Dear Deep Learning Munich enthusiasts,
I'm exited to announce the next meetup!
We will gather on Wednesday, 8th February, 6.30 PM, at Isarvalley / Google Munich, Erika-Mann-Str. 33.
All guests will have to enter through the main entrance and pick up a guest badge. The reception will make sure to have all badges printed beforehand, so that should be a relatively smooth process.
We can host 90 people this time so be quick!
Sponsors this time are NVIDIA, TNG Consulting, IBP, Google and free machines. We are grateful for the support.
Please ping if you'd like to join the organization team for future events.
Looking forward to a exiting year of Deep Learning innovation!
Dr. Damien Borth - Director, Deep Learning Competence Center, Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI)
Visual Sentiment Analysis with Deep Convolutional Neural Networks
Nowadays the Web, as a major platform for communication and information exchange, is shifting towards visual content.Unfortunately, visual content in form of images or videos is limited in its accessibility as compared to textual content. With recent advances in deep learning we are able to analyze the content of images and videos as not seen before. This talk will present the first framework able to extract sentiment from visual content by introducing the Visual Sentiment Ontology (VSO). This ontology consists of thousands of Adjective Noun Pair (ANP) concepts able to capture such polarities. Further, the talk introduces SentiBank, the associated deep convolutional neural network (CNN) used to detect the presence of up to 2089 ANPs in images. Originally designed to assess sentiment in visual content, DeepSentiBank was already shown to have a broad spectrum of application domains ranging from aesthetic assessment, image popularity prediction, filtering explicit content, human alike image captions generation, or wildfire detection for emergency response teams.
Dr. Damian Borth is the Director of the Deep Learning Competence Center at the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern, the Principle Investigator of the NVIDIA AI Lab at the DFKI, and founding co-director of Sociovestix Labs, a social enterprise in the area of financial data science.Damian’s research focuses on large-scale multimedia opinion mining applying machine learning and in particular deep learning to mine insights (trends, sentiment) from online media streams. His work has been awarded by NVIDIA at GTC Europe 2016, the Best Paper Award at ACM ICMR 2012, the McKinsey Business Technology Award 2011, and a Google Research Award in 2010.
Henrik Klagges - Geschäftsführer, TNG Technology Consulting GmbH
Strategies for AI deployment
The talk will pose some of the urgent questions regarding AI, like: What is the state of the art in AI, at least what is publicly known? What is the shape of the available tools and how can organizations use them? What are some of the operating problems of employing AI, and which methodological risks do we know? Are data worth more or algorithms? How will future AI-aided organization look like? The talk will attempt to give partial answers.
Henrik Klagges studied physics at in Munich and computation at the University of Oxford, from which he received an MSc. He was a member of the IBM Physics Group Munich and also spent time at the Lawrence Livermore National Laboratory. Henrik programmed the Unix Cockpit, for which he won the “German Entrepreneur Prize” of the “German Entrepreneur Fund” in 1995. In 2001, Henrik co-founded TNG Technology Consulting, a high-end software development forge in Germany. Under his co-lead, TNG grew to over 270 mathematicians, physicists, and computer scientists, 60% of whom hold PhDs. Henrik also coordinates TNG's deep learning projects. Already, the company has won the “Bavaria’s Best 50″ prize thrice.
Ralph Hinsche - NVIDIA
DGX-1 and SATURNV: The World’s Most Efficient Supercomputer for AI and Deep Learning
The NVIDIA DGX SATURNV taps into the compute power of 125 NVIDIA Pascal™-powered DGX-1™ server nodes to drive new levels of deep learning and AI analytics. 1,000 NVIDIA Tesla® P100 data center accelerators, coupled with NVIDIA’s deep learning software stack, provide high-performance deep learning training across multiple frameworks to deliver the best results in the shortest time. We will provide insights and details on this leap forward towards exascale computing.
With more than 30 years of HPC experience Ralph started as a student in 1987 at Parsytec (Transputer, OCCAM) in Aachen/Germany. This was followed by head of department activities at several SUN Microsystems partners with the focus on HPC and he contributed to a national development project (parallel computer GIGAmachine). In 1996 he became a sales engineer at EUREM with a focus on "Wide Area Automation" (distributed intelligence). In his last position, he was Key Account Manager at circular for nearly 10 years mainly in the field of HPC. Again, there were close cooperations with SUN Microsystems and DELL. Ralph is now responsible within the DACH region as a Business Development Manager for GPU-Computing (Tesla) and Deep Learning at NVIDIA since 2014.