Author: Paulo Maia

WDL – Solving Social Problems Using Data Science

This article describes the key points of my participation at the 2021 Edition of the World Data League. The Tech Moguls Team, composed of me, Tiago Gonçalves, Tomé Albuquerque and Joana Morgado, from INESC TEC, finished second place in this edition. World Data League (WDL) is a Data Science competition where groups of Data Scientists […]

Written by on Nov 29, 2021

Multiple Product Forecasting in the construction industry

In this article, we will cover a use case in the construction industry related to forecasting the needed materials for construction and the time in which they will be required. In the construction industry, there is a lot of uncertainty between the order time and the time in which it is actually executed, due to […]

Written by on Nov 9, 2021

You Have the Right to Remain Silent

The Miranda warning prevents us from self-incrimination. You have the right to remain silent. Anything you say will be used against you. If we hold ML models accountable for their predictions, shouldn’t we at least grant them that right? Can we expect ML models to know everything? I guess we don’t! Moreover, it would be […]

Written by on Aug 2, 2021

ML System Design: Federated Learning

NILG.AI, together with Neu.ro decided to try a format similar to a Reading Club, where the topic is not a specific paper but an entire research area. After a short discussion, we had a System Design part where the team described a specific use case to apply the new approach. Ideally, the discussion would stick […]

Written by on Jul 14, 2021

Speeding up Science: AI tools for Pharma

At NILG.AI, our motto is “Unlocking business capabilities using Data Intelligence”, since we see Artificial Intelligence as a powerful tool designed to maximize the potential of human activity. A lot of fields have been taking advantage of the AI revolution creating more efficient systems able to get more accurate results while saving time and operation […]

Written by on Jun 1, 2021

An Introduction to Multiple Instance Learning

Multiple Instance Learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag, opposedly to the instances themselves. This allows to leverage weakly labeled data, which is present in many business problems as labeling data is often costly: Medical […]

Written by on May 18, 2021

Embedding Domain Knowledge

In the good old days, working as a Machine Learning Engineer meant working 95% of the time on feature engineering and 5% on training models with the extracted features. This was a manually intensive and time-consuming process, that usually led to inflexible proofs of concept that could hardly be adapted to new settings. Fortunately, Deep […]

Written by on Feb 17, 2021

Reducing Unemployment using AI

With COVID-19, many were affected by the economic crisis and lost their jobs. In Portugal alone, between February and September, there was a 30% increase in unemployment! AI can be a powerful tool in allocating scarce resources in a more efficient way. Inspired by DSSG Fellowship’s Project in Partnership with IEFP (Instituto de Emprego e […]

Written by on Jan 18, 2021

Difficult Targets to Optimize: the ROC AUC

In many binary classification problems, especially in domains with highly unbalanced problems (such as the medical domain and rare event detection), we need to make sure our model does not become too biased for the more predominant class.  Thus, you may have heard that accuracy is not a good metric to validate classifiers in unbalanced […]

Written by on Dec 18, 2020

Fairness in AI

In collaboration with Data Science for Social Good Portugal, we are excited to announce a groundbreaking series of webinars focusing on AI topics that intersect with the greater social good. Our recent webinar, the second installment in this series, took place on the 29th of July and featured an insightful presentation by Francisca Morgado. She […]

Written by on Aug 18, 2020