Integrity in research
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This short document discusses integrity in research. It emphasizes the importance of rigor and avoiding superficial metrics like the number of users or deliverables when defining success. True success requires integrity and following ethical standards in research.
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For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.National Security Agency - NSA mobile device best practices
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
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Integrity in research
- 3. Rigour
- 4. “5 users are enough”
- 7. Thank you Lee McIvor @leemcivor
Editor's Notes
- Just want to talk about a few potential barriers to great research and insight in the UX field.\n\n
- Research is critical, but we need to learn methods properly, not just by rote.\n\nThere are lots of books out there, these are just a few more familiar ones.\n\nWe need to go beyond these old standards, and push ourselves to gain a deeper understanding.\n
- Because we always have to think about how rigorous our work is.\n\nBudget constraints matter, but we still need to communicate effect of savings.\n\nDiscount: Heuristic eval / cognitive walkthrough: No users. Impacts reliability. Tradeoff.\n\nInformal usability testing: Environment - busy coffee shop? Participants - reflect real users?\n\n2 other areas to consider:\n\nQuantitative vs qualitative - businesses love quantitative, even when it doesn’t help. What, not why.\n\nEvaluative vs generative. When, how, why? Methods above are evaluative, not good for innovation.\n\nAlso means we can avoid hanging onto comfort blankets.\n
- We often hear “5 users are enough” for usability testing. Really? Why? Because Neilsen said so on his website 20 years ago? Jared Spool questioned this assumption in 2001, as has Faulkner (2003) and my old teachers Woolrych & Cockton (2001).\n\nIt’s nice to talk in absolutes, but we need to conduct our own research and examine others because in many cases results vary.\n\nAnother barrier to effective research is hiding behind deliverables.\n
- We need to stop talking in deliverables. We’re not being paid for deliverables, but better solutions.\n\nPersonas are a prime example. Plethora of persona “checklists”, even Forrester are at it.\n\nWithout the research, personas are meaningless.\n
- If we don’t know our stuff and actually deliver better products and services.\n\nUltimately we’ll be seen as cowboys.\n\nDon’t want this to be seen as negative.\n\nBut these are real challenges that I think we need to consider for our industry to remain relevant in the future. \n\nIf we don’t demonstrably have the skills, then we won’t be useful.\n\n
- \n