CausaLens gets $45M for no-code technology that introduces cause and effect into AI decision making
The ability to forecast outcomes using algorithms trained on previous data has become one of the most widely used uses of artificial intelligence to date. But success isn’t always correlated with popularity: Predictive AI omits a significant amount of the context, cause-and-effect analysis, and nuance that goes into an outcome. As some have noted (and as we have seen), this results in instances when the “logical” conclusions generated by predictive AI can be devastating. This issue has been addressed by a company called CausaLens, which has created causal inference technology that is advertised as a no-code tool that can be used to add additional nuance, reasoning, and cause-and-effect awareness to an AI-based system.
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To date, one of the most widely used uses of artificial intelligence has been to make predictions using algorithms that have been trained on past data. However, success is not always a sign of popularity: As some have noted (and as we have seen), predictive AI often leaves out important details, context, and cause-and-effect reasoning that go into an outcome. As a result, the “logical” conclusions it generates can occasionally turn out to be disastrous. In order to add more complexity, logic, and cause-and-effect sensibility to an AI-based system, a startup called CausaLenshas developed causal inference technology, which is advertised as a no-code solution that doesn’t require a data scientist to use. Coadmin sentenced to years
Customers and partners of Londonbased causal seriesCausaLens today include businesses in a variety of industries, such as healthcare, financial services, and government, where its technology is applied not just to AI-based decision making but also to add additional cause-and-effect depth to the determination of results.
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The Mayo Clinic, one of the startup’s partners, has been utilisingcausaLens to find cancer biomarkers, and they are a good illustration of how this works.
According to the CEO and founder of the firm, Darko Matovski, “human bodies are complicated systems, thus applying basic AI paradigms you may uncover whatever pattern you want, correlations of any form, and you are not getting anywhere.” But you can comprehend more of the true nature of how one portion has an affect on another if you employ cause and effect tactics to comprehend the mechanics of how various bodies work.
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Given all the potential variables, it is the kind of big data problem that would be virtually impossible for an individual or group of individuals to solve yet is routine for a computer. This type of research is an important first step toward considering various treatments that are specifically adapted to the numerous permutations involved, even though it does not yet offer a cure for cancer.
Additionally, CausaLens’ technology has been used in healthcare in a less formal manner. With the help of causalLens’ causal AI engine, a public health organisation from one of the largest economies in the world was able to identify the reasons why some adults have been delaying receiving COVID-19 vaccinations so that the organisation could develop better strategies to convince them to do so (the plural “strategies” is the key word here because the point is that it’s a complex issue involving a variety of reasons depending on the individuals in question).
In areas like loan evaluations, where earlier AI systems were injecting bias into their choices by leveraging only historical data, other customers in industries like financial services have begun employing causaLens to feed automated decision-making algorithms. Hedge funds, on the other hand, employ causaLens to better grasp the potential development of market trends and to guide their investment strategies.
Also intriguing is the possibility of a brand-new consumer base emerging in the autonomous mobility sector. One area where the absence of human reasoning has impeded advancement in the field is this one.
No matter how much data is provided to autonomous systems, Matovski said of the problem, “it’s still simply historical correlations.” He claimed that causaLens is currently in discussions with two significant automakers about “various use cases” for its technology, with autonomous driving being one in particular “to help the systems grasp how the world works.” Not only are there pixels that are associated to a red light and a car halting, but there are also effects of the car slowing down at a red light. We are integrating AI with logic. The only chance for autonomous driving is causal AI.
People have always been examining cause and effect connections in science, according to Matovski. Even Newton’s equations might be considered causative. It is quite basic in science,” he claimed, but AI experts couldn’t figure out how to educate machines to accomplish this. It was simply too challenging, he remarked. “The technology and algorithms weren’t there.”
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As you might anticipate, causaLens is not the only company considering how to use developments in causal inference in more significant initiatives that rely on AI. With significant AI investments, Microsoft, Facebook, Amazon, Google, and other major tech companies are also active in this area. Startups include Causalis, which focuses on the potential of causal AI in the fields of medicine and healthcare, and Oogway, which looks to be developing a causal AI platform targeted at consumers, or what it calls a “personalised AI decision assistant.” All of this points to the potential for further development and a sizable market for the technology, which covers both niche commercial use cases and broader use cases.
To realise its full potential in the real world, AI must move closer to causal thinking. CausaLens is the first product to use Causal AI to model treatments and support computer-driven introspection, according to a statement from Daniel Freeman of Dorilton Ventures. The software that this top-notch team created combines the sophistication to woo serious data scientists and the usability to give business leaders more control. Dorilton Ventures is incredibly eager to help causaLens move forward with its mission.
Christoph Hornung, an investment director at Molten Ventures, continued, “Every organisation will use AI, not just because they can, but because they must. “At Molten, we firmly believe that causality is the essential component required to fully realise the potential of AI. The first causal AI platform in the world, causaLens has a track record of turning data into wise business decisions.
