Last week Belatrix attended the XXI Iberoamerican Congress on Pattern Recognition (CIARP) at the Pontifical Catholic University of Peru, one of the leading Peruvian universities. The event explored artificial intelligence and deep learning, and how they can be applied across different industries.
The event covered a wide range of topics from the challenges of using large data sets, to biometrics and authentication, to healthcare and improving medical diagnostics, to the future of artificial intelligence. I want to share some of my highlights of the event:
Some of the academic paper highlights at the event provided examples of the wide range of potential real-world use cases that we will see emerging for AI, machine learning, and pattern recognition in the coming years. For example in the world of healthcare, one academic examined deep brain stimulation (DBS) surgery for individuals with Parkinson’s Disease.This surgery involves placing an electrode near the basal ganglia part of the brain, which the medical experts then need to “tune” for it in order to work. In the past it would be tuned using a 3D image that allows the expert to see which brain structures are responding to the stimuli. However this was very time consuming and computationally very intensive. The idea presented at the conference involved using data-driven approximation, to create a method faster and more accurate than existing approaches.
Bringing the possible to real-world implementations
In Belatrix’s recent machine learning survey, 81% of respondents said they thought machine learning would have some or significant impact on their organization within the next 5 years. However at the same time it is evident that while many organizations see the potential, they are struggling to get started. Another survey, by the research company Forrester Research, found that in 2016 among business and technology professionals 58% are already researching AI, but just 12% are using AI systems. It’s our job now as a technology provider, to help organizations in their efforts to implement AI and machine learning, and bridge this gap between what organizations want to do and what they are currently doing.
October 10 / 2019
October 03 / 2019
November 19 / 2014
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