No products in the cart.
Generative AI Advancement
Generative AI advancement is being propelled by a surge in exploration and adoption.
Generative AI Advancement
Notably, the rise of stable diffusion, midjourney, ChatGPT, and large language models has fueled this growth.
In response, technology vendors are forming dedicated generative AI groups to prioritize the development of AI-enabled applications and tools. The year 2023 has witnessed the emergence of numerous startups dedicated to innovating with generative AI, with expectations of this trend continuing to expand. Additionally, certain governments are assessing the impact of generative AI and preparing to introduce regulatory measures.
Key technologies in this domain encompass:
- AI Simulation: Combining AI and simulation technologies to create AI agents and simulated environments for training, testing, and deployment.
- AI Trust, Risk, and Security Management (AI TRiSM): Ensuring governance, trustworthiness, fairness, reliability, robustness, efficacy, and data protection in AI models.
- Causal AI: Leveraging cause-and-effect relationships to move beyond correlation-based predictive models toward AI systems capable of prescribing actions and autonomous decision-making.
- Data Labeling and Annotation (DL&A): Enhancing data assets through classification, segmentation, annotation, and augmentation to improve data quality for analytics and AI projects.
- First-Principles AI (FPAI): Incorporating physical and analog principles, governing laws, and domain knowledge into AI models, expanding AI engineering into complex system engineering and model-based systems.
- Foundation Models: Large-parameter models trained on diverse datasets in a self-supervised manner.
- Knowledge Graphs: Machine-readable representations of both physical and digital worlds, capturing entities and their relationships according to a graph data model.
- Multiagent Systems (MAS): AI systems composed of multiple independent yet interactive agents capable of perceiving their environment and taking actions, including AI models, software programs, robots, and other computational entities.
- Neurosymbolic AI: A form of composite AI that combines machine learning and symbolic systems to create robust and trustworthy AI models, facilitating a broader range of problem-solving.
- Responsible AI: Addressing business and ethical considerations in AI adoption, encompassing organizational responsibilities and practices to ensure positive, accountable, and ethical AI development and operation
