Live Streaming Public Course: From Analytics to AI: Transforming Decision Making in Digital Business

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With the enormous growth of big data, especially from Internet of Things (IoT) devices, now is the time to start planning for and building skills and infrastructure in artificial intelligence (AI) to transform BI and analytics in support of decision making in your business.

AI has had a long, chequered history. Multiple periods of over-optimism have been followed by “AI Winters” since the 1950s. Today, AI has come of age and is being embedded in mainstream technology from cars to call centres, and smartphones to analytic systems. With the IoT instrumenting the physical world and social media doing the same for society, a massive deluge of data is driving extensive uptake of AI. It all suggests that this “AI Summer” is not going to fade.

Under a range of names—deep learning, autonomous vehicles, cognitive computing, robotics, algorithms and more—AI, combined with big data, IoT and automation, offer both the threat and the promise of revolutionising all aspects of IT, business and, indeed, society. What do you need to know about them? How should you prepare for and react to their growing importance in your business and IT environments, especially in their likely transformation of decision-making support?

In this one-day workshop, Dr Barry Devlin builds upon his two-day “Delivering the Digital Business: Starting from BI” course to enable you to take full advantage of emerging AI technology. Starting from familiar computing paradigms such as programming, operational systems, databases, analytics and business intelligence, we explore the relationship between big data and many types of deep learning. We position traditional and emerging BI tools and techniques in the practical application of AI in the business world. Extrapolating from the rapid growth of AI and IoT in the consumer world, we see where and how it will drive business and likely impact IT. Based on new models of decision making at the organisational and personal levels, we examine where to apply augmentation and automation in the roll-out of AI. Finally, we address the ethical, economic and social implications of widespread adoption of artificial intelligence.

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