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AI and The Future of Web and Mobile Development
AI and The Future of Web and Mobile Development
Download Audio: AI and The Future of Web and Mobile Development
Artificial Intelligence, AI, is set to revolutionise and impact every facet of human activity like no other technological advance has done in the past. There is no area of human endeavour that will not be touched by this technological revolution.
From predictive medicine to generative AI, Artificial Intelligence is having an unprecedented impact. But how will AI impact the field of Web and Mobile development? Will the net impact be positive or negative? Are developers ultimately replaceable? These are some of the common questions on the subject.
Some Common Use Cases
Over the years, AI has been slowly creeping into the Web and Mobile development landscape. From chatbots to predictive analytics, some of the features used in web and mobile apps are increasingly being powered by AI, often without users even being aware of it.
AI is packaged as models that are trained on existing data. AI extrapolates from that data in order to offer solutions to similar problems in new and creative ways. Whilst training of such models requires a lot of data and compute resources not usually available to typical developers, they still have access to the features provided by these models.
AI models that perform certain functions are increasingly being made available to developers via Application Programming Interfaces (API). Through API, developers without the capacity to train their own models can make use of pre-developed and pre-trained AI models to bring Artificial Intelligence to their applications at very low cost.
Some of the notable areas in which AI is finding its way into web and mobile development are as follows.
1. Personalisation
Machine learning is currently being used to personalise the user experience in web and mobile development through algorithms that analyse user interactions in order to make recommendations or adjust the user experience that is tailored to a particular user.
A good example of this is in music streaming services such as Spotify, where the algorithm makes recommendations based on the analysis of a user’s listening history and offers playlists with artists and music that the user is likely to enjoy.
Another good application in web and mobile apps is in e-commerce, where machine learning can be used to study a user’s browsing history and previous purchases on the website or app to generate recommendations on what products might interest them. It is even possible to generate an entire shop specifically curated for a user.
The high level of personalisation in web and mobile development results in higher customer engagement and engenders customer loyalty, as it results in a personalised experience for the customer.
2. Automation
Lately, you might have noticed a lot of websites and apps offering a chatbot that responds to your needs and queries in a conversational manner. Users can ask for information and the chatbot either responds directly, offers links to relevant resources, or links the user to a human operator for more assistance.
The Chatbots are powered by Artificial Intelligence through Natural Language Processing (NLP) algorithms. NLP is a branch of Artificial Intelligence concerned with giving computers the ability to understand text and spoken words, in much the same way human beings can.
By automating this aspect of doing business in web and mobile applications, simple customer requests and queries are automatically handled by the application, whilst freeing humans to be able to focus on more complex issues of customer service, and improve efficiency of the business.
Anyone who manages a web or mobile app has to contend with the data the application generates. This may be user data or data produced by the application as it runs. Developers can incorporate artificial intelligence to process such data, and categorise it in a format that humans can easily understand and analyse. This makes more sense the more data there is to contend with.
3. Predictive Analytics
Predictive analytics is the use of machine learning algorithms to analyse large sets of data, and identify patterns and trends that can be used to make predictions about future outcomes. AI is being used for predictive analytics in a variety of ways in web and mobile development.
The manner in which predictive analytics can be used in an application is domain specific. Depending on what the app is built for, developers can incorporate machine learning algorithms to analyse data and trends to predict future outcomes.
One example is in the finance industry where an algorithm can analyse market trends to predict future market outcomes. These analyses can aid investors to make informed decisions about their stock portfolio, for instance.
This approach can also be used in a health app. By leveraging a model trained on cases of certain health conditions, an algorithm can analyse patient data and make predictions about onset of disease and offer preventative measures before a condition manifests, or becomes critical, assisting doctors to diagnose and treat medical conditions.
4. Enhancing Security and Performance Monitoring
AI is being used to enhance security and perform performance monitoring in a number of ways in web and mobile development. One example of how AI is being used for security, is through the use of machine learning algorithms to detect potential threats.
It is possible to analyse network traffic and identify patterns that point to a cyber attack, such as a distributed denial of service (DDos) attack or detect a network intrusion by a machine learning algorithm, that analyses network traffic and identifies anomalies.
AIs are a strong preemptive tool for cyber security, as they are in a position to spot potential problems faster than their human counterparts, allowing for fast and preemptive responses to potential attacks.
In the same way, AI can be used in performance monitoring to optimise web and mobile application performance, by analysing user data and usage patterns. By doing so, AI can spot bottlenecks in the operation of an application, and offer suggestions to improve performance, ensure the smooth operation of systems, and also protect against potential threats.
Partnership Between Developers and Their Machine Counterparts
As much as AI is paving a way for humanity to do better across numerous fields, there is a fear going around that AI will replace us all, and no job is safe. It used to seem that developer jobs were safe from this threat, but more and more now, some developers are not so sure.
Recent developments in AI that have developers concerned about their role in the field is the development and emergence of tools like Open AI’s ChatGPT and Github’s Copilot.
ChatGPT is an AI model that is a chatbot and interacts with humans in a conversational way, to generate content and even code. It has a dialogue format and is able to answer follow-up questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.
GitHub Copilot on the other hand is a cloud-based artificial intelligence tool developed by GitHub and OpenAI, to assist users of Visual Studio Code, Visual Studio, Neovim, and JetBrains integrated development environments by autocompleting code.
It is eerie to watch these tools at work, and most developers may be justified in thinking that their job is at stake. As remarkable as these tools are, they should not be thought of as a replacement for developers, but as complementary tools. This should be thought of as forming a great partnership between humans and their machine counterparts.
As much as these tools can generate great code, they presently have no actual understanding of their output. They are just good at looking at millions of examples, and predicting the anticipated output. Until AI gets to the stage of practically understanding its output, the developer role is still valid and needed for a long time to come.
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