Introduction
Imagine sitting in a dimly lit room, surrounded by the hum of computers, as a group of eager minds gathers for a hackathon. The air is thick with anticipation and excitement, each participant ready to tackle the challenge of teaching machines to think like humans. This was my reality as I embarked on my journey into artificial intelligence (AI), driven by a fascination with intelligent systems that learn and adapt autonomously.
As an aspiring AI engineer, I often ponder the profound question: What does it truly mean to teach a machine to think? This inquiry has propelled me into the depths of AI, where I have encountered both exhilarating breakthroughs and daunting challenges. In this blog post, I will share my first attempt at teaching a machine to think, the obstacles I faced, and the invaluable lessons I learned along the way.
My Journey into Teaching Machines to Think
My fascination with AI ignited during my master's program in Digital Finance. It was here that I first applied AI concepts to real-world problems, such as financial customer segmentation. The thrill of seeing a machine analyze data and derive insights was akin to watching a child take their first steps—both exhilarating and transformative. This experience solidified my desire to explore the potential of machines that learn and adapt.
The Hackathon: A Crucible of Learning
My enthusiasm led me to participate in the Problematique Algorithm Solution (PAS) hackathon, which became the perfect testing ground for my skills.This event provided a unique opportunity to apply my theoretical knowledge in a practical setting.
The Challenge: Named Entity Recognition in African Languages
My team and I faced the challenge of named entity recognition (NER) in African languages, a task that seemed daunting yet exciting.
What is Named Entity Recognition (NER)?
Named Entity Recognition (NER) is a crucial technique in natural language processing (NLP) that identifies and classifies key elements in text into predefined categories such as names of people, organizations, locations, dates, and more. Imagine scanning a document to find all mentions of a specific company or person—NER automates this process, making it easier to extract structured information from unstructured text. For example, in the sentence "Steve Jobs co-founded Apple in 1976," NER would identify "Steve Jobs" as a person, "Apple" as an organization, and "1976" as a date. This capability is essential for various applications, including search engines, chatbots, and social media monitoring, as it helps machines understand the context and relationships within the text. This challenge pushed us to explore various models, including LSTM, BERT, RoBERTa, and XLM-RoBERTa.
How NER Contributes to AI Performance
NER plays a pivotal role in enhancing the overall performance of AI systems. Here’s how:
Teamwork
We divided our tasks efficiently:
The mentorship we received was invaluable, providing practical insights that boosted our confidence.
Training the Model
Training the model was a fascinating process. I watched in awe as the machine learned, adjusting its parameters to minimize errors. However, we faced challenges like overfitting, where the model excelled on training data but faltered on test data. To combat this, we implemented techniques like dropout and regularization, gradually improving the model's performance. The sense of accomplishment as our accuracy increased was exhilarating.
After extensive training and fine-tuning, it was time to test my model on new data. With a mix of excitement and nervousness, I ran the test. To my delight, the model accurately identified most of the handwritten digits! This exhilarating moment reaffirmed my belief in the importance of continuous testing and refinement in developing superior models.
From Model to Application
Image showing the NER app: An example of testing in Wolof
After refining our model, we moved to the next phase:
Although we didn’t win the hackathon, we were thrilled to place in the top 2 of the Kaggle challenge, competing against highly skilled ML engineers. The thrill of Kaggle competitions is unmatched; even as the competition nears its end, uncertainty looms over whether your score will hold. It was an exhilarating experience filled with anticipation.
Key Lessons Learned
Teaching a Machine to Think vs. Using Algorithms
Through this experience, I gained a deeper understanding of the distinction between programming machines to follow algorithms and enabling them to think and solve problems independently.
Teaching Machines to Think
Using Algorithms
Bridging the Gap
In our project, we found that combining both approaches yielded powerful results. We used algorithms for data handling and analysis within our machine learning model, while the learning component allowed for continuous improvement.
Conclusion
Understanding the distinction between teaching a machine to think and using algorithms is crucial for anyone in AI. Algorithms provide structure, but the ability to think and learn enables machines to adapt and solve complex problems that static programming cannot address. As I continue exploring AI, I am eager to see how these concepts can converge to create intelligent systems that truly learn and evolve. If this post has sparked your interest or if you have your own machine learning experiences to share, I’d love to hear from you!
