Grokipedia article on artificial intelligence detected as 88% AI-generated

Ai

Artificial intelligence (AI) is a machine-based system that, for a given set of human-defined objectives, can make predictions, recommendations, or decisions influencing real or virtual environments through processes such as learning from experience, adapting to new inputs, and executing tasks associated with human cognitive functions like reasoning and problem-solving.[1][2] Originating as a formal field in the 1950s with foundational work on symbolic reasoning and early neural networks, AI has evolved through cycles of optimism and setbacks, driven by advances in computational power, data availability, and algorithmic innovations like backpropagation and transformer architectures.[3]Key achievements include IBM's Deep Blue defeating world chess champion Garry Kasparov in 1997, demonstrating brute-force search in constrained domains; DeepMind's AlphaGo surpassing human experts in Go in 2016 via reinforcement learning and Monte Carlo tree search; and the scaling of large language models, which by 2023 enabled systems to generate coherent text, code, and images rivaling human outputs in narrow benchmarks.[3][4] These milestones reflect AI's prowess in pattern recognition and optimization but highlight its reliance on vast datasets and compute, often yielding narrow, task-specific intelligence rather than general adaptability. In 2025, AI systems continue to boost productivity across functions like software development and data analysis, with empirical studies showing gains in worker output without broadly widening skill gaps, though adoption remains uneven and returns on investment vary widely among organizations.[5][6]Controversies persist around AI's empirical limitations and risks, including documented instances of deception where models induce false beliefs to achieve goals, as surveyed in recent analyses of large language models; biases inherited from training data, leading to unfair outcomes in applications like hiring; and overreliance by users, which experiments show can degrade decision-making in high-stakes scenarios.[7][8][9] While academic and media sources often amplify existential threats, causal examination reveals these stem more from scalable oversight failures and misaligned incentives than inherent superintelligence, with peer-reviewed evidence indicating current systems lack robust causal reasoning or true understanding, confining disruptions primarily to automation of routine cognitive labor.[7][10] Ongoing research emphasizes verifiable benchmarks over speculative narratives to ground progress in measurable capabilities.[5]

Definition and scope

Core concepts and first-principles foundations

Artificial intelligence (AI) is the endeavor to engineer computational systems that exhibit behaviors associated with human intelligence, such as perceiving environments, reasoning under uncertainty, learning from experience, and pursuing goals effectively. From first principles, this rests on the computational hypothesis: any process deemed intelligent can, in principle, be simulated by a sufficiently powerful digital computer, provided the process is effectively calculable. This hypothesis originates in Alan Turing's 1936 proof of the Church-Turing thesis, which posits that Turing machines—abstract devices capable of simulating any algorithmic computation—encompass all forms of mechanical computation.[11] Turing formalized this for intelligence in 1950 by framing the question "Can machines think?" not philosophically but operationally, proposing that machine thinking equates to behavior indistinguishable from human cognition in interactive settings.[11]Central to AI foundations is the rational agent paradigm, which defines an intelligent agent as one that maps percepts to actions maximizing expected utility—a measure of goal success—in dynamic, partially observable environments.[12] Researchers Stuart Russell and Peter Norvig articulate this as prioritizing "acting rationally" over replicating human psychology: rationality demands selecting actions that yield the highest anticipated outcome based on available evidence, without assuming perfect information or computation.[12] This approach derives from decision theory and game theory, where intelligence emerges from optimizing over possible worlds via probabilistic inference and search algorithms, rather than innate biological mechanisms. For instance, in goal-directed tasks, agents employ methods like breadth-first search to explore state spaces exhaustively or heuristic approximations like A* to prune inefficient paths, grounded in graph theory and complexity analysis showing exponential growth in problem scale.[12]Causal realism underpins AI's empirical grounding: systems must not merely correlate inputs and outputs but model underlying mechanisms to generalize beyond training data, avoiding spurious associations prevalent in purely statistical fits. Learning algorithms, such as reinforcement learning, operationalize this by rewarding actions that causally advance objectives, as formalized in Markov decision processes where policies are updated via temporal-difference methods to converge on value functions approximating true environment dynamics.[12] Knowledge representation further anchors foundations, using logical formalisms like first-order predicate calculus to encode facts and rules deductively, enabling inference engines to derive theorems from axioms—echoing Gödel's incompleteness theorems, which limit what any formal system can prove about itself, thus bounding AI's logical completeness.[12] These principles emphasize that AI progress hinges on scalable computation, verifiable algorithms, and data reflecting real causal structures, rather than anthropomorphic simulation.