Practical lessons from an AI talk at Kathmandu University
On 24th Jan 2025, I attended a session organized by Department of Artificial Intelligence at Kathmandu University (KU). This session was by Dr. Saumendra Mohanty and was insightful; he looked at AI from angles I hadn’t considered.
Here are few takeaways from the session.
This was my first time hearing about this tool and I was like “man this is good”. This is a no code tool that you can do a lot with drag and drop interface.
I know you may have a ‘eek’ by hearing ‘no code’ but this can be a huge time saver if you are trying to find the best model for your data set.
The instructor demonstrated how you can use multiple models/strategies in the same dataset to find the optimal one for your data set. I think the demo was around 15-mins long, but we tried models like Linear Regression, Random Forest, SVM, Multi-Layer Perceptron, KNN etc, and for that dataset, the Random Forest was more accurate.
We generate the confusion matrix for all these models, tried with multiple datasets, analyzed the accuracy without any code and within 15 mins. It was surprisingly good stuff.
He explained that how companies use cosine similarity to filter out potential spams resume, and how we can think through it and optimize our resume. We had a practical demonstration where we calculated how likely a resume is going to be passed to a second round for a given job description.
The working behind this is, the job description is converted into a vector ( i need to read more on how this is done, If you are familiar on this, feel free to comment) and similarly the resume is also converted into a vector. Once this is done, calculate the cosine of the angle between these vector. The angles being closer to 0 means that the provided resume and the job description has a greater match.
Before you ask, yes we will also be taking into account all the irrelevant details in the resume, that is not the part of the job description.
Sentiment analysis
We also got to know how sentiment analysis is being used in industries to optimize sales. The Flipkart story stuck: a failing product turned top seller after the reviews were analyzed for sentiment and the product was changed accordingly. We briefly discussed other areas: customer feedback analysis, product success prediction based on customer data, stock market trends (we did one on Google data direct from Yahoo Finance with Orange Data Mining) e.t.c.
Localized AI
We also discussed about current trends on LLM and what the next focus is going to be. Professor explained that the next big thing on LLMs is going to be on the regional languages, how the local government can help subsidize this, and it’s challenges to improve accessibility in the rural areas so that AI is accessible.
Neural networks and reinforcement learning
We also briefly talked about the Neural Networks and machine learning method like Supervised Learning and Reinforcement Learning and its tradeoffs. He explained that reinforcement learning may not be the best for self driving car due to the accidents that may happen for the model to learn.
What I think is, although we are surrounded by AI and most of the repetitive tasks are being replaced by AI, we will still have an edge in sophisticated problem solving.
If you’re curious about Orange, cosine similarity, or AI in general, tell me in the comments.
PS, Yes I generate this thumbnail with AI