I'm game to take the other side of that wager.
My instinct from the last 5 years of advances in ML language research is that we're right at the cusp of having radically better natural language interfaces.
I chose a half-decade horizon because "Attention Is All You Need", the paper that introduced the transformer model, was published in 2017. Two of its co-authors are co-founders of Adept.
At this point ... think of google. How many tasks start with some vague statement of intent in a google search and then refinement by reviewing pages or better search queries: "How do I ..." Already many (most) internet users are using natural language to interface with the internet. Average don't go straight to wikipedia - they type a question and get a response where wikipedia is the first first or the knowledge graph except is sufficient. Or "plane ticket to $location" and get linked to airline sites (or the search engines interface).
We are much further along this path than many might suspect.
We just didn't have the tech that was able to take our high level instructions and carry them out for us like a human can. I think that is the long term goal of human computer interaction. This product seems like a significant step towards that.
For code-technical decisions, design documents, or the code itself provide a good record for the ever increasing details and scope, but we certainly haven't found the best way to capture everything. Perennial debates about code documentation, or what should go in PRs, or literate-style programing and more show we haven't figured it out. But at the same time, I definitely find myself having non-productive slack conversations, or PR threads or whatever, when my colleague (or myself) prompts: "hangout?" or similar to have call and talk through the issue. Something about the real-time conversation is able to cut-through written mis-communication very well - which then can be captured back in text for the benefits of other colleagues.
However, on your point I think natural language is the gateway to all those things. If you have a spec you will need to describe it in natural language first then use it.
Visual though is I think another way we should be able to communicate with a computer in the future and it seems like we are heading in that direction.
I guess how I imagine all this working is you provide all this high level instruction to the computer through writing/speaking as well as any diagrams and it figures it all out as well as queries you for any further clarification it needs. Just like interacting with your colleagues in day to day.
Non-verbal (body language) communication is a bigger part of our interaction than verbal communication (https://www.entrepreneur.com/leadership/you-dont-say-body-la...)
When I search for content, I use key terms to produce refined and better results. If you don't use such terms then what you're looking for may be difficult to find.
Here's my source: https://dl.acm.org/doi/pdf/10.1145/1753326.1753333?casa_toke...
Wouldn’t something similar apply here though, where after using it for some time you get an inherent understanding of what it works well with and does not?
There’s always some sort of mix of things to learn and make available to the end user though, so that they understand how to use the tool to be successful.
Disclaimer: I also work at Adept
The highest standard of UX is the genie that does as one wishes.
The best interface is no interface.
Certainly not - language models (including multi-lingual models) map words to some kind of (300ish?) multi-dimensional concept space. I wonder if we can translate that back to some sort of symbolic representation that is much more precise than human language. Some kind of IR where we could compile human into representing but also program against. I suspect the early prolog people were attempting something like this but were very wrong about framing reasoning as logical deduction rather than a stochastic process.