Collective intelligence
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The document discusses various aspects of collective intelligence and web 2.0 technologies including harnessing user data to improve experiences, mass collaboration through sites like Wikipedia, file sharing through peer-to-peer networks and BitTorrent, recommendation systems that provide suggestions unconsciously and through feedback, and Google's indexing and search systems that allow information to be easily found and accessed through collaboration.
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As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Keywords: AI, Containeres, Kubernetes, Cloud Native
Event Link: https://meine.doag.org/events/cloudland/2024/agenda/#agendaId.4211
Dandelion Hashtable: beyond billion requests per second on a commodity server
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
Astute Business Solutions | Oracle Cloud Partner |
Your goto partner for Oracle Cloud, PeopleSoft, E-Business Suite, and Ellucian Banner. We are a firm specialized in managed services and consulting.
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Collective intelligence
- 1. Collective Intelligence As a principle of Web 2.0
- 2. Who are we, & what do we have to say? Zia: Harnessing data to improve user experience. Manish: Mass collaboration and co-operation.
- 3. Who are we, & what do we have to say? Shalisa: File sharing and peer to peer networking. Shuja: Google and search engine indexing systems.
- 4. Improving Service Through Recommendations. Information supplied unconsciously…. …okay, it’s not perfect!
- 5. Improving Service Through Feedback. Information supplied consciously…. …very influential.
- 6. “Scrobbling” gathering data using open-source software. It’s the shizzle!
- 7. Wikipedia: a product of mass collaboration. E=MC2 Disagree? Amend the page.
- 8. Ad Hoc Communities Working to achieve a common goal.
- 9. Networks of Trust. Please don’t tag my drunken shenanigans… …and then email my boss.
- 10. Bit Torrent Uploading content for others to download.
- 11. P2P Networks Files shared without accessing a server.
- 12. Collective Intelligence Remembers things so you don’t have to!
- 13. Google Indexing System How it all works.
- 14. Google Top Search Because Googling stuff is fun…
- 15. Google Caffeine the Future of Indexing. The search engine… …with a boost.