New Forrester research outlines five key advances that are redefining AI use cases and shattering obstacles to enterprise AI adoption. Per Forrester, these advances are already entering commercial products, and forward-looking enterprises need to start preparing if they want to reap their competitive advantages.
They include the following:
- Transformer networks: Giant pretrained, customizable, hyperaccurate, multitasking deep learning models that can be used for any hard problem with a significant time or context dimension (e.g. understanding and generating text, software code, etc.).
- Synthetic data: Generative models and simulated virtual environments used to create or augment existing training data that can be used to accelerate the development of new AI solutions, improving the accuracy and robustness of existing AI models and protecting sensitive data.
- Reinforcement learning: Machine learning approaches that test their way to optimal actions via simulated environments or a large number of micro-experiments that can be used for constructing models that optimize many objectives/constraints or deciding on action based on positive and negative environmental feedback.
- Federated learning: A managed process for combining models trained separately on separate data sets that can be used for sharing intelligence between devices, systems, or firms to overcome privacy, bandwidth, or computational limits.
- Causal inference: Approaches such as structured equation modeling and causal Bayesian networks that help determine cause-and-effect relationships in data that can be used for business insights and bias prevention where insights and explainability are as important as prediction accuracy.
Forrester Analyst Kjell Carlsson is available for interviews to discuss further.