In 1930, John Maynard Keynes predicted we’d be having 15-hour workweeks by the end of the century. But by the time it was 2013, it was clear the great economist had gotten something wrong.
Welcome to the era of bullshit jobs, as anthropologist David Graeber coined it. Since the 1930s, whole new industries have sprung up, which don’t necessarily add much value to our lives. Graeber would probably call most jobs in software development bullshit.
I don’t share Graeber’s opinion, especially when it comes to software. But he does touch an interesting point: as more and more processes are automated, most jobs are obsolete at some point. According to one estimate, 45 percent of all jobs could be automated using current technology. And over time, they probably will.
In software development, where things move pretty fast anyway, you can see this happen in real-time: as soon as software testing became a hot topic, automation tools started springing up. And this is just one of the many areas where the bullshit-parts — the parts that are iterative and time-consuming — of software has been automated away.
This begs the question, though, whether developers are making themselves obsolete by building automation tools. If more and more machines can write code for themselves, what do we need humans for?
From designing logic to designing minds
Software developers are builders at heart. They build logical links, algorithms, programs, projects, and more. The point is: they build logical stuff.
With the rise of artificial intelligence, we’re seeing a paradigm shift though. Developers aren’t designing logical links anymore. Instead, they’re training models on the heuristic of these logical links.
Many developers have gone from building logic to building minds. To put it differently, more and more software developers are taking on the activities of data scientists.
The three levels of automation
If you’ve ever used an IDE, then you know how amazing assisted software development can be. Once you’ve gotten used to features like autocomplete or semantic code search, you don’t want to go without them again.
This is the first area of automation in software development. As machines understand what you’re trying to implement, they can help you through the process.
The second area is that of closed systems. Consider a social media app: it consists of many different pages that are linked among each other. However, it’s closed insofar as it isn’t designed to directly communicate with another service.
Although the technology for building such an app is getting more and more easy to use, we can’t speak of real automation yet. As of now, you need to be able to code if you want to create dynamic pages, use variables, apply security rules, or integrate databases.
The third and last area is that of integrated systems. The API of a bank, for example, is such a system since it is built to communicate with other services. At this point in time, however, it’s pretty impossible to automate ATM integrations, communications, world models, deep security, and complex troubleshooting issues.
Current developments
It might seem like developers are already using a lot of automations. But we’re only at the cusp of software automation. Automating integrated systems is almost impossible as of today. But other areas are already being automated.
For one, code reviews and debugging might soon be a thing of the past. Swiss company DeepCode is working on a tool for automatic bug identification. Google’s DeepMind can already recommend more elegant solutions for existing code. And Facebook’s Aroma can autocomplete small programs on its own.
What’s more, the Machine Inferred Code Similarity System, short MISIM,claims to be able to understand computer code in the same way that Alexa or Siri can understand human language. This is exciting because such a system could allow developers to automate common and time-consuming tasks, such as pushing code to the cloud or implementing compliance processes.
Exciting horizons
So far, all these automations work great on small projects, but are quite useless on more complex ones. For example, bug identification software is still returning many false positives, and autocompletion doesn’t work if the project has a very novel goal.
Since MISIM hasn’t been around for a long time, the jury is still out on this automation. However, you’ll need to keep in mind that these are the very beginnings, and these tools are expected to become a lot more powerful in the future.
Soon-to-come applications
Some early applications of these new automations could include tracking human activity. This isn’t meant like a spy-software, of course; rather, things like scheduling the hours of a worker or individualizing the lessons for a student could be optimized this way.
This, in itself, presents huge economic opportunities because students could learn the important stuff faster, and workers could serve during the hours in which they happen to be more productive.
If MISIM is as good as it promises, it could also be used to rewrite legacy code. For example, lots of banking and government software is written in COBOL, which is hardly taught today. Translating this code into a newer language would make it easier to maintain.
CREDIT:thenextweb
