In order to enable an iCal export link, your account needs to have an API key created. This key enables other applications to access data from within Indico even when you are neither using nor logged into the Indico system yourself with the link provided. Once created, you can manage your key at any time by going to 'My Profile' and looking under the tab entitled 'HTTP API'. Further information about HTTP API keys can be found in the Indico documentation.
Additionally to having an API key associated with your account, exporting private event information requires the usage of a persistent signature. This enables API URLs which do not expire after a few minutes so while the setting is active, anyone in possession of the link provided can access the information. Due to this, it is extremely important that you keep these links private and for your use only. If you think someone else may have acquired access to a link using this key in the future, you must immediately create a new key pair on the 'My Profile' page under the 'HTTP API' and update the iCalendar links afterwards.
Permanent link for public information only:
Permanent link for all public and protected information:
[Riken Seminar] James Halverson - A Triangle of Influence: Bringing Together Physics, Pure Mathematics, and Computer Science
Recent advances in machine learning have begun creating new bridges to physics and mathematics that have traditionally existed between the latter two. Given this progress, I will speculate about where we are and where things might be headed, including through the recently launched NSF AI Institute for Artificial Intelligence and Fundamental Interactions. Specifically, I'll survey well-known machine learning results in supervised learning, reinforcement learning, and generative models, and explain cases where these techniques are already impacting physics and math. In more detail, I will explain some remarkable similarities between neural networks and quantum field theory that might point towards a theoretical understanding of deep learning, and also how an AI agent's ability to unknot headphones might provide useful in cracking a foundational problem in topology.