One of the most popular uses of artificial intelligence is to predict things using data from the past. Some people have pointed out that popularity doesn't always mean success, and that there is a lot of context and cause-and-effect reasoning that goes into an outcome. A startup called causaLens has developed a no-code tool that doesn't require a data scientist to use to introduce more nuanced reasoning and cause-and-effect.
The CEO and co-founder of CausaLens, Darko Matovski, said that the aim of the company was to start to understand the world as humans understand it.
Since coming out of stealth a year ago, the startup has grown its revenues 500%, and today it is announcing $45 million in funding. This is being described as the first close of the round, meaning it is still open and could grow in size.
The round was led by Dorilton and Molten, with previous backers Generation and IQ Capital, and new backer GP Bullhound also participating. Sources say the round values causaLens at around $250 million.
CausaLens' technology is being used in healthcare, financial services and government to bring in more cause-and-effect nuances when arriving at outcomes.
An illustrative example of how this works can be found in the Mayo Clinic, one of the startup's customers, which has been using causaLens to identify biomarkers for cancer.
Darko Matovski, the CEO and founder of the startup, said that the human body is complex and that applying basic artificial intelligence paradigms is pointless.
The kind of big data problem that is nearly impossible for a human or even a team of humans to solve is called table stakes for a computer to work through. This kind of work is not a cure for cancer, but it is a significant step towards considering different treatments tailored to the many permutations involved.
CausaLens' tech has been applied in a less clinical way. A public health agency from one of the world's biggest economies used an artificial intelligence engine to figure out why some people are holding back from getting Covid-19 vaccinations.
Other customers in areas like financial services have been using causaLens to inform automated decision-making algorithms in areas like loan evaluations, where previous artificial intelligence systems were introducing bias into its decisions when using historical data alone. causaLens is used by hedge funds to gain better understanding of market trends.
One new wave of customers might be in the world of self-drive transportation. Progress in the field has been held back by lack of human reasoning.
No matter how much data is fed into the systems, it is still just historical correlations. He said that causaLens is in conversations with two major automotive companies to help the systems understand how the world works. It's not just a correlation between the number of points on a red light and a car stopping, but also the effect that will have on that car slowing down at a red light. Reasoning is being brought into the artificial intelligence. Causal artificial intelligence is the only hope for driving.
It seems like a no-brainer that the system would want to be as accurate as possible, which raises the question of why the improvement of machine learning hasn't been built into the system in the first place.
Matovski explained that people have been exploring cause and effect relationships in science. You could argue that the equations are not random. He said that it was too difficult to teach machines to do this.
He said that it started to change as academics started to publish initial approaches considering how to represent reasoning and cause and effect in artificial intelligence based on finding signals that contributed to existing outcomes. Matovski says that it doesn't need huge volumes of training data to work. The team is very heavy on PhDs and the startup considered 50,000 resumes while assembling its team. He said that the team has taken that baton and run with it. You can read more about it here.
causaLens is not the only player looking at how to leverage advances in causality in larger projects that rely on artificial intelligence. Microsoft, Facebook, Amazon, and other big tech players are working on the field. Causalis focuses specifically on the opportunity of using causality in medicine and healthcare, and Oogway appears to be building a platform geared at consumers. The opportunity to develop more and a pretty massive market for the technology, covering both specific commercial and more general use cases is what this speaks to.
The next step in Causal Artificial Intelligence is to model interventions and enable machine-driven introspection. Dorilton is very excited to support causaLens.
The investment director at Molten said that every company will adopt artificial intelligence.
All Rights Reserved © 2024
