Machine learning (ML) is no longer just a futuristic possibility that will allow robots to act and even replace people simply because they are smarter than their biological predecessors.
Artificial intelligence (AI), and more specifically machine learning, has already been put into action in millions of business applications across the globe, improving work processes and, with them, the lives of human beings.
Machine learning is not limited to self-driving vehicles. In fact, other industries, such as biotech and software development, have increasingly been relying on it.
Software development, in particular, has already gained a massive boost to its workflows and technological innovation.
ML has improved its processes and applications in three major ways:
- Machine learning teaches software how to use logic.
- Artificial Intelligence is now more mobile-friendly.
- Machine Learning capabilities have given software the ability to offer a vast array of strategic decision-making applications.
Before getting more in-depth into these three areas, a refresher on the basics of machine learning and A.I. is in order.
Difference between ML and AI
Although machine learning and artificial intelligence are related, they should not be considered synonymous. One has a more general meaning, while the other is more specific in its application.
Machine learning is all about algorithms and how they help computers, robots, and software learn autonomously through simulated and practical experiences.
For instance, applications like facial recognition and self-driving get progressively ‘smarter’ through continuous and constant testing.
Artificial intelligence, on the other hand, simply refers to intelligent behavior exhibited by machines.
Therefore, while AI will always rely on ML for intelligent behavior, machine learning and the technology associated with it do not necessarily rely on AI.
Put more simply, machine learning does not require artificial intelligence to run its applications, but artificial intelligence will always need machine learning to perform its tasks.
Now that we’ve cleared that up, let’s take a look at the standard way of developing software before diving deeper into how machine learning and AI are changing that standard.
Current software development methods
The ‘classic’ way of developing software uses a fixed model to lay out a set of rules for the software to perform.
Rule-based development allows software to do the same thing over and over again without any delimitation.
Under this standard practice, three kinds of codes are used:
- View (shows things)
- Model (defines things)
- Controller (decides things)
Using this development structure, rules are created in two ways: through a model or a controller.
The controller usually uses a fixed logic pattern in the form of ‘if this, then that’.
This means it does not deviate or recreate functions and applications outside of the pre-formatted scenarios it has been given.
Such fixed rules and fixed relationships make the development process slow and difficult, as every single part of the development must be mapped out and is difficult to change later on.
This is where machine learning and artificial intelligence come in to make software development easier and more flexible.
Software Logic
Machine learning allows developers to teach their software what to do without having to plan out every minor detail at the start of the development process.
Instead of defining the rules first and entering the data to come up with answers, ML allows developers to input data and answers in the beginning, so the software can create the rules as needed.
Combining machine learning with software development is referred to as data training.
It reduces the constant need to define code at the beginning, middle, and end of software development.
As a result of logic training, software can learn and change what it has to do in an almost unlimited number of situations.
When software can learn on its own, the development process becomes so simple that it usually needs only two lines of code to come up with practical and predictive rules:
classifier = sklearn.tree.DecisionTreeClassifier ( )
classifier = clf.fit (inputs, outputs)
Another great benefit of using ML-training models for software development is that if new data has to be imputed or replaced anytime in the development process, it is enough to replace a single file.
Almost always, that file is the initial input (model) itself.
There is no need for tedious and time-consuming database migrations or integrations when changes are made throughout the development phase.
While this type of logic-based ML training has not yet been utilized to its full capacity, there are many successful examples of applications that have used machine learning to make software smarter.
For example, the following three applications were designed with ML logic-based methods:
- Hit Factor (Image Recognition App)
- High Speed Vehicle Recognition (Image Recognition App)
- Neural Network Library (AI Neural Network)
While teaching software to learn on its own simplifies development and results in better applications, the question still arises as to how such models will run in the background of a mobile device.
Well, that is where artificial intelligence comes into play.
Mobile-friendly AI
AI-based apps need to run their machine learning models in the backend, which almost always means that a backend server is needed. This results in higher costs and usually additional programming training for developers.
Another problem with using AI models on mobile devices is that running them on a backend server means that ‘smart’ software can only run when connected to that server.
This is inconvenient to end-users.
However, companies like Apple have eliminated this online dependency in favor of a more convenient user experience, making life easier for developers.
For instance, Apple’s CoreML SDK offers developers offline functionality, drag and drop features and easy model conversions into the CoreML format.
This is not just a boon for developers either as Apple users can now access their applications both online and offline, because of CoreML SDK technology.
Let’s not forget about the benefits derived by the manufacturers who utilize AI technology, either.
Industry 4.0 technology can now integrate AI-empowered mobile apps and their machine learning capabilities with CRM (customer relationship management) software.
This allows users to automate and speed up tasks like order fulfillment, inventory checkups and sending email updates to customers even when offline.
While Apple has led the way to a better mobile app experience for those who wish to develop and use mobile applications, they are not the only company to create smarter and more user-friendly apps.
Google has also followed suit and developed a mobile-friendly framework named Mediapipe to build multiple machine learning pipelines.
Along with creating a better experience for manufacturers and customers, machine learning technology also helps developers cut down the time it takes to make key strategic decisions while building apps.
Strategic decision-making
If the average person makes 35,000 choices per day, how many do software developers make?
It seems safe to say a lot more than the average person, given the technical nature of their jobs.
Whatever the number of important decisions developers have to make during the development process one thing is for sure: their decisions must be relevant, practical, and timely.
The most important skill in software development is not coding but decision-making.
Since two of the key factors behind good decision-making are speed and accuracy, any technology that furthers these two factors will naturally make the software development process that much more expedient.
Moreover, it will improve the predictability of its outcomes.
How exactly does machine learning help developers make more consistent and more accurate decisions in less time?
In the following three ways:
- Problem Solving
- Opinion Mining
- Augmented Analytics
Let’s delve into each of these areas some more.
Problem Solving
AI can use machine learning to develop expert systems that mimic the thought-processes and experiential knowledge of experts.
Machine-learning systems utilize the best ideas by the topmost expert in a particular field.
They draw on the logical patterns behind those ideas, to come up with predictable decisions fairly quickly.
With such expert systems, developers can access past software development data, along with predictive models created by experts, even when those experts are not available to provide their suggestions and opinions.
Opinion Mining
In creating machine learning models for developing software and apps that end-users like and use, expert advice is not enough.
The opinions of customers are as important, if not more important, in creating successful software.
So, how does machine learning help in mining the opinions of customers and users to produce better apps?
AI and machine learning give software developers the ability to mine large quantities of customer data and organize it in such a way that it can be retrieved quickly and easily.
This fast ‘opinion mining’ helps lay out a clear picture of what customers want and why they want it.
As a result, developers avoid having to spend a great deal of time trying to figure it out through trial and error.
Ultimately, software developers can make decisions about how and what to develop based on what customers want and not what they think they want.
Augmented Analytics
Along with expert thinking and customer feedback, the decisions of a software development team and their upper management contribute to producing a truly outstanding product.
Machine learning uses a process called Augmented Analytics to manage and prepare data for developers and other decision-makers within a company, allowing them to share important data when and where necessary.
The result is a more efficient data mining and management process. This helps reduce the time it takes to get important information to those people in charge of making strategic software-development decisions.
Bottom Line
Machine learning techniques and models can accelerate the software development process and automate much of the decision-making that goes into it.
Three of the most important ways ML is now transforming software development are in the areas of software logic-training, mobile-friendliness, and strategic software decision-making.
Any development team or company set on creating a unique and practical app or piece of software should consider utilizing machine learning as part of their development strategy.
It will help cut down on time, cost, and the trial and error process traditionally associated with software development.
About the author:
Heather Redding is a content manager from Aurora. She loves to geek out about wearables, IoT and other tech trends.
She relaxes with her Kindle library and a hot coffee. Reach out to her on Twitter.
