What is consciousness, and could machines have it?

Dehaene, Stanislas; Lau, Hakwan; Kouider, Sid

doi:10.1126/science.aan8871

Cited by 525 publications

(354 citation statements)

References 92 publications

(86 reference statements)

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“…On the side of artificial consciousness, there is a recent review of cognitive systems inspired by how consciousness arise in humans made by Chella and Manzotti (2013) and another even more recent publication written by Dehaene et al (2017). We strongly agree with the first authors when saying that consciousness could be the missing step in the ladder from current artificial agents to human-like agents.…”

Section: Cognitive Systems In Social Roboticsmentioning

confidence: 51%

“…Compared to other robotic cognitive systems, some of which discussed in the state-of-the-art section, SEAI has still some shortages: homeostasis control is missing, the agent's physiological parameters are a symbolic representation, capabilities such as perspective-taking or mind-reading have been not yet considered. Most of the effort has been spent in the C1 meaning of consciousness, rather than in the C2 definition (Dehaene et al, 2017). On the other hand, SEAI stands out from the other systems thanks to the hybrid concept with which has been designed.…”

Section: Discussionmentioning

confidence: 99%

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SEAI: Social Emotional Artificial Intelligence Based on Damasio’s Theory of Mind

2018

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A socially intelligent robot must be capable to extract meaningful information in real time from the social environment and react accordingly with coherent human-like behavior. Moreover, it should be able to internalize this information, to reason on it at a higher level, build its own opinions independently, and then automatically bias the decision-making according to its unique experience. In the last decades, neuroscience research highlighted the link between the evolution of such complex behavior and the evolution of a certain level of consciousness, which cannot leave out of a body that feels emotions as discriminants and prompters. In order to develop cognitive systems for social robotics with greater human-likeliness, we used an "understanding by building" approach to model and implement a well-known theory of mind in the form of an artificial intelligence, and we tested it on a sophisticated robotic platform. The name of the presented system is SEAI (Social Emotional Artificial Intelligence), a cognitive system specifically conceived for social and emotional robots. It is designed as a bio-inspired, highly modular, hybrid system with emotion modeling and high-level reasoning capabilities. It follows the deliberative/reactive paradigm where a knowledge-based expert system is aimed at dealing with the high-level symbolic reasoning, while a more conventional reactive paradigm is deputed to the low-level processing and control. The SEAI system is also enriched by a model that simulates the Damasio's theory of consciousness and the theory of Somatic Markers. After a review of similar bio-inspired cognitive systems, we present the scientific foundations and their computational formalization at the basis of the SEAI framework. Then, a deeper technical description of the architecture is disclosed underlining the numerous parallelisms with the human cognitive system. Finally, the influence of artificial emotions and feelings, and their link with the robot's beliefs and decisions have been tested in a physical humanoid involved in Human-Robot Interaction (HRI).

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Section: Cognitive Systems In Social Roboticsmentioning

confidence: 51%

Section: Discussionmentioning

confidence: 99%

SEAI: Social Emotional Artificial Intelligence Based on Damasio’s Theory of Mind

2018

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“…IIT predicts that a sophisticated simulation of a human brain running on a digital computer cannot be conscious—even if it can speak in a manner indistinguishable from a human being . This is in stark contrast to the predictions of GNW which posits that machines (AIs) will be able to show consciousness at a number of different high levels independently of the substrate.…”

Section: Consciousness and Cognitionmentioning

confidence: 90%

Can Biological Quantum Networks Solve NP‐Hard Problems?

Wendin

2019

Adv Quantum Tech

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There is a widespread view that the human brain is so complex that it cannot be efficiently simulated by universal Turing machines, let alone ordinary classical computers. During the last decades the question has therefore been raised whether it is needed to consider quantum effects to explain the imagined cognitive power of a conscious mind. Not surprisingly, the conclusion is that quantum‐enhanced cognition and intelligence are very unlikely to be found in biological brains. Quantum effects may certainly influence signaling pathways at the molecular level in the brain network, like ion ports, synapses, sensors, and enzymes. This might evidently influence the functionality of some nodes and perhaps even the overall intelligence of the brain network, but hardly give it any dramatically enhanced functionality. The conclusion is that biological quantum networks can only approximately solve small instances of nonpolynomial (NP)‐hard problems. On the other hand, artificial intelligence and machine learning implemented in complex dynamical systems based on genuine quantum networks can certainly be expected to show enhanced performance and quantum advantage compared with classical networks. Nevertheless, even quantum networks can only be expected to solve NP‐hard problems approximately. In the end it is a question of precision—Nature is approximate.

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“…Some theorists suggest that building the right computational processes into machines is sufficient to make them conscious. For instance, Dehaene, Lau, and Kouider (2017) propose that machines are conscious if they can select information for global broadcasting, making it flexibly available for computations, and if they can self-monitor those computations. To support their proposal, they claim that a machine with both computational processes will behave 'as though it were conscious' (492).…”

Section: Fig 2 Two Overlapping Kinds Of Biological Speciesmentioning

confidence: 99%

“…Rather we can see them as jointly clarifying the more fine-grained taxonomy of conscious states needed in research on artificial consciousness. On one hand, Dehaene, Lau, and Kouider (2017) are investigating the kind of states which are defined purely in computational terms without reference to biological mechanisms; in particular they are interested in the explanatory structures associated with global broadcasting and self-monitoring. On the other hand, Haladjian and Montemayor (2016) and GodfreySmith (2016b) are interested in another kind of states, defined partly in biological terms; they raise different difficulties for realising such states in machines.…”

Section: Fig 2 Two Overlapping Kinds Of Biological Speciesmentioning

confidence: 99%

Artificial Consciousness: From Impossibility to Multiplicity

Chin

2018

Studies in Applied Philosophy, Epistemology and Rational Ethics

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Abstract. How has multiplicity superseded impossibility in philosophical challenges to artificial consciousness? I assess a trajectory in recent debates on artificial consciousness, in which metaphysical and explanatory challenges to the possibility of building conscious machines lead to epistemological concerns about the multiplicity underlying 'what it is like' to be a conscious creature or be in a conscious state. First, I analyse earlier challenges which claim that phenomenal consciousness cannot arise, or cannot be built, in machines. These are based on Block's Chinese Nation and Chalmers' Hard Problem. To defuse such challenges, theorists of artificial consciousness can appeal to empirical methods and models of explanation. Second, I explain why this naturalistic approach produces an epistemological puzzle on the role of biological properties in phenomenal consciousness. Neither behavioural tests nor theoretical inferences seem to settle whether our machines are conscious. Third, I evaluate whether the new challenge can be managed through a more fine-grained taxonomy of conscious states. This strategy is supported by the development of similar taxonomies for biological species and animal consciousness. Although it makes sense of some current models of artificial consciousness, it raises questions about their subjective and moral significance.

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What is consciousness, and could machines have it?

Cited by 525 publications

References 92 publications

SEAI: Social Emotional Artificial Intelligence Based on Damasio’s Theory of Mind

SEAI: Social Emotional Artificial Intelligence Based on Damasio’s Theory of Mind

Can Biological Quantum Networks Solve NP‐Hard Problems?

Artificial Consciousness: From Impossibility to Multiplicity

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