natural Language Processing
19:00 – 19:30 Amin Dorostanian – Machine Learning Engineer at bol.com
A human's attempt to understand intelligence
19:30 – 19:45 Break
19:45 – 20:15 Robert-Jan Bruintjes – Lead Deep Learning Engineer at Sightcorp
Face Analysis through Deep Learning
20:15 – 20:45 Koen Verstrepen – CEO & Founder Froomle
Lessons Learned from Building and operating more than 10 large scale recommendation AIs
20:45 – 22:00 Drinks

Daniël Heres - The Aha Moment of Natural Language Processing
Natural Language Processing (NLP) powers many technologies like search, voice recognition, question answering, image captioning and much more.
Despite improvements in some domains like machine translation, one limitation is that to achieve high performance we need a high volume of labeled data for each task.
Last year through several advances in language modeling, with models such as ELMo, BERT and GPT-2, we are now able to learn improved representations from textual data. This leads to huge improvements on NLP benchmarks, without the need of millions of labeled examples.
This Aha Moment of Natural Language Processing means that computers can understand, reason about and generate language much better in the coming years.

Amin Dorostanian - A human's attempt to understand intelligence
Will machines ever be intelligent enough to take over the world?
To solve complicated problems?
But what is Intelligence?
Let's dive into the topic of Intelligence!
Using a bit of history of Artificial Intelligence (AI), philosophy, mathematics, and physics, this talk will make you think deeper on things you might have neglected until now, and discover the wonders of what we as humans possess; Intelligence!

Robert-Jan Bruintjes - Face Analysis through Deep Learning
Face analysis has recently become a topic of interest for both press and science due to the implications that this technology has for our own life and privacy. Indeed, our face is inherently related to our identity. This information can be used for identification purposes, such as unlocking our phone, but it can also be used to extract personal information, such as age, gender and emotion, for commercial or statistical purposes. Therefore, given the delicate aspects of this technology, the problem has to be treated carefully. From a Machine Learning point of view, face analysis can be defined as, given an image, the extraction of necessary features to detect a face and then to identify personal information from it. Face detection needs to be the first step of any face analysis system, while auxiliary tasks such as age, gender, pose, smile classification, or even identification can follow from the extracted detection. How do we properly detect a face? How do we learn these face-related features? In this talk, I will show how Deep Learning has revolutionized computer vision for face detection and I will review the common architectures that can be used to tackle this problem. In particular, I will speak about the Single-Shot Multibox Detector (SSD) architecture as well as the implications of varying image resolutions on the detection of faces.