Artificial intelligence (AI) has rapidly advanced in recent years, providing capabilities that have long been thought of as uniquely human. Systems can now perceive the visual world, understand speech and language, make predictions and decisions, generate synthetic content, control robotic systems with dexterity, and much more. Exciting real-world applications like self-driving cars, voice assistants, game-playing algorithms, and image generators get much of the public attention and demonstrate the progress of AI.
However, behind these advancements lie core techniques and methods that provide the fundamental reasoning capabilities and computational architectures powering AI systems. While the applications come and go, understanding the foundational building blocks of artificial intelligence is key to driving forward progress in this multifaceted field. There is a dichotomy between the impressive applications that capture headlines, and the underlying algorithms and representations that operate behind the scenes to make those applications work.
Just as civil engineering requires mathematical, physical and chemical fundamentals to build bridges and skyscrapers, artificial intelligence relies on core disciplines like logic, knowledge representation, search, optimization and reasoning to construct systems that exhibit intelligence. Researchers compose and connect approaches from these fundamental areas to create AI systems capable of human-like cognition and behavior.
This article will survey key techniques and methods that provide the core technical capabilities underlying real-world AI applications today – from logical reasoning and knowledge representation, to search algorithms, optimization, automated reasoning, machine learning and generative modeling.
Understanding the foundations of AI is essential for moving the field forward and achieving the grand challenges like developing strong artificial general intelligence. While current AI has limits in reasoning, generalization and causality, progress in the fundamental methods will push towards more capable systems that act rationally and flexibly across different environments.
Logical Reasoning and Knowledge Representation
Two key aspects of human intelligence are the ability to reason logically and represent knowledge in a structured way. AI systems need to model these capacities in order to make rational decisions and draw conclusions like humans.
Formal logic provides a way to model human reasoning in computer programs using predefined syntax and semantics. Mathematical logic enables AI systems to infer new information from existing facts and rules.
Propositional logic manipulates simple propositions using operators like AND, OR and NOT to make inferences. It represents atomic facts as Boolean variables like “It is raining” and combines them to form Boolean expressions. While limited to simple true/false propositions, propositional logic provides the foundation for more complex logical reasoning.
First-order logic builds on propositional logic by introducing quantifiers like FOR ALL and THERE EXISTS to reason about objects and relationships. This allows expressing facts like “All humans are mortal” and makes first-order logic more expressive and closer to natural language than propositional logic. First-order logic enables AI systems to model real world domains.
Theorem proving uses axioms and inference rules to mathematically prove or disprove theorems. Systems like Coq implement theorem provers that allow users to state theorems and step-by-step construct formal proofs. Theorem proving has applications in verifying correctness of computer programs and mathematical proofs.
Logic programming languages like Prolog represent facts and rules to enable logic-based computation. Prolog programs define an initial set of facts and rules, which are then used to answer queries by searching for proofs. This inductive approach is widely used to build expert systems that can reason about specialized domains like medicine.
Applying rules of deductive reasoning enables AI systems to derive new logical consequences from initial premises or facts. This symbolic approach is fundamental to logical artificial intelligence. It enables making inferences that are provably sound.
Structured knowledge representation allows AI systems to effectively reason about real-world concepts, properties and relationships.
Knowledge graphs use a network structure to represent entities as nodes and relationships between entities as links. Large knowledge graphs containing billions of facts power search engines like Google and recommendation systems by enabling inference about entities.
Ontologies are conceptual models that formally define objects, classes, attributes and relations within a domain of knowledge using some shared vocabulary. Ontologies provide a way to explicitly represent the semantics of terms and relations in a machine-readable format. This enables knowledge sharing and reuse across applications.
Rules and constraints capture domain-specific logic in the form of if-then rules that encode heuristics experts use to reason about data. Constraints formally define restrictions that valid data must satisfy. Expert systems commonly use rules and constraints to reason about specialized domains like medical diagnosis.
The ability to represent knowledge systematically using formalisms like logic, knowledge graphs and ontologies is key to building intelligent systems that can perceive, interpret and interact with the real world.
Efficient Search Algorithms
Many complex AI problems involve navigating extremely large spaces of possible solutions to find the optimal ones. This requires efficient search algorithms to traverse these spaces, including:
Informed search algorithms use heuristics and domain knowledge to guide the search direction, pruning parts of the space that are unlikely to contain the goal. This reduces the number of nodes that need to be explored. The A* algorithm commonly used for path planning is an informed search method.
Optimization algorithms take an iterative approach to refining candidate solutions in order to minimize an objective function. Gradient descent is commonly used to optimize the weights of neural networks by incrementally moving in the direction that reduces loss.
Sampling-based methods evaluate only a subset of possible solutions based on some sampling strategy instead of exhaustively exploring the entire space. Monte Carlo Tree Search explores future game states by building a tree dynamically based on random sampling of the most promising nodes.
Constraint satisfaction algorithms efficiently find solutions that satisfy all the problem constraints by propagating constraints to prune infeasible partial solutions. Commonly used for scheduling, resource allocation and configuration problems.
Graph search algorithms like breadth-first search and depth-first search traverse graphs representing state spaces using different exploration strategies. Useful for pathfinding in navigation and game playing.
The ability to prune large search spaces and quickly hone in on promising solutions enables AI systems to tackle complex, real-world problems efficiently.
Logical Reasoning Methods
In addition to search, AI systems use logic-based methods like deduction, induction and abduction to draw conclusions from existing knowledge:
- Deductive reasoning derives conclusions that must logically follow from given premises according to rules of inference. It enables proving mathematical theorems from axioms.
- Inductive reasoning discovers patterns in existing data or experiences to form general rules that can be used to make predictions about new situations. It is used to make forecasts based on observations.
- Abductive reasoning infers the best explanation for an observed effect when the actual causes are unknown. This allows diagnosing faults from symptoms when the root cause is unclear.
- Non-monotonic reasoning involves revising old conclusions if new evidence arises that invalidates previous assumptions. It contrasts with deductive logic and better reflects real-world reasoning.
Combining logical reasoning with probabilistic methods enables AI systems to reason intelligently under uncertainty when full information is not available.
Many real-world problems involve finding optimal solutions according to mathematical objective functions, subject to certain constraints. Key optimization techniques used in AI include:
- Linear and convex optimization can efficiently find globally optimal solutions for problems that have linear objectives and convex constraints. Useful for applications like portfolio optimization.
- Gradient descent is an iterative algorithm that gradually refines solutions by taking steps proportional to the negative of the gradient to minimize an objective function. It is commonly used to optimize neural network weights.
- Evolutionary algorithms apply principles of mutation and cross-over modelled on natural evolution to evolve good solutions. Genetic algorithms and genetic programming are examples.
- Reinforcement learning algorithms learn optimal policies through trial-and-error interactions with an environment. Used to master games like chess and Go.
- Swarm intelligence algorithms are inspired by decentralized systems like insect colonies and flocking birds. They model simple local interactions between individuals to produce emerging intelligent global behavior.
The ability to frame real-world problems as optimization tasks and apply efficient algorithms to solve them is key to developing capable AI solutions.
Automated Reasoning and Theorem Proving
Automated reasoning refers to AI systems that can perform complex logical reasoning and mathematically prove theorems based on axioms and inference rules. Major methods include:
- Theorem proving deductively proves new theorems from basic axioms using rules of inference. It enables proving correctness of programs and mathematical theorems.
- SAT solvers determine the satisfiability of Boolean formulae efficiently, which has applications in circuit design, model checking and program analysis.
- Model checking verifies correctness of finite-state systems like hardware or software by exhaustively exploring state spaces.
- Program synthesis automatically generates programs from high-level specifications, reducing the need to manually code software.
Automated reasoning enables more reliable AI systems that can logically verify conclusions, prove properties about complex systems or automatically generate provably correct programs.
Machine Learning Models
At the core of many AI applications like computer vision and natural language processing are machine learning models like neural networks that can learn patterns from data and make predictions:
- Deep learning trains multi-layer neural networks on big datasets to perform tasks like image recognition, speech recognition, and language translation. They achieve human-level performance on many problems.
- Ensemble methods combine multiple models together to improve overall predictive performance. Popular methods include random forests and gradient boosting.
- Dimensionality reduction algorithms like principal component analysis (PCA) simplify high-dimensional data into fewer dimensions for more efficient processing and visualization.
- Transfer learning transfers knowledge from pretrained models to new tasks with limited data. This enables adapting models to new domains.
- Explainable AI techniques like LIME and SHAP explain the reasoning behind model predictions. This ensures transparency and fairness.
Advances in machine learning and deep learning fuel AI’s ability to accurately analyze data, identify patterns, and make human-like predictions and decisions automatically.
An active area of AI research is developing generative models that can create novel, realistic synthetic content like images, audio, video and text. Key techniques include:
- Generative adversarial networks (GANs) train two neural networks against each other to generate increasingly realistic synthetic data able to fool the discriminator network.
- Variational autoencoders (VAEs) learn the latent vector space representation of training data in order to generate new plausible data points by sampling the latent space.
- Autoregressive models generate content step-by-step, conditioning each step on previously generated outputs. Examples include PixelCNN for images and GPT-3 for text generation.
- Normalizing flows explicitly model data distributions through a series of reversible transformations to map simple noise distributions to complex real-world data distributions.
- Probabilistic programming languages allow flexible specification and inference of probabilistic generative models.
The ability to model complex high-dimensional distributions and efficiently generate new samples from them has enabled applications like creating photorealistic artificial humans.
This article provided an overview of core techniques and methods that enable modern artificial intelligence systems to exhibit human-like capabilities. Logical reasoning, knowledge representation, search, optimization, automated reasoning, machine learning, and generative modeling provide the fundamental building blocks powering many impressive AI applications today.
However, current AI systems still have notable limitations compared to human intelligence. While narrow AI has achieved human-level performance in specific domains like game-playing, image recognition and language processing, progress towards more general artificial intelligence has been slow. Existing systems lack the flexible reasoning, causal understanding, common sense knowledge and generalization abilities that humans readily employ for learning and problem solving.
Bridging these gaps to achieve human-level artificial general intelligence remains an open grand challenge. Advances in the foundational areas like reasoning, knowledge representation and generalization will be key to reaching this goal. Expanding the core capabilities of AI systems will enable tackling more complex real-world situations that require flexible behavior and transfer of knowledge across different tasks and environments.
Active research is underway focused on expanding the fundamental methods of AI. This includes work on representing common sense and world knowledge, performing causal and counterfactual reasoning, learning abstract concepts from few examples, and transferring knowledge between tasks. Integrating these capabilities will push towards more broadly intelligent systems. Technologies like deep learning show promise for learning representations that capture the complexity of the real world.
Understanding the foundational techniques powering modern AI provides insights into current capabilities and limitations. But more importantly, progress in the core areas of reasoning, knowledge representation, generalization and causality will drive forward the next generation of artificial intelligence systems. With imagination and persistence, researchers may one day fulfill the grand vision of developing strong artificial general intelligence rivaling humans across different domains. But this journey starts with advancing the fundamental building blocks.
With a passion for AI and its transformative power, Mandi brings a fresh perspective to the world of technology and education. Through her insightful writing and editorial prowess, she inspires readers to embrace the potential of AI and shape a future where innovation knows no bounds. Join her on this exhilarating journey as she navigates the realms of AI and education, paving the way for a brighter tomorrow.