AI & SAFETY | DIGITAL & IT


to be to improve quality, because that gets buy-in from the users. He says, “We always think about AI in terms of efficiency gains, but what we learned with hospitals is that you don’t get efficiency gains by using AI. People care about their work and they will never adopt a technology that makes them quicker if it doesn’t deliver better care. Never.” Focusing on delivering better care might include, for example, producing clearer, more precise clinical notes and scheduling faster or more appropriate appointments. “If the AI delivers a better outcome, that makes them feel they can rely on it and use it. Then you get the efficiency gain ‘for free’.” He summarises: “By encoding expertise you scale it up.”


Ways of addressing the world Prettejohn expands on some of the limitations of existing systems. “The main thing we try to do is get people away from thinking in the way their ‘source systems’ see the world.” He says source systems (which may be software programmes used to run or maintain an asset, or any type of information store) “have – almost by design – an idea of looking through a keyhole at the world.” For example, one system might show how much power is being delivered through a wire, but maintenance logs are in another system that does not ‘understand’ what is going on in the wire. Prettejohn imagines a user who has to make critical decisions about what is going on from this variety of systems, and wants information in a form that can be used easily: “You are looking for a view of the system that tells you ‘that wire is down, in that place, it is affecting this many customers, of which four users have medical need for power.’” At that point, he says, you are not worrying about whether the information is in a column in that database or a row in another database. “We try to build systems that mirror how people actually solve problems and the way they represent data when they solve problems. Facts and information and semantics. That requires a different sort of system,” which connects all those old and new representations of what is going on, does it in real time and presents it as an aid.


What does this mean in practice? With an issue like the


failed cable discussed above, typically there would be action points to fix the problem, but the details remain in the various software systems and in various different forms depending on the data source. He explains “We start to build these [details] into the software. But then you can start to run those actions against a digital twin. Your semantic model would ask ‘if we did this what would happen, what if we did this’? Finally, you get a schedule for action” that is backed with data, experience, technical detail etc, which the AI system can ‘write back’ to systems like engineering and maintenance, adding jobs to the queue, changing someone’s job or carrying out other actions. Most importantly, the user can ask unstructured questions using normal syntax, not coding. Prettejohn says, “Having that deep information in the


system means we are not a silo, not a dashboard, not an exhaust port of useful information. This is a control room for running a business that looks like how we want to talk about the business and understands how we want to think about the business.” He adds, “It is a far more effective control room than what was previously happening where someone would be logged into eight screens with a notebook trying to run the business. Now they can have it on one single panel” using syntax familiar to the user. Prettejohn adds that the system will develop “an


understanding of semantics and deep in that network they start to understand the relationship between entities, what the relationship is and the intent of what we are communicating versus the syntax”.


Safety cases Palantir is exploring using its AI to help civil nuclear operators write safety cases much more quickly. The company’s engineers have been looking at this opportunity. They say the nuclear industry now has new needs in writing safety cases, for a number of reasons. First is the scale of investment going in, with many new designs and new projects under way. “The volume of safety cases required has gone through the roof… We have a situation where safety cases need to be produced at scale very quickly” says Prettejohn. They compare that with the safety case writers available now: “They have generally been in organisations for a very long time and people underestimate the niche of that skills set – it is a very specialised skill. As we look at attrition and retirement you have a rise in demand for safety cases, with a decline in the number of people able to produce them. That is the crux of the problem. Something has to change.” The company has been looking at how it can speed up


the process. Prettejohn explains: “When we think about nuclear safety cases, they are not simple. Each is normally hundreds of pages and there are typically hundreds of documents in a safety case portfolio. This is a lot of paperwork.” The safety case for a single building can fill “lockers and lockers” – and that is a real image, because in some cases it involves original paperwork that is not captured digitally anywhere. The company is looking at how safety cases can be


created more quickly. One opportunity is to be able to reference old safety cases and pass appropriate parts forward to new safety cases. Palantir’s software uses embedding models to quickly scan text and find parts that have to be referenced in a future safety case. This is not


www.neimagazine.com | June 2024 | 21


Left: Nicolas Prettejohn, Head of AI, UK & Nordics at Palantir, summarises AI as software that helps companies tackle their most critical problems


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