Impossibility Results in AI: A Survey

An impossibility theorem demonstrates that a particular problem or set of problems cannot be solved as described in the claim. Such theorems put limits on what is possible to do concerning artificial intelligence, especially the super-intelligent one. As such, these results serve as guidelines, reminders, and warnings to AI safety, AI policy, and governance researchers. These might enable solutions to some long-standing questions in the form of formalizing theories in the framework of constraint satisfaction without committing to one option. In this paper, we have categorized impossibility theorems applicable to the domain of AI into five categories: deduction, indistinguishability, induction, tradeoffs, and intractability. We found that certain theorems are too specific or have implicit assumptions that limit application. Also, we added a new result (theorem) about the unfairness of explainability, the first explainability-related result in the induction category. We concluded that deductive impossibilities deny 100%-guarantees for security. In the end, we give some ideas that hold potential in explainability, controllability, value alignment, ethics, and group decision-making. They can be deepened by further investigation

Unpredictability of AI

The young field of AI Safety is still in the process of identifying its challenges and limitations. In this paper, we formally describe one such impossibility result, namely Unpredictability of AI. We prove that it is impossible to precisely and consistently predict what specific actions a smarter-than-human intelligent system will take to achieve its objectives, even if we know terminal goals of the system. In conclusion, impact of Unpredictability on AI Safety is discussed

Augmented Utilitarianism for AGI Safety

In the light of ongoing progresses of research on artificial intelligent systems exhibiting a steadily increasing problem-solving ability, the identification of practicable solutions to the value alignment problem in AGI Safety is becoming a matter of urgency. In this context, one preeminent challenge that has been addressed by multiple researchers is the adequate formulation of utility functions or equivalents reliably capturing human ethical conceptions. However, the specification of suitable utility functions harbors the risk of "perverse instantiation" for which no final consensus on responsible proactive countermeasures has been achieved so far. Amidst this background, we propose a novel socio-technological ethical framework denoted Augmented Utilitarianism which directly alleviates the perverse instantiation problem. We elaborate on how augmented by AI and more generally science and technology, it might allow a society to craft and update ethical utility functions while jointly undergoing a dynamical ethical enhancement. Further, we elucidate the need to consider embodied simulations in the design of utility functions for AGIs aligned with human values. Finally, we discuss future prospects regarding the usage of the presented scientifically grounded ethical framework and mention possible challenges

On Controllability of Artificial Intelligence

Invention of artificial general intelligence is predicted to cause a shift in the trajectory of human civilization. In order to reap the benefits and avoid pitfalls of such powerful technology it is important to be able to control it. However, possibility of controlling artificial general intelligence and its more advanced version, superintelligence, has not been formally established. In this paper, we present arguments as well as supporting evidence from multiple domains indicating that advanced AI can’t be fully controlled. Consequences of uncontrollability of AI are discussed with respect to future of humanity and research on AI, and AI safety and security. This paper can serve as a comprehensive reference for the topic of uncontrollability

Dynamic value alignment through preference aggregation of multiple objectives

The development of ethical AI systems is currently geared toward setting objective functions that align with human objectives. However, finding such functions remains a research challenge, while in RL, setting rewards by hand is a fairly standard approach. We present a methodology for dynamic value alignment, where the values that are to be aligned with are dynamically changing, using a multiple-objective approach. We apply this approach to extend Deep $Q$-Learning to accommodate multiple objectives and evaluate this method on a simplified two-leg intersection controlled by a switching agent.Our approach dynamically accommodates the preferences of drivers on the system and achieves better overall performance across three metrics (speeds, stops, and waits) while integrating objectives that have competing or conflicting actions

AI alignment and generalization in deep learning

This thesis covers a number of works in deep learning aimed at understanding and improving generalization abilities of deep neural networks (DNNs). DNNs achieve unrivaled performance in a growing range of tasks and domains, yet their behavior during learning and deployment remains poorly understood. They can also be surprisingly brittle: in-distribution generalization can be a poor predictor of behavior or performance under distributional shifts, which typically cannot be avoided in practice. While these limitations are not unique to DNNs -- and indeed are likely to be challenges facing any AI systems of sufficient complexity -- the prevalence and power of DNNs makes them particularly worthy of study. I frame these challenges within the broader context of "AI Alignment": a nascent field focused on ensuring that AI systems behave in accordance with their user's intentions. While making AI systems more intelligent or capable can help make them more aligned, it is neither necessary nor sufficient for alignment. However, being able to align state-of-the-art AI systems (e.g. DNNs) is of great social importance in order to avoid undesirable and unsafe behavior from advanced AI systems. Without progress in AI Alignment, advanced AI systems might pursue objectives at odds with human survival, posing an existential risk (``x-risk'') to humanity. A core tenet of this thesis is that the achieving high performance on machine learning benchmarks if often a good indicator of AI systems' capabilities, but not their alignment. This is because AI systems often achieve high performance in unexpected ways that reveal the limitations of our performance metrics, and more generally, our techniques for specifying our intentions. Learning about human intentions using DNNs shows some promise, but DNNs are still prone to learning to solve tasks using concepts of "features" very different from those which are salient to humans. Indeed, this is a major source of their poor generalization on out-of-distribution data. By better understanding the successes and failures of DNN generalization and current methods of specifying our intentions, we aim to make progress towards deep-learning based AI systems that are able to understand users' intentions and act accordingly.Cette thèse discute quelques travaux en apprentissage profond visant à comprendre et à améliorer les capacités de généralisation des réseaux de neurones profonds (DNN). Les DNNs atteignent des performances inégalées dans un éventail croissant de tâches et de domaines, mais leur comportement pendant l'apprentissage et le déploiement reste mal compris. Ils peuvent également être étonnamment fragiles: la généralisation dans la distribution peut être un mauvais prédicteur du comportement ou de la performance lors de changements de distribution, ce qui ne peut généralement pas être évité dans la pratique. Bien que ces limitations ne soient pas propres aux DNN - et sont en effet susceptibles de constituer des défis pour tout système d'IA suffisamment complexe - la prévalence et la puissance des DNN les rendent particulièrement dignes d'étude. J'encadre ces défis dans le contexte plus large de «l'alignement de l'IA»: un domaine naissant axé sur la garantie que les systèmes d'IA se comportent conformément aux intentions de leurs utilisateurs. Bien que rendre les systèmes d'IA plus intelligents ou capables puisse aider à les rendre plus alignés, cela n'est ni nécessaire ni suffisant pour l'alignement. Cependant, être capable d'aligner les systèmes d'IA de pointe (par exemple les DNN) est d'une grande importance sociale afin d'éviter les comportements indésirables et dangereux des systèmes d'IA avancés. Sans progrès dans l'alignement de l'IA, les systèmes d'IA avancés pourraient poursuivre des objectifs contraires à la survie humaine, posant un risque existentiel («x-risque») pour l'humanité. L'un des principes fondamentaux de cette thèse est que l'obtention de hautes performances sur les repères d'apprentissage automatique est souvent un bon indicateur des capacités des systèmes d'IA, mais pas de leur alignement. En effet, les systèmes d'IA atteignent souvent des performances élevées de manière inattendue, ce qui révèle les limites de nos mesures de performance et, plus généralement, de nos techniques pour spécifier nos intentions. L'apprentissage des intentions humaines à l'aide des DNN est quelque peu prometteur, mais les DNN sont toujours enclins à apprendre à résoudre des tâches en utilisant des concepts de «caractéristiques» très différents de ceux qui sont saillants pour les humains. En effet, c'est une source majeure de leur mauvaise généralisation sur les données hors distribution. En comprenant mieux les succès et les échecs de la généralisation DNN et les méthodes actuelles de spécification de nos intentions, nous visons à progresser vers des systèmes d'IA basés sur l'apprentissage en profondeur qui sont capables de comprendre les intentions des utilisateurs et d'agir en conséquence