In this compelling white paper, our friends over at causaLens highlight how ML has wrongly become synonymous with AI. We must shake off this misconception to start the real AI revolution. Data science must forgo its reliance on curve-fitting ML and return to its roots; to put the science back into data science. A growing number of leading scientists – from Turing Award winning Professors Judea Pearl and Yoshua Bengio, to Professor Bernhard Schölkopf, Director of Germany’s Max Planck Institute for Intelligent Systems – are advocating for the development of a new science of causality, that goes far beyond statistical pattern matching.
ML, for all its achievements, does not have the intelligence to address some of the highest value business problems because it lacks any concept of causality. For AI deserving of the name, we must build Causal AI.
Download the new white paper courtesy of causaLens to learn how the Causal AI Platform goes beyond predictions, providing transparent causal insights and suggesting actions that directly improve business KPIs.
causaLens is pioneering Causal AI, a new category of intelligent machines that understand cause and effect – a major step towards true AI. Its enterprise platform is used to transform leading businesses in Finance, IoT, Energy, Telecommunications and others.
Sign up for the free insideBIGDATA newsletter.
Nice blog . Yes, AI is very important enterprise platform for transforming leading business.