Artificial Intelligence: The Precise Discovery of Governing Patterns
Artificial Intelligence (AI) is essentially the imitation of human brain functions when processing information inputs. The human brain consists of nearly 85 billion neurons, which are intricately connected. The way these neurons interact is influenced by environmental information and corresponding patterns. In general, human intelligence involves the formation of neuronal connections in response to stimuli, followed by the recall of these patterns when similar stimuli occur in the future. AI aims to replicate the brain’s neurons by creating computational units and establishing logical, fuzzy connections between them to form a structure that mimics human brain intelligence. Fundamentally, AI consists of numerous computational neurons, and what creates intelligence is exposing these neurons to experimental data, forming patterns, and then correcting errors until the best predictive structure is achieved.
Unfortunately, AI’s understanding is limited to what it has learned and, unlike the human mind, it lacks creativity or the ability to generate ideas from nothing.
AI’s Computational Power and Cost Factor
What determines an AI’s ability to respond to information inputs is the number of parameters in the network and the quality of its training. The more diverse and extensive the training, the more precise the AI will be in its responses. For example, a weather forecasting AI system trained with more weather data over a longer period will be more accurate in predicting the weather for a region. For this reason, such systems are continuously updated with real-time information to reduce prediction errors. However, it is important to note that increasing the AI’s size and training duration requires more computational power, which consequently raises costs.
Artificial Intelligence: Human-Machine Interaction and Automation
Today, AI is expanding in two key dimensions. The first, which has the most widespread applications, is the automation of machines. In this context, “machines” refer to any tools performing a specific service, such as control cameras, self-driving cars, autopilot systems, and cloud servers. The second dimension, which has gained more public attention in recent years, involves direct human-AI interaction. This is exemplified by tools like ChatGPT, known for their ability to respond accurately and generally to a wide range of user inquiries. These tools have expanded rapidly, offering services like translation, editing, understanding, and the creation and editing of images, music, and videos.
AI and Imitating Industrial Expertise
Although AI currently plays a significant role in controlling industrial robots, the ideal application of AI in industries is to consolidate expert knowledge and scientific information to provide comprehensive solutions to industrial challenges in the form of a super-expert advisor. Expert knowledge is crucial because, in many industrial activities, particularly in food processes that involve a variety of raw materials and biological factors, there is no predefined solution for decision-making and achieving the desired quality. Decisions are made based on experience and data. With the advancement of AI, this experience can now be transferred to AI systems, allowing for sharing and collaboration among individuals.
The Future: AI and Food Industry Consulting
One of the challenges in the food industry is controlling microbial contamination. In some cases, products become contaminated with multiple pathogens, requiring data collection, tracking, and resolution of the issue. A microbiology expert’s involvement is key to quickly addressing contamination, but such experts are not always available. In such situations, an AI microbiologist can combine scientific knowledge and observations to provide the fastest solution for identifying and eliminating the contamination to managers.
