Artificial Intelligence and Machine Learning Drive The Future

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One of the disruptive technologies that has gained increasingly more attention after the turn of the century is Machine Learning. Machine Leaning – closely related and usually considered as a subfield of Artificial Intelligence (AI) – is the process of automatic detection of usable patterns within data. The detection of these patterns is performed with the help of machine learning algorithms which are specifically tailored to deal with complex and large data sets. Such powerful algorithms have the potential of drastically revolutionizing the way of doing business and how businesses operate. With this article I will provide an overview of opportunities that machine learning algorithms and Artificial Intelligence (AI) pose to the business environment.

AI-powered Maintenance Robots an Anomaly Detection

Many industrial production environments require periodic maintenance of their industrial machines. This has become especially important with the increasing awareness concerning workplace safety and climate change. However, most of the times, the critical components of such installations are difficult to reach and maintaining them requires a considerable effort from multiple service engineers, drastically increasing the cost that is associated with production and maintenance.

However, in the future, the work of several service engineers could become fully automated with the use of AI-powered robotics which are able to inspect, maintain or even repair production installations. Such smart robotics have the capability of independently navigating to the required location and perform certain actions which would otherwise be conducted by a human worker. This allows production facilities to reduce the workload of personnel, save budget and increase the safety of their workers without reducing the reliability of their production plant.

In addition, such industrial installations could be equipped with smart sensors which are able to collect data about production volumes, mechanical vibrations and temperature. Such data can be analyzed by machine learning algorithms with the purpose of finding production anomalies. For example: detecting a change in production volume may indicate a leak within the plant, a change in the mechanical vibration pattern of a machine may indicate that certain parts need replacement, and variations within the temperature data may suggest that the production plant needs to be calibrated.

Digital Twins Technology to Aid Model-Based System Engineering

Strict safety regulations and higher efficiency demands are resulting in an increasingly complex engineering environment, making the use of regular Computer Aided Engineering (CAE) systems cumbersome and inefficient. Therefore, new innovations such as Digital Twins, Machine Learning, and Artificial Intelligence (AI) are becoming increasingly relevant to Model-Based System Engineering (MBSE) and machine operations. Like traditional CAD-based systems, the Digital Twin technology represents a virtual model of a physical system such as industrial machines (e.g., gas turbines, mechanical drives or gas compressors). However, by linking a digital twin with sensory output data from the physical environment (usually captured by IoT sensory data), the technology allows the real-time monitoring of individual machine instances in a dynamic way

This linkage allows manufacturers to run simulations on Digital Twins, based on data obtained from physical systems that are already operational. In this way, Digital Twins can be provided with the following information during simulation:

  • Individual service and maintenance history
  • Operation history and environmental data capture by IoT devices
  • Product design history
  • Machine configuration during operation

Simulation results can then be compared to real-world outcomes (from the operational physical system) which – along with the CAD design files – can be analyzed by machine learning models to provide valuable insights about the physical twin and its possible design improvements and maintenance requirements within their specific production environments.

Speech Recognition using Recurrent Neural Networks

One of the technologies that is powered by Artificial Intelligence (AI) and Machine Learning and that has experienced a great leap forward is Speech Recognition. Speech recognition is the ability of a machine or program to receive, process and interpret spoken sentences, thereby enhancing the interaction between human and machines. Speech recognition poses many possible applications within the business environment, a few of which will be discussed in greater depth below.

The first class of speech recognition applications focuses on reducing costs for tasks that are currently being accomplished by a human attendant. An example of such task is the automation of operating services. Whereas before, calls to operating services were handled by human employees, the implementation of voice recognition software has enabled the full or partial automation of this process. Incoming calls are handled by speech recognition systems which detect the caller’s problem by asking it a series of relevant questions. Based on the answers, the caller may be redirected to a human attendant who is specialized in the matter regarding the caller’s problem. This has enabled operating services to increase efficiency and provide a better customer service – experience – using less resources.

Powerful speech recognition software can perform in-depth datamining on the audio files obtained from customer calls. Such data-analysis may provide key demographic information about the caller such as gender, age, accents, emotion and sentiment. This information allows businesses to gain powerful insights regarding their customer base, launch highly targeted marketing campaigns and improve support and sales performance.

Another application of speech recognition within a business context is the use of automatic text transcription software. Such software allows to convert audio and video fragments into perfectly accurate text documents which contain the spoken sentences from the imported file. This may be useful to acquire transcriptions of board meetings, conference calls or shareholders meetings in order to easily transfer this information to people which were not able to attend the event.

The use of Data Lakes for Artificial Intelligence (AI)

The fast-rising adoption of disruptive technology in all aspects of human life has resulted in a rise in connectivity between persons, customers and businesses and has brought with it in an increase in data transfer between different sorts of devices. Together with ever-improving storage capacities and far-evolved data collection techniques, this has led to large amounts of data becoming readily available to businesses.

However, businesses which enter the big data landscape should find solutions to efficiently manage such large amounts of data. This is usually done by using so-called Data Lakes: systems which allow the storage of raw, unstructured data – regarding customers and businesses – which the company might use at a future point in time. Data Lakes should not be confused with data warehouses (also referred to as Datamart), which are commonly used systems that store structured data ready for analyzation or for answering simple queries.

The advantages of adopting a Data Lake is that all data that enters the company walls will be stored for later use. It often happens that data points may not be useful at a certain point in time but may provide value to the business within the near future. In this way, Data Lakes provide a way to store this information and make it readily available in its original format when needed. keeping such large data records may be expensive or may require a large part of the available data storage capacity. Therefore, it is recommended that companies first quantify the costs and the benefits of implementing a data lake before proceeding to action.

Artificial Intelligence (AI) for IT Operations (AIOps)

IT and technology teams are experiencing at firsthand how the increase in data is transforming their jobs, requiring them to use new tools to monitor data, identify issues and propose possible solutions. However, it has turned out to be extremely difficult to manage these data streams in an efficient way, usually leading to delays when important issues need to be identified and resolved. Technologies such as Artificial Intelligence and Machine Learning have turned out to be very effective in aiding IT teams in managing and resolving IT related operations problems. This has resulted in an entirely new field, usually referred to as AIOps, where Artificial Intelligence is used for managing IT Operations.

AIOps platforms are able to partly – or fully – replace certain parts of IT operations such as monitoring performance, analyze IT-related events, and perform IT service management by using big data analytics and machine learning methodologies. In this way, they relieve the workload of IT personnel, allowing them to focus on IT activities that offer more strategic value to their business.

Machine Learning for Customer Intelligence and Marketing

Machine learning algorithms have proven to be effective in rapidly analyzing large numbers of data points and, consequently, offer useful insights that are comprised within this data. Therefore, machine learning algorithms can be employed with the purpose of gaining valuable insights into consumer behavior and to create a more effective marketing strategy. 

One of the machine learning methodologies that has proven to be especially well-suited for such purposes are recommender systems. Recommender systems, as the name suggests, are able to determine which products a certain user would be interested in, given data about the users previous purchases. Such systems are regularly used by companies such as Netflix, YouTube, and Amazon in order to provide its users with product recommendations that are aligned with their interest and needs. Implementing such recommender systems poses benefits to both customers and businesses. First, customers are targeted with products that are more closely aligned to their specific needs, therefore increasing their customer experience and level of satisfaction. Next, businesses are able to provide better customer support and simultaneously increase their level of sales due to the fact that customers are more likely to buy additional products, without having the initial intention in doing so.

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