Folio3 AI Meetup – Press Release

Folio3 AI and Lahore School of AI hosted a meetup in Lahore on Artificial Intelligence


[April 8th, 2019 Lahore, Pakistan] Folio3 AI and Lahore School of AI teamed up to host a meetup for AI enthusiasts hailing from enterprises, startups and academic institutions at Lahore Office of Folio3.

The AI meetup was an inaugural meet-up hosted by Folio3 AI to discuss the latest trends, challenges, opportunities etc. in the realm of Artificial Intelligence. With the collaboration of Lahore Chapter of School of AI which has been conducting similar kind of sessions in the past as well for the subject awareness and community building purpose within this field. 

The venue for AI Meetup was the new Lahore office of Folio3 in Gulberg III. The event took place on 5th April, 2019. AI enthusiasts from diverse backgrounds attended this event ranging from students, professionals, professors and entrepreneurs.

Ahmed Hassan, who is heading the Lahore office of Folio3, in his opening speech welcomed the audience introduce the guest speakers and spoke about Folio3 and its vision for innovation.

Followed by the introduction were the key speaker sessions, which was initiated by Kshitiz Rimal, who joined us remotely from Nepal. Mr. Rimal is a Google Developers Expert and Head of Research AID Nepal. He presented "Transfer Learning to Save the World" to the attendees and answered their queries with his vast experience on the particular subject.

Later on, a research excellency; Dr. Kashif Zafar Former Head of Department and Professor of CS Department - FAST Lahore contributed to the event with his informative presentation on "Evolution of AI and Industry 4.0." And also informed about the processes and different initiatives for aspiring AI professionals.

Last speaker but not certainly the least, Muhammad Usman presented and showcased on the significance of Big Data with his topic; "Domesticating your Big Data." He is a Data Scientist at IBM currently and addressed the attendees with a new perspective in regards to Big Data.

Concluding the event with memento presentation to the Speakers, refreshments to the attendees and a healthy continuous Q/A session focusing on the need for innovation and future possibilities with in AI.

About Lahore School of AI:  It is a community of AI practitioners spread across the globe working in tandem to solve real-world problems. It is part of the global community "School of AI" and their mission is to offer a world-class AI education to anyone on Earth for free.

About Folio3 AI: It is the innovation wing of Folio3 Software Inc., which is a Silicon Valley based software development and technology solution provider. With a global presence in over 5 countries and a worldwide workforce of more than 250+ professionals.

Folio3 AI has a team of dedicated Data Scientists and Consultants that have delivered end-to-end projects related to machine learning, natural language processing, computer vision and predictive analysis.

For Press Inquiries – Please Contact:

Bakhtiar Shah

Marketing Department – Folio3 AI

+1 (408) 365 4638



Farman Shah
Senior Software Engineer
Dec 19

Anomaly Detection is a widely used for Machine Learning as a service to find out the abnormalities in a system. The idea is to create a model under a probabilistic distribution. In our case, we will be dealing with the Normal (Gaussian) distribution. So, when a system works normally it’s features reside under a normal curve. And when it behaves abnormally, it’s features move to the far ends of the normal distribution curve. Middle area shows distribution of normal behavior and the red areas on the far ends show distribution of abnormal behavior. If you already don’t know, you should read the concepts of Mean, Variance and Standard Deviation first. In the next paragraphs I’ll be addressing how do we create a distribution curve for our system? The system I work on generates a file, daily. Having different number of lines in it every day. There is no defined range for the number of lines it should have. So, my problem was how to auto-detect if the file for today had too low number of lines or too high number of lines. 


Now that I had data for two weeks. I could find out the mean (average) number of lines. On the distribution curve in Figure 1, this would be the middle of the curve horizontally, i-e 0 on the x axis. But in the list of line counts above, it can be seen that actual values deviate from the mean, which is 55728.722222 in this case. For example, take 68336 which is reasonably away from the mean. I had the valid data, but I no false examples. That is, the examples that will guage the accuracy of my anomaly detection system. What I did was added a few examples that I consider as anomalous, and see if my system learns and predicts correctly.


It could be seen that our original data follows a pattern. Whereas the false examples we added later are scattered away. Those are the outliers we want to catch!! Let’s do some calculations to get mean and variance of our training dataset. What we do here is use mean and variance to model a normal (Gaussian) distribution like the one shown in Figure 1. And then we calculate f1score to find out a value (Epsilon) which we can set as best decisive threshold between our normal and abnormal values.