Seven technologies that underpinnings artificial intelligence (AI)


The recent successes of artificial intelligence (AI) have captured the wildest imagination of both the scientific communities and the general public. AI amplifies human potential, increases productivity, and moves from simple reasoning towards human-like cognitive abilities.

Current AI technologies are used in applications ranging from healthcare, manufacturing, transport, energy to financial services, banking, advertising, management consulting, and government agencies.

The global AI market was around 260 billion USD in 2016, and it is estimated to exceed 3 trillion by 2024. AI is a broad field of study, and the subfields listed below are critical to its advancement. In this post, we will briefly look at seven technologies underpinning artificial intelligence (AI): neural networks, fuzzy logic, evolutionary computation, and probabilistic methods.

Neural networks

Neural networks are based on connectionism, and their goal is to mimic how the nervous system processes information. Artificial Neural Networks (ANN) and variants have made significant progress in AI’s ability to perform “perception” tasks.

Many neural layers can be stacked in conjunction with today’s multicore parallel computing hardware platforms to provide a higher level of perceptual abstraction in learning its own set of features, eliminating the need for handcrafted features, a process known as deep learning. Deep layered ANN has several drawbacks, including 1) low interpretability of the learned model, 2) large volumes of training data, and the need for a lot of computing power to use these neural models effectively.

Deep learning

Deep learning is a subset of machine learning usually associated with deep neural networks that involve multilevel detail or data representation learning. Information is passed from low-level parameters to higher-level parameters through these layers. These levels correspond to various levels of data abstraction, which leads to learning and recognition.

Several deep learning architectures, including deep neural networks, deep convolutional neural networks, and deep belief networks, have been applied to fields like computer vision, automatic speech recognition, and audio and music signal recognition and have been shown to produce cutting-edge results in a variety of tasks.

Fuzzy logic

Fuzzy logic is concerned with the manipulation of often erroneous data. While our observations are always precise, our knowledge of the context is often incomplete or inaccurate, as it is in many real-world situations, according to most computational intelligence principles.

To improve the interpretability of a learned model, fuzzy logic provides a framework for working with data while assuming a level of imprecision over a set of observations and structural elements. It provides a framework for formalizing AI methods and a user-friendly interface for translating AI models into electronic circuits. However, because fuzzy logic does not provide learning capabilities, it is frequently used in conjunction with other techniques such as neural networks, evolutionary computing, and statistical learning.

Evolutionary computing

Natural selection or natural patterns of collective behavior are used in evolutionary computing. Genetic algorithms and swarm intelligence are two essential subfields. It has the most impact on AI in multiobjective optimization, where it can produce very reliable results. The interpretability and computing power limitations of these models are similar to those of neural networks.

Statistical Learning is aimed at AI that takes a more traditional statistical approach, such as Bayesian modeling, and incorporates the concept of prior knowledge. These methods make use of a wide range of well-proven techniques and operations inherited from classical statistics and a framework for developing formal AI methods. The main disadvantage is that probabilistic approaches express their inference as a population correspondence. The probability concept may not always be applicable, for instance, when vagueness or subjectivity must be measured and addressed.

Ensemble learning and meta-algorithms

The field of AI, known as ensemble learning and meta-algorithms, aims to create models that combine several weak base learners to improve accuracy while lowering bias and variance.

Ensembles, for example, can show greater flexibility when it comes to single model approaches that can be used to model some complex patterns. Bagging and boosting are two well-known meta-algorithms for constructing ensembles. Ensembles can use a large amount of computing power to train many base classifiers, increasing the ability to improve pattern search resolution. However, this does not always imply greater precision.

Logic-based artificial intelligence

Logic-based artificial intelligence is a type of AI commonly used to represent and infer task knowledge. It can represent facts, predicate descriptions, and semantics of a domain using formal logic in structures known as logic programs. Utilizing inductive logic, programming hypotheses can be derived over the known background.


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