I always enjoy using Mathematica for serious and less serious work. Don't want to spend all time on finding the right python libraries and check there vitality etc when most absolutely necessary. I think Mathematica is fantastic and thoroughly enjoy listening to Stephen wolfram's discussions with his outstanding team. I mean I don't know any other cto doing that. I also believe that Wolfram is really looking for "stuff" that is actually useful for his customers. Other languages have also great features and sometimes better syntax but that doesn't matter. The flexibility is incredible. I also enjoy the documentation and all the crosslink to other fields that might be useful. You always learn from it. No other languages has given me more pleasure programming than this Wolfram language. I once played with low code software mendix in combination with sap software. I didn't really like it. Low code for sure but many many many clicks and different windows.. Not for me.

A couple of recent comments led me to review the original post and some of the replies from community members which, I have to say, I find rather interesting (although I suspect Stephen Wolfram would perhaps beg to differ). A thought on this subject that I have been entertaining for some time is the concept of Mathematica as a kind of computational Wikipedia. Perhaps, if it has any relevance at all, the comparison could be better made between Wikipedia and Wolfram Alpha, rather than Mathematica.

In any event, what I find interesting is that for a long time Wikipedia looked like a hopeless quest: in the early days many subjects were barely covered and the quality of the articles was often very poor. But then, amazingly, the whole idea took off and Wikipedia became the kind of indispensable reference that Jimmy Wales imagined it could become.

I think something similar may be happening with Mathematica (or, rather, Wolfram Alpha) as its scope has enlarged over time (and the quality of the reference material has always been excellent). My sense is that it is close to some kind of tipping point, on the verge of becoming the kind of indispensable computational resource that Stephen Wolfram had in mind from the outset.

Do others agree? Or take an altogether different view?

I have found Mathematica essential in production settings for at least 20 years. Most of those were before the Wolfram Engine (and the more recent cloud offereings). I have re-analyzed some of the applications I mention below, and we could have cost-effectively used the Wolfram Engine if it had been available. Stability of the kernel has also dramatically improved.

I have used Mathematica in at least three different companies. In a consulting environment, we did all our in-house analyses using Mathematica. Notebooks would call large databases, issue SQL commands, and bring in the data for further processing and visualizations. Most algorithms we needed had to be developed from scratch, and Mathematica made it quick to go from reading a paper or a pencil and paper calculation, to something tested and working on data. The graphics would usually get some tweaking in Illustrator, but would often be close to camera ready. We also used Mathematica to create reference algorithms to be implemented in C++ or Java. Many of these where not numerical algorithms.

At my next company we used Mathematica directly as a compute engine. We had developed a very performant cyber security system that identified executing malicious code by observing the stream of system calls. All the machine learning steps were done in Mathematica (this was before the free Wolfram Engine). It allowed us to have version 1 of our system up way faster than if we had implemented them directly. We experienced memory leaks and random hangs that forced us to periodically kill and restart the compute engine, but we were able to engineer it so that those issues remained invisible to the user. Over time, because of costs, we replaced the Mathematica subsystem with Java code.

In my current work we still develop most of the algorithms in Mathematica. We are building a system that use Mathematica for some core data generation; reference algorithms are still in Mathematica. We also use it as a thinking tool (see my blog post). Having all that curated data at one's fingertips is great.

I feel costs have prevented a wider adoption. Wolfram Research now offers low cost versions for non-commercial applications, which should encourage wider adoption, but I sometimes worry that its market may be shrinking.

I agree Jonathan and I am also worried about the long term future.

In my opinion the following issues have to be addressed first:

1.) Polishing and bug fixing of existing features

• Inconsistencies in TimeSeries
• Bugs and missing features in Machine Learning (Batch Prediction, GPU-support, Support for Apple Silicon and ML engine, …)
• Performance of Dataset, …
• Improve Developer Tools (update Workbench, support for other editors - might be coming as far as I know)
• Fixing long standing issues in various kinds of functions. Very often when I try something new, I stumble over bugs which give an unpolished impression of the product and the features run out of gas after they are 90% finished.

2.) Generally „Big Data“ and Data Processing has to be handled better

• fast converter / importing routine for numbers, datetime, …
• Out of core storage
• Supporting CSV - Files with „;“ deliminator
• support for secure web sockets wss - Protocol
• => focus on performance for processing huge amount of data
• …

It is really surprising what basic features are missing in this area. Python is far ahead.

3.) Deployment for Production

• as far as I know efforts are made in this area (Application Server) but nothing really useable in an easy way has been released so far
• Licensing has to be pain free, no cloud credits or something like this but permanent license
• Deployment / packaging of an app has to be easy and stable
• Deployment on own infrastructure
• support long running services (eg. Bot)
• Automatic error recovery, restart, logging, …

4.) Predictable bug fixes and releases

• when a bug is reported it is simple unpredictable for a customer if it gets fixed at all and when it gets fixed
• This is especially annoying for regressions. Fixes have to be pushed out quicker.
• I wouldn‘t suggest WL for a critical app in an enterprise because of this
• as long as this is not worked out, WL is only useable for research and experimentation in my opinion

5.) Create and promote examples for real world use cases in enterprises (IoT, Predicitive Maintenance, Big Data, ML Recommendation engine, Trading Bot, …) outside of research and development. Whenever I talk to somebody about MMA nobody thinks about production usage of it. This is probably the hardest part to build up awareness.

That said I really like the concept and idea of Mathematica and I want it to last for a long time and be as stable as possible. I enjoy discovering new things and areas where I had no idea about before.

Mathematica has been the platform of choice for sensors-to-animation production by the film industry for decades. A colleague of mine is a system administrator at one of the sites.

Some comments from a long time programmer (coming up on my 50th anniversary) and Mathematica user (32 years):

First, Bernard Gress's comment that WL exists so that Stephen can get his projects done is truer than might be expected. Certainly, the best way to get feature 'x' implemented is to get Stephen interested.

Second, I think that there really is confusion about what Mathematica is for. I initially used it solely to solve maths problems, and for that it was, and is, superb. The first edition of the Mathematica book had the subtitle: "A system for doing mathematics by computer". Over the versions, the definition of what maths is has expanded, and a bunch of other stuff has been added.

The notebook interface is great for this type of investigation and interactive exploration.

However, it is rubbish at making stand-alone apps.

It is also far too hard to do computer-science or complicated programming. When I learned c, almost everything I needed to know was in the K&R book, some 125 pages or so, as I recall. Sure, you needed to understand the standard library, but the header files gave the information you needed. This level of documentation is missing in WL, although the general level of documentation otherwise is very good. With c, the step between the 'simple' stuff and a complex program was gradual and the simplicity of the core language made learning what to do relatively straight-forward. With Wolfram language, it is by no means clear what the core language is, and understanding it is not simple. This makes going beyond the basics much more difficult than it should be. At Apple in the early 2000s, they told developers that Xcode (and objective-c) made the regular stuff easy, and the hard stuff possible. With Mathematica, the mathematical functionality for interactive use is made very easy (compare to writing your own), but anything beyond that has been made much more difficult than it needs to be.

To give an example, it is not clear from the documentation how to pass a variable by reference -- if it is there, it is pretty well hidden.

Your comment about losing something when key people leave is very telling, especially for Mac users. When Theo Gray was involved, you could expect that Mathematica would make use of Apple technologies -- it was largely due to him that Mathematica remained a Mac program through the dark days. However, I am no longer feeling the love, specifically in the areas of UI and GPU utilization. As far as I can tell, Apple's neural engine is at least as good as anything based oil NVIDIA, yet it is not used at all for machine learning in Mathematica. Embracing other new OS features has been very slow -- it took far too long to move the front end to 64 bit, for example.

There is also the tendency to leave stuff at the 99% done level and move on to the next shiny object. There are simply too many bugs. There are too many similar functions that do pretty much the same thing, but which have subtle differences in the API or performance, and the documentation does not make that clear at all.

When I retired, I decided that I would let my skills in c (etc....) lapse in favor of doing all my coding in WL. I have come to regret this decision. When I use WL as part of my mathematical studies, I have (mostly) no problems. For anything else, it is a constant slog to achieve even moderate success.

I do have proposals for a solution:

1. Fix the bugs.

2. Market Mathematica (Wolfram|One, etc.) for what it does best -- interactive computation.

3. Fully implement the Wolfram engine so that it can be used as a resource when developing stand-alone code in pretty much any language for any platform. Specifically, this means making the engine work with iOS and iPadOS so that other developers can develop apps with a state-of-the-art UI and functionality, yet making use of the strengths of WL. (A similar case can be made for Android, as well.)

4. Make full use of the native capabilities of each hardware platform. This means making use of all the technologies in macOS (and iPad/iOS), Windows and Linux, although the most obvious mismatch is with macOS at the moment.

5. Institute a developer program (with nominal costs attached), so that developers will have access to essentially whatever or whoever tech support gets their undocumented answers from. I remember that the Wolfram Technology conference was originally called the Mathematica Developer's Conference.

6. As a corollary, provide some really state-of-the art developer tools. I tried using Eclipse, but compared to the tools that I used when developing Mac apps (Lightspeed c, Code Warrior, Xcode), the tool is clumsy and out-of-date. I have no experience in Java development or development on Linux or Windows; perhaps Eclipse is perfectly usable for these platforms. I know that discussing tools is likely to lead to a religious war -- after all there are those who think c++ is the best of all possible languages. However, if Wolfram Research wants to break out if its ghetto, it will need to integrate with modern IDEs, and Eclipse is not going to help with this (in my opinion, of course).

7. Consider making a new language. All older languages have had re-thinks about what the language means. C has had several successors, with different levels of success: c++, Objective-c, and Swift. While I am not fluent in Swift (yet) it seems to have captured the essence of what was good about c without the excesses of c++, and is easy to learn. A similar rethink of WL would be a really good idea. While many of the design objective of WL were good, not all were, and some of them are real howlers -- especially in the area of UI design and implementation. Done right, no old code would be obsolete (you can still use ANSI c in Swift), but things would be a lot easier both for typical users and experts. Of course, if Wolfram Engine were available for macOS and iPadOS, some enterprising person or group could re-implement WL from within Swift, so this might not be so far out after all. (Remember Swift is not proprietary -- and I am using that only as an example.)

When I first used Mathematica, I was developing (in-house) real time process control software for a large clinical lab. I would solve the mathematical problems and prototype graphs, etc., using Mathematica, then translate the results into c on a Macintosh. This combination worked extraordinarily well. If my suggestions were implemented, and I were doing the same thing today, I would do the prototyping in WL, then use the Wolfram Engine in a native program I would write in Swift for macOS and iPadOS that would make native use of Apple's Neural engine. This would result is a more useful and stable application than I could ever hope to do using WL alone.

So, to answer your question: what is Mathematica for?

In its current state, Mathematica is for interactive problem solving of primarily mathematical problems using the notebook interface.

I do not think it is effective when used for anything else (certainly by not anyone outside of Wolfram Research) : web deployment, etc. except as a way to publish notebooks interactively, seems to be a toy project.

With proper focus, as I outlined above, Wolfram Language could be a significant tool in the development and implementation of basically any type of scientific application.

Hi Raspi, in some ways I think you are an ideal Mathematica user - a total convert: MMA with everything! And I get it - it's a wonderful sandpit environment for every kind of experimentation.

But in broad terms, my thinking is that this is what Mathematica really is: a means of doing fast research, experimentation and prototyping - but not necessarily for production systems.

If you are not a professional developer that probably doesn't matter at all - everything is an experiment, or recreation! But for a professional developer, getting systems into production and making sure they are battle-ready is a critical part of what you do for a living. And its in this context that I see the greatest challenge for Mathematica - making WL applications that are sufficiently bullet-proof to hand over to an end user (rather than, say, a research co-worker).

BTW what do you use Raspberry Pi for?

Hi Tom,

I think a technically educated person should be writing with a word processor, doing reports with a spreadsheet, and thinking with a technical computing language as an extension of their thought process.

This strikes me as too prescriptive. Fifty years ago you would have had to write, perhaps: "a technically educated person should be writing with a fountain pen, doing reports using a pocket calculator and hardly thinking at all about technical computing languages, unless they are computer scientists".

The point is: time moves on. If you work at Wolfram Research I imagine you (try to) do everything in WL, whether it be writing text, producing some analysis, or programming an application. Certainly that's my understanding of Stephen Wolfram's expectation, anyway. For myself I do a large proportion of my work in WL, including carrying out analysis, creating presentations, and programming applications. It's very convenient to use a common UI and tech stack for all these varied tasks: it save shifting from one application to another, having to be concerned about file format compatibility, etc.

A technical computing language is not the right tool for producing commercial computer applications.

I understand your point, but I expect that there are legions of Fortran programmers, to say nothing of the good people at WR and Mathworks who would heartily disagree with you. And indeed on the pages of this post several examples are given of commercial applications developed in WL (including one or two of my own).

To your point, however, it's obviously a significant challenge to develop a commercial application in WL, even though it can be and has been done. I certainly wouldn't regard the development of commercial applications as a forte of Mathematica, in they way that, for instance, symbolic programming clearly is. Which goes to the topic of this post - i.e. what is Mathematica for? And I would have to concur that the development of commercial applications is not one of those things - although I am quite sure that Stephen Wolfram and others at WR would strongly disagree.

My answer: “it’s an extension of one’s thought process.”

This is a bold claim for any programming language/technology platform, including Mathematica. Could one not make a similar claim about C++, Java, or Julia? I suspect that, unless you are a professional developer, probably not. Mathematica has certain unique features that make it a natural fit for the rapid exploration of ideas, in a way that certainly isn't true of most other computer languages that I am aware of.

I think that your description encapsulates what Stephen Wolfram may be aiming for: a kind of cognitive sand-pit offering a plethora of computational tools that provide a unique way to explore ideas, whether experimentally, graphically, or analytically, that will assist the process of developing them into functional algorithms.

I think the ability to do symbolic math is underrated. I'm a hobbyist programmer, and I once encountered a problem that really did require to do symbolic math. I really, really didn't want to do the calculations with pen and paper so I bought a Mathematica license. Since then, I've learned to appreciate the capabilities of the language and the elegance of its design.