Juris Puce

The Challenges of Human Labour Automatization with Deep Learning in the Transport Industry

The key idea behind deep learning is to automate human labour, reduce the costs and reduce the time and increase precision in which a task can be done. In the transport industry things like cargo number recognition and counting of objects were first to be automated, and are now improved to a very high precision. However, there are many other tasks in the industry that can be automated, but computers are currently lacking the precision to guarantee the appliance with the industry security standards. This presentation will discuss how we have overcome some of the challenges and give an insight of the upcoming applications and their effects on industry.

Juris Pūce an adventurous entrepreneur, always looking for new challenges and business to build. Interested in all things technologically innovative and somewhat unknown, hence most of his companies are IT related. With over 15 years of experience in technology related business management, Juris Pūce currently divides his work between being a visionary for various start-ups as well as being the CTO of KleinTech, a company that specialises in complex machine vision and deep learning technology solutions for transport and security industries.

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