When human and machine intelligence meet
All businesses need intelligence to improve revenue, reduce costs, find efficiencies, or accurately predict customers’ needs. Traditionally, the minds of humans have exclusively provided intelligence in a business context; however, as demand for products and services increased, so too have the market for more human brainpower. Fast forward to 2021, where businesses can engage in artificial means of gaining product, service, or customer intelligence with less effort and less dependence on human intellectual input.
The views on AI are controversial and opposing. Some think AI will replace mundane human activities. In contrast, others believe AI will replicate humanity into machines – the consensus isn’t clear. As a result, AI has been reserved for solution-specific instances (to predict potential outcomes and patterns, automate tasks, and improve machine and human communication).
The reasons vary from distrust to the general understanding of AI and how it will benefit the business. Also, the specific skills required to build machine learning models and the amount of data needed upfront. The perception surrounding AI is that it is reserved for tech-heavy industries.
At IoT.nxt, we have a different view of AI – It should offer value to businesses, understand their customers better, and improve the product experience. This leads to a more competitive, efficient, and profitable business.
What is AI, ML and Deep Learning?
In layman’s terms, Artificial Intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.
Classical, or “non-deep”, machine learning is more dependent on human intervention to learn. For example, human experts perform feature engineering to understand the differences between data inputs, usually requiring more structured data to learn.
To inform its algorithm, deep learning can leverage labelled datasets, also known as supervised learning, but it doesn’t necessarily require a labelled dataset. It can ingest unstructured data in its raw form (e.g., text, images, or even IoT sensor data). It can automatically determine the hierarchy of features that distinguish different categories of data from one another. Unlike machine learning, it doesn’t require human intervention to process data, allowing us to scale machine learning more interestingly.
Why you should care about AI
AI solutions include tailored advertising, predicting human or livestock disease incidence and much more. At IoT.nxt, we have use cases where AI offers our clients direct value in the following areas:
- Predictive Maintenance (Equipment failure prediction is one part of this. Other tasks can also be augmented with AI, such as root cause analysis, remedy prescription and maintenance scheduling).
- Pattern Recognition – Anomaly or outlier detection
- Image Analytics – Object detection, recognition, and counting.
- Quality inspection
- Supply chain optimisation
- Manufacturing process optimisation
The industries that currently utilise AI in one form include healthcare, agriculture, finance, manufacturing, automobile, surveillance and robotics.
The examples above prove a widespread adoption of AI solutions across a broad range of industries. However, it could be daunting for any business that needs it but does not know where to start. IoT.nxt aims to remove the complexity and challenges, including AI and ML skills, understanding the technology, benefits and uses, and access to good quality and quantities of data.
AI, ML, and ultimately data analytics allows companies to reduce costs by finding more efficient ways of doing business, better customer service and improved decision making.
“Our data & analytics team’s top goal is to generate value driving insights and continuously develop tools to make this a frictionless experience for our clients.” – Ricardo Ludeke, Data & Analytics Manager at IoT.nxt
AI in IoT.nxt solutions
At IoT.nxt, we develop bespoke AI solutions ranging from unsupervised pattern recognition models to detect the operating behaviour of devices and highlight anomalies to forecasting models that can predict energy usage and carbon emissions. Our most advanced prescriptive analytics models can be used to prescribe the best actions to take to achieve the desired outcome and actuate control back to our Raptor gateway at the edge. Depending on the use-case or problem at hand, we can develop the right model to solve the problem.
To develop these bespoke solutions, you require highly skilled resources, a thorough understanding of the problem and the technology, and good quality data. Our Commander platform sets itself apart by easily making the right data available. However, the other challenges discussed earlier can still hold back the implementation and adoption of AI technologies.
With this in mind, we are in the process of developing a model library with a range of off-the-shelf models with targeted outcomes that ultimately focus on generating value for our clients. The model library will enable users to easily configure and deploy an AI solution to solve their problems without being an AI expert. This allows clients to focus on the implementation and value generation instead of acquiring skilled resources or understanding complicated technology to develop an AI solution from scratch.
Prediction for site carbon emissions and cost savings.
Some examples and benefits of the AI models that will be available in the model library include:
- General anomaly and outlier detection models will enable users to detect anomalies in the operation of their equipment or to detect outliers in a fleet. This allows users to effectively manage and maintain a fleet of equipment by identifying potential issues in operation before complete failure and take corrective actions to return the equipment to normal operation.
- Time-series forecasting and classification models will enable users to predict the future trend or categorise their equipment’s specific behaviour at a point in time. Knowing the future trend could be helpful in planning and decision making, while categorising how different equipment is operating could be valuable in identifying areas of concern to prioritise and focus our efforts.
- Our video intelligence models will enable a range of features, including object detection, counting, and tracking, which can monitor several video feeds for potential security issues.
- Other planned AI models include a predictive maintenance solution, an energy management solution, and a production yield optimisation solution. These are more comprehensive end-to-end solutions that will enable more complex workflows.
The model library will empower users to generate insights using their own data, focusing on what matters most, effectively reducing the time to value and further saving users time by getting rid of repeated tasks.
AI for any business
The fear that AI will develop its own consciousness and control humans should remain in Hollywood movies. However, with IoT.nxt, you control AI and not the other way around. Therefore, customers shouldn’t have to spend a vast number of hours or hire skilled data scientists to realise the value of AI.
We want the interaction with ML to be as low-touch as possible; we envision a user with a dataset and a need, for example, to improve efficiencies or detect failure trends. And we want the customer to apply our pre-set ML models to their data with a specific outcome in mind. With the soon to be released model library, more businesses can benefit from the powerful capabilities of AI.
Ultimately, the core of our IoT platform is to make the right data available.
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