Moving from rule based systems into Machine Learning by Aleksander Kijek (Nethone) at TECH Conference Europe 2019 (TCE2019)
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Machine learning and rules-based systems are widely used to make inferences from data. It has been found that the two approaches have their strengths and weaknesses, however, it is useful to have a grasp of both.
Although not as popular anymore, rules-based systems do still have their place and it is worth understanding the distinction between these methods and where they might be valuable. You can find more details by searching for “rules-based systems vs machine learning”.
Rules-based systems in a nutshell
Rules-based systems are a simple kind of artificial intelligence, which use a series of IF-THEN statements that guide a computer to reach a conclusion or recommendation.
Machine learning is an alternative approach which can help to address some of the issues with rules-based methods. Rather than attempt to fully emulate the decision process of an expert or best practice, machine learning methods typically only take the outcomes from the experts.
During the inaugural PICANTE Tech Conference Europe, Aleksander Kijek, Chief Product Officer at Nethone, will explain how moving from rule based systems into machine learning can benefit your company in a panel discussion dedicated to the growing use of Artificial Intelligence by Start-ups and SMEs.
Aleksander Kijek will be joined by Jiří Třečák (CEO at Supernova) and Vojtech Chloupek (Partner at Bird & Bird).
About Aleksander Kijek
Experienced leader and new technologies enthusiast fascinated by fintech and neuroscience. Profoundly passionate about combining his technological and people skills in the IT world. Prior to joining Nethone, he developed his technical and soft skills as a leader of PISAK project (an initiative stimulating the social inclusion of heavily disabled people through technology) and a coordinator of multiple projects at American Jewish Joint Distribution Committee.
At Nethone, Aleksander is responsible for business and product development, workflow management and ensuring comprehensive operational excellence at the company.
Aleksander studied Neuropsychology and Neuroinformatics at the University of Warsaw.
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