We have the means (big data architecture + IoT knowledge + our own deep learning libraries) to design innovative deep learning architectures, test concepts and validate models for biotech industries, consumer user experience (UX) and industry 4.0.
We are aimed by the possibilities of collective intelligence and mobile connectivity. Our goal is to spread mobile apps with deep learning algorithms inside. Just imagine the scope and the benefits of this technology. Go to skitag.eu project to experience the possibilities of deep learning algorithms executed on real time on iPhone (Swift).
Because together with our client’s innovation team we design, test and validate deep learning models while we explain what's inside the AI "black box".
We collect IoT raw data. Then, we process the data (big data analytics) and classify known events to train the algorithm to find hidden patterns (deep learning).
Collect information from an IoT device, i.e. mobile, BLE sensor, tweets, biomarkers, etc. and classify the event, i.e. ski/no ski; main/peripheral/outlier conversation; control/cancer;
We have the hability to anticipate the output in a sequential time serie. This kind of deep learning algorithms are very suitable for predictive maintenance; biological processes; crop evolution;
Seize the potential of the most widely available IoT device: the smartphone! We are taking advantage of the capacity of smartphones to process deep learning algorithms on real time to improve the consumer UX, to widely test models in biotech industry or to efficiently implement AI algorithms in the industry 4.0.
Find some of our main deep learning projects.
Neural network + social network algorithms to filter the main conversation in a bag of tweets. The algorithms learned to avoid the bias derived from hashtags and bots
Deep learning algorithms to classify diseases
(i.e. Alzheimer; cancer;)