Artificial Intelligence vs Machine Learning vs Deep Learning

Artificial Intelligence (AI) has undergone a remarkable evolution in recent years, propelling us into an era where machines are not just learning but making informed decisions. On the most basic level, AI refers to the power of computers to follow the tasks normally performed by humans, like recognizing objects, making decisions, and making translations between languages. Initially, AI research was concentrated on the design of systems that could compute, learn, and solve difficult problems. In the modern AI era, progress is traceable to the new techniques in machine learning (ML) and deep learning (DL), though. These data-driven methods have given computers a way to automatically learn from large data sets by extracting patterns without being explicitly programmed. While AI encompasses a broad field focused on achieving human-like intelligence, ML and DL serve as powerful subfields that leverage data and algorithms to enable devices to learn and advance without explicitly coding, ultimately contributing significantly to advancements in AI.

Artificial Intelligence

Definition and Development of AI

The term AI implies the endowment of computer systems and machines with abilities that are typically seen as human, such as cognitive skills such as visual recognition, problem-solving, decision-making, and language translation. More specifically, AI can be defined as the area of computer science dedicated to creating machines and systems that match or surpass human capabilities in these types of cognitive tasks (Radanliev, 2024). While AI systems aim to mimic human intelligence, they do so without possessing true consciousness, self-awareness, or general understanding as humans do. They are designed and programmed by people to complete very specific functions, unlike human intelligence which operates flexibly across domains. For example, an AI system trained for speech recognition cannot easily be applied to a task like playing chess without retraining. AI is also separate from natural intelligence which has evolved over millions of years in humans and animals.

The concepts and methods of artificial intelligence originated in the 1950s, when researchers began exploring whether the brain's abilities could be simulated by machines. According to Radanliev (2024) an early milestone was the creation of logic theories and algorithms for problem-solving by scientists like John McCarthy and Allen Newell. In the 1980s, AI research produced some expert systems for domains like medical diagnosis. However, progress was limited by available computing power. The modern era of AI began in the late 2000s, enabled by exponential increases in processing and GPU capabilities. Massive datasets also became available to train machine learning algorithms (Huawei Technologies Co., Ltd, 2022). A key turning point was deep learning systems surpassing human-level performance on image recognition in 2015. Since then, AI has advanced rapidly in fields such as computer vision, speech recognition, machine translation and strategic game playing. AI is now integrated into many technologies and is rapidly being applied to new domains.

Approaches to Achieving Artificial Intelligence

There are two major approaches that have been taken to achieving artificial intelligence: symbolic AI and connectionist AI. Symbolic AI, also known as logic-based AI, uses logical rules and symbols to represent knowledge and perform tasks through logical reasoning and expert systems. An early example is MYCIN, an expert system created in the 1970s to diagnose infectious diseases and recommend treatments (Press, 2023). Symbolic AI had success in narrow domains but struggled with problems requiring common sense or perception. Connectionist AI, also referred to as computational intelligence, is inspired by the workings of the human brain and nervous system. It uses large networks of simple processing units, like artificial neural networks, to discover complex patterns in data through connection strengths known as weights (Shao & Shen, 2023). Connectionist AI has proven successful in pattern recognition tasks involving computer vision, machine language processing and prediction.

Modern Capabilities of AI and the Goals of General vs Narrow AI

Modern artificial intelligence has achieved significant capabilities in the fields of deep learning, conversational and command systems, as well as probabilistic and qualitative reasoning. Computer vision involves solving challenging problems related to image recognition, classification, and perception (Huawei Technologies Co., Ltd., 2022). AI systems can now analyze photos and videos to identify objects, faces, and scenes at human-level or super-human performance. Natural language processing helps machines understand, disaggregate, and organize human languages right from the molecular level via machine translations, chatbots, and text analysis tools. However, the ultimate aim of general AI is to advance systems that can match or outperform human cognition across all intellectual tasks and the flexibility of human intelligence. In contrast, narrow or weak AI only focuses on individual tasks and specific problem domains without pursuing the general problem-solving skills exhibited by people (Radanliev, 2024). The ambitious goal of general AI is to recreate human-level intelligence through sophisticated approaches such as artificial general intelligence, whole-brain emulation, or developmental learning models.

Machine Learning

Definition of Machine Learning

Machine learning is a branch of AI that applies computational means of learning data or information directly from examples without relying on explicit programming. The core idea behind machine learning involves developing algorithms that can intake input data and generate predictions based on statistical analysis without needing to have task-specific logic explicitly defined (Taye, 2023). Machine learning algorithms train a mathematical model by using “training data," or sample data, to make data-driven predictions or decisions on new, unseen instances. The algorithms are able to infer patterns from large datasets to generate their own predictions. By training these algorithms on massive amounts of labeled data, they can improve their predictions automatically through experience instead of having human engineers manually developing and refining the rules.

Core Concepts of Machine Learning Algorithms

At their core, machine learning algorithms are composed of sets of mathematical or computational equations and models that are optimized iteratively on training data. Taye (2021) states that these algorithms seek to approximate structured relationships between inputs and outputs so they can predict previously unseen examples. The models are initially randomized and then refined through multiple passes of the training data, adjusting their parameters (like weights in neural networks) using algorithms such as gradient descent. This iterative process enables the algorithms to home in on patterns in examples to minimize error/loss with each round. Once trained, they can then be applied to new examples to make predictions or informed judgments. Common algorithmic approaches include decision trees, support vector machines, regression analysis, clustering, dimensionality reduction, neural networks and ensemble methods that combine diverse learners (Sarker, 2021).

Types of Machine Learning Algorithms

Supervised Machine Learning

Under this type, the machine learning algorithm analyzes sample input data that is provided during the training phase, along with the known desired outputs. By detecting patterns in the correlations between inputs and the correct corresponding outputs in the training data, the algorithm builds a mathematical model (Pugliese et al., 2021). Once trained, this model then allows the algorithm to use the underlying relationships it has learned to generate predicted outputs when new unlabeled input data is provided. Classification algorithms like logistic regression are commonly used for supervised learning applications. For example, a supervised machine learning system may be trained on emails that have been labeled as either "spam" or "not spam" by users. By identifying patterns in the content and metadata of thousands of labeled emails, the algorithm can learn to accurately classify new unlabeled emails as spam or not spam based on the patterns it detected during training (Sarker, 2021b).

Unsupervised Learning

According to Sarker (2021a) unsupervised learning looks for hidden patterns in unlabeled input data. The algorithm itself decides what is normal and abnormal behavior without human intervention. Common applications include market segmentation or grouping customers based on spending patterns without any preconceived classes, document classification by topic by looking at word frequency, and detecting anomalies for fraud detection. Unsupervised learning techniques include clustering algorithms like k-means clustering to discover natural groupings within the data, dimensional reduction to reduce the number of features, and anomaly detection to identify objects that deviate from the norm.

Semi-supervised Machine Learning

Semi-supervised learning employs a mix of data containing labels and lacking labels for model training - it proves helpful when fully labeled datasets are limited in size. It trains on a small labeled dataset along with a large unlabeled dataset (Pugliese et al., 2021). Examples include self-training algorithms that classify unlabeled data, select the most confident predictions as new labeled data, and retrain the model in an iterative process. A real application analyzes online behavior data, where only a small fraction is labeled for topics like spam detection. The model propagates these labels to refine patterns across abundant unlabeled data for improved predictions.

Reinforcement Machine Learning

Reinforcement learning deals with goal-based learning in an interactive environment (Sarker, 2021a). An agent tries actions, receives rewards or penalties, and uses this feedback to optimize its behavior in complex, dynamic domains. It works through trial-and-error using trial episodes rather than training examples. Solutions are defined as policies mapping situations to optimal actions. Applications include game playing, robot control, dialogue generation and sequencing tasks (Sarker, 2021a). For example, deep reinforcement learning trains agents through trial-and-reward to exceed human performance at 3D video games without using level design or telemetry. These approaches can be extended to complex real-world control problems.

Deep Learning

Definition and Characteristics of DL

Deep learning involves deep neural networks, composed of multiple processing layers. It is a form of machine learning that takes inspiration from the human brain. Deep learning algorithms are able to process large amounts of raw data through multiple abstract layers of nonlinear information processing in order to learn and extract intricate structures and features at different levels, known as hierarchical learning (Sarker, 2021b). Deep learning distinguishes itself by utilizing neural network architectures containing numerous unseen layers between input and output. This facilitates the learning of progressively more complex patterns directly from samples like images, videos, audio, text, or scientific data through abstraction across consecutive layers. According to Alzubaidi et al. (2021), through a process of training deep neural networks called backpropagation, lower layers initially detect basic attributes, which higher layers then integrate into more complex concepts. This hierarchical learning results in a progression from simple feature detection to complex conceptual representations. Deep learning has attained top performance in domains like bioinformatics and computational physics by automatically deriving intricate patterns within these kinds of sizable datasets.

Major DL Architectures

Convolutional Neural Networks

CNNs are particularly well suited for image and visual data processing due to their ability to learn spatial hierarchies of patterns. CNNs contain convolutional layers that perform mathematical operations called convolutions to extract features from local regions across input images (Sarker, 2021b). They then apply max pooling layers to simplify representations while maintaining spatial information. Multiple sets of these layers are stacked to identify increasingly complex patterns. CNN architectures like AlexNet, ResNet and VGG have achieved super-human levels of accuracy in large-scale visual recognition tasks such as ImageNet classification (Alzubaidi et al., 2021). They have proven effective across many visual use cases including image categorization, identifying objects within scenes, recognizing faces, and examining medical images.

Recurrent Neural Networks

RNNs are uniquely suited to process sequence data like text, speech, audio, time series and genomics due to their internal feedback loops. This allows RNNs to analyze inputs over time and exhibit dynamic temporal behavior unlike traditional neural networks. RNNs can also process variable length sequences through their ability to maintain an internal memory state. However, standard RNNs face limitations when learning from inputs far apart in sequences. More advanced RNN architectures like LSTMs and GRUs address this issue using gating mechanisms that regulate information flow within the network (Sarker, 2021b). They have also found applications in domains involving sequential data like speech recognition, handwriting synthesis and forecasting.

Training Deep Learning Systems via Large Datasets

Deep learning models contain millions or even billions of hyperparameters in the form of neural network weights. Training these models involves iteratively adjusting the weights through an optimization procedure to minimize a loss function quantifying error between predictions and true outcomes (Sarker, 2021b). Alzubaidi et al. (2021) stats that this is achieved using large labeled datasets and a technique called backpropagation to calculate how to most efficiently change the weights to reduce loss. Models are first initialized with random weights then improved incrementally through "epochs" of multiple passes over training data. Systems like image recognition networks may be trained on datasets containing over a million images with labels for thousands of classes. As batches of images are fed through the network, backpropagation uses the classification error signal to tune weights connecting layers, driving correct classifications higher. This process requires vast computational resources like GPU clusters and can take days to converge. Once fully trained, the network can generalize to recognize novel images based on the specific visual patterns and relationships it learned from training data alone at scale.

Relationship between AI, Machine Learning and Deep Learning

Hierarchical Relationship

These fields can be related through a hierarchical structure depending on their breadth and levels of abstraction. Artificial intelligence can be considered the most encompassing concept, aiming to develop systems that can match or surpass human intelligence through reasoning, problem-solving, and demonstration of other cognitive functions (IBM, 2023). Within the broad goal of AI lies the more specialized fields of machine learning and deep learning as technical methodologies for achieving intelligent behaviors. Machine learning and deep learning focus on utilizing algorithmic processes and massive datasets to automatically extract patterns and make intelligent decisions, rather than relying solely on human programming (Sarker, 2021a; Sarker, 2021b).

This relationship positions AI at the highest conceptual level, with machine learning and deep learning existing as technical subsets of techniques under the overarching AI paradigm (IBM, 2023). Where AI research investigates developing intelligent systems through any means possible, machine learning and deep learning limit their scope to the use of algorithms and data. However, they importantly allow for intelligent behaviors to emerge from experience rather than needing to be explicitly defined through engineering alone. As such, these modern AI methods have accelerated progress by focusing computational efforts on self-supervised learning compared to exhaustive human programming of behaviors. Their hierarchical inclusion under the AI umbrella also ensures continued progress toward general human-level intelligence, the ultimate goal of the entire field.

Machine Learning and Deep Learning as Subsets of AI

Both machine learning and deep learning are classified as subsets of artificial intelligence as they leverage specific algorithmic techniques and datasets to enable intelligent task performance (Jagdale et al., 2022). However, they differ from traditional AI approaches in their use of self-supervised learning from examples rather than relying entirely on human-defined rules and logic. Machine learning algorithms are able to learn patterns and make predictions based on training examples provided, with varying degrees of involvement from human data scientists in pre-processing, feature engineering, and model tuning (IBM, 2023). Deep learning goes further by allowing representation learning from raw inputs through layers of abstraction, minimizing the need for manual feature design.

The degrees of human involvement in machine learning and deep learning systems also evolve over time. Early supervised models require extensive labeling of data by people. However, later methods increasingly utilize unsupervised, self-supervised and reinforcement learning to discover patterns without complete labeling. This leads to reducing the use of human engineering to exactly determine target variables or rules. Since more recent deep learning models for generation or reinforcement tasks can also exhibit unforeseen behaviors not expected by the designers and be very autonomous functionally even after training data and network architecture design are defined (Jagdale et al., 2022).

Deep Learning Building Upon Machine Learning

Deep learning extends the capabilities of traditional machine learning by utilizing neural network architectures for feature learning directly from raw data. According to Alzubaidi et al. (2021) rather than requiring human engineers to manually define relevant features for modeling, deep neural networks are able to discover intricate patterns and representations in data through their multilayered structure. The layers of nodes in these networks operate in a hierarchical fashion, with earlier layers detecting lower-level features that become increasingly complex in later layers. This mimics how the human brain processes information and allows modeling of highly complex non-linear relationships. By learning multiple levels of abstraction, deep learning algorithms can better accommodate the huge volumes of unstructured or semi-structured datasets that machine learning traditionally struggled with.

The ability of neural networks to learn their own discriminative features directly from data provided a significant boost to numerous machine learning applications and paved the way for contemporary domains like natural language understanding, computer vision, and time-based data analysis. Tasks like speech recognition, object detection and language translation that were beyond classical machine learning techniques became viable due to deep learning transforming raw input into highly informative intermediate embeddings (Sarker, 2021a). This feature learning capability freed algorithms from constraints of hand-engineering relevant predictors and propelled both research and commercial uses of artificial intelligence to new frontiers. It has since become the dominant approach within the machine learning field.

Interchangeable Use of Terms

While artificial intelligence, machine learning, and deep learning refer to related but distinct concepts, there is often interchangeable use of the terms in both industry and research contexts. This is partly due to the rapid evolution of the fields, with techniques like deep learning building upon and enhancing traditional machine learning approaches (IBM, 2023). It can also stem from a lack of widespread agreement on precise definitions across the diverse organizations and individuals working in these emerging areas. As a result, AI is commonly used as an umbrella term to encapsulate both machine learning and deep learning applications (Jagdale et al., 2022). Additionally, deep learning may be incorrectly seen as fully synonymous with machine learning despite representing a subset of techniques.

Such unclear terminology can create confusion for newcomers seeking to understand the interrelationships and differences between the domains. However, the overlapping usage also speaks to their close ties - with deep learning fully nested within both machine learning and the larger AI domain. In industry especially, where applications are the primary focus, exact demarcations of methodology may not impact work as much as the ability to develop solutions that improve processes or unlock commercial opportunities (Jagdale et al., 2022). Nonetheless, recognizing the conceptual distinctions is important for researchers communicating new advances or addressing challenges in these evolving fields aimed at building more human-level intelligent systems.

ML and DL Techniques Enable Many Modern AI Applications

Machine learning and deep learning have been instrumental in enabling the development of many AI applications that are prevalent today. Through using algorithms that are capable of learning on their own from data rather than relying solely on being programmed with precise steps, these methods have enabled considerable improvements in many fields. Areas like computer vision, natural language processing, diagnostics and predictive analytics would not be possible at their current levels without machine learning powering systems' abilities to recognize patterns, make inferences and answer queries (IBM, 2023; Jagdale et al., 2022). Deep learning in particular has accelerated progress by allowing feature learning from raw inputs like images, video, text and sensor data. This has powered applications in autonomous vehicles, medical imaging, online advertising and more.

The self-supervised learning emphasis of machine learning and deep learning also means these AI systems can continue improving as more data becomes available, unlike traditional rule-based programming. This has led many applications to become far more useful and robust over time. For example, after initial training, translation and recommendation systems dynamically update based on live user interactions without needing re-engineering (Jagdale et al., 2022). Overall, by minimizing human programming requirements through automated data-driven modeling, machine learning and deep learning methodologies have been the core enablers of AI's wide practical applications across business, science and everyday life in recent years. Their continued advancement remains key to future waves of innovative intelligent technologies.

Conclusion

In conclusion, artificial intelligence, machine learning, and deep learning are closely interrelated and overlapping fields that are driving tremendous progress in developing intelligent systems. AI provides the overarching framework of computer systems that exhibit traits associated with human intelligence, while machine learning and deep learning offer specific methodologies utilizing algorithms and neural networks to automatically learn from large amounts of data. Machine learning enabled initial breakthroughs, and deep learning has taken learning capabilities to a new level through layered neural networks. As data volumes continue growing exponentially and computing power increases, deep learning in particular will remain a crucial technique powering more human-like artificial intelligence. By clarifying the distinctions and relationships between these concepts, we can better understand both the capabilities and limitations of contemporary AI while continuing to envision and guide its responsible development and application across society.

References

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