Next-Generation AI Technologies (R&D)
A
- Agent-Oriented Architecture
- Agentic AI Alignment
- Agentic AI for Customer Engagement
- Agentic AI for Decision Support
- Agentic AI for Knowledge Management
- Agentic AI for Predictive Operations
- Agentic AI for Process Optimization
- Agentic AI for Workflow Automation
- Agentic AI Safety
- Agentic AI Strategy
- Agile Development
- Agile Development Methodology
- AI Agents for IT Service Management
- AI for Compliance Monitoring
- AI for Demand Forecasting
- AI for Edge Computing (Edge AI)
- AI for Energy Consumption Optimization
- AI for Predictive Analytics
- AI for Predictive Maintenance
- AI for Real Time Risk Monitoring
- AI for Telecom Network Optimization
- AI Orchestration
- Algorithm
- API Integration
- API Management
- Application Modernization
- Applied & GenAI
- Artificial Intelligence
- Augmented Reality
B
C
D
E
G
I
L
M
N
P
R
S
T
V
At Xebia, Next-Generation AI Technologies in research and development refer to the emerging paradigms, architectures, and algorithms that go beyond current state of the art. These might include neurosymbolic AI, quantum machine learning, continual learning, self-supervised models, and hybrid reasoning systems. Xebia invests in exploring these technologies, running prototypes, and bringing the innovations forward into industrial applications.
What Are the Key Benefits of Next-Generation AI Technologies (R&D)?
- Ability to solve more complex and abstract tasks than current AI systems
- Improved sample efficiency (learning from fewer data points)
- Better generalization across domains by combining symbolic and neural approaches
- Continuous learning, adaptation and lifelong intelligence
- Lower dependence on labeled data through self-supervised or unsupervised methods
- Unlocking new frontiers such as quantum enhanced models or cognitive AI
What Are Some Next-Generation AI Technologies (R&D) Use Cases at Xebia?
- Neurosymbolic agents that combine logic and neural learning to reason with structured rules
- Continual learning systems in robotics that never stop adapting
- Quantum machine learning for optimization and simulation in finance and logistics
- Self-supervised models trained on massive unlabeled data for domain adaptation
- Hybrid models combining symbolic knowledge graphs with neural embeddings
- Advanced R&D pilots in generative AI that go beyond current LLM architectures
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