Bringing intelligence to systems across industries
We’re great at AI, collaboration, and innovation
How we implement AI
Leveraging 3D Engines for Data Generation in Deep Learning
Applied to the specific case of scene text detection.
In the field of computer vision, synthetic data generation is especially interesting since the number of relevant resources and tools have grown and improved significantly over the years. The development has not been in the field of machine learning though, but rather in game engines such as Unreal Engine, Blender and Unity. Often produced by professional designers, realistic scenes are produced offering great details.
Explainable AI in medicine: Detecting AF in ECG data
Machine learning models usually do not explain their predictions. This is a significant barrier to adaptation in domains like medicine, where understanding how the model works is vital.
In this blog post, we take a look at how explainability techniques can be used on a deep learning model predicting atrial fibrillation from sinus ECG (electrocardiogram) data.
Product recommendations for Ahlsell’s website and app
We helped out with kickstarting the in-house AI capabilities at Ahlsell by collaborating on the development of a recommender system for their website and smartphone app.
In close collaboration with Ahlsell’s data scientists, the team developed an end-to-end pipeline handling data ingestion, data processing, and model predictions. The recommendation system consists of a collaborative filtering model as well as a content-based model.
Detecting faulty transmission pole guy wires in drone images
The system consists of deep learning and traditional machine learning parts and is able of screening vast amounts of drone images to detect faulty guy wires.
The objective of the project was to create a machine learning pipeline to analyze images from power lines and detect lack of tension in guy wires. To solve this problem, the team broke down the problem in different stages and created a multi-model AI solution, in close collaboration with the client.
How to organize an AI conference with Josef Lindman Hörnlund
Josef Lindman Hörnlund is a co-founder of Modulai and one of the organizers and co-founders of the yearly conference GAIA (Gothenburg Artificial Intelligence Alliance).
After moving from Stockholm to Gothenburg back in 2014, after working in machine learning in Stockholm for a few years, I realized that in Gothenburg very few companies were working with machine learning. So I joined a small meetup-group of people that discussed machine learning, and we had meetups with the few companies working within the field, and ML students from Chalmers and Gothenburg University.
Detecting food intake in glucose time-series from diabetes patients
Together with Digital Diabetes Analytics, we created a meal detection system using glucose level data to help people with insulin-treated diabetes.
Today, more than 5,5 million people worldwide live with type 1 diabetes and need insulin-based treatment to survive. When it comes to diabetes, there’s no “one treatment fits all”, therefore doctor’s need support in optimizing treatments for each patient.
Recommendation system for Skincity
Providing accurate skin product recommendations online is difficult. We built a system that combines the knowledge of the skin therapists with product- and customer behavior data to increase product recommendation accuracy with the aim of driving higher conversion rates and customer satisfaction.
The client is an online skincare clinic that offers a finely-tuned selection of professional skin care products and make-up. Central to their operation are the customized product recommendations customers receive from their experienced skincare therapists.
Detecting underlying paroxysmal atrial fibrillation for Zenicor
Together with Zenicor Medical Systems AB, we trained a deep learning model to predict an underlying paroxysmal atrial fibrillation (AF) condition in patients.
Every day, thousands of people suffer from stroke due to an underlying undiagnosed AF condition. Stroke often results in conditions such as paralysis or, in many cases, death. For this reason, precise and early diagnosis of AF is an essential part of stroke prevention.
Real-time and In-session: state-of-the-art recommender system for Ahlsell
The Modulai team worked together with Ahlsell’s Applied AI team to develop a real-time session-based recommender system. The system weighs a customer’s intent in a given session on their website or smartphone app to provide contextually relevant recommendations.
In a case like this, careful balancing of model complexity and predictive performance is key to best meet Ahlsell’s needs. Higher predictive performance means customers getting recommendations better suited to their needs, indirectly increasing Ahlsell’s revenue. However, real-time, low-latency inference in the cloud is not free, and customer experience is negatively impacted by slow(er) load times.
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Classification of hypoglycemia causes in blood sugar time series
We give an overview of a study conducted in collaboration with Daniel Espes and Per-Ola Carlsson at Uppsala University aiming to improve the treatment of type 1 diabetes.
Type 1 diabetes is one of the most common chronic disorders among children and adolescents but affects people of all ages and globally more than 5 million people are affected.
Read moreBayesian hierarchical model for company credit risk
We developed a credit system to assess the credit risk associated with small to medium-sized enterprise (SME) lending.
The client is an American software company that provides solutions to financial institutions. Their offer includes technology for compliance, credit risk, and lending solutions used to manage risk.
