Are you ready for AI?

Implementing Artificial Intelligence (AI) in operational processes of critical applications is a strategic decision that can significantly boost the efficiency and competitiveness of an organization. However, before diving directly into the full implementation of AI, it is crucial to carefully consider the associated phases and cost-benefit, as well as adopting intermediate approaches for more efficient control.

I often mention in my presentations that attempting to implement Artificial Intelligence before establishing a reliable data collection framework and a proactive alarm control system for the operational team is equivalent to trying to run a marathon without knowing how to walk or run. Like any virtuous cycle, planning and intelligent focus are necessary. Many IoT projects simply do not materialize because they are overly ambitious and pose a financial and operational risk to companies.

Generally, AI systems are highly specific, focusing on a single aspect or problem of the overall application. The ideal scenario involves creating an ecosystem where AI can enhance operational intelligence with various functions necessary for a specific application.

For example, an AI system capable of identifying the lifespan of an electric motor through vibrations, temperature, and noise may not have the ability to recognize variations in water supply or temperature increases in power buses, which are equally crucial to the operation.

To achieve this goal, it is crucial for companies to make long-term choices. In other words, they should opt for interoperable modular systems or platforms that allow horizontal growth without the need to acquire a new system for each new application, enabling integration with any AI of interest for any given application.

It is quite common for teams and executives, driven by board pressure and immediate results, to make dead-end decisions. A specific and closed AI system may generate impactful gains in the short term, but in the medium term, it may become unfeasible compared to various other specific systems for different applications.

Implementation Phases of AI

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) has ushered in a new era of technological innovation. Simply put, AI in IoT involves integrating intelligent algorithms into connected devices, enabling them to process data, make decisions, and autonomously respond to changing conditions.

The process of reliable AI implementation must be executed with care and attention. There are thousands of cases where the expected outcome was not achieved and are typically associated with failures or inadequate speed in the deployment of project phases.

The rigor and cyclical execution in each of these phases are essential factors for the success of AI implementation and are associated with machine and human resource costs.

Always keep in mind that this is a continuous learning project between machines and humans where process maturity is always evolving.

Reliable Data Collection

The use of AI systems is inherently linked to the availability of reliable data. The essential first step involves collecting relevant and trustworthy data for the task at hand.

These datasets can include information from sensors, controllers, machines in general, and other sources of information.

This is a crucial moment in the project, as the choice between an intelligent modular IoT platform and a specific data collection system can make the difference between a future of expansions and a dead-end.

Processing Reliable Data

After collection, the data undergo real-time processing, being organized and structured. Techniques applied include handling missing data, normalizing values, and removing outliers. This step is fundamental to ensure that the AI model is trained with high-quality and reliable data.

Extraction of Relevant Features

The next step involves extracting features from the dataset, which includes selecting and transforming relevant aspects (features) of the data essential for the AI model's task.

For example, in natural language processing, features can be words or phrases, while in image identification, features can be specific patterns or textures. The goal is to represent the data in a way that captures its important characteristics.

Training and Machine Learning

Machine learning is a subset of artificial intelligence that focuses on creating models capable of learning patterns from data.

The AI model is exposed to a subset of data (training data) and learns to make predictions or classifications. The model's performance is evaluated using a loss function, measuring the difference between its predictions and the actual results.

Common machine learning techniques include:

- Supervised Learning: The model is trained on a labeled dataset where correct outputs are provided.

- Unsupervised Learning: The model learns patterns without labeled data.

- Reinforcement Learning: The model learns through trial and error, receiving feedback in the form of rewards or penalties.

Optimization algorithms, like gradient descent, are then used to iteratively adjust the model's internal parameters, minimizing loss and improving performance.

The collection of information from a reliable data source is fundamental because AI models are trained based on historical data, and their reliability depends on the quality and representativeness of this data. If training data is biased or incomplete, it can result in unreliable predictions or decisions. Ensuring the reliability of AI models is crucial, especially in critical applications where security is a primary concern.

Additionally, the training process involves feeding input data into the model, calculating the predicted output, comparing it with the actual output, and adjusting the model's parameters to reduce prediction error. This iterative process continues until the model achieves satisfactory performance on the training data. Hyperparameter tuning, regularization, and other techniques are employed to finely adjust the model's behavior.

Validation and Further Testing

The next phase involves validation and additional testing. The trained model is evaluated on new, previously unseen data to assess its generalization ability. Testing involves measuring the model's performance on a separate test dataset, while validation involves adjusting hyperparameters and model architecture based on performance metrics.

This step ensures that the model not only memorizes the training data but can make accurate predictions on new, similar data.

Inference

We then move to the inference phase, where the trained model is used to make predictions or classifications on new, unseen data. The model's ability to generalize from training data to real-world scenarios is crucial. In many applications, real-time or near-real-time processing is required for timely decision-making.

Ethical Considerations of the Model

Finally, we reach the last phase of the ethical dimension of AI model development, involving addressing biases in data, ensuring transparency in AI decision-making processes, and considering the broader impact of applications.

The development of AI requires careful consideration of fairness, responsibility, transparency, and ethics throughout the entire model lifecycle.

By understanding and carefully implementing these components, developers aim to create AI systems and models that are accurate, reliable, and ethical. Ongoing research and advances in AI techniques contribute to continuous improvement in these processes.

What would be the short-term solution?

As discussed earlier in this article, implementing Artificial Intelligence (AI) is a process that demands a structured approach, and it is essential to treat it as a complex project.

Our stance is favorable to AI, recognizing its results as unquestionable when conducted professionally and judiciously.

However, it is crucial to emphasize that results can be catastrophic when execution is done inadequately.

Unfortunately, often due to external pressures, teams do not have time to implement an AI process properly and safely, compromising the project's results.

I firmly believe that the most effective approach for companies looking to maximize their operational processes and identify short-term issues is the gradual adoption of IoT through a modular multi-sector platform. This platform should not only allow immediate intelligent parameterization based on control limits, calculations, and behavioral changes but also enable intelligent proactive notification, contributing to a holistic view of the process. It should represent a comprehensive solution to challenges associated with AI implementation.

This approach may involve simultaneously preparing the platform for effective AI implementation, even through specific plugins, ensuring alignment with data and company maturity. Efficient synchronization of these elements is crucial to ensuring that AI implementation is not only successful

but also adaptable to the organization's specific needs, providing immediate financial returns from the first day of the deployment process.

In this model, a significant reduction in initial investments and the time needed for the complete execution of the project is observed. This benefit is crucial to providing security and agility in return on investment, while simultaneously mitigating the risks associated with inadequate calibration of artificial intelligence.

Conclusion

In summary, the advocated approach highlights the importance of a careful and strategic implementation of AI, emphasizing the thoughtful choice of a modular multi-sector platform that allows immediate intelligent parameterization of operations and harmonization with data maturity.

Additionally, it emphasizes economic gains by reducing initial costs and ensuring efficient return on investment in a shorter time frame, without compromising the integrity and effectiveness of the AI to be implemented.