4,334 research outputs found
On Fodor on Darwin on Evolution
Jerry Fodor argues that Darwin was wrong about "natural selection" because (1) it is only a tautology rather than a scientific law that can support counterfactuals ("If X had happened, Y would have happened") and because (2) only minds can select. Hence Darwin's analogy with "artificial selection" by animal breeders was misleading and evolutionary explanation is nothing but post-hoc historical narrative. I argue that Darwin was right on all counts. Until Darwin's "tautology," it had been believed that either (a) God had created all organisms as they are, or (b) organisms had always been as they are. Darwin revealed instead that (c) organisms have heritable traits that evolved across time through random variation, with survival and reproduction in (changing) environments determining (mindlessly) which variants were successfully transmitted to the next generation. This not only provided the (true) alternative (c), but also the methodology for investigating which traits had been adaptive, how and why; it also led to the discovery of the genetic mechanism of the encoding, variation and evolution of heritable traits. Fodor also draws erroneous conclusions from the analogy between Darwinian evolution and Skinnerian reinforcement learning. Fodor’s skepticism about both evolution and learning may be motivated by an overgeneralization of Chomsky’s “poverty of the stimulus argument” -- from the origin of Universal Grammar (UG) to the origin of the “concepts” underlying word meaning, which, Fodor thinks, must be “endogenous,” rather than evolved or learned
Deep reinforcement learning from human preferences
For sophisticated reinforcement learning (RL) systems to interact usefully
with real-world environments, we need to communicate complex goals to these
systems. In this work, we explore goals defined in terms of (non-expert) human
preferences between pairs of trajectory segments. We show that this approach
can effectively solve complex RL tasks without access to the reward function,
including Atari games and simulated robot locomotion, while providing feedback
on less than one percent of our agent's interactions with the environment. This
reduces the cost of human oversight far enough that it can be practically
applied to state-of-the-art RL systems. To demonstrate the flexibility of our
approach, we show that we can successfully train complex novel behaviors with
about an hour of human time. These behaviors and environments are considerably
more complex than any that have been previously learned from human feedback
Evolutionary Robotics: a new scientific tool for studying cognition
We survey developments in Artificial Neural Networks, in Behaviour-based Robotics and Evolutionary Algorithms that set the stage for Evolutionary Robotics in the 1990s. We examine the motivations for using ER as a scientific tool for studying minimal models of cognition, with the advantage of being capable of generating integrated sensorimotor systems with minimal (or controllable) prejudices. These systems must act as a whole in close coupling with their environments which is an essential aspect of real cognition that is often either bypassed or modelled poorly in other disciplines. We demonstrate with three example studies: homeostasis under visual inversion; the origins of learning; and the ontogenetic acquisition of entrainment
On Fodor on Darwin on Evolution
Jerry Fodor argues that Darwin was wrong about "natural selection" because (1) it is only a tautology rather than a scientific law that can support counterfactuals ("If X had happened, Y would have happened") and because (2) only minds can select. Hence Darwin's analogy with "artificial selection" by animal breeders was misleading and evolutionary explanation is nothing but post-hoc historical narrative. I argue that Darwin was right on all counts. Until Darwin's "tautology," it had been believed that either (a) God had created all organisms as they are, or (b) organisms had always been as they are. Darwin revealed instead that (c) organisms have heritable traits that evolved across time through random variation, with survival and reproduction in (changing) environments determining (mindlessly) which variants were successfully transmitted to the next generation. This not only provided the (true) alternative (c), but also the methodology for investigating which traits had been adaptive, how and why; it also led to the discovery of the genetic mechanism of the encoding, variation and evolution of heritable traits. Fodor also draws erroneous conclusions from the analogy between Darwinian evolution and Skinnerian reinforcement learning. Fodor's skepticism about both evolution and learning may be motivated by an overgeneralization of Chomsky's "poverty of the stimulus argument" -- from the origin of Universal Grammar (UG) to the origin of the "concepts" underlying word meaning, which, Fodor thinks, must be "endogenous," rather than evolved or learned
Cyber-Agricultural Systems for Crop Breeding and Sustainable Production
The Cyber-Agricultural System (CAS) Represents an overarching Framework of Agriculture that Leverages Recent Advances in Ubiquitous Sensing, Artificial Intelligence, Smart Actuators, and Scalable Cyberinfrastructure (CI) in Both Breeding and Production Agriculture. We Discuss the Recent Progress and Perspective of the Three Fundamental Components of CAS – Sensing, Modeling, and Actuation – and the Emerging Concept of Agricultural Digital Twins (DTs). We Also Discuss How Scalable CI is Becoming a Key Enabler of Smart Agriculture. in This Review We Shed Light on the Significance of CAS in Revolutionizing Crop Breeding and Production by Enhancing Efficiency, Productivity, Sustainability, and Resilience to Changing Climate. Finally, We Identify Underexplored and Promising Future Directions for CAS Research and Development
AGI for Agriculture
Artificial General Intelligence (AGI) is poised to revolutionize a variety of
sectors, including healthcare, finance, transportation, and education. Within
healthcare, AGI is being utilized to analyze clinical medical notes, recognize
patterns in patient data, and aid in patient management. Agriculture is another
critical sector that impacts the lives of individuals worldwide. It serves as a
foundation for providing food, fiber, and fuel, yet faces several challenges,
such as climate change, soil degradation, water scarcity, and food security.
AGI has the potential to tackle these issues by enhancing crop yields, reducing
waste, and promoting sustainable farming practices. It can also help farmers
make informed decisions by leveraging real-time data, leading to more efficient
and effective farm management. This paper delves into the potential future
applications of AGI in agriculture, such as agriculture image processing,
natural language processing (NLP), robotics, knowledge graphs, and
infrastructure, and their impact on precision livestock and precision crops. By
leveraging the power of AGI, these emerging technologies can provide farmers
with actionable insights, allowing for optimized decision-making and increased
productivity. The transformative potential of AGI in agriculture is vast, and
this paper aims to highlight its potential to revolutionize the industry
Music as complex emergent behaviour : an approach to interactive music systems
Access to the full-text thesis is no longer available at the author's request, due to 3rd party copyright restrictions. Access removed on 28.11.2016 by CS (TIS).Metadata merged with duplicate record (http://hdl.handle.net/10026.1/770) on 20.12.2016 by CS (TIS).This is a digitised version of a thesis that was deposited in the University Library. If you are the author please contact PEARL Admin ([email protected]) to discuss options.This thesis suggests a new model of human-machine interaction in the domain of non-idiomatic
musical improvisation. Musical results are viewed as emergent phenomena
issuing from complex internal systems behaviour in relation to input from a single
human performer. We investigate the prospect of rewarding interaction whereby a
system modifies itself in coherent though non-trivial ways as a result of exposure to a
human interactor. In addition, we explore whether such interactions can be sustained
over extended time spans. These objectives translate into four criteria for evaluation;
maximisation of human influence, blending of human and machine influence in the
creation of machine responses, the maintenance of independent machine motivations
in order to support machine autonomy and finally, a combination of global emergent
behaviour and variable behaviour in the long run. Our implementation is heavily
inspired by ideas and engineering approaches from the discipline of Artificial Life.
However, we also address a collection of representative existing systems from the
field of interactive composing, some of which are implemented using techniques of
conventional Artificial Intelligence. All systems serve as a contextual background and
comparative framework helping the assessment of the work reported here.
This thesis advocates a networked model incorporating functionality for listening,
playing and the synthesis of machine motivations. The latter incorporate dynamic
relationships instructing the machine to either integrate with a musical context
suggested by the human performer or, in contrast, perform as an individual musical
character irrespective of context. Techniques of evolutionary computing are used to
optimise system components over time. Evolution proceeds based on an implicit
fitness measure; the melodic distance between consecutive musical statements made
by human and machine in relation to the currently prevailing machine motivation.
A substantial number of systematic experiments reveal complex emergent behaviour
inside and between the various systems modules. Music scores document how global
systems behaviour is rendered into actual musical output. The concluding chapter
offers evidence of how the research criteria were accomplished and proposes
recommendations for future research
- …