Machine Learning: Adopt or Die

With the use of cloud computing, high-capacity systems, and high-performance computing, organizations these days can collect loads of data – often referred to as “Big Data”. But what can organizations do with all that data? This has been an important question for quite some time. Enter machine learning (ML).

Google has been blazing the path for big data for over two decades. Solutions such as BigQuery allow organizations to take advantage of data analytics, visualization, and ML.

Machine learning is a branch of artificial intelligence. It provides computers with the ability to learn based on data input in the system, without the need to be explicitly programmed. The simplified idea is that computer programs teach themselves when exposed to new data. According to IBM, “Machine learning focuses on applications that learn from experience and improve their decision-making or predictive accuracy over time”.

Machine Learning Use in Business

Use Case 1: Amazon

Amazon utilizes machine learning to provide a very personal user experience. Based on a customer’s past purchases, Amazon’s systems use machine learning with predictive analytics to determine what you might like. They also predict whether a given user will become a paying customer, and much more. The more a customer uses its app, the more Amazon’s systems learn.

Use Case 2: Cyber Defense

Neuromorphic technology can be used to increase threat detection capabilities. This neural network technology can be programmed and trained to perform the data analysis within a network intrusion detection system.

With this analysis performed, a neural computing system can take measures it has been trained to take and protect the network. The neural network can then dynamically and autonomously reconfigure itself. After reconfiguration of a system, the neural network can be trained to document the change to a change database.

Because the neural system has reacted and reconfigured autonomously, it has effectively prevented a risk. This readjustment and improvement is critical for the healthy maintenance of a system. With the use of neuromorphic chips in information systems, specifically network systems, it can learn from an event, and use that learned capability to apply protection measures to other events based on cognitive learning.

With this cognitive learning capability, cyber defense systems can perform highly sophisticated searches within a specific domain. These systems can then find relevant information and patterns, and harvest insight from continually updated data. Partnerships with various organizations will cause a neural system to think better and process better than any human brain by itself. 

Machine Learning Benefits

Here are two main ways organizations can benefit from machine learning.

Simulations and Predictions

Utilizing machine learning and predictive analytics, organizations can run simulations against a data set to determine the best route to take.

Enhance User Experience

Machine learning can be used to provide customers with an experience that is unique to them much like the Amazon app provides recommendations and displays products that are most relative to the customers purchase habits.

Adopt or Die?

Large organizations are using machine learning to stay ahead of the competition using simulations, predictive analysis, etc. However, companies such as Google (Google Cloud), Amazon (AWS), and Microsoft (Azure) have made services available and affordable for organizations of any size to utilize the power of ML.

The issue with many small organizations is that they often lack the data to support ML. Although, there are freely available data sets on services such as Kaggle, those data cannot always be guaranteed to be accurate nor representative of the data required for the intelligence the organization is seeking.

Organizations must start asking what data they should be collecting and what do they seek to learn with that data. As an example (simplified): A vehicle maintenance company might start collecting component failure data based on vehicle make, model, year, and mileage to predict failure rates. This same predictive analysis can help in establishing an effective inventory policy to ensure availability of necessary components.

How does, or can, your organization use machine learning to stay competitive?