Artificial intelligence has become synonymous with everyday life. Whether it’s how we do our daily chores or our interaction with the internet. AI seems to be an integral and constant part of it all. So it is no surprise that over time, Artificial Intelligence has also become a part of our industrial world. A world characterized by consistent competition amongst various companies. Also, an unending pursuit of launching products. They are innovative and provide companies with an edge over their competitors.
Industrial Artificial Intelligence
To institute AI into industries means a gigantic shift from the rather traditional methods of training staff/chains to inculcating a strategy. Whereby learning is rather automated and without any issue. Which also contributes to its predictive ability. Ultimately the transformative impact it can have across each industry.
The transportation sector companies are using AI. To predict arrival times or any issues/complications that might arise, by farmers. To figure out what kinds of food to grow in particular seasons and how to grow more of them. Also, in use by the healthcare sector. To provide insights into patients, detect predictive patterns. Moreover, learn how to effectively treat certain diseases.
With the implementation of AI into the business mechanisms. Owners expect to have a huge impact on the growth and productivity of the business. This would be complemented by a rise in innovation. Resulting in the creation of jobs that would help complete the process. Not only this, but companies expect that through AI, they’ll be able to solve strategic challenges within the organization. Also, provide a unique experience unlike any before.
However, to train AI in the industrial world and prepare it for its incorporation. Organizations need to gather data sources that could push start this move. This ‘training data’ so becomes the most important tool for organizations working in the industrial arena.
Why Are Data Sources Essential?
Any predictive or fully automated system will need data. So that it can be trained correctly. If there is no data, then how can you train anyone? Moreover, it is the quality of that very data that helps define the operational results that the AI system will deliver. The larger the work for the AI, the larger the dataset has to be for the AI.
Still a little confused? Think of a dog.
The first time you bring a pet dog into your home, it is untrained. It will do its business anywhere and everywhere and will not come to you if you call out their name. However, if you being training the dog into how it’s supposed to act, and what name it responds to. You will see over time that the animal ‘learns’ how to behave. The better you train it, the better its behavior becomes.
So the AI, therefore, is simply put, like a dog that requires training.
Data Flow Process
When introducing an AI project in an industrial context. Organizations need to consider the general workflow required to build a usable AI.
Historical Data
The very first step is to gain access to all relevant historical data. Which may show up in the form of multiple files or databases with the required information. This huge collection of data is what we call the “Data lake”. This lake contains all forms of unstructured and structured data. Which generally is in its raw format i.e. no preprocessing of the data has taken place. The entirety of this data has been retrieved from relevant sensors or historical recordings. So will have many formats ranging from images to video to text and even just plain audio.
Preprocessing
The second step is where the preprocessing takes place i.e. the data processes, looked at, visualized and the quality assessed. Here the data is cleaned and reduced. In an attempt to ensure that the raw data now transforms into data that is more attuned. To the objectives that the business is trying to achieve.
Once this data has been processed. It forms the base/foundation for developing predictive models that will shape AI. Here the machine learning algorithms are applicable. To learn from the data, which learns, trains, practices. Then develops a final form for the model.
Once the AI components are ready to go. They integrate on an enterprise scale. This integration is rather twofold.
The first aspect deals with embedding AI into devices and hardware solutions present on machinery that is available on site. The second is the integration into enterprise systems. In the form of software components.
Either way, these systems (whether software or hardware) have AI integrated into them. Hence enabling them to perform much more effectively.
Industrial AI Applications
Having read so much about the categories of applied industrial AI and the potential it holds for industries. There’s no doubt that AI has the potential to change not just products/services. Also how they’re delivered to customers.
One field is the manufacturing industry. Whereby you could create tools that could self-diagnose themselves. Improve the overall performance during the operational installation. This leads to improvements in the reliability of the tool. While enhancing the longevity of the machines themselves.
Data collected from automation can also be available. To fuel what researchers call “hyper-automation”. Whereby industrials can boost the processes by utilizing the gathered data/insights. To further develop and grow their processes. An example would be the usage of data from autonomous driving cars or smart robots. tTo train industrial autonomous vehicles/machines. Using already gathered data from similar subjects to train new subjects.
Engineering systems can also greatly enjoy AI by using it to discover more tools/resources. Identify root causes of problems and eliminate any/all risks using AI itself. Many critical areas can provide a lot of important data through sensors and logs. Here is where AI could step in to detect data that would’ve otherwise gone undetected. Also, create patterns that would be rather “unexpected”. Establishing relations between past incidents and current readings.
Now that you’re up to speed with industrial Artificial Intelligence. You’re probably thinking of how you can get your hands on some open data sources. To fuel your journey into incorporating AI – well don’t worry, we’ve got you covered.
Open Data Sources For Industrial AI
To find open sources suited to your individual AI projects. It’s essential to use specific search engines, catalogs, and aggregators. With these tools, you’ll be able to search through many repositories. Also, find relevant data sets rather quickly.
These operate like classic search engines. You type what you’re looking for, and you get a plethora of options.
The Google Dataset Search
By accessing this, you will get an overview of freely available data sets. Coupled with not just simple links to the repositories. Also provides you direct information about the data formats provided and the way that data can be accessible. At the moment, it is home to over 25 million publicly available datasets.
The Registry Of Research Data Repositories
A text-based search that is rather comprehensive. This source offers you the opportunity to explore graphically. Conducting searches under the “search by subject” tab to find open data. Yet this will only provide you with links to the repositories. Where you will have to continue your search. But it is great at building a bridge. Actually connecting you to the repositories that you might have otherwise been unaware of.
Kaggle & Unearthed
These are relatively new entries into the world of open-source data platforms. They offer industry-related repositories. Focus on solving data science challenges and providing a service unlike any other.
AI Is Here To Stay
Industrial AI is only beginning to create ripples in the market. With time, we can’t wait to see what it’ll bring to the table. Industry leaders globally are in favor of the argument for implementing AI into their industrial processes. They have picked up speed to integrate them into their work. Use this time to get ahead of their competitors.
So, what are you waiting for?
