Shape Classification Demonstration

We have created a demonstration of using SensorCognition™ to classify shapes drawn by an accelerometer sensor.

Best viewed full screen.

This example is simplistic so as to demonstrate the concepts and workflow. In practice, analysis of the output features requires more sophisticated analysis. The features can also be analysed to detect anomalies and predict.

Focus on Practical Matters

There’s currently a shortage of skills due to Google, Apple, Facebook, Amazon et al consuming the majority of AI talent. At the same time, there’s the misconception that you need candidates with PhDs in AI in order to successfully implement machine learning.

Deloitte’s recent article on AI’s ‘most wanted’: Which skills are adopters most urgently seeking? questions whether you really need a AI superstar. Companies say, with hindsight, that people who can work out how best to integrate AI into the organisation become more important:

The less-experienced AI adopters are placing too much emphasis on finding AI researchers.

AI might start as experiments run on servers in small labs or under the data scientist’s desk. But highly successful AI demands infrastructure that can scale beyond experimentation

IBM: Decoding the 7 traits of companies achieving success with AI

Research by Peltarion shows that while 99% of companies believe in and are trying to use AI, only 1% have deployed it extensively. Problems cited include complexity, lack specialist skills, scalability, lack of available data and integration. While most AI researchers are expert at solving first two challenges the last three often thwart successful rollouts.

The Deloitte article advocates approaches using ready-made AI tools and services that need less AI expertise. Read about Sensor Cognition™.