Impact of Embedding Dimensionality on Machine Learning Interpretability | Slides

Read and see slides describing significant impact of embedding dimensionality on the interpretability and explainability of machine learning models. It highlights the trade-off between interpretability, explainability, and performance, emphasizing that while lower-dimensional spaces may be simpler to understand, they may lack accuracy, and higher-dimensional spaces, though more accurate, may be complex to interpret.
SLIDE12
SLIDE12
        


The Impact of Embedding Dimensionality on Interpretability and Explainability

Embedding dimensionality is a critical aspect of machine learning models, particularly those that use deep learning techniques. It refers to the number of dimensions in the vector space where the model maps its input data. The choice of embedding dimensionality can significantly impact the interpretability and explainability of the model's output.

Interpretability

Explainability

Interpretability refers to the extent to which a human can understand the cause of a decision made by a machine learning model. A model with high interpretability is transparent, meaning its internal workings can be understood by humans. The embedding dimensionality can affect interpretability. For instance, a lower-dimensional space may be easier for humans to understand, but it may not capture all the nuances of the data. On the other hand, a higher-dimensional space may capture more information, but it can be harder for humans to interpret.

Explainability, on the other hand, refers to the extent to which the internal workings of a machine learning model can be explained in human terms. A model with high explainability can provide clear reasons for its decisions, which can be understood by humans. The embedding dimensionality can also impact explainability. For instance, a model that uses a lower-dimensional space may be able to provide simpler explanations for its decisions, but these explanations may not always be accurate. Conversely, a model that uses a higher-dimensional space may provide more accurate explanations, but these may be more complex and harder for humans to understand.

In conclusion, the choice of embedding dimensionality in a machine learning model can significantly impact its interpretability and explainability. While lower-dimensional spaces may be easier for humans to understand and interpret, they may not always provide the most accurate or comprehensive explanations. Conversely, higher-dimensional spaces may provide more accurate and comprehensive explanations, but they may be harder for humans to understand and interpret. Therefore, when designing machine learning models, it is important to carefully consider the trade-off between interpretability, explainability, and performance.



Challenges-in-good-embeddings    Chunking-and-tokenization    Chunking    Dimensionality-reduction-need    Dimensionality-vs-model-perfo    Embeddings-for-question-answer    Ethical-implications-of-using    Impact-of-embedding-dimension    Open-ai-embeddings    Role-of-embeddings-in-various   

From the blog

Build Dataproducts

How Dataknobs help in building data products

Enterprises are most successful when they treat data like a product. It enable to use data in multiple use cases. However data product should be designed differently compared to software product.

Be Data Centric and well governed

Generative AI is one of approach to build data product

Generative AI has enabled many transformative scenarios. We combine generative AI, AI, automation, web scraping, ingesting dataset to build new data products. We have expertise in generative AI, but for business benefit we define our goal to build data product in data centric manner. Our Product KREATE enable creation of data, user interface, AI assistant. Click to see it in action.

Well Governed data

Data Lineage and Extensibility

To build a commercial data product, create a base data product. Then add extension to these data product by adding various types of transformation. However it lead to complexity as you have to manage Data Lineage. Use knobs for lineage and extensibility

Build Budget Plan for GenAI

CIO Guide to create GenAI Budget for 2025

CIOs and CTOs can apply GenAI in IT Systems. The guide here describe scenarios and solutions for IT system, tech stack, GenAI cost and how to allocate budget. Once CIO and CTO can apply this to IT system, it can be extended for business use cases across company.

What is KREATE and KreatePro

Kreate - Bring your Ideas to Life

KREATE empowers you to create things - Dataset, Articles, Presentations, Proposals, Web design, Websites and AI Assistants Kreate is a platform inclide set of tools that ignite your creatviity and revolutionize the way you work. KReatePro is enterprise version.

What is KONTROLS

KONTROLS - apply creatvity with responsbility

KONTROLS enable adding guardrails, lineage, audit trails and governance. KOntrols recogizes that different use cases for Gen AI and AI have varying levels of control requirements. Kontrols provide structure to select right controls.

What is KNOBS

KNOBS - Experimentation and Diagnostics

Well defined tunable paramters for LLM API, LLM fine tuning , Vector DB. These parameters enable faster experimentation and diagosis for every state of GenAI development - chunking, embedding, upsert into vector DB, retrievel, generation and creating responses for AI Asistant.

Kreate Articles

Create Articles and Blogs

Create articles for Blogs, Websites, Social Media posts. Write set of articles together such as chapters of book, or complete book by giving list of topics and Kreate will generate all articles.

Kreate Slides

Create Presentations, Proposals and Pages

Design impactful presentation by giving prmpt. Convert your text and image content into presentations to win customers. Search in your knowledbe base of presentations and create presentations or different industry. Publish these presentation with one click. Generate SEO for public presentations to index and get traffic.

Kreate Websites

Agent to publish your website daily

AI powered website generation engine. It empower user to refresh website daily. Kreate Website AI agent does work of reading conent, website builder, SEO, create light weight images, create meta data, publish website, submit to search engine, generate sitemap and test websites.

Kreate AI Assistants

Build AI Assistant in low code/no code

Set up AI Assistant that give personized responss to your customers in minutes. Add RAG to AI assistant with minimal code- implement vector DB, create chunks to get contextual answer from your knowlebase. Build quality dataset with us for fine tuning and training a cusom LLM.

Create AI Agent

Build AI Agents - 5 types

AI agent independently chooses the best actions it needs to perform to achieve their goals. AI agents make rational decisions based on their perceptions and data to produce optimal performance and results. Here are features of AI Agent, Types and Design patterns

Develop data products with KREATE and AB Experiment

Develop data products and check user response thru experiment

As per HBR Data product require validation of both 1. whether algorithm work 2. whether user like it. Builders of data product need to balance between investing in data-building and experimenting. Our product KREATE focus on building dataset and apps , ABExperiment focus on ab testing. Both are designed to meet data product development lifecycle

Innovate with experiments

Experiment faster and cheaper with knobs

In complex problems you have to run hundreds of experiments. Plurality of method require in machine learning is extremely high. With Dataknobs approach, you can experiment thru knobs.

RAG For Unstructred and Structred Data

RAG Use Cases and Implementation

Here are several value propositions for Retrieval-Augmented Generation (RAG) across different contexts: Unstructred Data, Structred Data, Guardrails.

Why knobs matter

Knobs are levers using which you manage output

See Drivetrain appproach for building data product, AI product. It has 4 steps and levers are key to success. Knobs are abstract mechanism on input that you can control.

Our Products

KreateBots

Setup chatbots in minutes
  • Pre built front end that you can configure
  • Pre built Admin App to manage chatbot
  • Prompt management UI
  • Personalization app
  • Built in chat history
  • Feedback Loop
  • Available on - GCP,Azure,AWS.
  • Add RAG with using few lines of Code.
  • Add FAQ generation to chatbot

    • KreateWebsites

    LLM and AI based Website generation
  • AI powered websites to domainte search
  • Premium Hosting - Azure, GCP,AWS
  • AI web designer
  • Agent to generate website
  • SEO powered by LLM
  • Content management system for GenAI
  • Buy as Saas Application or managed services
  • Available on Azure Marketplace too.

    • Kreate CMS

    CMS GenAI
  • CMS for GenAI
  • Lineage for GenAI and Human created content
  • Track GenAI and Human Edited content
  • Trace pages that use content
  • Ability to delete GenAI content

    • Generate Slides

    Effortless slides creation
  • Give prompt to generate slides
  • Convert slides into webpages
  • Add SEO to slides webpages

    • Content Compass

    Your story writing is automated
  • Generate articles
  • Generate images
  • Generate related articles and images
  • Get suggestion what to write next