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Interdisciplinary AI
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1Quantum Revolution& Advanced AI2HumanAugmentation3Eco-Regeneration& Geo-Engineering4Science& Diplomacy1.11.21.31.42.12.22.32.43.13.23.33.43.54.14.24.34.44.5HIGHEST ANTICIPATIONPOTENTIALAdvancedArtificial IntelligenceQuantumTechnologiesBrain-inspiredComputingBiologicalComputingCognitiveEnhancementHuman Applications of Genetic EngineeringRadical HealthExtensionConsciousnessAugmentation DecarbonisationWorldSimulationFuture FoodSystemsSpaceResourcesOceanStewardshipComplex Systems forSocial EnhancementScience-basedDiplomacyInnovationsin EducationSustainableEconomicsCollaborativeScience Diplomacy
1Quantum Revolution& Advanced AI2HumanAugmentation3Eco-Regeneration& Geo-Engineering4Science& Diplomacy1.11.21.31.42.12.22.32.43.13.23.33.43.54.14.24.34.44.5HIGHEST ANTICIPATIONPOTENTIALAdvancedArtificial IntelligenceQuantumTechnologiesBrain-inspiredComputingBiologicalComputingCognitiveEnhancementHuman Applications of Genetic EngineeringRadical HealthExtensionConsciousnessAugmentation DecarbonisationWorldSimulationFuture FoodSystemsSpaceResourcesOceanStewardshipComplex Systems forSocial EnhancementScience-basedDiplomacyInnovationsin EducationSustainableEconomicsCollaborativeScience Diplomacy

Sub-Field:

1.1.4Interdisciplinary AI

As AI shifts away from huge datasets and brute-force computing approaches, this will create incentives and opportunities for combining with alternative approaches such as neuromorphic computing (chips mimicking neural networks directly into the hardware; see 1.3) and biocomputing (information processing based on biochemical components such as nerve cells, DNA or metabolic processes in the cell and which takes advantage of naturally occurring stochasticity and evolutionary processes to manipulate information; see 1.4).
Hybrid architecture combining these approaches with traditional machine learning might yield unexpected advantages. Additionally, running machine learning algorithms on quantum computers (see 1.2) might prove useful for problems with small data sets and dealing with quantum objects, such as simulating chemical reactions or new materials.18,19 Quantum computing researchers are already working in collaboration with machine learning experts to assess the potential of leveraging a partnership between these fields.20

Future Horizons:

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5-yearhorizon

The era of quantum machine learning begins

Research identifies clear quantum advantage in machine learning applications, proving that quantum computers can assist classical machine learning algorithms to perform tasks more efficiently than either would achieve alone.

10-yearhorizon

Neuroscience accelerates AI development

Explorations of small-scale circuits in the human brain provide new interconnection models that inspire interesting new AI implementations in the lab. AI researchers and neuroscientists spin-out new startups aimed at exploiting these ideas.

25-yearhorizon

Quantum-based AI makes scientific breakthroughs

Quantum machine learning running on quantum computers proves useful for problems with small data sets and dealing with quantum objects, such as simulating chemical reactions or new materials.

Interdisciplinary AI - Anticipation Scores

How the experts see this field in terms of the expected time to maturity, transformational effect across science and industries, current state of awareness among stakeholders and its possible impact on people, society and the planet. See methodology for more information.

GESDA Best Reads and Key Resources