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We empower people with better information to make their best decisions. Knowledge Representation & Reasoning Our vision is to investigate, innovate, propose, and build symbiotic systems that mutually exploit language models (LM) and knowledge graphs (KG) in a continuous and (semi-) automated learning paradigm to address current challenges. Data AI Symbiosis Our work in the data-management-for-AI a
erik@localhost:~$ gcloud alpha cloud-shell get-mount-command ~/my-cloud-shell sshfs ekuefler@35.197.73.198: /home/ekuefler/my-cloud-shell -p 6000 -oIdentityFile=/home/ekuefler/.ssh/google_compute_engine erik@localhost:~$ sshfs ekuefler@35.197.73.198: /home/ekuefler/my-cloud-shell -p 6000 -oIdentityFile=/home/ekuefler/.ssh/google_compute_engine erik@localhost:~$ cd my-cloud-shell erik@localhost:~$
A couple of years ago, I started (read: was made) to adopt scrum in my work. I didn’t like it. The concept of estimation was vague to me: How do we estimate effort for data exploration or research? And after we move something from In Progress to Done, can we move it back? This happens often (in data science) where we need to revisit an upstream step, such as data preparation or feature engineering
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