What are the key areas driving AI innovation?
Updated: Nov 24
More businesses are leveraging Artificial Intelligence (AI) to drive transformative customer experiences and real-time business decisions. Still, organizations need AI that is accurate, fast, and secure to get a competitive advantage.
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.
The key elements of AI 2.0 identified by the recent Forrester report driving innovation to address the accuracy, speed, and security issues.
Transformer networks – To train the large model with fever data.
Synthetic data – Use extensive synthetic data to improve accuracy.
Reinforcement learning – To respond to changes in data.
Federated learning – Train models in a 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.
The ideas mentioned above are meant as information to ease your organizational processes. However, if you would like a more detailed overview, do not hesitate to reach out to me at firstname.lastname@example.org.
I have years of experience building Technology and providing Technology Due Diligence as a CTO, and I am available for fruitful discussions.