AI Advancements: History
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Contributor:
Hamed Taherdoost
,
Mitra Madanchian

Artificial intelligence (AI) has seen remarkable advancements, stretching the limits of what is possible and opening up new frontiers. Beginning with the fundamentals of AI, including traditional machine learning and the transition to data-driven approaches, the narrative progresses through core AI techniques such as reinforcement learning, generative adversarial networks, transfer learning, and neuroevolution.

  • artificial intelligence (AI)
  • emerging frontiers
  • cutting-edge techniques

1. Introduction

Since the advent of computers that required human manipulation in the 1950s, researchers have been focusing on enhancing computers’ capacity for independent learning. This development ushers in a new era for business, society, and computer science. In a sense, computers have advanced to the point where they can now complete brand-new tasks independently. To adapt to and learn from people, future artificial intelligence (AI) will interact with them using their language, gestures, and emotions. Due to the popularity and interconnectivity of various intelligent terminals, people will no longer only live in actual physical space, but will also continue to exist within the digital virtualized network. In this cyberspace, the lines between people and machines will already be blurred [1][2].
Robots exhibit AI as compared to humans. Human intelligence and animal intelligence both display consciousness and emotions, whereas the other does not [3]. Alan Turing popularized the idea that computers might one day think similarly to humans in 1950 [4]. Since it has been growing for more than 60 years, AI has evolved into an interdisciplinary field that combines several scientific and social science fields [5][6][7]. There is a growing scholarly interest in the possibility that machine learning and AI could replace people, take over occupations, and alter how organizations run [8]. The underlying assumption is that, given specific restrictions on information processing, AI may produce results that are more accurate, efficient, and high-quality than those produced by human specialists [9][10].
Devices that can perform mental functions like learning and problem-solving in a manner that is comparable to how humans think are usually referred to as AI [11][12]. Artificial agents are defined as systems that observe their environment and take actions to enhance their chances of achieving their objectives. AI is a class of sophisticated machines that can successfully understand human speech [13]. The ability to use objects as well as convey knowledge, reasoning, planning, learning, and processing are among the core objectives of AI research [14][15]. AI aims are pursued using a variety of strategies, including computational intelligence and statistical modeling. In addition to having an impact on computer science, AI also draws researchers from languages, mathematics, and engineering [16][17][18].
Exploring new AI frontiers is crucial because they develop technology, tackle new problems, boost performance, speed up research, and have positive societal and economic effects. By conducting a thorough literature review, introducing cutting-edge AI techniques such as reinforcement learning, generative adversarial networks, transfer learning, neuroevolution, explainable AI (XAI), and quantum AI with real-world applications, addressing ethical concerns, and outlining future directions. By doing this, the article supports creativity, promotes ethical AI adoption, and stimulates additional research, ultimately advancing AI and its advantageous effects on a variety of industries as well as society at large. It helps people make informed judgments about technology adoption, ethics, and the future of AI by giving historical context, multidisciplinary ideas, and a glimpse into cutting-edge AI techniques. The evaluation helps grasp AI’s disruptive potential and difficulties, enabling ethical and beneficial integration across sectors and society.

2. Evolution of AI Techniques

Over time, there have been notable advancements in the field of AI techniques. The field has gone through several stages of evolution and revolution, which have increased impact and given rise to new technologies. AI’s history began in the 1940s, about the time that electronic computers were first introduced [19]. The area of AI was officially founded in 1955, when the phrase “AI” was first used in a workshop proposal [20]. AI has developed over time, moving from theoretical ideas to machine learning, expert systems, machine logic, and artificial neural networks [21][22][23].
Interesting patterns of knowledge inflows and trends in AI research themes were found in a study by Dwivedi et al. The study presented in [24] focuses on the evolution of AI research in technological forecasting and social change (TF&SC). By balancing development and revolution in research, the field of AI in education (AIED) has also undergone refinement and audacious thinking [25]. AI development has been marked by important turning points, breakthroughs, and depressing times called “AI winters” [26]. Training computation in the field has grown exponentially, which has resulted in the development of increasingly powerful AI systems [27].

2.1. Emergence of Deep Learning and Its Impact

Deep learning has a significant influence that is still being felt today, changing the way intelligent systems function and opening up new possibilities for AI applications. Deep learning will likely become more significant as research into it advances, greatly influencing the potential and capabilities of intelligent computers across a range of fields. The amalgamation of historical turning points, technological breakthroughs, and a wide range of applications defines deep learning as a pillar in the continuous AI story.
AI has been greatly impacted by the advent of deep learning, which has revolutionized machine learning techniques. Several factors came together in the early 2010s to catapult deep learning—a class of machine learning algorithms that gradually extracts higher-level features from raw input—into the public eye [28][29]. Hardware advancements have been crucial in that they have made it possible to train massive deep neural networks with the processing power required, especially in the case of GPUs and specialized accelerators [30][31][32]. Deep learning has a wide range of applications, including natural language processing, computer vision, and medical diagnostics [33]. Deep learning has drawn praise and criticism alike, and its significant influence is still being felt today, shaping intelligent system functioning and broadening the scope of AI applications. Novel studies in the field of deep learning are constantly emerging as a result of the remarkable advancements in hardware technologies as well as the unpredictable growth in data acquisition capabilities [34]. The significance of deep learning research is expected to grow as it advances, potentially influencing the potential and capabilities of intelligent computers across multiple domains.

2.2. Transition from Rule-Based Systems to Data-Driven Approaches

Rule-based systems, which depended on explicit programming of predetermined rules to control system behavior, were prevalent in the early phases of AI development [35]. Although these systems were quick and simple to construct, their reliance on hardcoded rules and inference limited their capacity to handle the complexity and unpredictability of real-world data. When they were not in their area of expertise, they were rarely accurate. Machine learning systems were far more difficult to comprehend, adjust, and maintain than rule-based systems. But when it came to solving problems with a large number of variables, where it was difficult for humans to come up with a comprehensive set of rules, they encountered difficulties [36][37][38].
The development of data-driven techniques became apparent as AI advanced and changed the game. This paradigm change made it possible for AI systems to use data for learning and adaptation [39], and it was fueled by developments in machine learning, particularly supervised learning. These systems may generalize patterns from enormous datasets, enabling adaptability in the face of varied and dynamic settings, as opposed to being restricted by strict rules [40].
Data-driven methods were demonstrated by deep learning, a form of machine learning that uses multi-layered neural networks. Notwithstanding their benefits, certain obstacles still exist, such as the requirement for representative datasets [41], worries over bias, and problems with interpretability [42]. However, the transition from rule-based to data-driven methodologies has unquestionably transformed the AI environment, unleashing hitherto unrealized potential and shaping the course of continuing research and development.

3. Core AI Techniques

AI research and development have advanced quickly over the years, resulting in the introduction of cutting-edge methods that push the limits of what AI is capable of. These cutting-edge techniques can transform entire industries, resolve difficult issues, and open up new research directions. While classic AI approaches have been effective in solving particular problems, they frequently have trouble adapting, scaling, and dealing with novel scenarios. The exploration of these cutting-edge approaches has been driven by the need for AI systems to complement human capabilities, learning from experience, generalizing information, and performing tasks effectively. Emerging AI techniques are characterized by their capacity to let robots learn from data, emulate human reasoning, and get better over time. These methods accomplish amazing feats by utilizing sophisticated algorithms, robust computational resources, and, occasionally, natural inspiration [43][44][45]. For example, reinforcement learning empowers robots to acquire knowledge by means of experimentation, thereby facilitating the execution of intricate operations like object manipulation and tool application. By analyzing massive datasets, recognizing patterns, and generating forecasts, machine learning, on the other hand, enables robotics to enhance their performance gradually [46][47][48].

3.1. Reinforcement Learning

Through contact with an environment where an agent senses the state of that environment, reinforcement learning is a learning framework that enhances a policy in terms of a given aim [49]. Reinforcement learning was created at the nexus of concepts from cognitive science, neurology, and AI. To create the notions employed in computational reinforcement learning algorithms, many behaviorist principles were transformed. Every time an artificial agent finds itself in a position where it has a choice of actions, reinforcement learning can be used as a general-purpose framework for making decisions. Robot control is one area where reinforcement learning has been used [50][51].
The reinforcement learning process involves several steps as shown in Figure 1. An agent interacts with its environment in this iterative cycle by receiving observations that represent the system’s current state. Equipped with a policy, the agent subsequently decides on an action by these observations. By applying the selected action to the environment, a reward signal is generated and the environment undergoes a transition to a new state. Both the reward and the amended observation are received by the agent; these are essential for the agent to learn and refine its policy. As the agent seeks to maximize cumulative rewards over time this cyclical interaction persists, and it eventually teaches the agent the optimal strategy for navigating and making decisions in the given environment.
Figure 1. Reinforcement learning process.
Reinforcement learning refers to the practice of increasing rewards through a variety of environmental behaviors. Implementing the behaviors that optimize these rewards is part of this learning process. The agent needs to learn on his own using hit-and-trial mechanisms for maximal reward in this sort of learning, which acts similarly to natural learning [52]. Machine learning can be divided into supervised, unsupervised, and semi-supervised categories. Unsupervised and supervised learning is not the same as reinforcement learning (semi-supervised). The goal of supervised learning is to map the input to the corresponding output and learn the rules from labeled data. There is a set of instructions for each action. Depending on whether the value is continuous or discrete, a regressive or classification model is utilized. As opposed to supervised learning, unsupervised learning requires the agent to find the hidden structure in unlabeled data [53]. In contrast to supervised learning, unsupervised learning can be used when the amount of data is insufficient or the data are not labeled. However, in reinforcement learning, the agent has an initial point and an endpoint, and to get there, the agent needs to choose the best course of action by modifying the environment. Agents are rewarded for finding the solution, but they are not rewarded if they do not, therefore agents need to study their surroundings to collect the most benefits [54]. In reinforcement learning, the issue formulation is carried out using the Markov decision process (MDP), and the solution can be model-based (Q-learning, SARSA) or model-free (policy). In this method, the agent engages with the environment, generates policies based on incentives, and then the system is trained to perform better [55][56].
The utilization of reinforcement learning in the domains of robotics, gaming, marketing, and automated vehicles was examined by Wei et al. [57]. Their primary area of interest was Monte Carlo-based reinforcement learning control in the context of unmanned aerial vehicle systems. Wang et al. [58] investigated the use of a Monte Carlo tree search-based self-play framework to learn to traverse graphs. Maoudj and Hentout [59] introduced a novel method for mobile robot path planning that is optimal, utilising the Q-learning algorithm. Intayoad et al. [60] developed personalized online learning recommendation systems by employing reinforcement learning based on contextual bandits.

3.2. Generative Adversarial Networks

Generative adversarial networks, a brand-new generative model, were put forth by Goodfellow et al. [61] in 2014. Generative adversarial networks engage in both a competitive and a cooperative process because they are made up of two neural networks: the discriminator (D) and the generator (G). While the discriminator seeks to distinguish between actual and synthetic data, the generator is tasked with creating synthetic data samples that resemble real data. Through this competitive training, generative adversarial networks gain knowledge from one another, causing the generator to produce more and more accurate data until it reaches an equilibrium where it is impossible to tell the difference between generated and genuine data. Figure 2 shows the process of generative adversarial networks.
Figure 2. Process of generative adversarial networks.
Generative adversarial networks are used in many different fields, such as image synthesis, style transfer, and picture-to-image translation. Additionally, they have demonstrated potential in the areas of data augmentation, medication discovery, and building lifelike virtual environments for AI training. Despite their effectiveness, generative adversarial networks still encounter problems including instability during training and mode collapse, which results in a lack of diversity in the generated samples. Additionally, due to their potential abuse in the production of deep fakes and deceptive information, ethical considerations surface [62].

3.3. Transfer Learning

It is expensive or not practical in many applications to recollect the ideal training data to update the models. Transfer learning or knowledge transfer between the task domains would be necessary in such circumstances. By transferring the useful parameters, transfer learning helps a classifier learn from one domain to another [63]. “What to transfer”, “how to transfer”, and “when to transfer” are the three key questions in transfer learning [64]. The question “what to transfer” asks what information should be transferred across domains or tasks. Knowledge can be specialized for particular tasks and domains that may or may not be useful. However, certain knowledge might be shared across several domains and could improve performance in the target domain or activity. Learning algorithms need to proceed after determining the portion of knowledge that has to be conveyed to transfer the beneficial knowledge. Therefore, “how to transfer” becomes the following issue. The final issue, “when to transfer”, asks under what conditions transmitting should occur. The majority of TL research now in existence focuses on “what to transfer” and “how to transfer”, implicitly presuming that the source domain and the target domain are related [65].
Choosing a basic model trained on a source task and then fine-tuning it with a smaller, task-specific dataset for the target task are typical steps in the process. Through this adaptation, the model can preserve its general knowledge while adapting its learned representations to the specifics of the current task. In the transfer learning process (Figure 3), a pre-trained model is chosen based on a related source task, and a target task dataset is collected and pre-processed accordingly. The model is then modified either through feature extraction, where new layers are added while keeping pre-trained layers frozen, or fine-tuning, where some pre-trained layers are unfrozen. The modified model is trained on the target task dataset, and its performance is evaluated on a separate dataset. If the performance is satisfactory, the trained model can be used for making predictions on new data. If not, adjustments such as hyperparameter tuning or model modifications can be made to improve performance. Once the desired performance is achieved, the transfer learning process is complete, and the model is ready for practical use in the target task.
Figure 3. Transfer learning: from source task to target task.

3.4. Neuroevolution

Artificial neural networks, or ANNs, are created using evolutionary methods by the AI branch known as neuroevolution. This approach uses evolutionary algorithms to train the neural networks. Even though we would think it has something to do with deep learning, neural evolution is not quite the same as what deep learning is at its core. As previously mentioned, neuroevolution is a method of machine learning that uses evolutionary algorithms to create artificial neural networks, drawing inspiration from the evolution of organic nervous systems in nature [66]. The process as shown in Figure 4 begins with initializing a population of neural networks with random parameters, representing potential solutions. These networks are then evaluated based on predefined metrics, determining their fitness for the given task. The top-performing networks are selected to proceed, mimicking natural selection. Through reproduction and crossover, new offspring with random changes and combined traits are generated. This process iterates for multiple generations, continuously refining and improving the neural networks’ performance.
Figure 4. Process of neuroevolution.
The capacity of neuroevolution to examine a wider variety of network designs and hyperparameters than conventional techniques is one of its main advantages. Because of this, it is especially well suited for complicated situations where the ideal network structure might not be obvious or simple to build by human experts. Success in a variety of fields, including robotics, gaming, optimization problems, and control systems, has been demonstrated via neuroevolution [67]. Additionally, it has the potential to build neural networks using fewer computer resources, which makes it appealing for applications in contexts with limited resources [68].
Neuroevolution, like many AI techniques, is not without its difficulties and limitations. The evolutionary process can be computationally expensive, and it may take several generations to find the best solution. Another crucial issue that researchers need to carefully handle is balancing exploration and exploitation to prevent early convergence [67]. Despite these difficulties, neuroevolution represents an innovative strategy in AI research, offering a potent substitute for conventional training techniques [68].

This entry is adapted from the peer-reviewed paper 10.3390/ai5010003

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