400 research outputs found
Talking About Large Language Models
Thanks to rapid progress in artificial intelligence, we have entered an era
when technology and philosophy intersect in interesting ways. Sitting squarely
at the centre of this intersection are large language models (LLMs). The more
adept LLMs become at mimicking human language, the more vulnerable we become to
anthropomorphism, to seeing the systems in which they are embedded as more
human-like than they really are. This trend is amplified by the natural
tendency to use philosophically loaded terms, such as "knows", "believes", and
"thinks", when describing these systems. To mitigate this trend, this paper
advocates the practice of repeatedly stepping back to remind ourselves of how
LLMs, and the systems of which they form a part, actually work. The hope is
that increased scientific precision will encourage more philosophical nuance in
the discourse around artificial intelligence, both within the field and in the
public sphere
Consciousness as integrated perception, motivation, cognition, and action
This commentary has two aims: first to clarify the behavioural grounds for the ascription of consciousness to non-human animals (including insects), and second to show how Klein & Barron’s views can be reconciled with the core claims of global workspace theory
Classifying Options for Deep Reinforcement Learning
In this paper we combine one method for hierarchical reinforcement learning -
the options framework - with deep Q-networks (DQNs) through the use of
different "option heads" on the policy network, and a supervisory network for
choosing between the different options. We utilise our setup to investigate the
effects of architectural constraints in subtasks with positive and negative
transfer, across a range of network capacities. We empirically show that our
augmented DQN has lower sample complexity when simultaneously learning subtasks
with negative transfer, without degrading performance when learning subtasks
with positive transfer.Comment: IJCAI 2016 Workshop on Deep Reinforcement Learning: Frontiers and
Challenge
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