Artificial intelligence is not just an idea from science fiction anymore. Machines have already made their way into many people’s everyday lives and its importance is only continuing to increase.
Machine learning is revolutionizing the way that the modern world works in different ways. This includes tasks traditionally taken on by teams of individuals at companies – including the process of software development.
The State of Current Software Development
For over forty years, the process of software development has pretty much stayed the same. High-level development languages, lower-level languages, scripting languages, methodology, and necessary tools for the development process have come and go, but the capabilities of these items have not advanced very far. Essentially, although languages, tools, and testing have changed quite a little, their capability levels remain nearly the same.
A software development company, such as BairesDev, goes through the same outline of a Software Development Life Cycle as companies have for the past several decades. While this process has obviously been a successful one, this does not mean that the most advanced technology of today should not be applied to it to make it as efficient as possible.
Understanding Machine Learning
An incredibly powerful subset of artificial intelligence, machine learning is able to make sense of an otherwise limitless world of big data while processing nearly unfathomable amounts of information. It is modern science’s way of finding patterns and making educated decisions or predictions based on data from data mining, statistics interpretation, analytics, and pattern recognition.
The real value of machine learning is in its ability to create models to act as a guide for future actions and to discover patterns that otherwise would have been invisible. With this technology, organizations are able to move beyond simply looking into the past to generate reports and manage expectations. Now, they are able to predict what will happen in the future based on a computer-generated analysis of their data.
Harnessing Machine Learning in Software Development
Although machine learning is still considered to be in its early stages, its applications and use cases are nearly limitless. At its most basic level, Machine learning is merely artificial intelligence powered-pattern recognition on the grandest of scales, but it already has and will continue to open up a world of possibilities in the field of software development. These advancements include:
- Code Replacement and Neural Networks – Machine learning technology is already capable of taking hundreds of thousands of lines of code and condensing it down to mere hundreds. This massive reduction of code is incredibly beneficial both in initial programming effort and the bandwidth required to maintain said code.
However, the most significant factor is how the code actually works. Instead of using thousands of lines of statistical code, developers deploy a neural network trained to translate. This neural network can be retrained on new data without being rewritten while adjusting for usage shifts, language changes, and many other factors. Neural networks can create new programs as well by combining existing modules and uses execution traces from other programs for training.
- Management of Data and Infrastructure – A rival to traditional database indexes, machine learning programs are capable of predicting the storage location of data or if said data even exists. This functionality is fairly limited at this initial stage, but machine learning is already proving to be much faster than traditional indexes and requires significantly less memory.
Programmers and engineers are already working towards building data science programs that can work on multidimensional learned indexes, further query optimization, shortening the retraining process, and many other factors to make the management of data easier with machine learning.
- The Role of the Developer – Machine learning is not expected to replace the role of a software developer (at least not in the near future). Instead, it will change the way that developers work with data. Software developers must have the skillsets to manipulate raw data and mold it into a format that is machine learning-friendly. They also need to know how to monitor predictive model accuracy and understand the inner workings of the pairing between the machine learning aspects and the software itself.
However, software developers are not data scientists. While both data scientists and software devs participate in the building of a machine learning project, they alone will not be able to harness the full power of the technology.
Machine learning is an incredibly powerful technology for the world of modern business. Companies who do not choose to integrate it into their software workings will face major disadvantages in the future. Although it is relatively new in the field of software development, it has already begun to reduce the amount of code and the effort to maintain it, improved data management and infrastructure, and adjusted the role of a software developer.
Laila Azzahra is a professional writer and blogger that loves to write about technology, business, entertainment, science, and health.