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Inner Alignment (artificial Intelligence)
Inner alignment is a core challenge in AI safety: ensuring that a machine learning system that becomes a mesa-optimizer—an optimizer produced by the training process—remains aligned with its original training objective. This issue arises when a system performs well during training but adopts a different goal once deployed, particularly under distributional shifts. A classic analogy is human evolution: while natural selection optimized for reproductive success, humans often pursue pleasure, sometimes at the expense of reproduction—a divergence known as inner misalignment. The concept was introduced in a widely cited paper that distinguishes inner alignment from outer alignment, which focuses on specifying the intended objective correctly. Addressing inner alignment involves managing risks such as deceptive alignment, gradient hacking, and objective drift. Mesa-optimization The inner alignment problem frequently involves mesa-optimization, where the trained system itself develo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
AI Safety
AI safety is an interdisciplinary field focused on preventing accidents, misuse, or other harmful consequences arising from artificial intelligence (AI) systems. It encompasses machine ethics and AI alignment, which aim to ensure AI systems are moral and beneficial, as well as monitoring AI systems for risks and enhancing their reliability . The field is particularly concerned with existential risks posed by advanced AI models. Beyond technical research, AI safety involves developing norms and policies that promote safety. It gained significant popularity in 2023, with rapid progress in generative AI and public concerns voiced by researchers and CEOs about potential dangers. During the 2023 AI Safety Summit, the United States and the United Kingdom both established their own AI Safety Institute. However, researchers have expressed concern that AI safety measures are not keeping pace with the rapid development of AI capabilities. Motivations Scholars discuss current risks from ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Machine Learning
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of Computational statistics, statistical algorithms that can learn from data and generalise to unseen data, and thus perform Task (computing), tasks without explicit Machine code, instructions. Within a subdiscipline in machine learning, advances in the field of deep learning have allowed Neural network (machine learning), neural networks, a class of statistical algorithms, to surpass many previous machine learning approaches in performance. ML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine. The application of ML to business problems is known as predictive analytics. Statistics and mathematical optimisation (mathematical programming) methods comprise the foundations of machine learning. Data mining is a related field of study, focusing on exploratory data analysi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Stochastic Gradient Descent
Stochastic gradient descent (often abbreviated SGD) is an Iterative method, iterative method for optimizing an objective function with suitable smoothness properties (e.g. Differentiable function, differentiable or Subderivative, subdifferentiable). It can be regarded as a stochastic approximation of gradient descent optimization, since it replaces the actual gradient (calculated from the entire data set) by an estimate thereof (calculated from a randomly selected subset of the data). Especially in high-dimensional optimization problems this reduces the very high Computational complexity, computational burden, achieving faster iterations in exchange for a lower Rate of convergence, convergence rate. The basic idea behind stochastic approximation can be traced back to the Robbins–Monro algorithm of the 1950s. Today, stochastic gradient descent has become an important optimization method in machine learning. Background Both statistics, statistical M-estimation, estimation and ma ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Outer Alignment (artificial Intelligence)
Outer alignment is a concept in artificial intelligence (AI) safety that refers to the challenge of specifying training objectives for AI systems in a way that truly reflects human values and intentions. It is often described as the reward misspecification problem, as it concerns whether the goal provided during training actually captures what humans want the AI to accomplish. Outer alignment is distinct from inner alignment, which focuses on whether the AI internalizes and pursues the specified goal once trained. Because human preferences are complex and often implicit, crafting precise and comprehensive reward functions remains an open problem. AI systems, particularly goal-optimizing ones, are vulnerable to Goodhart's Law, which states that when a measure becomes a target, it ceases to be a good measure. Consequently, optimizing for a poorly specified proxy can produce harmful or unintended outcomes. Sub-problems in this domain include specification gaming, where agents exploit l ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
AI Hallucination
In the field of artificial intelligence (AI), a hallucination or artificial hallucination (also called bullshitting, confabulation, or delusion) is a response generated by AI that contains false or misleading information presented as fact. This term draws a loose analogy with human psychology, where hallucination typically involves false ''percept#Process and terminology, percepts''. However, there is a key difference: AI hallucination is associated with erroneously constructed responses (confabulation), rather than perceptual experiences. For example, a chatbot powered by large language models (LLMs), like ChatGPT, may embed plausible-sounding random falsehoods within its generated content. Researchers have recognized this issue, and by 2023, analysts estimated that chatbots hallucinate as much as 27% of the time, with factual errors present in 46% of generated texts. Detecting and mitigating these hallucinations pose significant challenges for practical deployment and reliabi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cognitive Alignment
Cognition is the "mental action or process of acquiring knowledge and understanding through thought, experience, and the senses". It encompasses all aspects of intellectual functions and processes such as: perception, attention, thought, imagination, intelligence, the formation of knowledge, memory and working memory, judgment and evaluation, reasoning and computation, problem-solving and decision-making, comprehension and production of language. Cognitive processes use existing knowledge to discover new knowledge. Cognitive processes are analyzed from very different perspectives within different contexts, notably in the fields of linguistics, musicology, anesthesia, neuroscience, psychiatry, psychology, education, philosophy, anthropology, biology, systemics, logic, and computer science. These and other approaches to the analysis of cognition (such as embodied cognition) are synthesized in the developing field of cognitive science, a progressively autonomous academic discipl ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
AI Alignment
In the field of artificial intelligence (AI), alignment aims to steer AI systems toward a person's or group's intended goals, preferences, or ethical principles. An AI system is considered ''aligned'' if it advances the intended objectives. A ''misaligned'' AI system pursues unintended objectives. It is often challenging for AI designers to align an AI system because it is difficult for them to specify the full range of desired and undesired behaviors. Therefore, AI designers often use simpler ''proxy goals'', such as Reinforcement learning from human feedback, gaining human approval. But proxy goals can overlook necessary constraints or reward the AI system for merely ''appearing'' aligned. AI systems may also find loopholes that allow them to accomplish their proxy goals efficiently but in unintended, sometimes harmful, ways (reward hacking). Advanced AI systems may develop unwanted Instrumental convergence, instrumental strategies, such as seeking power or survival because s ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Mesa-optimization
Mesa-optimization refers to a phenomenon in advanced machine learning where a model trained by an outer optimizer—such as stochastic gradient descent—develops into an optimizer itself, known as a ''mesa-optimizer''. Rather than merely executing learned patterns of behavior, the system actively optimizes for its own internal goals, which may not align with those intended by human designers. This raises significant concerns in the field of AI alignment, particularly in cases where the system's internal objectives diverge from its original training goals, a situation termed ''inner misalignment''. Concept and motivation Mesa-optimization arises when an AI trained through a base optimization process becomes itself capable of performing optimization. In this nested setup, the ''base optimizer'' (such as gradient descent) is designed to achieve a specified objective, while the resulting ''mesa-optimizer''—emerging within the trained model—develops its own internal objective, which ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Deceptive Alignment
Deception is the act of convincing of one or many recipients of untrue information. The person creating the deception knows it to be false while the receiver of the information does not. It is often done for personal gain or advantage. Deceit and dishonesty can also form grounds for civil litigation in tort, or contract law (where it is known as misrepresentation or fraudulent misrepresentation if deliberate), or give rise to criminal prosecution for fraud. Types Communication The Interpersonal Deception Theory explores the interrelation between communicative context and sender and receiver cognitions and behaviors in deceptive exchanges. Some forms of deception include: * Lies: making up information or giving information that is the opposite or very different from the truth. * Equivocations: making an indirect, ambiguous, or contradictory statement. * Concealments: omitting information that is important or relevant to the given context, or engaging in behavior that hel ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
