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CausaLens gets $45 million for no-code technology that introduces cause and effect into AI decision-making – TechCrunch

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CausaLens gets $45 million for no-code technology that introduces cause and effect into AI decision-making – TechCrunch

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One of the most popular applications of artificial intelligence to date has been to use it to predict things, using algorithms trained with historical data to determine a future outcome. But popularity doesn’t always mean success: predictive AI leaves out much of the nuance, context, and cause-and-effect reasoning that goes into an outcome; and as some have pointed out (and as we have seen), this means that sometimes the “logical” answers produced by predictive AI can prove disastrous. A startup called causaLens has developed causal inference technology – touted as a no-code tool that doesn’t require the use of a data scientist to introduce more cause-and-effect nuance, reasoning, and sensitivity into a system based on AI – what she believes can solve this problem.

The goal of CausaLens, said CEO and co-founder Darko Matovski, is for AI to “begin to understand the world the way humans understand it.”

Today, the startup is announcing $45 million in funding after seeing some success with its approach, growing revenue by 500% since exiting stealth a year ago. This is described as a “first close” of the round, meaning it is still open and will potentially increase in size.

Dorilton Ventures and Molten Ventures (Draper Esprit’s renowned VC) led the round, with previous backers Generation Ventures and IQ Capital, and new backer GP Bullhound also participating. Sources tell us the round values ​​London-based causaLens at around $250 million.

CausaLens customers and partners currently include organizations in healthcare, financial services, and government, among a number of other verticals, where its technology is used not only for AI-based decision making. , but to bring more nuances of cause and effect when obtaining results. .

An illustrative example of how this works can be found in the Mayo Clinic, one of the startup’s partners, which uses causaLens to identify cancer biomarkers.

“Human bodies are complex systems, and so by applying basic AI paradigms, you can find any pattern you want, correlations of all kinds, and you’re not going anywhere,” said said Darko Matovski, CEO and founder of the startup, in an interview. “But if you apply cause and effect techniques to understand the mechanics of how different bodies work, you can better understand the true nature, how one part impacts another.”

Considering all the variables that could be involved, it’s the kind of big data problem that’s nearly impossible for a human, or even a team of humans, to compute, but it’s table stakes for a computer to solve. While not a cure for cancer, this type of work is an important step in beginning to consider different treatments suited to the many permutations involved.

CausaLens’ technology has also been applied less clinically in healthcare. A public health agency in one of the world’s largest economies (causaLens cannot publicly disclose which) used its causal AI engine to determine why some adults held back from getting vaccinated against Covid-19, so that the agency can devise better strategies to get them on board (‘strategies’ in the plural is the operational detail here: the point is that it’s a complex issue involving a number of reasons depending on the individuals in question).

Other customers in areas such as financial services have used causaLens to inform automated decision-making algorithms in areas such as loan appraisals, where previous AI systems introduced bias into their decisions when they only used historical data. Hedge funds, on the other hand, use causaLens to better understand how a market trend might develop in order to inform their investment strategies.

And interestingly, a new wave of customers could be popping up in the self-driving world. This is an area where the lack of human reasoning has held back progress on the ground.

“No matter how much data is fed into autonomous systems, it’s still just historical correlations,” Matovski said of the challenge. He said causaLens is currently in talks with two major automotive companies, with “many use cases” for its technology, but one in particular is self-driving “to help systems understand how the world works. It’s not just about correlated pixels related to a red light and a stopping car, but also the effect that this car will slow down at a red light.We introduce the reasoning into AI. Causal AI is the only hope for autonomous driving.

It seems obvious that those who use AI in their work would want the system to be as accurate as possible, which raises the question of why the brilliant improvement of causal AI has not been incorporated into AI algorithms. and machine learning in the first place.

It’s not that more reasoning and answering “why” weren’t early priorities, explained Matovski – “People have been exploring cause and effect relationships in science for a long time. You could even say that Newton’s equations are causal. It’s super fundamental in science,” he said – but it was that AI specialists couldn’t figure out how to teach machines to do this. “It was just too difficult,” he said. “Algorithms and technology did not exist.”

That started to change around 2017, he said, as academics began publishing initial approaches looking at how to represent “reasoning” and cause and effect in AI based on finding signals that contributed to existing results (rather than using historical data to determine results), and build models based on that. Interestingly, it’s an approach that Matovski says doesn’t need to ingest huge volumes of training data to work. The CausaLens team is heavily loaded with PhDs (you could say the startup really ate its dog food here: it reviewed 50,000 resumes while building its team). And this team took that slack and ran with it. “Since then, it’s been an exponential growth curve” in terms of discovery, he said. (You can read more about that here.)

As you’d expect, causaLens isn’t the only player looking to leverage advances in causal inference in larger projects that rely on AI. Microsoft, Facebook, Amazon, Google and other big tech players with substantial investments in AI are also working in the field. Among the startups are also Causalis which specifically focuses on whether to use causal AI in medicine and healthcare, and Oogway appears to be building a consumer-facing causal AI platform, a “personalized AI decision assistant” as he describes himself. All of this speaks to the potential for further development and a fairly massive market for the technology, spanning both specific and more general business use cases.

“AI must take the next step towards causal reasoning to reach its potential in the real world. causaLens is the first to leverage Causal AI to model interventions and enable machine-driven introspection,” said Daniel Freeman of Dorilton Ventures, in a statement, “This world-class team has created software with the sophistication to appeal to serious data scientists and the ease of use to empower business leaders. Dorilton Ventures is very pleased to support causaLens on the next leg of its journey.

“Every business will embrace AI, not just because they can, but because they must,” added Christoph Hornung, chief investment officer at Molten Ventures. “At Molten, we believe that causality is the key ingredient needed to unlock the potential of AI. causaLens is the world’s first causal AI platform with a proven ability to convert data into optimal business decisions.

CausaLens gets $45 million for no-code technology that introduces cause and effect into AI decision-making – TechCrunch

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