So I think programming will eventually become a tool used by everyone rather than something for only for programmers and other tech-savvy individuals. It'll be something that everyone uses the same way basic arithmetic is a tool that everyone uses these days. These days programming is reserved for the few because in order to program you have to learn a language with very specific grammar and the level of abstraction is still quite low. The underlying operations being expressed by current languages are not themselves intuitively hard to understand but the average person is prevented from doing so because of the language barrier. The level of abstraction that the average programmer is working with is moving continuously higher. In the past there was a time when the average programmer was working at the level of assembly language but today the average programmer is working at much higher levels of abstraction. There are obvious disadvantages to current visual programming systems as you've described but it may be the case that we just haven't developed the right abstractions and interfaces that work best for visual programming.
There is a distinction between incidental complexity - complex tools and languages - and inherent complexity - the problem itself, it's domain and the emergent properties of it all combined with a Turing complete machine.
You can't really do much to reduce the inherent complexity, and even professional programmers are struggling with the inherent complexity in all but the smallest problems.
Some programmers are even struggling more than others which suggests to me that programmers need to be a bit above average IQ to be efficient, and it might even be a lower limit were nothing useful happens at all.
What could potentially happen is that we could invent AI agents that could help us tackle some of the inherent complexities, and maybe non professionals could string together intents that will be interpreted by AI, but that won't happen soon.
Edit:
The insights about incidental and inherent complexity is not mine. They were popularised in the great book The Mythical Man-Month by Fred Brook that have many insights that are applicable to software development in general apart from those insights that are applicable to writing large operative systems.
As the book is 40+ years old and the follow-up essay "There is no silver bullet" - that expand on the complexity topic - is 30 years old, there is little reason that we are still trying to invent the silver bullet.
I guess it could even be described as a failure of the educational systems, because I'm sure there is a lot research in information theory that have been done or at least should have been done, that touches these topics.
Things have changed since Fred Brooks wrote his book: accidental complexity has increased considerably and is now at least an order of magnitude more than essential complexity. I estimate you can take any non-trivial system and reduce it to around a tenth of the size, without removing essential functionality, without changing languages. Chuck Moore goes even further: http://web.archive.org/web/20071009222710/http://www.colorfo...
In many cases what differentiates a programmer who is better at dealing with inherent complexity than another has a lot to do with the ability to apply the right patterns, tools, or strategies under the right circumstances.
This ability is largely influenced by having been exposed to similar problems in the past and having applied various strategies to solve those problems. One reason why a lot of programmers struggle is because our current educational systems do not expose students to computational thinking at an early age. They've simply not have had enough exposure to that kind of thinking.
Even though computational thinking draws on computer science as a formal discipline the insights are applicable to various other domains and virtually everyone will benefit if they're able to apply the same kind of thinking to other problems in their personal and professional lives. Without having to solve any problems specific to computer science people can still acquire the ability to effectively apply computational thinking by solving other problems in their lives because the inherent complexities of those other problems are going to have some similarities to the inherent complexities in designing software systems.
Now some people may already have highly developed computational thinking abilities without ever having touched a computer because intuitively they've understood how to solve analogous problems in a different domain. Such people may be far better at dealing with inherent complexity than some or most professional programmers but they could never apply those abilities in this domain due to the barrier to entry posed by the incidental complexity associated with the tools and languages.
A friend of mine, a physicist, is an example of the above. Her approach to dealing with complex problems is already far better than many developers I know, but she wouldn't be a good programmer simply because she doesn't understand the tools. However, if she were to invest significant time to understand the tools then she'd be a better developer than most. Not only that, if she had familiarity with the tools then her already developed computational thinking skills would mature much further. If she were to have access to tools that allowed her to apply her ability to deal with complexity without getting in her way then that would be the ideal outcome. What I'm trying to say is that we are a very long way away from minimizing the incidental complexities introduced by our tools and languages.
He mentions that if you were to ask a literate individual in a past society with a 10 percent literacy rate what would be the maximum possible literacy rate they would've said something like 20 percent (80 percent of the population is incapable of overcoming the inherent complexity associated with gaining literacy). But our educational systems have progressed far enough to achieve a 99 percent literacy rate, which would've been impossible for someone in a society with 10 percent literacy to even consider as a possibility. Our estimates of where the boundary between incidental and inherent complexity lies is generally going to be strongly biased.
I wholeheartedly agree to almost everything you say and especially that everyone is probably able to learn some programming in a suitable language, just as they can learn to read and do math - and it would be good for them if it was taught well, if not because it gives you a tool for solving problems that you might not otherwise acquire. But I am certain that not all, or perhaps not even the majority, have the skills required to pursue a professional career.
I doubt however, that there is much to gain from simplifying the languages, and especially not by going the graphical programming route - it's a chimera occluding the actual problems.
Graphic programming could be used as learning tool, just like a set of training wheels, but just as them it would eventually become a hindrance as both make simple problems simple and everything else much more complex or virtually impossible.
The tools and languages can be improved of course but I do not think there will be any ground breaking improvements, just like there probably will not be any ground breaking simplifications in mathematics, or the process of learning it.
As an side note, I personally wonder why not more work have been done in incorporating first class support for entity-relation and/or graphs in languages - which is probably useful when modeling actual real world problems. But as it is still possible to build using generic language constructs, no one seems to care.
Or maybe the problem is that the educational system is so bad that too many are actually struggling to not topple over with their bicycle even after some years in university.
> Graphic programming could be used as learning tool, just like a set of training wheels, but just as them it would eventually become a hindrance as both make simple problems simple and everything else much more complex or virtually impossible.
For visual languages such as Squeak, and Skov, the idea is indeed to make programming easier for people.
However, visual programming has another important raison d'être. MIMD programs algorithms are more naturally expressed as dataflow, so that task scheduling and load-balancing become almost trivial, and dataflow is more naturally expressed graphically.
Now we have multicore machines and it is difficult to make optimal use of resources with mainstream imperative programming languages.
Graphs are, as you point out, a very flexible data structure, and there ought to be something to be gained in representing programs as graphs, and having programs operate on graphs. Lisp and Prolog benefit from homoiconicity.
Developers who've significant experience with functional programming understand that it would be easier and more reliable to solve many (or most) problems using a functional language over an imperative one. But the majority of developers have little to no experience with functional programming. In almost every conceivable way widespread adoption of functional programming would make everyones jobs significantly better. And these days we are beginning to see mainstream imperative languages starting to incorporate functional concepts slowly because people are starting to realize the value of it.
Right now we have far superior tools available to us than the ones used by the average programmer, but due to a combination of historical reasons, poor education, and sheer inertia of past commitment to inferior tools, we don't yet see widespread adoption of the superior tools that are already available.
The visual languages that I've seen so far are a kind of adaptation of existing languages to make them simpler and more accessible to beginners. What I'm talking about is not merely simplification of existing languages, but more a shift to an entirely different kind of paradigm. A shift that would require the development of new abstractions that are uniquely suited for visual programming that are not just adaptations of existing abstractions.
But as with all tools there are limitations. Certainly visual languages will not be best tool for all jobs, but there are certain classes of problems that are going to be easier to tackle with a visual language. There is also the possibility of solving a particular problem (or different parts of the problem) with both visual and text-based programming concurrently. First class support for data structures like graphs are easier to do with a visual language. I use Neo4j for graphs and the cypher query language is quite good, but it's effectiveness comes from the fact that it incorporates visual elements within the text. Something like MATCH (n)-[:REL]->(y) is a query for matching a part of the graph but you're still limited by the one dimensional structure of the language. You could imagine a visual query language for graphs being vastly more powerful in ways that a text based language couldn't possibly be.
