Don’t forget the plumbing! AI Anchoring

A practical guide to leverage the power of anchoring for improving the performance and accuracy of your natural language processing applications.

If you're looking for ways to improve AI, look no further than this article. It's compiled from real-world experience, and provides concrete steps you can take to make your AI better.

What is anchoring and why does it matter?

Anchoring is a technique that helps large language models to learn from specific data sources and domains, by providing them with relevant keywords, phrases, and entities that act as anchors or guides for the model. Anchoring helps the model to focus on the most important aspects of the data, and to avoid being distracted by irrelevant or noisy information. Anchoring can also help the model to adapt to new or changing data, by updating the anchors accordingly.

Anchoring is especially important for large language models, such as GPT-3, BERT, or T5, that are trained on massive amounts of text from various sources and domains. These models have impressive generalization abilities, but they may also suffer from some limitations, such as data sparsity, domain mismatch, or semantic drift. Data sparsity means that some domains or topics may not have enough data to train the model effectively. Domain mismatch means that the model may not perform well on a specific domain or task, because it was trained on different or broader data. Semantic drift means that the meaning of some words or concepts may change over time or across domains, and the model may not capture these nuances.

Anchoring can help to overcome these limitations, by providing the model with more specific and relevant data, and by aligning the model's representations with the domain or task at hand. Anchoring can also help to improve the interpretability and explainability of the model, by making it more transparent and accountable for its predictions and decisions.

How to use Microsoft Syntex and SharePoint to create anchors for your large language models?

Microsoft Syntex is a cloud-based service that leverages the power of large language models to automate content processing and understanding. Syntex can help you to extract valuable insights and information from your unstructured data, such as documents, emails, images, or videos. Syntex can also help you to create and manage your own custom models, by using a low-code or no-code approach.

SharePoint is a web-based platform that enables you to store, share, and collaborate on your content and data. SharePoint can help you to organize and manage your data, by using features such as libraries, lists, metadata, workflows, or permissions. SharePoint can also help you to integrate your data with other Microsoft products and services, such as Teams, Power BI, or Azure.

By using Syntex and SharePoint together, you can create and use anchors for your large language models, by following these steps:

·?????? Create a SharePoint library or list to store your data. You can use existing data, or upload new data from your local or cloud sources. You can also use metadata to add more information and context to your data.

·?????? Create a Syntex model to process and understand your data. You can use one of the built-in models, such as form processing or entity extraction, or you can create your own custom model, by using the Syntex model builder. You can also use the Syntex model tester to evaluate and improve your model.

·?????? Apply the Syntex model to your SharePoint library or list. You can use the Syntex model gallery to select and apply the model to your data. You can also use the Syntex model feedback to review and refine the model's results.

·?????? Use the Syntex model's results as anchors for your large language models. You can use the extracted keywords, phrases, entities, or metadata as anchors to guide and improve your large language models. You can also use the Syntex model's confidence scores or explanations as anchors to measure and explain your large language models' performance and accuracy.

What are the benefits of using anchoring with Microsoft Syntex and SharePoint?

By using anchoring with Microsoft Syntex and SharePoint, you can enjoy the following benefits:

·?????? You can improve the results of your large language models, by providing them with more specific and relevant data, and by aligning them with your domain or task.

·?????? You can save time and resources, by using a low-code or no-code approach to create and manage your anchors, and by using the cloud-based and scalable services of Microsoft.

·?????? You can enhance the trust and transparency of your large language models, by using the interpretability and explainability features of Syntex, and by making your anchors visible and accessible.

Thank you for reading this post, and I hope you found it useful and informative. If you have any questions or feedback, please feel free to leave a comment or contact me via LinkedIn.