Machine Learning or Deep Learning for decision-making: which AI to choose?

Machine Learning

Artificial Intelligence has a strong potential to support business growth.. As explained previously, there are 4 different types of Machine Learning. However, when one thinks of Artificial Intelligence these days, it is always a Deep Learning application that comes to mind: Boston Dynamics robots, Alexa or a Tesla Car. So, let’s dive deeper (pun intended) into the topic to see if it can help business experts!

Deep learning: a definition

Deep learning is a branch of Machine Learning. Just like Machine Learning, Deep Learning combines a data set and an algorithm that will find correlations and patterns within the data to explain a target outcome. However, Deep Learning goes much further than traditional Machine Learning algorithms by mimicking Human cognition but also Human brain structure.

To do so, Deep Learning uses a specific set of algorithms called Artificial Neural Networks. These are made of different layers of computing units called “neurons” – hence the name. For each layer of the Artificial Neural Network, neurons analyze a specific feature of the input data. They then transmit the information to the appropriate neuron of the following layer. The process is repeated until a satisfactory output is found.

The benefit of such algorithms is that they can process a vast quantity of information without human intervention. Of course, they are actually much more complex than traditional Machine Learning algorithms, with tens or even hundreds of hidden layers! This complexity also means that tremendous computing power is needed, which limits the number of use cases.

Tesla’s autopilot mode is a good example of how Artificial Neural Networks work. Cars take videos of a stop sign. Each neuron of the algorithm will analyze some of the specificities of the sign. Layer after layer, the network will learn to recognize the STOP sign and the car will be able to stop automatically – the outcome layer in that case. Great use case, but can it inspire business experts? More importantly, does it make sense for them?

The limits of Deep Learning vs. the simplicity of Machine Learning

Deep Learning is very powerful. But it is also very expensive, resource-hungry. If you are a business expert who needs to make fast and trusted decisions, Deep Learning is not the best fit!

Indeed, Deep Learning needs petabytes of data to deliver good outcomes. Unfortunately, Business experts mostly use Small Data sets to drive their decisions. Moreover, business experts must support their decisions with fast, understandable and actionable insights from data. Deep Learning outcomes can be actionable, but they are neither fast nor understandable.

The good news is that at MyDataModels, we spent the last 10 years developing a groundbreaking technology. It makes Artificial Intelligence accessible to every business expert in every industry. To do so, we developed a proprietary Machine Learning algorithm called Zoetrope Genetic Programming (ZGP). Its strength is its ability to do Supervised Learning on small sets of data to predict with a great accuracy the designated outcome. 

By mimicking the Darwinian evolution process, ZGP builds predictive models out of 400-row, 15-column datasets in a snap. We encapsulate ZGP within custom Decision Intelligence Platforms for business experts. These solutions connect to their data sources, automate the data analysis process and provide them with predictive models to try different scenarios through clear dashboards and guide them towards the best decisions.

Companies face a major challenge these days as they need to make AI accessible to every department. MyDataModels’ Decision Intelligence Platforms integrate with existing workflows, data sources and embedded systems to help them make fast, actionable and explainable decisions.

So, if you’re looking to take your decision-making moments to the next level with Machine Learning, contact us! We’ll be happy to discuss your business needs and build exciting use cases with you!

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