More and more businesses are leveraging Artificial Intelligence (AI) to drive transformative customer experiences and real-time business decisions, but to get the competitive advantage the organizations need AI that is accurate, fast and secure.
AI 1.0 focused on pattern recognition, task-specific models, and centralized training of models and their execution. AI 2.0 on the other hand is defined by the establishment of models to generate language, images and other data, as well as the universal applicability of AI, centrally or locally – at the Edge.
Following are the key elements of AI 2.0 identified by the recent Forrester report that are driving the innovation to addresses the accuracy, speed, and security issues.
- Transformer networks – To train large model with less data
- Synthetic data – Use large synthetic data to improve accuracy
- Reinforcement learning – To respond to changes in data
- Federated learning – Train models in distributed manner
- Casual inference – To avoid non-optimal business decisions
The firms that will increase their efficiencies and create new business models using progressive technologies like AI will continue to survive and thrive in any economy.