PlantBrain Co-Pilot :Powered by Generative AI

PlantBrain Co-Pilot is a digital assistant integrated into PlantBrain, enabling access to enterprise-wide data. PlantBrain Co-Pilot empowers our customers to draw valuable insights from a diverse range of Enterprise data sources, driving informed decisions and advancing operational efficiency

Overview

Generative AI makes data, analytics, and forecasts universally accessible across an organization, democratizing access to these resources beyond the realm of data analysts. Consequently, personnel throughout an enterprise can leverage this powerful technology, augmenting efficiency, productivity, and crucially, strategic planning. A fundamental aspect of business transformation lies in the value generative AI brings to enterprise search experiences - Employing a search engine that provides precise access to the necessary resources within your business, facilitating users to retrieve relevant information, segments of reports, and insights from predictive analytics from both your enterprise data and external systems.

The Need for Generative AI in Industries

An integral part where the need for Generative AI became apparent was to augment operators in manufacturing and energy industry. Tasked with monitoring equipment performance and manufacturing conditions, operators bear the responsibility for alarm management, urgent issue responses, and ensuring operations safely meet production targets and quality specifications. The role's demanding nature leaves operators and engineers little time for delving into detailed manuals/ design standards, root cause analysis or aggregating information across systems in case of anomalies or issues in production, assets or project execution.

Compounding these challenges is an aging workforce, which has seen a vast reservoir of expertise slowly depleting as seasoned operators retire. This situation posed a significant problem for business continuity and demanded a solution capable of retaining and transferring this vital knowledge.

Generative AI offered an opportunity to surmount these challenges. We realized that a large language model (LLM) could be trained on a corpus of enterprise data, including historical machine failures, P&IDs, work order logs, inspections, production performance data, and OEM operating manuals. This trained model could synthesize information and provide valuable recommendations to less experienced operators.

Challenge of using Generative AI directly by Enterprises

Generative AI, despite its numerous advantages, does pose certain challenges that need to be addressed for it to be effectively utilized in an enterprise setting. Large language models (LLMs), like GPT-4, excel at understanding and manipulating general, publicly available knowledge but their capacity to comprehend proprietary, non-public information is notably absent, Most of the workflows in Enterprises heavily rely on proprietary information that's critical for decision making and strategic planning. However, when dealing with proprietary, non-public information, these models can’t be implemented directly.

The journey of implementing Generative AI in an industrial context also has its own set of unique challenges. One such obstacle was the inherent limitation of large language models (LLMs) when used directly through chatbots or APIs. Despite their prowess in language understanding, these models often lack context and domain-specific knowledge that are integral to accurate and relevant responses in an enterprise setting. Hence, there was a need to overcome these challenges and integrate a Generative AI-powered component that is integrated into the interactive UI dashboard of PlantBrain.

Algo8 PlantBrain Co-Pilot

Algo8 has developed a customized application layer which facilitates the creation of an enterprise-specific knowledge base to train the LLMs. By integrating domain-specific data and contextual knowledge, we enhanced the LLMs' capacity to provide more accurate and relevant responses, thus increasing their value to our platform’s end users.

We went a step further by enabling access to all enterprise-wide data, both structured and unstructured, through a single interface. This broad access empowers our users to draw valuable insights from a diverse range of data sources, driving informed decisions and advancing our operational efficiency.

These development efforts led to the birth of Algo8 Co-Pilot, an AI-powered assistant designed to meet the specific needs of our enterprise, while effectively addressing the noted limitations.

Users can still view critical KPIs, receive alerts, and uncover insightful data through the dashboards generated by PlantBrain. However, the true power of Algo8 PlantBrain Co-pilot comes into play when users want to delve deeper into the provided insights. With a simple question posed to the Algo8 Co-pilot, users can troubleshoot problems, gain deeper understanding of the KPIs, or garner more contextual information around insights, making it an invaluable tool in the decision-making process.

In essence, the Co-pilot, combined with the capabilities of the PlantBrain platform, revolutionizes how enterprises interpret and act on their data. By offering a way to navigate through complex datasets and providing actionable insights, PlantBrain and Algo8 Co-pilot together serve as an intuitive, intelligent aid to achieving improved operational efficiency and informed strategic decision-making.

Implementation Process

When applying PlantBrain Co-Pilot to enterprise data for industrial Enterprises, we've leveraged six key concepts— Connect and Curating various data sources, Enterprise Features, Prompt Engineering, Context Relevance, Model fine tuning and Response validation —to elevate the performance and relevance of PlantBrain Co-Pilot.

• Connecting and Curating Data Sources: In order to create meaningful outputs, Generative AI and Large Language Models (LLMs) require a rich understanding of the context within which they operate. PlantBrain allows our users to connect & curate various Enterprise IT & OT data. PlantBrain's robust metadata management layer can help ensure data consistency by standardizing data formats and making sure the model obtains contextual information needed to operate effectively.
• Enterprise Features:

1. User Access Control: PlantBrain provides Role-based access control (RBAC) and attribute-based access control (ABAC) to define and manage the level of access that different users or groups of users have. This includes controlling access to specific data or system functionalities.
2. Usage Monitoring and Audit Trails: PlantBrain makes it possible to monitor usage of the Co-Pilot and maintain audit trails
3. Hallucination Control: Through pre-training and fine-tuning processes, responses of Co-pilot stays within the defined boundary and provides accurate outputs
4. Feedback Control: A strong feedback loop corrects the model whenever it generates incorrect or nonsensical outputs

• Prompt Engineering and Context Relevance: By prepending our query with context—information about the proprietary data—we improve the model's ability to deliver accurate and relevant responses.
• Embeddings: This method transforms text into numerical vectors, thereby encoding similarity in information as proximity in a multi-dimensional space. By embedding the enterprise data and comparing it to the prompt's embedding, we identify the most similar pieces of information, which are then provided to the model as context
• Fine-Tuning: We take a foundation model—already equipped with general knowledge—and fine-tune it to reflect specific enterprise knowledge. This approach allows the model to understand enterprise-specific concepts without including them in each prompt, thereby making the inference process more cost-effective.

Partnership with MS Azure

In the implementation process, we've also capitalized on the robust capabilities of Azure OpenAI service and Azure Cognitive Search. Together, they've proven integral in managing and utilizing data that is proprietary and external to the ChatGPT Large Language Model. By utilizing Azure Cognitive Search, we've been able to effectively index and retrieve our enterprise data. This innovative solution enhances the language model's ability to generate accurate and meaningful responses, based on the specific context of your enterprise.

Most importantly, your enterprise data remains entirely under your control. By working on an instance of GPT model in Azure and fine tuning it, the knowledge sourced from your proprietary data lives outside of the openly available ChatGPT model and is not used for training purpose by Open AI. This structure ensures that your sensitive data is handled securely and privately, making the application of Generative AI in your enterprise not only powerful but also safe and trustworthy.

Benefits: Post Implementation

As we transition into the post-Generative AI implementation process, the tangible benefits of Algo8 Co-Pilot become increasingly apparent. When trained with your enterprise's specific data, it more than just responds to user queries. It yields policy-compliant and contextually relevant responses, which adds immense value to your enterprise.

Going beyond merely accessing existing enterprise data, sophisticated it is capable of calculating metrics and analyzing your proprietary data to produce value-added content.

Illustrative example:

Imagine an operator posing a question to our PlantBrain Co-Pilot, : "I'm noticing some unusual vibration patterns on compressor C-102. What could be causing this?" The application swiftly retrieves the pertinent troubleshooting steps from the compressor's Standard Operating Procedure (SOP) document, complemented with insights from recent work orders specific to compressor C-102.

Through this interaction, operators with varying experience levels can instantly access the cumulative knowledge and wisdom of their predecessors, even without spending decades on the job. Plantbrain Co-Pilot synthesizes all relevant information, delivering it in a user-friendly and easily understandable format, all at a simple prompt entered into the enterprise search bar. This immediate access to critical knowledge transforms even a novice operator into a more proficient, more effective worker, contributing to improved operational outcomes for our business.

Challenges

As promising as the application of Generative AI is for enterprises, the path towards effective implementation is not devoid of risks and potential hiccups. Recognizing and addressing these risks proactively is essential for the successful deployment and operation of these systems.

• Security and Privacy Risks: Using sensitive data for training or operating the model could expose the enterprise to security and privacy risks if unauthorized access occurs. Vulnerabilities in the model architecture could potentially be exploited, leading to data breaches. Additionally, opaque data handling practices could lead to potential regulatory noncompliance. It is crucial for organizations to have robust security measures in place and to maintain transparency in their data handling practices.
• Intellectual Property Risks: Enterprises using public-facing Generative AI systems could face intellectual property infringement risks. Since these systems are often trained on publicly available internet data, they might unintentionally generate content similar to copyrighted works, potentially leading to legal issues. The ongoing debates around the authorship and ownership of AI-generated content could further compound these legal challenges.
• Bias and Inaccuracy: Bias present in the training data can be perpetuated or amplified by the AI model, leading to outcomes that could be discriminatory, legally risky, or unsafe. Additionally, outdated or irrelevant data, or a phenomenon known as 'model hallucination', could result in inaccuracies that might exacerbate existing issues or create new ones.
• Prompt Injection: If a question lacks clarity or is constructed in a way that the AI doesn't understand, it can lead to the generation of incorrect or misleading information. Furthermore, the AI may generate sensitive or proprietary information if asked a question that it shouldn't answer. This risk becomes more pronounced as models are trained on a vast range of data sources, which may inadvertently include confidential information. Moreover, malicious actors could potentially exploit the AI system, tricking it into revealing information that should remain private, or using it to access data that they should not have access to.

The risks highlighted above underscore the importance of Algo8’s approach of developing application layer for Generative AI models that are trained on an enterprise's proprietary data and deployed atop their own data infrastructure and applications. By doing so, enterprises can harness the power of Generative AI while mitigating associated risks.

Unleashing Industrial Excellence

In the dynamic landscape of industrial operations, the emergence of the Algo8 PlantBrain Co-Pilot marks a significant stride towards enhanced operational prowess. Shifting away from conventional dependency on human expertise, this ingenious AI-powered solution becomes a transformative catalyst. Driven by Generative AI, it adeptly navigates the intricate industrial landscape, reshaping efficiency, precision, and progress. The Algo8 PlantBrain Co-Pilot emerges as an indispensable partner, empowering enterprises with an intelligent collaborator to amplify data-driven insights. This technological marvel ushers industries into an era of untapped possibilities, where human ingenuity merges seamlessly with AI's computational might.