While AI and its subset technologies such as machine learning, deep learning and generative AI have been steadily advancing through the years, it’s hard to deny that the past couple of years have caused a significant explosion in popularity for the very same. As a result, top software companies in Sri Lanka and abroad that offer their services on an outsourcing basis have also embraced the AI bandwagon, with developers now at the forefront of building intelligent technologies for their clientele.
While much buzz surrounds the topic of different tools that are being built to suggest recommendations, identify patterns, automate tasks and have contextual interactions with chatbots, emphasis towards the technology stacks that are used to build said applications are less in the limelight – especially for business leaders who aren’t as tech-savvy.
In this article, we do just that, by introducing JavaScript, its strengths, weaknesses and what you need to look out for, when evaluating your stack options for an AI project.
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JavaScript is an object-oriented scripting language that is used to build dynamic websites and web applications. As an established programming language, JavaScript is home to numerous libraries, frameworks and developer communities, to enable developers of all skill levels to learn and grasp the intricacies of working with it. Although JavaScript is popularly used as a client-side scripting language (as it executes code in users’ browsers), it can also render its functions as a server-side scripting language.
Additionally, JavaScript is commonly combined with other programming languages such as Python to deliver optimal performance, making it a highly versatile programming language among software and IT outsourcing companies. This makes it a highly valuable addition for AI and ML use cases, especially where large amounts of data are processed for insights.
JavaScript is suitable for predictive analytics, which includes (but isn’t limited to) trend forecasting and contextual suggestions while typing, thanks to its prototype-based capabilities. With prototypes not utilising classes, properties for an object are shared and reused between instances, which enable effective trend identification and model training.
Additionally, an extensive collection of libraries and frameworks such as TensorFlow also help developers build, test and ship code faster, making JavaScript ideal for any data analytics and data science use cases in general.
JavaScript and software libraries such as TensorFlow that are used to build and train machine learning models form the foundation of NLP tools and technologies. NLP, in turn, is the foundation for numerous other analytics use cases such as social listening and sentiment analysis, to help identify emotions behind language used by audiences across the web, and even in offline channels such as voice.
Again, software libraries allied to JavaScript provide developers a base to train large amounts of data in order to accurately execute speech and facial recognition – each of which can be applied across different business units, and across different industries as well.
Speech recognition, for example, is a highly resourceful capability within CX software solutions, as it can streamline self-service for customers; instead of typing, a user can simply use voice commands to navigate through an IVR, or interact with a chatbot. On the other hand, facial recognition solutions serve security needs, in areas such as access authorisation.
Being a programming language that has been extensively formulated to build websites since its inception, JavaScript may be slow when processing images and computing large amounts of data. Extra reinforcements may be needed by means of executing pre-compiled code within the browser itself, in order to improve performance.
In spite of being a programming language that is used by most websites today, JavaScript’s popularity isn’t as high as other programming languages (such as Python), when it comes to AI-based applications. This is because writing JavaScript code from scratch to serve AI models may be a more complex and time-consuming task. However, machine learning/deep learning technology stacks such as TensorFlow can help streamline this, thanks to an extensive range of libraries, tools and frameworks.
JavaScript code is visible to every user that visits a site built with it. This makes the code susceptible to manipulation from hackers, who can gain unauthorised access and cause data breaches. Additionally, codes are also modified to serve different platforms. This also increases the attack surface, as different codes increase the chances of security loopholes being present.
Before embarking on your AI project, it is imperative to know which programming languages and technology stacks shall be best suited to your business’s unique needs and objectives. In-house and offshore software development companies alike need to do a thorough assessment to ascertain this beforehand, so that teams are all aligned at the start of the project. Additionally, an assessment will also influence (and determine) whether any skill gaps exist, and who needs to be recruited.
Using JavaScript or not is ultimately dependent on what your product is, and what it is meant to achieve. Plus, other languages may also need to be used, in order to obtain required outcomes. While such an assessment may be more relevant for your software development teams, business teams can join in on the conversation to deliver feedback surrounding objectives.
Key questions to ask in order to determine whether JavaScript is suitable for your unique AI project include (but aren’t limited to):
Answering the above questions in the affirmative may mean that extra technology stacks and tools will be required, if JavaScript is being implemented. All of this can now be addressed, following feedback and clarity from the assessment.
JavaScript can be a highly valuable programming language for building AI-powered solutions, while circumventing the shortcomings it may bring in terms of limited compute and code building. Being an established object-oriented scripting language that uses a prototype-based approach, greater nuance can be delivered as objects aren’t restricted to classes – and properties can be assigned based on behaviour. This makes JavaScript a great option for predictive analytics and data science, as both fields revolve around identifying insights and trends from existing data sources – instead of the other way around.
However, certain challenges surrounding the use of JavaScript for AI (such as public code bases, which can be a security risk), require thorough assessments from both software development and business teams, so the right decisions can be made when it comes to technology stacks, well before your AI project commences. The same also applies to whether other programming languages (such as Python) need to be combined, to build a technology stack that is fully equipped and scalable.
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