AI in Software Development: 7 things you need to Know

Statistics state that 38% of companies have already invested in AI and using AI technologies for various operations, meanwhile, 62% of them plan to use those in the next 12 months, i.e. by the end of the year. Helped by the power of big data and cloud computing, AI is revolutionizing the digital world faster than anyone can imagine.

We are already witnessing the change- from Amazon’s Alexa to Google Photos and Tesla’s self-driving cars. However, how does AI the development of the software that underlies most of these services? What are the things that from the perspective of a developer, that are necessary to be considered for AI software development?

Artificial Intelligence relies on Big Data

Each year the amount of data produced is doubled in amount and it is estimated that by the next decade there will be 150 billion networked sensors. This data is crucial in assisting the AI devices to learn about how the human mind thinks and feels – which accelerates their learning curve and enables the automation of data analysis.

To perform the required task, an AI model must be trained on a huge and comprehensive data set. Furthermore, the bigger the data set is the better will be the result. Generally, a standard training data set for a Machine Learning model may comprise millions or billions of entries. AI is now even capable of learning without the human support. For e.g., Google’s DeepMind algorithm learned itself how to win 49 Atari games.

These days, the software development services are not restricted by technical limitations of the past and permit collecting, processing and analyzing big data even in real time.

Artificial Intelligence needs Cloud computing

At the initial stage, Machine learning needs substantial computing resources, meanwhile, the data processing stage is not so challenging. Previously, this varying requirement in computing resources was difficult for those who wanted to implement machine learning but were unwilling to make big one-time investments to purchase servers that were adequately powerful. As the cloud technology emerged, the possibility of satisfying this requirement became easy. software development services can rely on either the corporate or commercial cloud, for e.g. Microsoft or AWS etc.

Artificial Intelligence in real-time or near real-time

As artificial intelligence techniques become mature, more are interested in using these practices to control complex real-world systems that have solid deadlines. Some of the industries where AI applications are in real-time include fraud detection, voice recognition (voice-to-text), facial recognition, virtual assistants (Siri, Cortana), translators (Google Translator), chatbots, self-driving cars and smart homes etc.

Best programming languages for AI

AI is a huge field and with a wide area to cover, it is difficult to just one single programming language. Of course, there are a variety of programming languages that can be used but not all offer the best value for your effort. These languages are considered to be best options for AI considering their simplicity, prototyping capabilities, usefulness, usability speed – they are Python, Java, Lisp, Prolog, C++ etc.

AI beyond ML algorithm application

Algorithms are, indisputably crucial in Machine Learning software development. There are other elements, however that play into the success, such as –

1. Training data and training processes for Machine Learning & Artificial Intelligence (AI)

Training data is the key to success, if an organization doesn’t have enough data or if they are biased or low quality, the AI software is likely to make wrong decisions.

Meanwhile, the training process in Machine Learning can take more than one form. The first form is called “supervised learning” in which the machine learning model is provisioned with both the training data and the desired output.

Machine Learning Mastery writes “It is called supervised learning because the process of an algorithm learning from the training dataset can be thought of as a teacher supervising the learning process”.

The second form is called “unsupervised learning”, in this the output is not known and the model has to learn the underlying structure or distribution of the data in order to learn more about the data

Machine Learning Mastery explains “these are called unsupervised learning because unlike supervised learning above there are no correct answers and there is no teacher”.

2. Integration into day-to-day business processes

AI should become an integral part of the day-to-day business processes to bring any practical value. In order to achieve this, the software should be integrated with other systems – from those it received new data from and to those it translates the output to.

Machine Learning solution must be observed and confirmed

Even the best of technology cannot replace the judgment and expertise needed to filter and process the data and evaluate the meaning of the risk score. While this problem is eliminated through rule-based techniques- the lack of inspectability is a major drawback of certain ML-based approaches.

Even if some errors are slight (like a wrong batch of products recommended in e-commerce), there are times when they can be quite grave (like overlooked fraud case in banking). To avoid the negative effect of these mistakes, the architecture of the ML software should comprise of a second control loop which will monitor AI decisions and detect errors. This task can be done either by human or another AI software module that looks into the AI level-1 decisions.

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