Hackathon: Artificial Intelligence for Sustainability Hackathon, Munich, Germany, 24th June 2020
#IndustrialAI makes a difference for #business and to pursuit of a #sustainable future. Super stoked that great #partners from the #AI #ecosystem joined forces with the Siemens #AILab to tackle AI-driven #sustainability #challenges that will shape our #future! Geeks join…
Co-Chair: Human Centered AI: Trustworthiness of AI Models and Data, AAAI 2019 Fall Symposium Series in Arlington, VA, USA, November 7–9, 2019
Join and discuss with us #HumanCenteredAI at the #AAAI 2019 #FallSymposiumSeries in Arlington, VA, #USA, November 7–9, 2019 – agenda is set and registration is now open: https://bit.ly/2nveduf #HumanCentered #AI: #Trustworthiness of AI #Models and #Data To facilitate the widespread…
Talk: AI Meets Society: responsibly designing smart systems & infrastructures, 19th September 2019, Zug, Swiss
Trust is not necessarily about Transparency, but about Interaction. In current times: Industrialization was/is all about gaining more efficiency and productivity; Digital platforms target attention spans and the predictive behaviour, trying to maximize engagement for profits – sure that shall…
Talk & Panel: Responsible AI: Trust, Fairness, and Transparency in Machine Learning at the appliedAI Ecosystem Meetup in Munich, 14. January 2019
Around 150 attendees participated in the appliedAI Ecosystem Meetup on Artificial Intelligence in Munich and Partners. Challenging talk on #ResponsibleAI: Trust, Fairness, and Transparency in Machine Learning – and interesting panel with Leon Szeli and Fabian Mader. AI and ML…
MOOC: Industrial Artificial Intelligence at Siemens – from the impact of Deep Learning, the creation of Conversational AI, to AI-centric API ecosystem
Awesome – finishing the MOOC on Artificial Intelligence at Siemens – from the impact of Deep Learning, the creation of Conversational AIs, to the acceleration via an AI-centric API ecosystem. Proud to push the Siemens AI Lab further – accelerating…
End-to-End Trainable Attentive Decoder for Hierarchical Entity Classification
Abstract: We address fine-grained entity classification and propose a novel attention-based recurrent neural network (RNN) encoderdecoder that generates paths in the type hierarchy and can be trained end-to-end. We show that our model performs better on fine-grained entity classification than…
AI & Machine Learning Research with Meaningful Impact
I am curious about the foundations of computational intelligence, specifically methodologies that bridge the areas of connectionist and symbolic learning applied to real-world AI, Machine Learning and NLP applications. I am passioned about enabling an open and interdisciplinary research and…
Natural Language Access to Enterprise Data
Abstract:This paper describes USI Answers — a natural language question answering system for enterprise data. We report on the progress towards the goal of offering easy access to enterprise data to a large number of business users, most of whom…
USI Answers: Natural Language Question Answering Over (Semi-) Structured Industry Data
Abstract: The paper reports on the progress towards the goal of offering easy access to enterprise data to a large number of business users, most of whom are not familiar with the specific syntax or semantics of the underlying data…
Market Intelligence: Linked Data-driven Entity Resolution for Customer and Competitor Analysis
Abstract: In this paper, we present a linked data-driven method for named entity recognition and disambiguation which is applied within an industry customer and competitor analysis application. The proposed algorithm primarily targets the domain of geoparsing and geocoding, but it…
