On October 19th, 2024, TIMETOACT GROUP Austria hosted the Hackathon “Sustainability meets LLMs”, organized by AIM – AI Impact Mission. This event brought together talented minds from JKU, TU, Universität Wien, WU, and industry, all focused on creating innovative Retrieval-Augmented Generation (RAG) solutions within one day to tackle real-world sustainability challenges using public ESG and sustainability reports. In his insightful keynote, Daniel Weller (Senior Software Developer & AI Expert at TIMETOACT) set the scene by highlighting key facts and embedding them within the broader context of ESG reporting, offering participants a compelling foundation for the day’s work. With a focus on impactful AI applications, participants addressed key issues like greenwashing detection, ESG report relevance mapping, and compliance with the European Green Deal.
Datum
05.11.2024
content.autor.writtenBy
The Top 3 Teams
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First Place: SAM – Sustainability Advanced Model
The winning team, nexus. Group AI, developed SAM, an AI-powered ESG reporting platform designed to help companies streamline their sustainability compliance. SAM leverages RAG and NLP to generate real-time ESG compliance scores and insights, aligning with European Green Deal goals. By utilizing data from multiple sources and providing an intuitive dashboard, SAM empowers businesses to manage sustainability proactively.
Read more about the winning project here →
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Second Place: Trustpilot for ESG
This solution tackled greenwashing by detecting misleading claims in ESG reports. The NightWalkers designed a scalable tool that assigns trustworthiness scores based on various types of greenwashing indicators, including unsupported claims and inaccurate data. This tool is especially valuable for investors, NGOs, and consumers seeking to hold companies accountable for their sustainability claims.
Read more about Trustpilot for ESG here →
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Third Place: ESG Relevance Mapping
The Venturers team created an architecture to identify the relevance of each section. Their prototype classified sentences based on its content, making it easier to identify concrete facts versus promotional, vague statements. This relevance map helps users quickly navigate reports, enabling standardized comparisons between companies and incentivizing clarity in ESG reporting.
Read more about ESG relevance mapping here →
We want to acknowledge the incredible efforts of all participating teams, who demonstrated creativity, technical skill, and a strong commitment to sustainability! Each team brought unique insights and learned valuable lessons throughout the hackathon, contributing to an inspiring event that showcased the potential of AI to drive positive environmental and social impact.
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Third Place - AIM Hackathon 2024: The Venturers
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Second Place - AIM Hackathon 2024: Trustpilot for ESG
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SAM Wins First Prize at AIM Hackathon
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ChatGPT & Co: LLM Benchmarks for January
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ChatGPT & Co: LLM Benchmarks for December
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ChatGPT & Co: LLM Benchmarks for November
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ChatGPT & Co: LLM Benchmarks for September
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LLM Performance Series: Batching
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ChatGPT & Co: LLM Benchmarks for October
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Let's build an Enterprise AI Assistant
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The Intersection of AI and Voice Manipulation
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So You are Building an AI Assistant?
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Strategic Impact of Large Language Models
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Part 4: Save Time and Analyze the Database File
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Part 1: Data Analysis with ChatGPT
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Part 3: How to Analyze a Database File with GPT-3.5
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Creating a Social Media Posts Generator Website with ChatGPT
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Open-sourcing 4 solutions from the Enterprise RAG Challenge
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Sustainability
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Part 1: TIMETOACT Logistics Hackathon - Behind the Scenes
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AI Workshops for Companies
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Introduction to Functional Programming in F# – Part 6
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Introduction to Functional Programming in F# – Part 5
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Introduction to Functional Programming in F# – Part 4
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Introduction to Functional Programming in F# – Part 12
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My Weekly Shutdown Routine
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Introduction to Functional Programming in F#
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Introduction to Functional Programming in F# – Part 10
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Introduction to Functional Programming in F# – Part 11
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Innovation Incubator Round 1
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ADRs as a Tool to Build Empowered Teams
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From the idea to the product: The genesis of Skwill
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Introduction to Functional Programming in F# – Part 2
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Introduction to Functional Programming in F# – Part 9
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Introduction to Functional Programming in F# – Part 3
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Introduction to Partial Function Application in F#
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Innovation Incubator at TIMETOACT GROUP Austria
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Learn & Share video Obsidian
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Introduction to Functional Programming in F# – Part 8
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So, I wrote a book
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Introduction to Functional Programming in F# – Part 7
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Running Hybrid Workshops
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Using a Skill/Will matrix for personal career development
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Ways of Creating Single Case Discriminated Unions in F#
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5 lessons from running a (remote) design systems book club
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Celebrating achievements
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Learning + Sharing at TIMETOACT GROUP Austria
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Inbox helps to clear the mind
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Common Mistakes in the Development of AI Assistants
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8 tips for developing AI assistants
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Process Pipelines
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AI Contest - Enterprise RAG Challenge
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License Plate Detection for Precise Car Distance Estimation
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5 Inconvenient Questions when hiring an AI company
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Database Analysis Report
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Crisis management & building a sustainable future with AI
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Artificial Intelligence in Treasury Management
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Expanding Opportunities with Generative AI
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Google Workspace: AI-supported work for every company
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Boosting speed of scikit-learn regression algorithms
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Dashboards & Reports
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Designing and Running a Workshop series: An outline
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Part 2: Data Analysis with powerful Python
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Why Was Our Project Successful: Coincidence or Blueprint?
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The Power of Event Sourcing
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How to gather data from Miro
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Designing and Running a Workshop series: The board
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Tracing IO in .NET Core
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Understanding F# Type Aliases
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My Workflows During the Quarantine
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Understanding F# applicatives and custom operators
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Building a micro frontend consuming a design system | Part 3
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Part 1: Detecting Truck Parking Lots on Satellite Images
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Make Your Value Stream Visible Through Structured Logging
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Building A Shell Application for Micro Frontends | Part 4
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They promised it would be the next big thing!
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Introduction to Web Programming in F# with Giraffe – Part 3
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Introduction to Web Programming in F# with Giraffe – Part 2
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Isolating legacy code with ArchUnit tests
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How we discover and organise domains in an existing product
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Machine Learning Pipelines
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Part 2: Detecting Truck Parking Lots on Satellite Images
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Event Sourcing with Apache Kafka
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Using Discriminated Union Labelled Fields
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Using NLP libraries for post-processing
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Revolutionizing the Logistics Industry
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Creating solutions and projects in VS code
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State of Fast Feedback in Data Science Projects
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Announcing Domain-Driven Design Exercises
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Building and Publishing Design Systems | Part 2
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Introduction to Web Programming in F# with Giraffe – Part 1
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Creating a Cross-Domain Capable ML Pipeline
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Data Governance
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