Agriculture and farming is one of the oldest and most important professions in the world. Humanity has come a long way over the millennia in how we farm and grow crops with the introduction of various technologies. As the world population continues to grow and land becomes more scarce, people have needed to get creative and become more efficient about how we farm, using less land to produce more crops and increasing the productivity and yield of those farmed acres.
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For many years banks have been at the forefront of using technology to help with both front-of-house and back-of-house operations. It’s no surprise then that banks are adopting AI to help in a variety of ways.
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The UK has played an important role in the history and development of AI. Alan Turing, a British mathematician, is considered to be the father of theoretical computer science and has deep roots in AI as well. In addition to crafting the foundations for modern computing, Turing envisioned the Turing test, which aims to determine a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.
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Artificial intelligence has been around for many decades. However, In the past decade, interest and investment in AI have skyrocketed. In the past few years, companies and organizations in just about every industry and across the world are adopting a wide range of cognitive technology solutions spanning the seven patterns of AI with applications in finance, retail, automotive, healthcare, government applications and many more industries showing adoption.
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Companies of all sizes are implementing AI, ML, and cognitive technology projects for a wide range of reasons in a disparate array of industries and customer sectors. Some AI efforts are focused on the development of intelligent devices and vehicles, which incorporate three simultaneous development streams of software, hardware, and constantly evolving machine learning models. Other efforts are internally-focused enterprise predictive analytics, fraud management, or other process-oriented activities that aim to provide an additional layer of insight or automation on top of existing data and tooling. Yet other initiatives are focused on conversational interfaces that are distributed across an array of devices and systems. And others have AI & ML project development goals for public or private sector applications that differ in more significant ways than these.
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