> There is an interesting third group emerging: People who acknowledge the quality problem, but think they can deal with it by applying more AI to the output.
Ah yes, the known unknowns.
The discussion reminds me of a talk Zizek gave in which he discusses the speech Rumsfeld gave regarding the evidence Iraq supplying weapons to terrorist[0].
Zezik argues the unknown knowns are far more interesting (and the reason why USA was losing in Iraq). While Rumsfeld focused on the unknown unknowns.
I've noticed that domain experts who implicitly know the the known unknowns of their field distrust LLMs because they can identify their shortcomings. Those subtle mistakes LLMs make. I argue this is why domain experts using LLMs get such a boost. They can identify and avoid pitfalls sometimes before they happen. But in other fields the same people are in awe of LLM capabilities precisely because the known unknowns are a mystery.
The Unknown Unknowns of LLMs are the IMO the most interesting. The so called emergent capabilities of the technology. The use of LLMs in others fields such as biology, eg in protein language models, is really cool.
Everyone focuses on replacement of people workers when I think opening new fields of work for humans should be the goal of LLMs by leveraging the tech to discover.
The other interesting caregory is unknown knows. But that's another topic for another time.
As an aside, the mass mockery in response to Rumsfeld's statement always bothered me because it's the single most intelligent statement he ever made about the Iraq war, and if he had started out with that mindset things probably would not have gone nearly as pear-shaped as they did.
This is one of those classic "sounds dumb / doesn't play well on TV but is actually smarter than most of the other people babbling about it" things. Nassim Taleb has written for example about how maddening it is to watch world-class economists who are also just sort of awkward and a little nerdy go on TV and "lose" to blowhards who don't actually know what the hell they're talking about but appear confident and look good on camera. Thankfully in Rumsfeld's case I think as time has gone on it's become a pretty respected statement about risk even if people still occasionally find the phrasing a bit amusing.
I think for most companies EPIC is too much, and EPIC tends to agree, they won't talk to you unless you have at least a thousand providers.
I can't pick one over all, it really depends on what your health care company does. I researched dozens to find one for our company, and it was one focused on behavioral health.
In general I'd say stay away from Nextgen like the plague, and avoid Netsmart too. Those are the worst I've ever seen. I could write a small book about Nextgen's failures.
While the definition changes, the expertise shifts and with it the field. Computers eventually became statisticians and data scientists. Printers became graphic designers.
What I found most interesting is that when positions undergo such evolution (printer -> graphic designer), a number of skills which were previously different expertise altogether, combine to create a new field. In other words, a new multidisciplinary field is born.
I think a good example is data science, the field at it's core is applied statistics using modern techniques such as data management and computing [0].
The question is, what is the new evolution of a programmer? Lots of folks like to use the term "engineer", and previously I thought this was silly. But now with LLMs, maybe that is a good descriptor; software engineer.
The moniker already exists which we need to revive and repurpose for the LLM era;
"Systems Engineer" i.e. one who does Systems Engineering - https://en.wikipedia.org/wiki/Systems_engineering Because the focus is no longer on coding alone but involves specification, verification (formal and testing), traceability and correctness. All using a whole plethora of third-party infrastructure, tools and components.
In the early days there used to be "Systems Analyst" and "Systems Designer" in addition to the above. All of them go together. The Systems Analyst is business requirements facing, The Systems Designer maps it to implementation architecture and The Systems Engineer pulls everything together (including costs/risks/specific implementation technologies etc.) to produce the complete functional system.
Ah yes, the known unknowns.
The discussion reminds me of a talk Zizek gave in which he discusses the speech Rumsfeld gave regarding the evidence Iraq supplying weapons to terrorist[0].
Zezik argues the unknown knowns are far more interesting (and the reason why USA was losing in Iraq). While Rumsfeld focused on the unknown unknowns.
I've noticed that domain experts who implicitly know the the known unknowns of their field distrust LLMs because they can identify their shortcomings. Those subtle mistakes LLMs make. I argue this is why domain experts using LLMs get such a boost. They can identify and avoid pitfalls sometimes before they happen. But in other fields the same people are in awe of LLM capabilities precisely because the known unknowns are a mystery.
The Unknown Unknowns of LLMs are the IMO the most interesting. The so called emergent capabilities of the technology. The use of LLMs in others fields such as biology, eg in protein language models, is really cool.
Everyone focuses on replacement of people workers when I think opening new fields of work for humans should be the goal of LLMs by leveraging the tech to discover.
The other interesting caregory is unknown knows. But that's another topic for another time.
[0] https://en.wikipedia.org/wiki/There_are_unknown_unknowns
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