Blog Categories

Artificial Intelligence

  • AI in Banking and Financial Services

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    Innovation has always been at the forefront in the BFSI sector. Take the case of ATMs, OCRs in cheques, plastic money via credit cards and debit cards or the most recent digital wallets, technology has been used to enhance not just operational efficiencies but also business effectiveness and cost savings. With the advent of artificial intelligence, there is a multitude of use cases for banking in wide ranging areas from risk management to portfolio management, from treasury to investment banking, from customer relationship management to credit control and compliance.

  • AI is Not a Tool, it is Cognitive Infrastructure!

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    There is a quiet ‘category error’ sitting at the centre of much of the AI conversation right now, and it is shaping decisions that will outlive the people making them. We keep calling AI a tool. Tools are picked up and put down. Tools sit in a drawer until needed. A hammer does not change what a wall is, what a house means, or how a neighbourhood feels. The framing is comfortable because it puts us in charge: we choose, we wield, we set down. I would argue, though, that it is the wrong frame. It is wrong in the way that calling electricity “a better candle” was wrong in 1890. The artefact is recognisable; the substrate it creates is not. What we are actually building, and stitching into the daily fabric of work and life, is starting to behave more like cognitive infrastructure. Something that increasingly mediates how we perceive, decide, coordinate, and remember. You do not “use” infrastructure the way you use a tool. You live inside it. The question of who designs it, who maintains it, and who gets to question it becomes a different kind of question entirely. This distinction is not academic. It changes the strategy. Rohit Mahadevu (AI and Digital Transformation Consultant, Texavi Innovative Solutions) expresses his views on Artificial Intelligence as a cognitive infrastructure framework. Read on...

  • Technologies, platforms and infrastructure in AI!

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    The technology stack powering the AI revolution unravelling in front of our eyes is touching fields far and wide. From the surge in coding libraries of programming languages (e.g., Python, R, Xcode, Java, Dot Net etc) to the comprehensive full-stack platforms such as TensorFlow, Scikit and Keras, there is a vast new playfield with numerous tools, platforms and SDKs. Besides the software, there is also a massive growth of hardware, networking and infrastructure aspects too. These include data centres, servers, cloud computing, parallel computing i.e., multi-core processors, sensors, actuators, controllers to name a few. It won't be an exaggeration to say that the speed and scale of changes in the AI technology landscape is definitely breakneck, and getting so much more difficult to make sense of those in our lifetime.

  • AI Robotics and their Applications

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    AI Robotics is the latest addition of AI techniques, tools, algorithms and models offering autonomy and speed to the already-existing automation offered by robotic machines. So now we have robots that are not just automatic but also autonomous, meaning they just don't do complex and repetitive tasks but also learn constantly, make decisions, improve and improvise their work. Besides the hard-handling machines in heavy industries and destructive testing in military and defence, there are newer applications and use cases for this discipline. Increasingly robotic AI is making in-roads into the numerous real-world applications eg., surgical robots and clinical tests in healthcare to rovers in space exploration and beyond.