AI: First New UI Paradigm in 60 Years?
This article isn&#x27;t too helpful.<p>There have been many &quot;UI Paradigms&quot;, but the fancier ones tended to be special purpose. The first one worthy of the name was for train dispatching. That was General Railway Signal&#x27;s NX (eNtry-Exit) system.[1] Introduced in 1936, still in use in the New York subways. With NX, the dispatcher routing an approaching train selected the &quot;entry&quot; track on which the train was approaching. The system would then light up all possible &quot;exit&quot; tracks from the junction. This took into account conflicting routes already set up and trains present in the junction. Only reachable exits lit up. The dispatcher pushed the button for the desired exit. The route setup was then automatic. Switches moved and locked into position, then signals along the route went to clear. All this was fully interlocked; the operator could not request anything unsafe.<p>There were control panels before this, but this was the first system where the UI did more than just show status. It actively advised and helped the operator. The operator set the goal; the system worked out how to achieve it.<p>Another one I encountered was an early computerized fire department dispatching system. Big custom display boards and keyboards. When an alarm came in, it was routed to a dispatcher. Based on location, the system picked the initial resources (trucks, engines, chiefs, and special equipment) to be dispatched. Each dispatcher had a custom keyboard, with one button for each of those resources. The buttons lit up indicating the selected equipment. The dispatcher could add additional equipment with a single button push, if the situation being called in required it. Then they pushed one big button, which set off alarms in fire stations, printed a message on a printer near the fire trucks, and even opened the doors at the fire house. There was a big board at the front of the room which showed the status of everything as colored squares. The fire department people said this cut about 30 seconds off a dispatch, which, in that business, is considered a big win.<p>Both of those are systems which had to work right. Large language models are not even close to being safe to use in such applications. Until LLMs report &quot;don&#x27;t know&quot; instead of hallucinating, they&#x27;re limited to very low risk applications such as advertising and search.<p>Now, the promising feature of LLMs in this direction is the ability to use the context of previous questions and answers. It&#x27;s still query&#x2F;response, but with enough context that the user can gradually make the system converge on a useful result. Such systems are useful for &quot;I don&#x27;t know what I want but I&#x27;ll know it when I see it&quot; problems. This allows using flaky LLMs with human assistance to get a useful result.<p>[1] <a href="https:&#x2F;&#x2F;online.anyflip.com&#x2F;lbes&#x2F;vczg&#x2F;mobile&#x2F;#p=1" rel="nofollow noreferrer">https:&#x2F;&#x2F;online.anyflip.com&#x2F;lbes&#x2F;vczg&#x2F;mobile&#x2F;#p=1</a>