Best Reads
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Best Reads
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Best Reads
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5.5SyntheticBiology5.4Science ofthe Originsof Life5.3FutureEconomics5.2Future ofEducation5.1ComplexSystemsScience4.4Democracy-affirming Technologies4.1Science-basedDiplomacy4.2Advances inScience Diplomacy4.3Digital Technologiesand Conflict3.7InfectiousDiseases3.6Solar RadiationModification3.5OceanStewardship3.4SpaceResources3.3Future FoodSystems3.2WorldSimulation3.1Decarbonisation2.6FutureTherapeutics2.5Organoids2.4ConsciousnessAugmentation2.3RadicalHealthExtension2.2HumanApplicationsof GeneticEngineering2.1CognitiveEnhancement1.6CollectiveIntelligence1.5AugmentedReality1.4BiologicalComputing1.3Brain-inspiredComputing1.2QuantumTechnologies1.1AdvancedAIHIGHEST ANTICIPATIONPOTENTIAL
5.5SyntheticBiology5.4Science ofthe Originsof Life5.3FutureEconomics5.2Future ofEducation5.1ComplexSystemsScience4.4Democracy-affirming Technologies4.1Science-basedDiplomacy4.2Advances inScience Diplomacy4.3Digital Technologiesand Conflict3.7InfectiousDiseases3.6Solar RadiationModification3.5OceanStewardship3.4SpaceResources3.3Future FoodSystems3.2WorldSimulation3.1Decarbonisation2.6FutureTherapeutics2.5Organoids2.4ConsciousnessAugmentation2.3RadicalHealthExtension2.2HumanApplicationsof GeneticEngineering2.1CognitiveEnhancement1.6CollectiveIntelligence1.5AugmentedReality1.4BiologicalComputing1.3Brain-inspiredComputing1.2QuantumTechnologies1.1AdvancedAIHIGHEST ANTICIPATIONPOTENTIAL

Appendices:

Best Reads

    The GESDA Best Reads provide a carefully curated list of recent key articles and resources in relation to the scientific emerging topics described in the trend section. The GESDA BestReads are available as a monthly newsletter at the following link.

    Article

    Why AI is Harder Than We Think // 28.04.2021, arXiv

    Published:

    7th Aug 2021
    Since its beginning in the 1950s, the field of artificial intelligence has cycled several times between periods of optimistic predictions and massive investment (“AI spring”) and periods of disappointment, loss of confi- dence, and reduced funding (“AI winter”). Even with today’s seemingly fast pace of AI breakthroughs, the development of long-promised technologies such as self-driving cars, housekeeping robots, and conversational companions has turned out to be much harder than many people expected. One reason for these repeating cycles is our limited understanding of the nature and complexity of intelligence itself. In this paper I describe four fallacies in common assumptions made by AI researchers, which can lead to overconfident predictions about the field. I conclude by discussing the open questions spurred by these fallacies, including the age-old challenge of imbuing machines with humanlike common sense.

    1.1.1Deeper Machine Learning

    1.1.2Human-centred AI

    1.1.3Next-level AI

    1.1.4Interdisciplinary AI

    1.2.1Quantum Communication

    1.2.2Quantum Computing

    1.2.3Quantum Sensing and Imaging

    1.2.4Quantum Foundations

    1.3.1Neural Network Architectures

    1.3.2Neuromorphic Systems

    1.3.3Neural Network Algorithms

    1.3.4Neuromorphic Benchmarking

    1.4.1Bio-architectures

    1.4.2Bio-computational Logic and Strategies

    1.4.3Programmable Bio-synthesis

    1.4.4Novel Bio-computing Paradigms

    1.5.1Augmented reality hardware

    1.5.2Augmented experiences

    1.5.3AR platforms

    1.5.4Human factors of AR

    1.6.1Large-scale collaboration