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We all see the term artificial intelligence (AI) thrown around in many contexts. AI is a buzzword in the tech industry in particular. However, many people (including those in IT) don’t actually understand what AI is nor the challenges and opportunities it presents. Today, I’m beginning a series of blogs to share the basics of all things AI.

Why Does AI Matter?

As computer systems become ever more capable of performing the tasks that traditionally are staffed by human employees, the evolution will affect nearly every industry. In the short term, there will be positions that are replaced by machines, leading to job loss. However, over the longer term, there will be increasingly higher skilled workers needed to manage and maintain these machines and systems.

What Is Machine Learning?

At its most basic, machine learning (or AI) involves “teaching” a computer to learn and change when given a vast amount of data. The computer is not necessarily explicitly programed for these changes, but instead learns to spot patterns and make connections. Therefore, the machine learns (get it?) to complete the task on its own. (Source:

What Is Deep Learning?

Deep learning, or as it is alternatively known, cognitive computing, is a more advanced version of machine learning—utilizing neural networks to emulate human thought processes. The synapses of the human brain are mimicked utilizing networks of small computing nodes. This allows the computer to solve complex, non-linear problems (Source:

Strong AI and Weak AI

There are two popular classifications of artificial intelligence—Strong AI and Weak AI. Strong AI is aimed at replicating human thought, while Weak AI is generally about just getting the systems to do a specific task. Strong AI is farther from realization at this point, but is very likely to become a reality (though the timeline for this is subject to much debate). Weak AI might be able to copy a human thought pattern to some extent, but it does not go deep into how the human decision process works.

Weak AI technology is currently in broad use, with explosive growth expected as systems are vetted and standardized. Industry needs vary, and while there are many companies utilizing Weak AI, there are many more that would benefit from a robotic transition leading to reliability, cost saving, and functionality improvements.

Future of Machine Learning Depends on Interconnectivity

Interconnectivity will be the core of success for the AI and machine learning revolution. SAP is utilizing the Internet of Things (IoT) to connect systems as never before. Our market leading SAP HANAS/4 platform will continue to drive Big Data, pushing machine learning to analyze and process in ways never before imaginable.

As SAP moves forward into this new market, we will be working towards navigating the complex and evolving commercial framework. New challenges will be faced from a business, legal, and social standpoint, but these will also open market-making opportunities and a chance to lead with empathy. These will be addressed in the next installment of my AI for Dummies series.

Learn more about machine learning and artificial intelligence by reading our other Thursday series blogs.