We are looking for talented scientists to work at Amazon Alexa AI in Dresden/Germany! If you are interested in machine learning, knowledge graphs, NLP & language models, please consider to apply at https://t.co/fxLjBWT9tI (researchers at different experience levels can apply).
I am extremely excited to join @amazon@alexa99 AI today and start a new chapter in my scientific career working in an excellent environment on question answering, knowledge graphs, reasoning, natural language understanding and more generally the future of conversational AI.
We are looking for top talents for our new project OpenGPT-X in which we aim to improve large neural language models and their usability for dialogue systems & question answering. Very large supercomputing facilities are available. You can apply here: https://t.co/3xcGEv1Opo
Recently, I got many questions about the role of neural language models in #AI (due to our new OpenGPT-X project) and decided to write a blog post on the topic: https://t.co/fmQOpTzbM5 #languagemodels#gpt3#ArtificialIntelligence#OpenGPTX
Very happy that the SPEAKER proposal was awarded at the German AI innovation competition. SPEAKER will build a B2B conversational AI platform. Thanks to the currently approx. 50 organisations participating in SPEAKER for their support. - https://t.co/jcIOEBIbTq #ConversationalAI
@AnkasZBW And in case you wonder what an #OpenResearch#KnowledgeGraph is, its a #semantic and interlinked representation of research contributions facilitating reproducibility, comparability and peer-review. Our #ORKG is still in alpha mode. See an overview at: https://t.co/Op0ME4VEqK
Our Open Research #KnowledgeGraph#ORKG is advancing step by step. In case you are interested to stay tuned or get involved, here are your options: https://t.co/zMqcI6sKX4
Co-located with @SemanticsConf we are organizing the 3rd workshop on the Open Research #KnowledgeGraph on Sep 9th in Karlsruhe https://t.co/axwZqniHxi Please get back to us if you want to present or contribute!
https://t.co/EFvWqt1DJs is an interesting paper, which shows that the way neural networks solve tasks differs substantially from how humans do it. They often defeat benchmarks in ways they were not meant to be defeated, which can lead to an overestimation of their abilities.