How AI Will Impact The Roles Of Developers?

The steadily increasing demand for AI and other automation technologies come with the potential of disrupting labor markets significantly. While AI has the power to augment the productivity of employees, it can also replace the work performed by others. Almost every industry will likely to be transformed at least to some degrees and software development is no different.

Humans have an intuitive and innate number sense which helped us to develop computers in the first place. Unlike a computer, we can identify the abstract concept of the thing which is common in some objects, without having to count them.

This demonstrates the difference between the machine and the human mind and helps understand why we’re not even close to creating AI that have the broad intelligence possessed by humans. However, according to a new study, which was published in ‘Science Advances’, an AI has developed a human-like number sense spontaneously. With the help of unsupervised learning systems, modern AI may detect you or any other object for that matter when offered millions of training images of any kind – just like humans do. Deep neural networks, a machine learning technique, work in a similar way, to some extent, to the human brain. The depth originates from several layers in the network – the commonalities observed become more abstract as the information moves deeper into the network.

As scientists continue to discover more about creating artificial learning techniques and identify new ways to comprehend the brains of living organisms, they unlock more of the puzzles of adaptive, intelligent behavior.

An Evans Data Corp survey, which was conducted on 550 software developers, revealed that 29 percent of those developers consider that ‘they and their development efforts are replaced by AI’ as the most worrying things in their career. ‘The Future of Employment’ study conducted by the Oxford University alerts that software engineers may become computerized due to the advances in machine learning. And algorithms will be optimizing software design choices.

Impact of AI on software development

Though there’re lots of hypes around AI in the context of development, it’s still in a nascent stage and it’ll probably be years until we can use it on a larger scale. However, there’re some areas where AI can certainly make a difference. Let’s have a look at them.

Automatic debugging: The deep learning algorithm can expedite the debugging process by flagging known errors. It can also learn how to fix them. The machine might be able to correct an array of mistakes after receiving sufficient training.



Automated code generation: An AI system might generate code by gathering some predefined modules, once the necessary patterns are learned by it. So, it can be said that at some point of time in the future, AI won’t replace the work of developers, but it’ll certainly become a coding partner.



Strategy: It’s common during the development of a software product to spend a significant amount of time over which features to immediately include and which ones to leave for later. With the help of AI, simulations can be generated and a hierarchy of the most relevant features can be obtained by analysing the voice of the consumers, as retrieved from social media and product reviews or based on the use rates for similar products.



Automated testing: Throughout the SDLC, testing is an extremely important component of any software product. The testing domain contains two major challenges – developing a comprehensive list that includes most likely cases and some extreme situations that could leave a significant impact on the program’s performance. This can be managed by AI by looking at earlier logs and automatically developing a list of test cases to run through the system.

Impact of AI on developers

The widespread adoption of machine learning, specifically neural systems, will need developers to learn new skills together with developing another mindset. Usually, conventional developers think in straight algorithmic ways, which isn’t usually what’s required when developing machine learning algorithms. It’s also going to expect developers to hold a more profound business comprehension.

Also, we shouldn’t forget the black box effect. Although lots of AI-powered algorithms offer great automatization and predictions, they come with a definite downside, which is the way algorithms learn is completely opaque to an outside observer. The observer can do nothing but feeding the algorithms new datasets and observing the outputs. This is a quite inefficient way when we talk about fine-tuning. In addition, it can result in very dangerous and biased results.

Conclusion

We’re still no way close to having the capacity of telling a machine about our necessities and after that, the machine makes the last application. So, developers shouldn’t be concerned about losing their jobs because of the widespread adoption of AI. Instead, they should search for ways to leverage the abilities of AI and utilise them to become better developers. So, we can expect to see some expansive moves in the necessary skillsets of the developers.