人工通用智能的火花：GPT-4的早期实验
S´ebastienBubeck Varun Chandrasekaran Ronen Eldan Johannes Gehrke
Eric Horvitz Kamar Peter Lee Yin Tat Lee Yuanzhi LiScott Lundberg
Adaptive and Distributed Operator Placement for Streaming Workflows in Edge-Cloud Environments
Klara Nahrstedt
Department of Computer Science
Supporting Stateful Edge Services by Enforcing Deterministic Behavior
Jason Flinn and Matthew Furlong
Elevating the Edge to be a Peer of the Cloud
Enabling next generation technologies such as self-driving cars or smart cities requires us to rethink the way we support their applications. The emergence of these technologies is fueled by the proliferation of a large number of devices in the Internet of Things. These devices have the potential to generate massive amounts of data, and applications supporting them often require this data to be processed in a timely manner. Because of these requirements, we must augment and extend the Cloud computing model to better serve such applications. The backhaul links connecting clients to Cloud data centers could quickly become overwhelmed by such data, and the physical distance of these data centers from clients prevents low-latency response times. To meet the challenges posed by emerging IoT applications, we must provide Cloudlike functionality closer to the edge of the network, where clients and their data live. We propose to elevate the Edge to be a peer of the Cloud for addressing these challenges.
Digital Tapestry
Carsten Rother Sanjiv Kumar Vladimir Kolmogorov Andrew Blake
Microsoft Research, Cambridge, UK Carnegie Mellon University, Pittsburgh, USA
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