NOVEMBER 2020CIOAPPLICATIONSEUROPE.COM8IN MYOPINIONhe last few years have seen a surge of interesting artificial intelligence (AI) and for good reason. AI has the potential to transform every sector, reimagining processes, and extracting valuable insights from data previously unobtainable to us. With less than one percent of the data generated in the world being collected and analysed, much of that still represents `deep data'--data that hasn't yet been dug up, mined, and examined for its insights and value. In this era of exponential growth of data, there are significant opportunities for organisations to learn on an exponential scale and become disruptors within their industries. However, the successful use of AI is not engineering-free. For optimal performance, beyond a simple initial proof of concept, the AI components need to be correctly implemented and configured. Data, the critical aspect of any AI project, must be carefully defined and cleansed. The AI capability must be integrated into a broader end-to-end solution. The whole solution must be designed in a way that is scalable and sustainable. All of these aspects require the skills of experienced data scientists and engineers.There is a perception that the current success of AI is due to a major algorithmic breakthrough in the domain of machine learning (ML) and, specifically, deep learning. Whilst there have been some great developments in the underlying algorithms, the real enablers behind our current success in AI are:· the increased availability of data,· the increase in processing power and storage, and· the rising desire to use AI to deliver operational benefits.The first task of any engineer looking to apply AI in an organisation is to select the right business problems to focus on and understanding the feasibility of applying AI to those problems. First and foremost, we need to understand what success looks like and whether it is possible to measure business impact. Data is critical and it is important to ensure the data exists in the right form and at the right level of quality to support the intelligence. Ensuring that AI performs in the TAI Engineering Through the HypeJAMES LUKE, CTO, IBM DISTINGUISHED ENGINEER
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