Last updated on 18 de set. de 2024

Você encontrou contratempos em projetos de IA. Como você pode transformá-los em insights para o sucesso futuro?

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Omid YOUSEFI

PhD Student | Business Intelligence Enthusiast | Pioneering Urban Planner Committed to Social Equity and Resilience |…
Stanislav Dalence, PMP, PSM, LBBP

Top Artificial Intelligence (AI) Voice - Director de Proyecto Senior in NICE CXone

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Timo Leiser

CEO | Founder von Leiser AI Consulting | Ich helfe Finanzdienstleistern, ihre Geschäftsprozesse durch KI zu…

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Omid YOUSEFI

PhD Student | Business Intelligence Enthusiast | Pioneering Urban Planner Committed to Social Equity and Resilience | Athlete | Bodybuilding & Crossfit Judge & Coach
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One thing I've found helpful when facing setbacks in AI projects is to focus on ethical AI frameworks. While frameworks provide principles like beneficence and justice, translating these into system requirements can guide the development of more ethical AI solutions. Another strategy is to prioritize sustainability by considering the environmental impact of training large models. Strubell et al.'s research shows that re-purposing models is more energy-intensive than initial training, suggesting a need for sustainable AI practices.

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Stanislav Dalence, PMP, PSM, LBBP

Top Artificial Intelligence (AI) Voice - Director de Proyecto Senior in NICE CXone
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This was my star project last year that allow my team to: 💡 Facing constraints, we embraced creativity, developing a secure, budget-conscious chatbot using Retrieval-Augmented Generation (RAG) within our company's internal knowledge base. Without relying on the internet, we ensured sensitive information stayed protected, leveraging open-source models to eliminate risks of third-party compromise. 🚀 Despite lacking budget approval for an on-premise setup, we completed a successful proof of concept. This initiative now seeds future projects, including FedRAMP-enabled, secure, offline LLMs deployments in compliance with strict security regulations, providing significant value even in limited environments.

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Timo Leiser

CEO | Founder von Leiser AI Consulting | Ich helfe Finanzdienstleistern, ihre Geschäftsprozesse durch KI zu automatisieren und die Effizienz ihrer Mitarbeiter um 30% zu steigern.
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Root Cause Analysis is essential for understanding the fundamental issues that led to setbacks in AI projects. By dissecting errors and tracing them back to their origin, teams can devise strategies to avoid similar mistakes in the future. This technique not only helps in troubleshooting but also in refining the project development processes.

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Muhammad Usman Shahbaz

Artificial Intelligence 🤖 | Deep Learning 👁️ | Neural Networks 🧠 | DSA 📊
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When I encounter setbacks in AI projects, I turn them into valuable insights by analyzing the root causes of the issues and identifying what went wrong at each stage. I treat these setbacks as learning opportunities, allowing me to refine our processes and improve our approach moving forward. By documenting lessons learned and sharing them with the team, we can adjust our strategies, whether it's related to data quality, model selection, or project management. I also encourage a growth mindset within the team, promoting resilience and creative problem-solving. This approach ensures that setbacks are stepping stones to future success, helping us build stronger, more adaptable AI solutions.

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Amaete Umanah 💪🏾

I help you turn your low ROI tasks to high ROI | Leverage global talent | Scale your business faster, smarter and more affordably!
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One thing I've found helpful is to conduct thorough post-mortem analyses after encountering setbacks in AI projects. This process allows us to dissect what went wrong, identify root causes, and extract valuable lessons for future endeavors. In my experience, fostering a culture of open communication and psychological safety is crucial. When team members feel comfortable sharing their mistakes and challenges without fear of blame, it leads to more honest discussions and richer insights. Creating a knowledge base of past setbacks and their solutions not only prevents repeating mistakes but also serves as a valuable resource for new team members and future projects.

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