By Artem Oppermann, teaching deep learning for the production environment
- The consulting firm McKinsey & Company estimates that Artificial Intelligence has the potential to create between $ 3.5 Trillions and $ 5.8 Trillion in value across nine business functions in 19 industries — annually
- Artificial Intelligence will generate up to $ 2.6 Trillions in additional value in Marketing and Sales
- Additional $ 200 Billion in value will be added to Pricing & Promotion and $ 100 Billion
- The study of McKinsey shows that 69 % of time Artificial Intelligence and Deep Neural Networks are able to improve the performance beyond what existing analytic techniques were able to deliver.
These takeaways and many other fascinating insides are taken from the McKinsey Global Institute Discussion Paper “Notes from the AI frontier: Applications and Value of Deep Learning”
The discussion paper draws on McKinsey Global Institute research and the firm’s applied experience with the artificial intelligence of McKinsey Analytics, assessing the practical applications and the economic potential of advanced AI techniques. The discussion paper’s findings are based on intensive McKinsey Global Institute analytics collated and integrated with more than 400 use cases across 19 industries and nine business functions.
Before we go any further and analyze which markets are affected by AI and to what extent, let’s discuss first what exactly Artificial Intelligence means.
1. Artificial Intelligence in its many Forms and Facets
A large number of companies claim nowadays to incorporate some kind of “Artificial Intelligence” in their applications or services. But artificial intelligence is only a broader term which describes applications when a machine mimics “cognitive” functions that humans associate with other human minds, such as “learning” and “problem-solving”.
On a lower level, an AI can be only a programmed rule that determines the machine to behave in a certain way in certain situations. Instead, when speaking of AI it’s only worthwhile to consider two different approaches: Machine Learning and Deep Learning.
Machine Learning incorporates “classical” algorithms for various kinds of tasks such as clustering, regression or classification. Many of these algorithms have been used for dozens of years. Deep Learning, however, is a relatively young field that incorporates neural networks that are capable to solve the classical as well as more advanced tasks, such as image recognition and language generation, just to name a few. I won’t go any further on the explanation of the various kinds of neural network architectures and how they can be used in the specific business fields at this point (These are topics of the upcoming articles however).
One thing you must take with you is that Deep Learning is the way to go if a company wants to incorporate artificial intelligence in their services or products.
This can be shown in Figure Nr. 1 from the McKinsey Global Institute Discussion Paper, where we can see that Deep Learning (Blue Color) is much more likely to be used in an AI application than classical Machine Learningalgorithms (Black Color). Even Transfer Learning and Reinforcement Learningare techniques that only unfold their full potential with the usage of neural networks.