Turku.ai November meetup

Hi guys! To ease the dark evenings of the Finnish autumn, we are bringing you the next Turku.ai meetup! The next event is on the Nov 2 in Werstas, starting at 1800. Our host will be Silo AI.

The speakers for Nov 2 will be Prof. Filip Ginter (University of Turku) and Samuel Rönnqvist (Åbo Akademi University / THM University of Applied Sciences). See below for more details. Hope to see you all in Werstas!

"Forget words! Modelling language with neural networks character at a time" by Prof. Filip Ginter (University of Turku)

Filip is a top-of-the-line NLP researcher with thousands of citations. For someone working on the coolest technology (NLP, deep learning, LSTM, autoencoders, word embeddings and what not), he's a modest man: "I am a researcher at the Department of Information Technology, University of Turku. My research is in the area of natural language processing. See bionlp.utu.fi (https://fginter.github.io/bionlp.utu.fi), the pages of our research group, for more details. I was born in 1978 in Ostrava, Czech Republic (Czechoslovakia back then) In 2001, I got a M.Sc. in computer science at the computer science department of VSB - Technical University Ostrava. My major subject was artificial intelligence. I gained a PhD in computer science in 2007. The title of my thesis is Towards Information Extraction in the Biomedical Domain: Methods and Resources. As of 2016, I am an assistant professor of language technology."

"A tutorial on deep learning for NLP" by Samuel Rönnqvist (Åbo Akademi University / THM University of Applied Sciences)

Samuel is a PhD candidate on his way to Germany for a postdoc, with interests ranging from networks to deep learning to visual analytics: "My research centers around text mining, NLP, machine learning and artificial intelligence, particularly knowledge-lean (resource-lean) approaches to text mining, applied toward the study of financial risk and stability among other areas. It builds upon and combines:

• natural language processing: distributional semantics, semantic role labeling, discourse parsing, topic modeling, sentiment analysis, etc.

• machine learning: neural networks, deep learning, NLP applications, data and dimension reduction

• visual analytics: interactive visualization of models to support reasoning

• network analysis: studying phenomena as complexly connected systems, quantitatively and visually

The areas of application include financial risk, business analytics, computational biology and history."