Dr. Darko Matovski

CEO at CausaLens and Quantitative Researcher

Dr. Darko Matovski is the CEO of causaLens. The company is leading Causal AI research, a way for machines to understand cause & effect, and serves some of the most sophisticated organizations. Darko has also worked for cutting edge hedge funds and research institutions. For example, the National Physical Laboratory in London (where Alan Turing worked) and Man Group in London. Darko has a PhD in Machine Learning and an MBA.

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Dr. Darko Matovski

Advances in artificial intelligence and machine learning in recent years have produced remarkable results in a wide variety of fields. Image recognition and computer vision have demonstrated great potential in their ability to detect disease, enable self-driving capabilities and produce realistic deep fakes videos, among many others. Other significant successes in the field of machine learning include the defeat of the champions of intellectual games such as jeopardy, chess and go; as well as advances in promising fields like 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. In fact, estimates show that our brain operates at 20 W (similar to a low power lightbulb), while training a typical deep learning model produces more CO 2 than a car. 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.

Machine learning approaches used nowadays are unable to identify the true causal drivers behind the
variations in sales, revenues, stock prices or real estate yields. In current common practice, predictive
models are, in essence, curve fitting exercises that do not even attempt to identify cause and effect. As a
consequence, 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 a new theory of how to build intelligent machines is required. 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.