Major Fields of Artificial Intelligence and Their Applications

Artificial Intelligence (AI) has moved beyond familiar applications like machine translation and image recognition. It is now solving highly complex problems in engineering, from optimizing manufacturing workflows to automating early stages of the design process.

Machine learning (ML), a part of AI, drives this shift. With large datasets we update daily, ML algorithms reveal patterns and generate solutions unattainable with traditional programming. For example, generative AI can propose new designs for objects or structures, starting with historical data as input. These capabilities reshape how engineers approach tasks that once relied exclusively on human expertise and traditional computer programs.

Progress in computer systems and hardware, particularly GPUs, has accelerated the adoption of AI tools. These tools now assist human intelligence in solving complex problems, such as predicting failure points in materials and refining product designs to meet strict efficiency criteria.

This article examines the exciting ways AI can perform tasks to assist engineering or life sciences. AI has transitioned from theory to a powerful ally in problem-solving. We will discuss how optimization techniques in Machine Learning can enhance predictive maintenance and boost product design productivity.

This article discusses the current status of weak AI or narrow AI. Weak AI excels at one task. It doesn’t “understand” the task it performs; it simply follows patterns or instructions based on processing data supplied for its training. Virtually all current AI systems, including virtual assistants, fall into this category.

Intelligent Machines. How Do Machines Learn?

Machines learn through several methods. Learning is classified into three categories: supervised (labeled data), unsupervised, and reinforcement learning. Machines can mimic specific aspects of human intelligence, such as recognizing an object. The foundation of learning processes data statistically. This enables various applications, such as image recognition, natural language processing, or self-driving cars.

Supervised Learning Vs. Unsupervised Learning

In supervised learning, machines are trained with labeled data to predict outcomes based on input-output pairs. Unsupervised learning dives into the data without labels, discovering inherent patterns or clusters.

Reinforcement Learning

Reinforcement learning introduces a different dynamic. Reinforcement learning involves teaching a computer to fulfill a multi-step process with defined rules. In this dynamic, an AI tool learns to make decisions through trial and error, receiving rewards or penalties that guide its learning process toward optimal behavior.

Natural Language Processing

AI leverages natural language processing techniques to understand human language. Supervised and unsupervised methods help to understand natural language, from parsing sentences to interpreting context. These methodologies enable intelligent machines to handle tasks and evolve, adapting their knowledge bases and decision-making capabilities.

Large Language Models (LLMs)

LLMs are a specific technological approach within deep learning (DL) that addresses NLP tasks. They are not synonymous with NLP but represent a subset of methods used to tackle NLP challenges. Large Language Models (LLMs) are advanced AI systems built on DL architectures, particularly transformer models like GPT. They are designed to generate coherent text, summarize long documents, and provide context-aware responses.

Machine Learning

Machine Learning is the backbone of modern AI systems. The majority of advancements in AI today are due to machine learning models. Machine Learning empowers systems to learn from data, thereby autonomously improving their decision-making capabilities.

Unlike traditional programming, where explicit instructions dictate outcomes, Machine Learning algorithms develop models by analyzing data, identifying patterns, and making predictions or decisions based on these patterns. The previous chapter outlined the various types of learning processes.

Machine learning optimizes functions over datasets.

For instance, in linear regression, “y = mx + b," y is the predicted output. We minimize the error between predicted and actual values using metrics like Mean Square Error (MSE). Methods like gradient descent adjust m and b to reduce this error.

The figure summarizes the main Machine Learning algorithms.

Applications of Machine Learning - Classification and Regression

Machine learning is essential in modern Artificial Intelligence because it analyzes data to extract patterns and facilitate decision-making.

In predictive analytics, machine learning algorithms process historical data to forecast future outcomes. Two primary approaches are classification and regression.

Classification assigns data points to discrete categories, such as predicting whether a customer will stop using a product or service.

Conversely, regression predicts continuous numerical values, such as estimating future sales trends.

In autonomous systems, machine learning enhances decision-making by processing real-time sensory input. Self-driving vehicles use computer vision algorithms to classify objects and regression models to estimate distances or trajectory paths. Similarly, drones learn from past interactions to adapt to new environments.

Other Branches of Artificial Intelligence

The primary artificial intelligence field we discuss in the article is machine learning; however, the other branches of artificial intelligence with applications in engineering are also worth mentioning. 

Natural language processing (NLP) allows machines to understand and communicate in human language. Key applications include chatbots, language translation, and sentiment analysis. NLP aids engineers in documenting and sharing knowledge, ensuring technical information is accessible. It also enhances user interaction and feedback through chatbots and virtual assistants. 

Computer Vision and Robotics

Computer vision has many applications. It allows computers to understand digital images and videos. Practical implementations are facial recognition, object detection and medical image analysis. Furthermore, it can detect defects in manufacturing processes that the human brain might overlook.

Robotics integrates AI technologies with physical systems to solve complex challenges. In precision assembly and manufacturing, AI-driven robots perform tasks and adjust to new situations without human reprogramming.

Robotics automates processes and transforms industries by designing and constructing robots and their control systems to perform labor-intensive tasks. Looking ahead, AI researchers are advancing robots with machine learning, enabling more natural social interactions.

Expert Systems

An expert system is a program specializing in a singular task, similar to a human expert. Still, in the rapidly evolving landscape of technology, expert systems may be characterized as somewhat outdated. While these Artificial Intelligence applications once effectively emulated human decision-making in specific fields, relying on a static knowledge base and an inference engine to apply rules derived from human expertise, their limitations have become evident.

For instance, one of the pioneering expert systems developed in the 1960s assisted chemists in identifying the molecular structure of organic compounds. Although revolutionary, such systems now face challenges from more advanced AI methods.

Modern applications, especially in fields like medical diagnosis or engineering, have moved towards more adaptive forms of AI that can learn and evolve without being bound to a fixed set of rules. As a result, while expert systems laid groundwork, they are surpassed by new technological advancements.

DL learns from data and manages complexities that traditional systems struggle with, such as pattern recognition in images and voices. It generalizes from examples to handle unpredictable situations and can be retrained with new data if necessary.

This gives DL an edge over expert systems that require manual knowledge updates to reflect new information. An exciting example is the decisive technology of 3D Deep Learning, which can recognize object shapes and predict their behavior with engineering-grade accuracy.

We will address this new paradigm in the next chapter.

Deep Neural Networks and Deep Learning

Neural networks are computational systems that mimic the human brain’s neural networks. The story started in the 40s with McCulloch and Pitts, who proposed a model for artificial neurons (perceptions) and artificial neural networks.

A neural network activates when its output exceeds a threshold. During training, weights are adjusted to enhance accuracy.

Key Applications of Deep Learning Algorithms

Deep learning algorithms have revolutionized how machines learn and process information, unlocking advanced capabilities across industries. Below are some key applications that demonstrate the power of deep learning.

Image Recognition.

Deep learning is a specialized branch of artificial intelligence that utilizes neural networks with many layers—hence the term “deep”—to analyze and interpret complex patterns in data. How do deep learning predictive models significantly outperform traditional algorithms?

Convolutional Neural Networks (CNNs), a specific type of neural network, are the backbone of modern image recognition systems. CNNs emulate the human brain and visual cortex by breaking down images and analyzing them hierarchically to enhance classification effectiveness.

CNNs classify images on platforms like Google Photos using large datasets to recognize objects, faces, and scenes. They can identify a dog’s breed and automatically sort it into the “Pets” folder, which helps to efficiently manage large visual data.

Speech Processing

Speech recognition converts voice data reliably into text data. Neural networks, mainly Recurrent Neural Networks (RNNs) and their advanced variants, such as Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs), are at the heart of speech recognition technology.

RNNs are designed to handle sequential data; in speech recognition, they can process audio signals over time by maintaining a memory that captures information from prior inputs in the sequence. 

Deep learning models are used in healthcare to diagnose diseases from medical images, such as cancer detection from X-rays or MRIs, with higher accuracy than traditional methods.

In autonomous vehicles, neural networks process real-time sensor data to recognize objects, and make decisions on routes.

In video games, neural networks help create realistic graphics and character behaviors or dynamically generate music that fits the gameplay.

Let's transition from gaming to engineering!

Deep Learning and Engineering Optimization

Neural networks are instrumental in solving complex optimization problems in engineering. Deep learning is a branch of Artificial Intelligence that has immediate dividends for engineering.

ConvNets

Convolutional Neural Networks (ConvNet) can learn from vast amounts of engineering data, such as Computer-Aided Engineering (CAE) simulation data and Computer-Aided Design (CAD) data, which are used for AI training.

AI technologies like Neural Concepts help R&D and product development departments create innovative solutions tailored to complex, real-world scenarios. The underlying AI systems are inference models encapsulated in existing software or delivered as bespoke solutions, making them immediately usable by non-experts.

Generative Design

Through techniques like Generative Adversarial Networks (GANs), neural networks can create new designs by learning from existing ones. These neural networks iteratively generate and refine outputs and propose optimized solutions that meet predefined criteria, such as efficiency or aesthetics. 

This approach is widely used in automotive and aerospace. It starts with quality training data and leverages predictive models built with machine learning techniques. This enables the creation of unique, high-performance structures or objects that would be challenging for humans to conceive manually. 

Fuzzy Logic: Reasoning with Approximate Values

Fuzzy logic is not central to AI, but it's intriguing and mathematically formally similar to some aspects of Deep Learning (sigmoid functions). This type of logic is a computational approach to partial truth; fuzzy logic considers all existing possibilities between digital values of ‘yes' and ‘no.'’ This method enhances reasoning in uncertain situations or when decision-making resembles human behavior, though its role is secondary to foundational AI techniques.

Mathematical Foundation

Fuzzy membership functions measure an element's belonging to a set. For example, temperature can be classified as “Hot” to varying degrees, reflecting real-world nuances.

Comparison with Classical Logic/Symbolic AI

Classical Logic/Symbolic AI uses a binary system where statements are either true or false, leading to clear outcomes. It is precise and deterministic but can struggle with ambiguity and incomplete information.

Fuzzy Logic deals with degrees of truth, allowing statements to be “somewhat true.” Thus, it excels at handling imprecise data and human-like reasoning in complex systems. You can check out this LinkedIn article on Fuzzy Sets.

Applications of AI in Engineering and Design

Deep learning is revolutionizing engineering design by integrating AI into simulation workflows. Neural Concept’s platform exemplifies this by enabling designers to use real-time predictive analysis tools.

Underlying Technology - Neural Concept

The platform leverages various branches of AI and Computer Science, especially ConvNets. AI engineers and designers collaborate to exploit datasets to feed models and enable them to predict engineering results based on geometry and other design changes. While the most well-known applications are in image recognition and classification, Neural Concept engineering intelligence aims to provide designers with inference capability. AI in aerospace predicts numerical values such as pressure distributed over an aircraft surface.

Aerospace and Automotive Industries

AI tools have also been used for thermal management in industries like automotive and aerospace, optimizing heat exchangers and cold plates with genetic algorithms and other built-in functions on the NC platform.

For example, in an automotive battery crash case, the platform predicted whether a crash would cause contact between a battery and its encasing. The AI tool app achieved impressive reliability: out of hundreds of designs, it produced no false positives and only one false negative. This boosted engineers’ confidence in using AI models to optimize designs.

They designed ultrasonic piezoelectric actuators and performed real-time electromagnetic simulations for antennas and motors, allowing designers to execute complex tasks almost instantly.

Future Trends in AI Fields

Three key areas are emerging as pivotal for the development and application of Artificial Intelligence (AI):

• Explainable Artificial Intelligence,
• Artificial Intelligence Ethics,
• Industry Integration.

Recent advancements in Artificial Intelligence facilitate responsible and understandable technology. Explainable AI focuses on creating interpretable and transparent systems that enable users to understand AI decision-making. This aims to clarify the “black box” nature of deep learning models, ensuring transparency and accountability of AI models.

AI ethics addresses issues like bias and accountability as AI becomes more integrated into daily life. It works to prevent the reinforcement of societal biases.

Another focus is integrating AI models within niche sectors, such as engineering simulation and 3D printing. This integration highlights the increasing adoption of these systems. 

Conclusion

From human language processing to facial recognition and sentiment analysis to advanced engineering simulations, the branches of Artificial Intelligence are revolutionizing nearly every aspect of our lives and engineering work.

NLP enables seamless communication between humans and machines, while computer vision interprets visual data for applications like autonomous vehicles and defect detection.

In the industrial context of engineering and design, fields like deep learning are playing a transformational role by optimizing complex processes and enhancing simulations for faster, data-driven decisions. They leverage statistical techniques and 3D deep convolutional networks to solve real-world problems.

Artificial Intelligence continues to bridge the gap between theoretical innovation and practical, data-driven problem-solving.

FAQ

What industries benefit most from AI fields?

Industries like automotive, aerospace and electronics use AI for predictive maintenance, optimizing operations and minimizing downtime. In healthcare, AI enhances diagnostics and drug discovery; in retail, it enables personalized marketing and inventory management. These sectors demonstrate the power of AI in predictive maintenance and automation.

What is the most popular sub-field within Artificial Intelligence nowadays?

Machine Learning is the most popular branch of Artificial Intelligence. It drives innovations in image and speech recognition,, natural language processing, autonomous systems, and engineering design.

How fast is the field of Artificial Intelligence progressing?

Artificial Intelligence is progressing at an unprecedented pace, with exponential growth in research and application. This was also due to breakthroughs in computer algorithms and more efficient and dedicated hardwaare.

What sub-areas of Artificial Intelligence will be a big deal in the future and why?

Sub-areas like Explainable AI (XAI) will be crucial for trust and transparency in AI decisions. Quantum Artificial Intelligence and other Machine Learning algorithms could revolutionize problem-solving. Their ability to solve complex computations and cryptography challenges makes them powerful tools.

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