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Banking meets Artificial intelligence

Banking meets Artificial intelligence

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Global Business Review Magazine
Thursday, August 11, 2022
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Banking meets Artificial intelligence

Banking meets Artificial intelligence

Dictated by the decisions we make, our entire lives are made both inside and outside. As we are hardwired to continuously learn even in our subconscious , from our mistakes , from our surroundings. Especially to avoid taking bad decisions and questioning our capabilities of improving our decision making when faced with consequences in the future. The concept for machine learning (ML). AI (artificial intelligence) brains are, by and large, programmed the same way as a human brain. Both are meant to learn from human decisions , asking the same questions and reinforcing the same principles.

A machine learning algorithm watches by identifying the good decisions and learning from the bad ones, just like a human brain does by constantly evaluating the decisions. And that’s the maturity level which we now can be found accelerating along with us, one built on ML algorithms and AI models that are beginning to operate, inexorably, in the same way that we do, through constant positive and negative re-enforcement feedback. Technology on a whole has become more available and it is not a secret at all. It is more accessible and democratized. The main reasons AI and ML have been able to continue their relentless march is because of specific open-source mathematical software such as Tensorflow (deep learning) and Kubernetes (distributed computing), which have made data science infinitely more efficient and effective. We can expect more ideas and innovations as and when the more people become fluent in Tensorflow and Kubernetes. The ideas will help us flow and flourish and more the advance artificial intelligence more the machine learning will become. Machine learning pipelines is now industrialized and operational and commercialized. The process of machine learning was a task done offline traditionally using data outside the production. Now it is built on algorithms and models that are efficiently learning as and when the data flows through systems. The deep learning code has been cracked by the brands and will most definitely they will keep their cards close to their chest as it is so valuable.

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The use of AI and ML has increased exponentially in the way in which their insights are presented and the human trust and connection that enriches. Data scientists were able to find out and extract the information from machine produced data. Since it was written by a machine, it was intangible and had no links to the human’s thought process patterns. With the changing times and with the technology evolving, our way of presentation is also altering. The way we present algorithms now are different and have become very personal. The gradual shift that is being experienced as AI and ML are evolving from an intangible ideology to a more rational practical execution is because of the increased sophistication. 63 percent of people don’t even realize they’re already using AI techs.  Just think of your Spotify ‘s song recommendations or the reroutes taken by G Maps, then the understanding and trust on this technology will strengthen.  The key here is that artificial intelligence is starting to solve single issues across entire organizations by overcoming issues from the management end to the ones where everyone can relate. It’s a self-fulfilling prophecy. The more we trust this technology, the more we interact with it on a human level and more the interaction, the more human it becomes.  In a nutshell, you receive data, you make transformations, you make a prediction, and then you learn from it. Your machine brain is always learning from new insights given to it, just like a human brain.

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Global Business Review Magazine

Global Business Review is a online print magazine focusing on the updates and information about on emerging markets, Finance, Banking, Technology. Global Business Review provides news, features, analysis, commentary, and interviews from industry across the globe.

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  • Twitter questions centre on Musk’s efforts to scuttle a $44 billion deal
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