Kayode Makinde
AI Researcher, Lawyers Hub
Lagos, Nigeria
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Kayode Makinde
AI Researcher | Data Scientist
Kayode Makinde is an AI researcher at Lawyers Hub, Africa's leading AI policy organization, where he explores the intersection of AI, law, and technology to drive ethical AI development and policymaking in Africa. He is also a multi-award-winning data scientist at Perizer, specializing in AI-driven research analysis, market trends, and optimization.
With a background in engineering, Kayode has gained recognition for his innovative research, including training reinforcement learning agents to play Ayo, a traditional Nigerian game. This project earned him the Best Poster award at the DSN AI Bootcamp and 3rd place at IndabaX Nigeria 2024. His achievements also include winning the Bluechip Data and AI Summit hackathon, the DataFest hackathon, and receiving the prestigious Mr. Algorithm award for excellence in AI.
Kayode is a skilled speaker and educator who has delivered impactful sessions at leading tech events, including DataFest Africa 2024 and TECH MEET Abeokuta 3.0, where he has shared insights on computer vision, object detection with YOLO, and reinforcement learning. His diverse projects demonstrate his practical approach to AI, from developing an AI-powered ripe orange plucking robot to designing optimization algorithms for real-world applications.
A passionate advocate for AI innovation in Africa, Kayode combines deep technical expertise with a commitment to community engagement. He leverages AI to solve local challenges in agriculture, cultural preservation, and beyond, while inspiring the next generation of AI practitioners to harness AI’s transformative potential for sustainable development.
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Area of Expertise
Transforming Industries with Computer Vision: Object Detection using YOLOv5
**Join the session with this link:
https://tinyurl.com/mmvz569m
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Abstract:
This workshop on advanced object detection using YOLOv5 directly addresses the DataFestAfrica 2024 theme "AI-Enabled Transformation: Shaping the Future Together". As computer vision revolutionizes industries from retail to healthcare, mastering object detection has become crucial for AI-driven innovation and efficiency.
Object detection is a cornerstone of many AI applications, enabling automated inventory management, quality control, and even patient monitoring in healthcare. This workshop bridges the gap between theoretical knowledge and practical implementation, equipping participants with the skills to deploy state-of-the-art AI solutions in their respective fields.
Through the workshop, participants will:
- Create and annotate custom datasets for industry-specific object detection tasks
- Configure and optimize YOLOv5 for maximum performance and accuracy
- Train and evaluate models using best practices in deep learning
- Deploy and test models in real-time, simulating real-world applications
By the workshop's conclusion, attendees will have developed a functional object detection model ready for real-world application. This hands-on experience will provide deep insights into the entire pipeline of implementing computer vision solutions, from data preparation to model deployment.
This workshop is designed for data scientists, AI engineers, and tech-savvy business leaders looking to leverage computer vision for strategic advantage. Participants will leave with not just theoretical knowledge, but with hands-on experience in implementing cutting-edge AI technology.
Reinforcement Learning Agents for Ayo: A Traditional Nigerian Game
Abstract:
Ayo, a traditional mancala-style game widely played in Nigeria, presents an intriguing challenge for artificial intelligence research. This study explores the application of various reinforcement learning algorithms to train agents capable of playing Ayo at a high level. By leveraging the rich cultural heritage of this game, we aim to showcase how AI can be harnessed to preserve and promote traditional African games while advancing the frontiers of machine learning research.
The project implements several reinforcement learning algorithms, including Monte Carlo Tree Search (MCTS), Minimax with alpha-beta pruning, and heuristic-based agents. These agents are evaluated through extensive simulations, providing insights into their performance and decision-making strategies. Additionally, a graphical user interface (GUI) is developed using the Tkinter library, enabling human players to engage with the trained agents and experience the dynamic gameplay of Ayo. This interactive component not only facilitates human-agent interactions but also serves as a platform for further research and experimentation.
The research findings contribute to the understanding of reinforcement learning techniques applied to traditional games and highlight the potential of AI in preserving and promoting cultural heritage. By presenting this work at the IndabaX Nigeria conference, we aim to foster discussions on the intersection of AI and cultural preservation, while showcasing the potential of AI-driven innovations for Nigeria's digital economy.
How LLMs Are Revolutionizing Public Perception Analysis and Social Listening
Submisison Type: Talk + Q&A (30 mins)
Understanding public sentiment is critical for brands, government agencies, and organizations seeking to make informed decisions. This process, known as social listening, was traditionally labor-intensive, involving manual annotation and review of thousands of online opinions. Conventional sentiment analysis tools, such as VADER, often struggled with nuanced language, sarcasm, and context, leading to unreliable insights and a continued dependence on human involvement.
The advent of large language models (LLMs), such as Google’s Gemini, is transforming this space by automating thematic and sentiment analysis with remarkable accuracy. Thanks to their large context window and advanced natural language understanding, LLMs can decode nuanced public opinions, detect sarcasm, and extract actionable insights at scale. This automation reduces human involvement, accelerates results, cuts costs, and democratizes access to powerful sentiment analysis tools for individuals and brands alike.
In this session, we’ll explore how LLMs are reshaping public perception mining and examine real-world use cases demonstrating their impact on social listening, from crisis management to brand monitoring and public policy evaluation.
Key Takeaways:
- How LLMs improve sentiment analysis by detecting nuance, sarcasm, and context.
- The role of LLMs in automating public perception mining at scale.
- Practical strategies for using LLMs in social listening and sentiment tracking
Indabax Nigeria 2024 Sessionize Event
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