Use the future to build the present
Next-level AI
Comment
Stakeholder Type
,
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:

1.1.3Next-level AI

Moving beyond the machine learning paradigm towards more flexible AI is likely to involve coupling the strengths of ML with the strengths of other approaches to AI. The aim here is to move towards the kind of intelligence displayed by human beings, where learning happens without vast data resources, without intensive training, at low computational cost.15 In addition, humans gain knowledge in a way that allows them to use “common sense”, and to transfer knowledge and experience between domains by representing data in compact hierarchical structures based on concepts and their relationships.16
As our survey results made clear, replicating human level intelligence (often referred to as strong AI, or Artificial General Intelligence (AGI)) remains a distant goal, but even small steps in this direction will open up a host of transformative applications. One approach with potential is Symbolic AI, which has the advantages of being adaptable to context, and having a degree of transparency, allowing us to understand, validate and live comfortably with AI-sourced decisions, whether in healthcare, the judicial system, workplace recruitment or other domains.

Future Horizons:

×××

5-yearhorizon

AI systems display potential for “common sense”

Symbolic AI algorithms demonstrate basic knowledge transference across domains and begin to perform basic functions without extensive training.

10-yearhorizon

AI begins to display more human-like learning

Artificial curiosity expands the scope for learning in situations where tasks are not yet well defined. Algorithms can look up and integrate knowledge found in encyclopaedias. Continuous learning includes memory effects, working with dynamic data (e.g. cumulative rainfall) that can introduce changes to the algorithm’s operation. Research helps uncover algorithms’ vulnerabilities, understand their limits and devise possible strategies to protect them from malicious data.

25-yearhorizon

AI becomes more like human intelligence

AI may reach a number of milestones towards human abilities within this time frame. These include tasks such as understanding people’s motivations (testable by answering open-ended questions about the hypothetical scenarios shown in a video sequence), transferring knowledge between different tasks, emulating analogies, or guessing how an appliance works and using it in a real-world situation.17

Next-level 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