| 1. Lack of Spatial Intelligence |
Develop models with advanced 3D spatial understanding, like Large World Models (LWMs). |
World Labs, Nvidia Omniverse, Meta Reality Labs |
| 2. Limited Real-Time Decision Making |
Integrate AI with real-time processing systems using edge computing. |
Nvidia (Jetson), OpenAI (plugins for real-time operations), Tesla FSD |
| 3. Narrow Multimodal Integration |
Create models that seamlessly handle text, images, audio, and video data together. |
Google DeepMind, OpenAI (DALL-E and Whisper integrations), RunwayML |
| 4. Energy-Intensive Training |
Optimize algorithms for lower energy consumption and use renewable energy sources. |
Cerebras Systems, OpenAI (energy-efficient clusters), Graphcore |
| 5. Black Box Nature (Lack of Explainability) |
Develop AI systems with inherent interpretability and transparency. |
Anthropic (AI alignment), DARPA XAI, IBM (Watson Explainable AI) |
| 6. Limited Domain Expertise |
Fine-tune large models with domain-specific datasets for better contextual results. |
Jasper AI (marketing), Glean AI (finance), PathAI (healthcare) |
| 7. Poor Physical Interaction Capability |
Enhance robotics integration with AI for better real-world interaction. |
Boston Dynamics, Waymo, Tesla, OpenAI (robotics experiments) |
| 8. Inability to Handle Complex Emotions |
Incorporate advancements in affective computing and emotional AI. |
Affectiva, Cogito, Microsoft (emotional recognition research) |
| 9. Dependency on Massive Data |
Use synthetic data generation and few-shot learning to reduce reliance on large datasets. |
Datagen, OpenAI (fine-tuning with less data), Google AI |
| 10. Security Vulnerabilities |
Focus on adversarial training and robust cybersecurity frameworks. |
Microsoft Azure AI, Palantir, MIT Lincoln Labs |