Software Engineering Insights

Mobile banking, investment apps, wealth management applications, wallets, and stock trading applications have reshaped the finance industry for the good. However, this has also introduced security challenges. Fintech applications have become increasingly more exposed to cyber threats, such as data breaches, phishing, identity theft and financial fraud.

Recommendation engines stand as the backbone of personalized online experiences, guiding users to discover content, products, and services that meet their preferences. From driving online shoppers to their next favorite product, or suggesting the next binge-worthy movie according to the viewer’s genre history, recommendation engines are the hidden force behind driving user-preferred actions on digital platforms. 

Imagine opening your food ordering app and being greeted with your favorite food list, or visiting an online store and finding the items you've been longing for already lined up, waiting for your approval. This isn't magic; it's predictive UX at work. It's like a friend who remembers your preferences and surprises you with choices that feel handpicked—with a touch of Artificial Intelligence(AI).

Artificial intelligence (AI) solutions are redefining daily life and global business operations, offering unprecedented speed and efficiency that surpass human capabilities. However, as this new "intelligence" unfolds, a critical question arises: Can it embody qualities beyond mere logic and reasoning? Specifically, will emotion play a role in AI's functionality, and can it ever rival human capacity for sensing and feeling?

Growing up, sci-fi movies always fascinated me. Remember the distress message from Princess Leia, in the form of a 3D hologram in Star Wars. Or the video calling scene from the movie Thunderbirds. By far one of my favorites is the character, J.A.R.V.I.S. an AI (Artificial Intelligence) from Iron Man, which also resembles the innovation of chatbots in today's time. Sci-fi has been the inspiration behind many such innovations.

Less than two decades ago, Deep Learning (DL), or the simulation of human neural networks, was only a concept limited to theory. Fast-forward to the present day and it is being leveraged to solve real-world problems such as converting audio-based speech to text transcripts and in the various implementations of computer vision. The underlying mechanism behind these applications is known as the Attention Mechanism or Attention Model.

Artificial Intelligence (AI) and its multiple sub-domains are being increasingly employed in various industries and businesses to aid in repetitive processes. But there has been a burgeoning interest from established tech giants and startups in using AI to make everyday life a walk in the park.

The accuracy of deep learning models depends upon the quality, quantity, and contextual meaning of the training data set. However, the scarcity of this up-to-the-market data set is one of the major challenges in building DL models. 

Artificial Intelligence (AI) is everywhere. The possibility is that you are using it in one way or the other and you don't even know about it. One of the popular applications of AI in custom software development is Machine Learning (ML), in which computers, software, and devices perform via cognition (very similar to the human brain). Herein, we share few examples of machine learning that we use every day and perhaps have no idea that they are driven by ML.

Chatbots have redefined the way customers interact with businesses. According to HubSpot, “47% of consumers are open to buying items through a chatbot”. Thus, majority of organisations have joined the race of augmenting or building these virtual agents on their websites.