The Emergence of KnowledgeOps
12see more slides, notes, and other material here: https://github.com/Aggregate-Intellect/practical-llms/
https://www.linkedin.com/in/amirfzpr/
** KnowledgeOps and Development Processes
1/14: #KnowledgeOps is about managing an organization's or community’s knowledge assets and processes to enable reuse and collaboration. #DevOps and #ModelOps (#MLOps) are specific examples that help develop software faster and with lower chances of failure.
2/14: #ShiftingLeft and #CI/CD are important #DevOps concepts that bring testing and quality assurance to the beginning of the software development process and provide tools to automate them for consistency and repeatability.
3/14: Although automation has improved many parts of the development process, the step of discovering and planning is still largely manual, requiring continuous communication and collaboration with teammates, which creates a single point of failure.
4/14: #ModelOps (e.g., #MLOps) has allowed for the automation of data and model handling, but the manual interpretation and decision-making steps are still present. #Automation
** Generative AI, and Continuous Exploration / Continuous Integration / Continuous Delivery
5/14: Generative AI can help us shift further left into the exploration, planning, and coding steps, significantly improving our ability to explore options and conduct experiments. #GenerativeAI #SoftwareDevelopment
6/14: There will eventually be more automation in our problem-solving processes, but in the meantime, tools built with generative AI will significantly augment our ability for interpretation and decision-making in ensuring the success of the development of complex software systems. #MachineLearning #AI
7/14: Emerging tools for thinking allow for a more experimental approach to knowledge-intensive work, allowing for continuous hypothesis generation and experimentation leading to CE/CI/CD. #ContinuousExploration #ContinuousIntegration #ContinuousDelivery
8/14: There is a trend towards interfacing generative copilots, retrieval systems, and other knowledge-intensive systems to create thinking machines with memory and reasoning skills. #GenerativeAI #RetrievalSystems #Reasoning
9/14: Language models are essential for these tools because they need to interpret users’ instructions usually provided in natural language, communicate results, and facilitate human-human communication. #LanguageModels #Communication
10/14: Language models can give us the ability to articulate and communicate complex ideas effectively to stakeholders and team members enabling more efficient problem solving in communities and organizations. #LanguageModels #Communication
** Adoption of Knowledge-Ops
11/14: In bigger companies, beyond technology, the biggest barriers to implementing #KnowledgeOps are cultural problems; eg. political reasons that prevent a unified and integrated knowledge and expertise system connected to knowledge bases of all teams across the enterprise. #CulturalProblems
12/14: Since all #GPT can reliably provide in short term is the language skill, primarily NLU/NLG, smaller language models trained on internal knowledge can be built to avoid privacy and data access issues. #NaturalLanguageUnderstanding #NaturalLanguageGeneration
13/14: Adoption of #LLM enabled thinking tools will start in smaller companies, and with improvements in corporate culture and maturing technology, we will see bigger companies joining the movement.
14/14: With these tools being able to talk to us, remember our context, and reason about the world around us without the barriers of coding and formal language, we can accelerate #KnowledgeOps to the point where no idea is too expensive to try. #Automation