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Swiss
Army
Challenge
Agenda
• Stakeholder and Requirements
• Proceeding
• Project plan
• High Level Architecture
• Data
• Foundation Model
Stakeholder
● Cyber Command of Swiss Army
● Characteristics: The army must be able to independently carry out tasks and have an impact in
cyber and electromagnetic space. For example, it must be able to detect and thwart a cyber
attack on its IT systems.
● Job: Detection and mitigation of Cyber security risks. Making strategic decisions based on
received information. Implementing security measures for Swiss country (people, residents,
government, public infrastructure).
● Pains: Data/Information Overload, Foreign adversary risk, Fast evolving technologies
Requirements
● An anticipation engine which allows Swiss Armed Forces Cyber Command to navigate future
trends in accordance with predefined business rules should be developed
● For interaction with findings a user interface should be created
Proceeding
Anticipation Engine for Swiss Armed Forces Cyber Command
● Technique Selection: Retrieve Augmented Generate (RAG) Model
○ Why RAG Model?
■ Bias Mitigation: Effectively mitigates biases by combining retrieval-based and
generative approaches.
■ Accuracy and Reliability: Provides accurate and reliable insights tailored to specific
business rules and objectives.
■ Customization: Can be customized to generate relevant and actionable insights.
■ Time Efficiency: Requires less time and computational resources compared to fine-
tuning large language models.
March April May
65%
70%
70%
50%
0%
0%
0%
March 3 – Apr 23
March 3 – Apr 23
April 1 – April 23
April 9 – April 23
Apr 23 – May 28
Apr 23 – May 7
April 23 – May 7
CONCEPT
REALISATION
Research
Sources
System Architecture
Data Architecture
Data Source
LLM
Milestone
% completion
0%
Orchestrator (Prompt and Context with LLM)
0%
User Interface May 7 – May 28
April 30 – May 7
0%
Orchestrator (Search in Database) April 30 – May 7
High Level Architecture
High Level Architecture with Technologies
Data Collection Tools and Sources
• Types of sources: News sites, academic articles, think tank’s publications.
• Initially planned to use Python for scraping.
• Switched to Watson Discovery after the Lab 5. Advantages:
■ User-Friendly Interface
■ AI Capabilities e.g, tech domain concepts
■ Integrated Database Functionality
Challenges in Data Collection
● Data quality issues across all sources.
● Structural differences in scraped websites.
● Some High-quality sources resistant to scraping.
Analysis and Modelling/Next steps
● Enhancing data pre-processing.
● Analysis ideas:
■ Frequency
■ Sentiment
■ Correlation
● Topic Modelling: Latent Dirichlet Allocation (LDA) or Non-negative Matrix Factorization (NMF).
● Time-Series Analysis: Timestamped data, analysis how certain topics or terms trend over time.
Foundation Model "Granite-13B-Chat-v2"
● Model Overview
■ Large language model designed for conversational interactions, capable of understanding and generating
human-like text.
● Applicability to Use Case
■ Provides the ability to aggregate, analyze, and disseminate complex and rapidly changing information
related to military technology advancements, use cases, capabilities, and global defense trends.
● Strengths:
■ Conversational capabilities facilitate user interaction and engagement.
■ Versatile and well-suited for understanding and generating text in the military domain.
Foundation Model "Granite-7B-Lab"
● Model Overview
■ Large language model designed for labelling tasks, capable of processing and classifying large amounts of
data.
● Applicability to Use Case
■ Useful for data labeling tasks such as identifying trends, patterns, and actionable insights from diverse and
reliable sources.
● Strengths
■ Efficiently processes and classifies large datasets, aiding in trend identification and predictive analysis.
■ Helps in addressing the pain points of data overload and accuracy and reliability.
Foundation Model "Flan-T5-XXL-11B"
● Model Overview
■ Extremely large language model based on T5 architecture, capable of performing a wide range of natural
language processing tasks.
● Applicability to Use Case
■ Provides a comprehensive solution for developing the anticipation engine, offering accurate insights and
predictions.
● Strengths
■ Versatile and capable of performing various NLP tasks, including text analysis, summarization, and
generation.
■ Well-suited for handling complex and rapidly changing information in the military domain.
"Why Flan-T5-XXL-11B is the Best Choice"
● Comprehensive Solution: The Flan-T5-XXL-11B model is an extremely large language model based on the
T5 architecture, capable of performing a wide range of natural language processing tasks. It provides a
comprehensive solution for developing the anticipation engine, offering accurate insights and predictions.
● Versatility: The model is versatile and capable of performing various NLP tasks, including text analysis,
summarization, and generation. It is well-suited for handling complex and rapidly changing information in
the military domain.
March April May
65%
70%
70%
50%
0%
0%
0%
March 3 – Apr 23
March 3 – Apr 23
April 1 – April 23
April 9 – April 23
Apr 23 – May 28
Apr 23 – May 7
April 23 – May 7
CONCEPT
REALISATION
Research
Sources
System Architecture
Data Architecture
Data Source
LLM
Milestone
% completion
0%
Orchestrator (Prompt and Context with LLM)
0%
User Interface May 7 – May 28
April 30 – May 7
0%
Orchestrator (Search in Database) April 30 – May 7
Recommendation System using RAG Architecture

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Recommendation System using RAG Architecture

  • 2. Agenda • Stakeholder and Requirements • Proceeding • Project plan • High Level Architecture • Data • Foundation Model
  • 3. Stakeholder ● Cyber Command of Swiss Army ● Characteristics: The army must be able to independently carry out tasks and have an impact in cyber and electromagnetic space. For example, it must be able to detect and thwart a cyber attack on its IT systems. ● Job: Detection and mitigation of Cyber security risks. Making strategic decisions based on received information. Implementing security measures for Swiss country (people, residents, government, public infrastructure). ● Pains: Data/Information Overload, Foreign adversary risk, Fast evolving technologies
  • 4. Requirements ● An anticipation engine which allows Swiss Armed Forces Cyber Command to navigate future trends in accordance with predefined business rules should be developed ● For interaction with findings a user interface should be created
  • 6. Anticipation Engine for Swiss Armed Forces Cyber Command ● Technique Selection: Retrieve Augmented Generate (RAG) Model ○ Why RAG Model? ■ Bias Mitigation: Effectively mitigates biases by combining retrieval-based and generative approaches. ■ Accuracy and Reliability: Provides accurate and reliable insights tailored to specific business rules and objectives. ■ Customization: Can be customized to generate relevant and actionable insights. ■ Time Efficiency: Requires less time and computational resources compared to fine- tuning large language models.
  • 7. March April May 65% 70% 70% 50% 0% 0% 0% March 3 – Apr 23 March 3 – Apr 23 April 1 – April 23 April 9 – April 23 Apr 23 – May 28 Apr 23 – May 7 April 23 – May 7 CONCEPT REALISATION Research Sources System Architecture Data Architecture Data Source LLM Milestone % completion 0% Orchestrator (Prompt and Context with LLM) 0% User Interface May 7 – May 28 April 30 – May 7 0% Orchestrator (Search in Database) April 30 – May 7
  • 9. High Level Architecture with Technologies
  • 10. Data Collection Tools and Sources • Types of sources: News sites, academic articles, think tank’s publications. • Initially planned to use Python for scraping. • Switched to Watson Discovery after the Lab 5. Advantages: ■ User-Friendly Interface ■ AI Capabilities e.g, tech domain concepts ■ Integrated Database Functionality
  • 11. Challenges in Data Collection ● Data quality issues across all sources. ● Structural differences in scraped websites. ● Some High-quality sources resistant to scraping.
  • 12. Analysis and Modelling/Next steps ● Enhancing data pre-processing. ● Analysis ideas: ■ Frequency ■ Sentiment ■ Correlation ● Topic Modelling: Latent Dirichlet Allocation (LDA) or Non-negative Matrix Factorization (NMF). ● Time-Series Analysis: Timestamped data, analysis how certain topics or terms trend over time.
  • 13. Foundation Model "Granite-13B-Chat-v2" ● Model Overview ■ Large language model designed for conversational interactions, capable of understanding and generating human-like text. ● Applicability to Use Case ■ Provides the ability to aggregate, analyze, and disseminate complex and rapidly changing information related to military technology advancements, use cases, capabilities, and global defense trends. ● Strengths: ■ Conversational capabilities facilitate user interaction and engagement. ■ Versatile and well-suited for understanding and generating text in the military domain.
  • 14. Foundation Model "Granite-7B-Lab" ● Model Overview ■ Large language model designed for labelling tasks, capable of processing and classifying large amounts of data. ● Applicability to Use Case ■ Useful for data labeling tasks such as identifying trends, patterns, and actionable insights from diverse and reliable sources. ● Strengths ■ Efficiently processes and classifies large datasets, aiding in trend identification and predictive analysis. ■ Helps in addressing the pain points of data overload and accuracy and reliability.
  • 15. Foundation Model "Flan-T5-XXL-11B" ● Model Overview ■ Extremely large language model based on T5 architecture, capable of performing a wide range of natural language processing tasks. ● Applicability to Use Case ■ Provides a comprehensive solution for developing the anticipation engine, offering accurate insights and predictions. ● Strengths ■ Versatile and capable of performing various NLP tasks, including text analysis, summarization, and generation. ■ Well-suited for handling complex and rapidly changing information in the military domain.
  • 16. "Why Flan-T5-XXL-11B is the Best Choice" ● Comprehensive Solution: The Flan-T5-XXL-11B model is an extremely large language model based on the T5 architecture, capable of performing a wide range of natural language processing tasks. It provides a comprehensive solution for developing the anticipation engine, offering accurate insights and predictions. ● Versatility: The model is versatile and capable of performing various NLP tasks, including text analysis, summarization, and generation. It is well-suited for handling complex and rapidly changing information in the military domain.
  • 17. March April May 65% 70% 70% 50% 0% 0% 0% March 3 – Apr 23 March 3 – Apr 23 April 1 – April 23 April 9 – April 23 Apr 23 – May 28 Apr 23 – May 7 April 23 – May 7 CONCEPT REALISATION Research Sources System Architecture Data Architecture Data Source LLM Milestone % completion 0% Orchestrator (Prompt and Context with LLM) 0% User Interface May 7 – May 28 April 30 – May 7 0% Orchestrator (Search in Database) April 30 – May 7