Category: Technical

How to deal with the annoying implications of changing data sources

Let’s discuss a common scenario in AI consulting. The client provides access to data sources in formats such as CSVs or databases that aren’t in a production environment. Why? Usually, they’re exploring the value of the project, do not want to disclose too much data and want to prevent technical problems from happening at the […]

Written by on Nov 20, 2022

Stop removing outliers just because!

Outliers are data points that stand out for being different from the remaining data distribution. An outlier can be: An odd value in a feature A data point distant from the centroid of the data A data point in a region of low density, but between areas of high density. Suppose you have been working […]

Written by on Nov 14, 2022

Detecting duplicates in text

A common use case seen across several industries is the creation of systems capable of detecting the similarity between pairs of objects – images and texts. For example, duplicate detection in marketplaces, or recommendation systems that show similar objects to the ones the user has searched for, can use such systems. They can also be […]

Written by on Oct 25, 2022

Turning classes into inputs

Let’s face it, we all have worked on an ML project where we had to predict a ridiculously high number of classes. Large enough to make the number of observations per class into an embarrassingly small subset. Most people model these tasks as a multiclass classification problem where, for each input observation, we must predict […]

Written by on Sep 22, 2022

Privacy Preserving Machine Learning

Trip data is any type of data that connects the origin and destination of a person’s travel and is generated in countless ways as we move about our day and interact with systems connected to the internet. But why is trip data sensitive? The trips we take are unique to us. Researchers have found that […]

Written by on Aug 16, 2022

Local vs. global optimization

Is the fastest route always the best? This article may give you a different perspective if your answer is yes. Normally there are multiple ways to tackle a given problem or task, and the optimization field is no different, as there are different approaches we can take to find an optimal solution. The choice of […]

Written by on Jun 23, 2022

Self-Supervised Learning workshop at the XIII Symposium on Bioengineering

We are happy to announce our workshop in Self-Supervised Learning at the XIII Symposium on Bioengineering, the biggest Portuguese Bioengineering congress!  The Symposium on Bioengineering is organized by Bioengineering Students’ Association for students and researchers, and is guided by three simple principles: promoting scientific excellence and innovation; building global bridges between participants, speakers, and the […]

Written by on Mar 23, 2022

Teaching Models With Free Data

“The more I see, the less I know” might be a saying, but it does not apply to AI models. It’s well known that the performance of an artificial neural network is highly dependent on the volume and on the diversity of the data that was shown to the model. This happens because exposing the […]

Written by on Feb 23, 2022

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

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