Hello! I’m L, a PhD candidate specialising in explainable AI (XAI) for robotics. I started this blog to share my research journey, insights, and discoveries in this exciting field.
What is Explainable AI?
Many powerful AI methods are black-box, making it difficult to understand and explain their outputs. This severely limits applications of AI systems in risk-averse fields where transparency is essential such as healthcare, finance, and security.
Explainable Artificial Intelligence (XAI) are algorithms that can show internal processes and explain decisions. These methods aim to achieve the performance of black-box models with the explainability necessary to operate in risk-averse fields.
Why Robotics?
The mammalian brain is one of the main inspirations for many artificial intelligence methods. It evolved over millions of years as a control system for organisms, allowing them to interact with their environment in increasingly complex manners.
As organic intelligence evolved to control organic bodies, a natural pathway for the development of artificial intelligence is as a method of controlling artificial bodies, i.e., robots.
What to Expect
In this blog, I’ll be sharing:
- Updates on my research progress
- In-depth technical articles
- Tutorials on tools and techniques
- Reviews of significant literature in the field
- Discussions on practical applications of XAI in robotics
I’m excited to share my journey with you and hope you find the content informative and engaging. Feel free to reach out with any questions or suggestions!
Stay tuned for my next post, where I’ll dive into the basics of XAI and its role in robotics.
Best,
L
AI In Depth 
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