Why Causal AI?

Our CTO Maksim Sipos speaks at the Hong Kong Machine Learning Meetup

Advances in artificial intelligence and machine learning in recent years have produced remarkable results in a wide variety of fields: from self-driving capabilities to advances in protein folding.

These impressive advancements have been primarily driven by increases in computational power and deep learning. However, scaling this approach further will not lead to the development of general AI. A fundamental shift in the direction of AI research is required to develop truly intelligent machines that can understand their environment and adapt to reach the goals they are designed to achieve.

In current common practice, predictive models are, in essence, curve fitting exercises that do not even attempt to identify cause and effect. Models are driven by parameters that happened to correlate in training but do not have true predictive power when deployed in the real world. Correctly identifying causality is the key to overcoming this challenge, producing models that can forecast the future accurately and rapidly adapt to changing market conditions.

At causaLens we believe that machines need to be capable of understanding “cause” and “effect” in order to advance machine learning and bring us one step closer to general AI.