Over the last decade, one of the most transformative pieces of technology to shake up a range of industries has most definitely been
AI. Not only has AI been adopted in healthcare, but it has also been made available to consumers in a wide range of products such as AI smartphones. This being said, it should come as no surprise then that AI has also transformed
software development.
Machine learning technologies such as AI give us the capabilities to accelerate our already existing technology by pushing the
traditional software development lifecycle (
SDLC). By doing so, it presents an entirely new paradigm for innovation of new technologies. Developing computer programs traditionally involve specifying what the developer wants the system to do and then engineering the features of the technology to do so.
Before the introduction of AI, computers were still quite powerful. Their downfall, however, was that teaching them rules in a rule-based way was too rigid, which meant that without AI, it would be difficult for them to identify particular media for specifics. Our smartphones now are a great example of this. This need is not possible from
traditional software development which is why AI has changed the landscape in a
colossal way.
Software development changed with machine learning
AI techniques in the form of machine learning would prompt the software developer to curate and prepare
domain-specific data. This is then fed into learning algorithms which are continuously improved and iteratively trained. By doing so, the developer is giving the device machine learning capabilities, a model, which can deduce essential features and patterns from data without the need for
human encoding. This new way of working means models can highlight perspective details that even humans may not have thought of. With this in mind, it is evident that the most profound impact of AI on computer programming is how we as humans, have in the past, executed software development and its capabilities, which has now changed.
Machine learning boosts traditional software
Software development in the traditional manner is not by all means at an end. Training machine learning models is just one step towards practising
AI technology. It is thought that only a fraction of real-world machine learning is actually composed of codes. This means that important components of these systems will still need to be handled by regular software. These include aspects of the software such as interfaces and security, much like smartphones from Smartphone Chcker. However, technologies developed using traditional SDLC still have the potential to benefit from MAchine learning and here's how.
Prototyping
It can take months and years of planning to turn business requirements into technology products.
Machine learning can help shorten the process by enabling domain experts who are less technical to use
visual interfaces and natural language when developing technologies. This will then help cut the prototyping process in half and let the business move on to reach their goals quicker.
Intelligent programming
In most cases, vast amounts of time is spent by
developers debugging and reading through documentation. By using
smart programming assistance, the time they initially put into debugging would be reduced. This means that intelligent aid would be able to offer just-in-time support through recommendations, best practice and code examples for the developers to use to solve issues more quickly.
Error handling and Automatic analytics
The nature of
programming assistance means that they can learn from past experiences which can help identify common errors during the development process. This means in the future, and it may be possible for the software to change dynamically and respond to mistakes without the need for human intervention.
Smartphone cameras
The use of AI in cameras allows them to
automatically identify scenes in the capture as well as available light and the nagle of the scene. The Ai in the camera behaves like a human and acts as if it knows what it's looking at. Through the use of machine learning the Ai chooses the best exposure and adjusts the colour, providing the perfect shot. In most cases, smartphone cameras lack the type of zoom a DSLR camera gives. However, Samsung's newly released
S20 range has an impressive zoom function. Machine learning is used in AI enabled cameras to develop image recognition engines. These then put in place neural networks that have been trained on millions of images.
Code refactoring
For
team collaboration and long-term maintenance, clean code is critical. As large organisations upgrade their technologies, large scale refactoring is unavoidable, causing a multitude of issues within the organisation. Machine learning can be used to help analyse codes and automatically optimise it for performance and interpretability.
Can AI create AI
Considering future development, the question that still remains is if AI can create Ai then there will be no need for humans to be involved in the development process. We already see the massive growth in
AutoML solutions. These technologies aim to automate pieces of the machine learning model training process. It helps to reduce the workload on data scientists and engineers by enabling domain experts to train
production-quality models.
AI Testing
One of the biggest roles of
AI in connection to the lifecycle of software development is in testing. AI in software testing refers to AI powered tools for software testing and
testing AI based products.
the last decade, one of the most transformative pieces of technology to shake up a range of industries has most definitely been
AI. Not only has AI been adopted in healthcare, but it has also been made available to consumers in a wide range of products such as AI smartphones. This being said, it should come as no surprise then that AI has also transformed
software development.
Machine learning technologies such as AI give us the capabilities to accelerate our already existing technology by pushing the
traditional software development lifecycle (
SDLC). By doing so, it presents an entirely new paradigm for innovation of new technologies. Developing computer programs traditionally involve specifying what the developer wants the system to do and then engineering the features of the technology to do so.
Before the introduction of AI, computers were still quite powerful. Their downfall, however, was that teaching them rules in a rule-based way was too rigid, which meant that without AI, it would be difficult for them to identify particular media for specifics. Our smartphones now are a great example of this. This need is not possible from
traditional software development which is why AI has changed the landscape in a
colossal way.
Software development changed with machine learning
AI techniques in the form of machine learning would prompt the software developer to curate and prepare
domain-specific data. This is then fed into learning algorithms which are continuously improved and iteratively trained. By doing so, the developer is giving the device machine learning capabilities, a model, which can deduce essential features and patterns from data without the need for
human encoding. This new way of working means models can highlight perspective details that even humans may not have thought of. With this in mind, it is evident that the most profound impact of AI on computer programming is how we as humans, have in the past, executed software development and its capabilities, which has now changed.