Artificial Intelligence (AI) and machine learning have influenced multiple industries by changing the dimensions of the data and technology. In the rat race of being the top, firms believe that artificial intelligence and machine learning are magic wands which could transform colossal data into decisions. There are many times, companies start their machine learning journey without having s lucid objective. In such a scenario, companies may not have specific key performance indicators (KPI’s) and simply drain research and development budgets.
Sometimes, managers are in a dilemma whether to incorporate AI and machine learning into their business, but not all of them require the hands of machine learning. Machine learning has its own limitations and would not be able to apply to all business domain. Broadly speaking, machine learning can be classified as:
• Classification: These models are extensively used in image recognition and natural language processing, which is used to disintegrate a large dataset into smaller datasets.
• Regression: These models are used to develop predictions. Also, mostly they are used in sales forecast that takes into account a lot of factors ranging from macroeconomic indicators to political indicators.
Next question arises is which type of machine learning is apt for the current business.
Supervised learning is where you have input variables (x) and an output variable (Y) and an algorithm is used to learn the mapping function from the input to the output.
Y = f(X)
The goal is to approximate the mapping function so well that there is a new input data (x) prediction of the output variables (Y) is obtained. The algorithm stops when it achieves an acceptable level of performance. It is so called supervised learning because the algorithm is learning from the training data sets.
Unsupervised learning is a black box where certain variables are given as input and no target variables. Machine learning model then groups data with respect to its own reasoning. The advantage of unsupervised machine learning is that algorithms work through humongous data set and often find patterns and relationships.
Reinforcement learning is subjected to specific rules like in a game. There is an environment where the learning (game) takes place and in the end, there is a final victory. As the algorithms start running the code, they innately try different strategies and learn from their previous experience to maximize their output. The best application of reinforcement learning is Google Alpha.
Deep learning can be used in all three types of machine learning but mostly used in supervised learning. This technique utilizes artificial neural networks and is used in classifying objects based on their features. Say, for example, it can be used to categorize pictures different objects with high precision.
Next question arises what kind of strategy organization take to adopt machine learning?
The first strategy would be building a machine learning strategy from the scratch. But research suggests that only 10 percent of machine learning project succeed which indicates it is a risky option.
The second strategy would be engaging more in machine learning with the help of cloud engines from Google, Amazon etc. But as every coin has two sides, the cloud will limit the type of modeling for the projects which add constraints for deeper analysis. This implies that complex machine learning projects require custom solution development.
The third strategy would be acquisitions and mergers. For this strategy, consider the perfect example of Disney’s acquisition of Pixar where Disney got a cutting-edge enhancement in their technological dimensions. So the big companies could pursue the action of acquisition and mergers of other firms to enhance their machine learning and artificial intelligence quotient.