BRINGING CLARITY TO INDUSTRY BUZZWORDS – AI AND OTHER TERMINOLOGY

Artificial Intelligence (AI) has become a buzzword, however it is often misunderstood and misrepresented in its true capabilities. In this Questions and Answers session with IT expert Peter Spring at Mettler-Toledo Product Inspection, we will unravel the industry buzzwords, clarify AI’s distinction from Machine Learning, and explore how these technologies are transforming the food industry.

 

What is AI? Is it here already in the food industry? Where can we see it?

Simply put AI is intended to think on its own in a manner which matches or surpasses human intelligence. It is designed to learn and adapt, to make a decision tomorrow that is better than today[i].

 

To do this, AI needs a lot of data – it involves the utilisation of advanced algorithms and models to analyse the vast amounts of data, identify patterns and derive meaningful insights. Unlike traditional computing, AI systems can handle complex tasks, solve problems and exhibit a level of intelligence that enables them to respond effectively to diverse scenarios.

 

While AI is present in high-end systems and applications, it is not yet significantly impacting production lines. However, it is extensively used for analysis, modelling and prediction. For instance, in food safety, AI can enhance the security of supply chains, increase productivity and detect machine issues before they occur.

 

What is Machine Learning?

Machine Learning is a subset of AI that focuses on the development of algorithms and statistical models that enable computers to learn from data and improve their performance. Often it focuses on a specific task such as a voice recognition system. The system may sound intelligent, and you may think it is AI, however it will not have an advanced understanding of the language it simply listens for key sounds and on detecting these will perform certain tasks[ii].

 

Machine Learning algorithms are designed to simply learn and adapt from the data, refining their performance over time. An example of Machine Learning in the food industry is the Predictive Maintenance feature on some production machines. Within these systems, data from machines is analysed to predict potential breakdowns and optimise part replacements, ultimately reducing downtime.

 

How do they differ?

While Machine Learning is a component of AI, AI encompasses more than just learning from data. AI possesses the ability to think, reason and adapt to new situations, enabling it to come up with novel solutions that have not been pre-set. Machine Learning, on the other hand, focuses on training models on data to make predictions or perform tasks.

 

Why is there confusion?

The confusion surrounding AI stems from its wide application and the misuse of the term. Often, AI is used interchangeably with Machine Learning or other technologies, leading to misconceptions about its true capabilities. It is crucial to understand that AI represents intelligent decision-making and problem-solving abilities beyond mere data processing.

 

What benefits do they bring?

Both AI and Machine Learning offer numerous benefits to the food industry. AI can enhance food safety and security, streamline logistical processes and improve productivity. By automating manual tasks, companies can leverage AI to make their workforce more valuable by training employees to work alongside intelligent systems. Machine Learning, in particular, enables Predictive Maintenance, optimising machine performance and reducing costly breakdowns.

 

Are there disadvantages?

While the potential of AI is immense, there are certain risks and challenges. The quality of data fed into AI systems is paramount, as “rubbish in, rubbish out” applies here. Incorrect or biased data can lead to flawed decisions. Additionally, if AI systems operate autonomously without proper safeguards, a small error or malfunction in one part of the system can have cascading effects. Ensuring human oversight and implementing safeguards are essential to mitigate these risks.

 

How far are we from having AI in this industry?

AI is already making its presence felt in high-end systems and applications within the food industry. However, its widespread integration into production lines is yet to be realised fully. As technology advances and connectivity improves, AI’s potential for transforming operational processes will continue to increase.

 

How can Product Inspection work with AI?

Product Inspection technology solutions can play a crucial role in enhancing AI capabilities. By integrating Product Inspection with AI systems, comprehensive data from multiple applications, devices and processes can be accessed, enabling more informed decision-making. Our Product Inspection technology provides a vast array of data related to food production processes, such as quality control, contamination detection and package integrity. This wealth of data can be analysed by AI algorithms to identify patterns, predict outcomes and optimise various aspects of food production. For example, AI can utilise our data to optimise energy consumption, identify environmental influences and create predictive maintenance schedules, thereby streamlining operations and enhancing overall efficiency in the food industry.

 

What is the difference between digitization and digitalization in food safety?

In the context of food safety, digitization refers to the process of converting analog information into digital format. This includes the transformation of manual processes into automated systems, creating a digital footprint of various operations within the food supply chain. On the other hand, digitalization is a broader concept that encompasses the integration of digital technologies into all aspects of business operations, leading to fundamental changes in how business is conducted. In the realm of food safety, digitalization involves leveraging advanced technologies, such as AI and Machine Learning, to optimise processes, enhance transparency and aid in compliance.

 

What is Food Safety Digital Maturity?

Food Safety Digital Maturity refers to the progressive digitalisation of food safety processes within the entire food supply chain. In essence, it involves the collection, storage and sharing of data related to food safety practices. This digital transformation spans from the initial stages of production to the end consumer, incorporating advanced technologies to enhance transparency, compliance and efficiency. This journey towards digital maturity enables the industry to move away from traditional manual processes, embracing automation and connectivity for better traceability and regulatory adherence. It signifies a pivotal shift towards a more modern, data-driven approach to managing food safety.

 

Why is Food Safety Digital Maturity important?

First and foremost, Food Safety Digital Maturity aligns with the growing demands for transparency in the food supply chain, driven by both regulatory bodies and consumers, particularly retailers. The importance of this digital transformation lies in its ability to provide enhanced traceability and track and trace capabilities, ensuring swift and targeted responses in the event of a food safety incident or product recall.

 

From a business perspective, embracing digital maturity can translate into increased productivity, efficiency and profitability. The automation of processes, coupled with continuous monitoring, reduces production costs by identifying and rectifying negative performance trends promptly. The streamlined access to critical production data facilitates quicker decision-making, contributing to operational efficiency and resource optimisation.

 

The digitalization of food safety processes mitigates risks associated with product quality issues. Automated, paperless documentation of rejected products not only helps to facilitate compliance but also aids in trend analysis, potentially reducing rework costs.

 

The significance extends to audit and compliance management, where digital systems outperform manual processes. Continuous monitoring of machinery and processes enhances data capture, making audit preparation faster, more efficient and less error-prone. This not only saves on personnel costs but also acts as a preventive measure against potential fines.

 

In essence, Food Safety Digital Maturity is vital for food manufacturers as it promises tangible benefits in terms of cost reduction, operational efficiency, and regulatory compliance. Beyond the quantifiable aspects, it fosters complete traceability, better brand protection and improved relationships within the supply chain, positioning it as an indispensable element for the future of the food industry.

 

What is the next big thing in IT within the food industry?

The digitization of the food industry, driven by initiatives like Track and Trace systems, holds immense potential for AI integration. By leveraging AI, the industry can enhance food safety, improve productivity and optimise logistical processes. Furthermore, the seamless integration of AI with existing systems and applications can enable comprehensive data analysis and informed decision-making, leading to greater efficiency and automation.

 

Conclusion

AI and Machine Learning are powerful technologies with the potential to revolutionise the food industry. While AI represents the pinnacle of intelligent systems capable of adaptive decision-making, Machine Learning focuses on data-driven predictions and tasks. By harnessing the benefits of AI and Machine Learning, manufacturers can enhance food safety, streamline operations and make better-informed decisions. As technology continues to advance, the future holds exciting possibilities for AI integration, leading to a more efficient, productive and safe food industry.

By Peter Spring, Product Manager ProdX™, Mettler-Toledo Product Inspection

About ProdX™: www.mt.com/prodx-pr

[i] https://www.splunk.com/en_us/form/5-big-myths-of-ai-and-machine-learning-debunked/thanks.html

 

[ii] https://www.splunk.com/en_us/form/5-big-myths-of-ai-and-machine-learning-debunked/thanks.html