Why Collaborative Learning and Conversational Intelligence Are Important
Learn how the convergence of CL (collaborative learning) and CI (conversational intelligence) enables a future where AI handles data and tasks while humans focus on creativity.
As AI rapidly advances, new applications within AIOps are gaining momentum. Dr. Maitreya Natu, writes how Collaborative Learning and Conversational Intelligence have emerged as important trends in 2024, driving seamless cooperation between humans and AI in IT operations.
As artificial intelligence (AI) rapidly advances, new applications and capabilities within AI operations (AIOps) have gained momentum. Amongst these, collaborative learning and conversational intelligence have emerged as important trends in 2024, driving a paradigm shift towards seamless human-AI cooperation and symbiotic intelligence amplification. This has ushered in a transformative era for the application of AI and machine learning (ML) in IT operations (ITOps).
The true potential of AI emerges when it combines the power of pattern-mining in complex data with the intuition and experience of natural intelligence. CL and CI play crucial roles in this human-AI, facilitating shared learning and natural interactions that drive increased autonomy, predictive capabilities, and enhanced collaboration. Essentially, they are reshaping the landscape of AI-powered IT operations.
To understand the impact of CL and CI, it’s important first to grasp what they are, what they bring to the table, and how they are being applied in real-world scenarios.
Collaborative Learning: Moving Beyond Isolated AI Models
Traditional AI models continuously learn and adapt but often fail to capture human intuition. AIOps relies not just on data-driven reasoning but also on subject matter experts to understand technological and domain nuances.
CL enables AI systems to learn dynamically from human experts, experiences, and real-world interactions through an interactive approach where AI solutions incorporate human-in-the-loop (HITL) mechanisms, integrating human intelligence and oversight into automated or AI-driven processes. Through this collaborative approach, AI models can continuously expand their knowledge, refine their decision-making, and stay aligned with evolving human preferences and environmental contexts.
The implications of CL are far-reaching, as it allows AI to complement and amplify human intelligence rather than operate in isolation. Humans can provide domain expertise, contextual nuances, and creative insights to AI while benefiting from the computational power, scalability, and pattern recognition of AI. This paves the way for transformative breakthroughs across industries, from healthcare to finance and scientific research to creative projects.
Collaborative Learning (CL) offers valuable applications in a variety of use cases, including:
- Augmenting AI-driven Insights: When an AI system mines data and generates observations or insights, CL can bring human experts into the loop to help translate those findings into actionable recommendations.
- Enhancing AI-powered Triage and Resolution: For an AI solution that performs automated triage and resolution, CL can enable human experts to handle exceptions and unknown conditions that the system has not yet learned to address. This human-in-the-loop approach helps the AI system improve over time.
- Validating AI-automated Tasks: If an AI solution automates repetitive tasks, CL can involve human experts to ‘derisk’ the automation by providing expert validation and corrections. This helps ensure the reliability and accuracy of the automated processes.
- Validating AI-generated Insights: When an AI system generates insights, CL can bring human experts into the review, validation, and approval process. This helps organizations leverage AI’s insights for more effective and proactive planning.
In an example of a CL scenario, an AI system first mines data to identify the root cause of a problem and recommends a fix. Human experts then review and validate the AI’s recommended fix, approving it or providing an alternative solution. The human experts also serve as designated handlers for exceptions that the AI system has not yet learned to address. Through this iterative human-AI interaction, the AI system continues to learn and enhance its auto-triage and resolution capabilities. This, in turn, increases the overall coverage, effectiveness, explainability, and trustworthiness of the automated processes.
While CL offers numerous benefits, it can also present certain challenges. For instance, CL systems may inundate human experts with excessive questions, and there is a risk of overreliance on human expertise. Therefore, two key principles should be adhered to when implementing CL:
- Ask the right questions at the appropriate time: Leverage the available data, the enterprise context, and insights from similar past conversations to make as many inferences as possible. Engage human experts only for the missing information.
- Assess when to trust human intuition versus data-driven insights: Evaluate the insights based on factors such as the level of support, confidence, recency, and consistency to determine which approach to trust.
Conversational Intelligence: Bridging the Human-AI Communication Gap
AIOps often generate a wealth of insights, but users can struggle to make the most of them due to insight fatigue – being overwhelmed by the volume and unsure of what to prioritize or how to trust the insights.
CI represents an interaction model that allows humans to engage in intelligent dialogues with machines. Powered by advancements in natural language processing (NLP), CI is revolutionizing human-AI interaction. Instead of relying on fixed-form interfaces in the form of reports, notifications, or dashboards, CI enables natural, intuitive communication, making it easier for humans to effectively use an AI product.
CI-enabled AIOps platforms enable IT professionals to interact with AI systems using plain language. CI assistants can understand complex queries, provide contextual responses, and engage in multi-turn dialogues that mimic human conversations.
CI can address insight fatigue by enabling users to identify focus areas and discover insights through simple dialogues. It also brings explainability and trustworthiness to AI-derived insights. For example, a business leader asks a CI system about areas needing attention. The CI engine comprehends the user’s context, their organization’s landscape, past preferences, and business criticality to provide insights into focus areas.
The CI system can then guide the conversation, helping users prioritize these areas, furnish details, and recommend actions. Importantly, the CI engine adapts to the discussion flow, offering root-cause analysis or identifying similar issues. Through each interaction, the CI engine expands its understanding and refines responses to suit the user’s needs, building trust and providing increasingly valuable, explainable insights.
When implementing CI, two key principles should be considered:
- Engage in intelligent conversations with humans: CI systems should capture the context of the conversation, form a point of view, guide the dialogue, and adapt their responses based on user feedback. This allows the system to engage in meaningful, dynamic exchanges with users.
- Bring explainability to AI insights using explainable intelligence: CI should leverage concepts from the field of explainable AI to provide both textual and visual evidence of the reasoning process behind its insights. This helps users better understand and trust the AI-driven outputs.
See More: 5 Lessons To Help You Avoid AIOps Pitfalls
Exponential Power: CL and CI Drive Transformative Change in AIOps
The convergence of CL and CI is creating new metaphors of augmented intelligence and transforming AIOps platforms. CL enables AIOps solutions to continuously learn by understanding systems, data, and operational knowledge, connecting dots, filling gaps, and undergoing ongoing training and validation from human experts. CI empowers humans to effectively leverage AI-powered insights, making them explainable and easy to consume, increasing trust and AIOps adoption.
Combined, CL and CI pave the way for conversational, explainable, trustworthy operational intelligence, where humans and AI work in true partnership. As they mature, they provide the means to tackle complex IT challenges, drive innovation, and expand knowledge with greater accuracy by enabling various use cases such as :
- Improving real-time anomaly detection and prediction by combining AI pattern recognition with human contextual understanding
- Enhancing automated incident resolution through contextual understanding and efficient human-AI collaboration in the resolution of new and exceptional cases
- Providing highly tailored recommendations considering environmental, business, and operational factors
- Fostering trust and confidence in AIOps, driving higher adoption
The powerful convergence of CL and CI unlocks a collaborative future where artificial and human intelligence work symbiotically. AI rapidly processes vast data, identifies patterns, and automates tasks accurately. Humans provide intuition, creativity, emotional intelligence, and contextual framing that AI lacks. Integrating the two empowers humans with AI’s capabilities while allowing them to focus on higher-order cognitive tasks and strategic decisions.
CL and CI are game-changers because they bridge the gap between powerful AI capabilities, human expertise and wisdom. Organizations can optimize IT operations proactively, make informed choices, and drive agility through this human-AI symbiosis in AIOps. This partnership amplifies what humans and machines can achieve across many domains in 2024 and beyond.
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