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Hackathon:_Humanizing AI Text
A Hackathon Challenge
Humanizing AI Text
Join us for an exciting challenge where OUr aims to bridge the gap between AI-generated text & human-authored content.
The goal of this 3-day challenge is to develop solutions that make AI-generated text appear indistinguishable from human-authored content. Participants can use AI tools, various programming paradigms, and CS/math concepts such as lambda calculus, type theory, and category theory to enhance AI-generated text, making it NATURAL HUMAN BEING TEXT.
The goal of this 3-day challenge is to develop solutions that make AI-generated text appear indistinguishable from human-authored content. Participants can use AI tools, various programming paradigms, and CS/math concepts such as lambda calculus, type theory, and category theory to enhance AI-generated text, making it NATURAL HUMAN BEING TEXT.
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Innovations in Authentic Text Generation
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Description:
[Hackathon Rules]
Team Composition:
- Teams can have from 1 to 5 members
- Participants can join multiple teams but can only lead one team
Tools and Technologies:
- Participants can use custom AI models, third-party API, or no AI
- IF AI, Only open-source and publicly available datasets are permitted
Submission Requirements:
- Each team must submit a working prototype of their solution
- Submissions should include a detailed description of the approach, the code, and an exposed URL where the solution can be accessed — Upon request, we can provide a Linux VM with SSH access for the duration of the hackathon.
- A description emphasizing the technical complexity and the efforts to humanize the AI-generated text
Intellectual Property:
- Teams retain ownership of their solutions
- we may contact you to request permission to showcase the solutions for media purposes
Ethical Considerations:
- Solutions must adhere to ethical guidelines for AI development, including fairness, transparency, and accountability.
- Any form of plagiarism or unethical methods will result in disqualification
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Cost-Effectiveness
- Resource Utilization: Efficient use of computational resources and runtime efficiency.
- Scalability Potential: Potential for the solution to be scaled cost-effectively for larger datasets and real-world applications.
- Adaptability: Flexibility of the solution to work with various types of input text and different humanization requirements
- Maintenance and Operational Costs: Long-term viability of the solution, including ease of updates, debugging, and ongoing operational expenses
20%
[Judging Criteria]
- Algorithmic Sophistication: The complexity and innovation of the algorithms used to humanize the text.
- Functional Programming Techniques: Effective use of functional programming paradigms to enhance the solution.
- Mathematical Rigor: Integration of advanced mathematical concepts such as lambda calculus, type theory, category theory, and those involving lexemes and syntactical structures to refine text generation.
- Novel Techniques: Use novel AI techniques, such as transformers, reinforcement learning, and meta-learning to improve text humanization.
40%
Technical Complexity:
- Authenticity Check Pass Rate: The goal is to achieve the lowest possible percentage of AI-generated text using tools like A, B, and C.
- Readability and Naturalness: The text should be coherent, contextually appropriate, and exhibit natural language patterns.
- Stylistic Diversity: Ability to generate text in various writing styles and tones.
- Emotional Intelligence: Incorporation of appropriate emotional cues and nuances in the text.
Human-Likeness:
40%
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Cost-Effectiveness
- Resource Utilization: Efficient use of computational resources and runtime efficiency.
- Scalability Potential: Potential for the solution to be scaled cost-effectively for larger datasets and real-world applications.
- Adaptability: Flexibility of the solution to work with various types of input text and different humanization requirements
- Maintenance and Operational Costs: Long-term viability of the solution, including ease of updates, debugging, and ongoing operational expenses
20%
[Judging Criteria]
- Algorithmic Sophistication: The complexity and innovation of the algorithms used to humanize the text.
- Functional Programming Techniques: Effective use of functional programming paradigms to enhance the solution.
- Mathematical Rigor: Integration of advanced mathematical concepts such as lambda calculus, type theory, category theory, and those involving lexemes and syntactical structures to refine text generation.
- Novel Techniques: Use novel AI techniques, such as transformers, reinforcement learning, and meta-learning to improve text humanization.
40%
Technical Complexity:
- Authenticity Check Pass Rate: The goal is to achieve the lowest possible percentage of AI-generated text using tools like A, B, and C.
- Readability and Naturalness: The text should be coherent, contextually appropriate, and exhibit natural language patterns.
- Stylistic Diversity: Ability to generate text in various writing styles and tones.
- Emotional Intelligence: Incorporation of appropriate emotional cues and nuances in the text.
Human-Likeness:
40%
[Judges]
United States
Sai Kalyana Pranitha Buddiga
Expert leader in the advanced AI and ML algorithms
United States
Piyush Ranjan
Piyush Ranjan is a tech leader with 18+ years in AI, cloud, and security; holds two patents.
United States
Roshin Unnikrishnan
GTM Transformation, market access and launch, product launches, and Global Sales Ops
Poland
Eugene Alooeff
Certified Senior Software Engineer
United States
Igor Kuzevanov
Distributed Systems Expert, Oracle Senior Tech Staff, SettleTON CTO, Open League Hackathon 2024 Winner
United States
Pradeep Chintale
Pradeep is a Cloud Architect, cybersecurity expert, IEEE Sr Member, mentor, and industry judge.
Canada
Stepan Mikhailiuk
Principal Software Engineer at lumen5, public speaker, lead course at ITMO University
Russia
Egor Grushin
Senior software architect with 11+ years in highload B2B/B2C projects. Expert in JS, MongoDB, Kafka.
United States
Achinto Banerjee
Senior Principal Software Engineer at Oracle, leading AI and cloud projects, driving innovation.
United States
Khaled Abughoush
AI expert, journal reviewer, innovator in ML-based bank compliance automation.
United States
Rahul Vadisetty
Sr software engineer US BANK
New Zealand
Sophia Willows
Head of Engineering at Rye. Previous Engineering Manager (AI) at Crimson Education. AI & NLP expert.
United States
Nikita Klimov
ADP QA Certified Scrum Master™ & SAFe Agilist® 6SIGMA Lean Thinker IEEE, BSC, Raptor.dev, ACM, PMI
United States
Deepanjit Singh Kohli
Senior Business Intelligence & Data Engineering Lead at Amazon, AI Innovator & Dean’s Scholar
UAE
Andrew Aluev
Senior Software Engineer in Aparavi. Expert in software engineering and AI
United States
PONNARASAN KRISHNAN
Computer science and Engineering in the field of AI & ML
Russia
Anna Chumakova
Head of QA and software development Project
Brazil
Svetlana Repina
Senior Data Analyst at Skyeng. Expert in product analytics and user behavior analysis
Canada
Haseab Ullah
Founder of automatic.chat. Expert in building custom no-code RAG tooling
United States
Kalyan Gottipati
VP, Principal Solutions Architect. Expertise in Digital Transformation in Banking & Cybersecurity.
Germany
Mustafa Issabayev
Security Expert with +10 years of experience, publisher, Senior Security Engineer @ Veeam Software
United Kingdom
Akshat Kapoor
Director, Product Line Management at Alcatel-Lucent Enterprise
UAE
Maksim Kirillov
Solutions Architect @ Presight AI. Expert in solutions design, ex consulting & engineering lead.
United States
Brij kishore Pandey
Principal Engineer @ ADP, Architect, Strategist, Data Engineering, Technical Leader
Mykhailo Shumilov
CTO at Vadimages and IT enthusiast
Ukraine
Team Composition:
- Teams can consist of 1 to 5 members.
- Participants can join multiple teams but can only lead one team.
Evaluation Process:
- Judges will use 10 diverse AI-generated texts to evaluate each submission.
- Evaluation will focus on:
- Human likeness (authenticity, readability, stylistic diversity, emotional intelligence, faithfulness to the original text)
- Technical complexity (based on the technical report)
- Cost-effectiveness (resource use, FEASIBILITY)
Scoring:
- Solutions will be scored based on the wide range of diverse texts provided by the judges:
- Authenticity Check Success Rate Using Market-Available AI-CHECKER Tools
- Readability and naturalness assessed by real humans, also known as judges
- Technical complexity, as evidenced in the report and output
- Resource efficiency and scalability potential
Join us in this unique challenge to push the boundaries of AI text generation! Let's humanize AI text and make it indistinguishable from human-authored content.
[Submission Process]
Technical Report:
- Submit a detailed technical report (PDF format) outlining (at least one of the following):
- Algorithmic approach and techniques used for text humanization
- Functional programming techniques employed
- Mathematical concepts integrated (e.g., lambda calculus, type theory, category theory)
- Novel AI techniques utilized (e.g., transformers, reinforcement learning, meta-learning)
- Resource utilization strategy and scalability considerations
- Adaptability features for various input types
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[Prizes]
200$
You did a great job!
3st
2st
and JETBRAINS "ALL PRODUCT PACK" ($289)
300$
1st
and featured showcase on MEDIA
1000$
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[Prizes]
200$
and a curated collection of classic interactive fiction
3st
2st
and an exclusive mentoring session
300$
1st
and featured showcase on our platform
1000$
[TIMETABLE]
31.10 — 11.11.2024
Registration Ends:
31.10.2024 (THURSDAY)
AT 18:00 (UTC)
AT 18:00 (UTC)
Hackathon Kickstart:
31.10.2024 (THURSDAY)
AT 18:00 (UTC)
AT 18:00 (UTC)
Submission Deadline:
03.11.2024 (SUNDAY)
AT 15:00 (UTC)
AT 15:00 (UTC)
Judging Begins:
03.11.2024 (SUNDAY)
AT 15:00 (UTC)
AT 15:00 (UTC)
judging Ends:
10.11.2024 (SUNDAY)
AT 12:00 (UTC)
AT 12:00 (UTC)
Announcement of Winners:
11.11.2024 (MONDAY)
AT 12:00 (UTC)
AT 12:00 (UTC)
Let’s together transform the overwhelming flood of information into a curated, engaging, and enlightening experience.
Humanizing AI Text_//2024//
