SITUALAB

We are scientists!

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.

Seize the potential of IoT + Big Data + Deep learning.

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).

Why an innovation lab?

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".

TRAINING & LEARNING

With a deep learning innovation lab you'll get an algorithm trained to find patterns, classify events or anticipate outputs. The AI algorithm will learn to detect whatever you trained it for.

FIND HIDDEN PATTERNS

An AI algorithms finds non linear patterns much more faster than humans do. It also exceeds the capabilities of the descriptive statistics used to find parameters and describe patterns. AI simply goes a step forward.

PERFORMANCE IMPROVEMENTS

What do you get with AI? You'll get more relieable outputs. You'll have it faster and at a lower tuning cost. Just show an AI algorithm what you are looking for and it will find it for you.

How does an algorithm learns?

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).

EVENTS CLASSIFICATION

Linear, binary or multiple categories

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;

EVENTS ANTICIPATION

Processing sequential data

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;

APPs WITH DEEP LEARNING INSIDE

iOS and Android Apps

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.

Situalab innovation labs

Find some of our main deep learning projects.

Skitag

Deep learning +
clustering algorithms
Visit Skitag.eu

Tweetlab

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

Biotech

Deep learning algorithms to classify diseases
(i.e. Alzheimer; cancer;)