One of the most popular applications of artificial intelligence to date has been to use it to predict things, using algorithms trained on historical data to determine a future outcome. But popularity doesn't always mean success: Predictive AI misses a lot of the nuance, context, and cause-and-effect reasoning that goes into an outcome; and how wired indicates and as has been seen in several cases, this means that sometimes the “logical” answers produced by predictive AI can be disastrous. the startup CauseLens has developed causal inference technology, presented as a no-code tool that does not require a data scientist to use it to introduce more nuance, reasoning, and cause-and-effect sensitivity into an AI-based system, which it believes aims to solve this problem.
The purpose of CauseLens, CEO and co-founder Darko Matovski said, is for AI to "start to understand the world as humans do."
The startup secured $45 million in funding after the initial success of its approach, increasing revenue by 500% since it appeared a year ago.
Dorilton Ventures and Molten Ventures (the VC that rebranded Draper Esprit) led the round, with participation from previous sponsors Generation Ventures and IQ Capital, and new sponsor GP Bullhound. Various sources say that the round values CauseLens, based in London, at around $250 million.
Customers and partners of CauseLens they currently include healthcare, financial services, and government organizations, among other verticals, where their technology is used not only for AI-driven decision-making, but also to bring more nuance of cause and effect when arriving at results. .
An illustrative example of how this works can be found at Mayo Clinic, one of the startup's partners, which has been using CauseLens 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 any kind, and you won't get anywhere," said Darko Matovski, CEO and founder of AI. startup. "But if you apply cause and effect techniques to understand the mechanics of how different bodies work, you can better understand the true nature of 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 game for a computer model. While it doesn't produce a cure for cancer, this type of work is a significant step in beginning to consider different treatments tailored to the many permutations involved.
Technology CauseLens it has also been applied in a less clinical way in health care. An undisclosed public health agency in one of the world's largest economies used its causal AI engine to determine why certain adults have been refraining from getting vaccinated against COVID-19, so the agency could devise better strategies.
Other clients in areas such as financial services have been using CauseLens to inform automated decision-making algorithms in areas like loan valuation, where previous AI systems introduced biases into their decisions when using only historical data. On the other hand, the funds use CauseLens to better understand how a market trend might develop to inform your investment strategies.
And interestingly, a new wave of customers could be emerging in the world of autonomous transportation. This is an area where a lack of human reasoning has held back progress in the field.
"No matter how much data is fed into autonomous systems, it's still just historical correlations," Matovski said of the challenge. He said that CauseLens is in talks now with two major automotive companies, with "many use cases" for their technology, but one in particular is autonomous driving "to help systems understand how the world works." It's not just about correlated pixels related to a red light and a car stopping, but also what will be the effect of that car slowing down at a red light. We are bringing reasoning to 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 begs the question why the brilliant improvement of causal AI hasn't been integrated into AI algorithms and machine learning in the world. first place.
Not that reasoning further and answering “why” weren't priorities from the start, Matovski explained: “People have been exploring cause-and-effect relationships in science for a long time. One could even argue that Newton's equations are causal. It's super fundamental in science,” he said, but AI specialists couldn't figure out how to teach machines to do this. “It was too difficult,” he said. "The algorithms and the technology did not exist."
That started to change around 2017, he said, when academics began publishing initial approaches considering how to represent "reasoning" and cause and effect in AI based on finding cues that contributed to existing results (instead of using historical data to determine the results), and build models based on that. Interestingly, it's an approach that Matovski says doesn't need to ingest large volumes of training data to work. The team of CauseLens he has many doctorates. And this team has taken this challenge and met it. "Since then, it's been an exponential growth curve" in terms of discovery.
As expected, CauseLens he's not the only player looking to harness advances in causal inference in larger projects that rely on AI. Microsoft, Facebook, Amazon, Google and other big tech players with substantial investments in artificial intelligence are also working in the field. Among the startups there are also causalis specifically focusing on the opportunity to use causal AI in medicine and healthcare, and oogway it appears to be building a causal AI platform aimed at consumers, a "personalized AI decision assistant" as it describes itself. All of this speaks to the opportunity to further develop and a fairly massive market for the technology, covering 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. CauseLens it is the first to leverage Causal AI to model interventions and enable machine-driven introspection,” said Daniel Freeman of Dorilton Ventures. “This team of talent has built software with the sophistication to win over data scientists and the ease of use to empower business leaders. Dorilton Ventures is very excited to support causaLens on the next stage of their journey."
“All companies will embrace AI, not just because they can, but because they must,” added Christoph Hornung, chief investment officer at Molten Ventures. “At Molten we are convinced that causality is the key ingredient needed to unlock the potential of AI. CauseLens It is the world's first causal AI platform with a proven ability to turn data into optimal business decisions."
