Artificial Intelligence
Artificial Intelligence is now widely adopted in applications that require interpretation of events, decision support, or automated actions. The development and adoption of AI is progressing exponentially as it automates many day-to-day aspects of analysis, decision, and communication of business operations. AI is also becoming more integrated with systems, processes, and humans as it moves from model-based to platform-based implementations.
Beyond the buzzword
Unsupervised Machine Learning
Reinforcement Learning
Generative models
Indeed, AI is a catalyst for process automation, human augmentation, and enhanced engagement. However, much progress still needs to be made to improve the technology and its applications to avoid chronic issues such as bias and interpretability. There is also the problem of AI technical debt: the proliferation of vendors and models has made it difficult for companies to have a consolidated implementation and governance of AI systems.
A global trend is underway to replace analog systems with modern, digital information exchange systems supporting automation and improved decision-support. Public-private collaboration programs NextGen (U.S.) and SESAR (Europe) are spearheading modernization of air traffic control systems to make air travel safer, more efficient, and more economical.
To maximize the utility of AI, it is recommended to focus on specific business problems it can solve. Most AI is currently specialized to specific domains and industries. This approach requires a mix of resources when implementing an AI solution: data scientists (models), software engineers (development), and subject matter experts (application knowledge). Only when these pillars are working together can an AI project be successfully implemented and solve the problem it intends to.
A model suite customizable to your application
Skymantics MLOps architecture provides a built-in AI model suite that can be used in replicable, scalable, and reliable development frameworks. Its explainable, modular structure makes the job easier for subject matter experts and developers who integrate it in enterprise applications to solve specific business problems. This approach guarantees sustainability as it facilitates governance, operationalization, and roadmap evolution of AI-based products in the organization.
Different model components can be applied to specific applications (click to learn more):
Models can also be combined to produce composite AI models. This allows a broader scope of the business problems being solved.
The Skymantics approach to production-ready AI
At Skymantics, we thrive at making new concepts a reality. For AI-based solutions, we rely on three pillars to achieve fast operationalization:
Explainability
Models that are interpretable and understandable by humans have a higher applicability and adoption rate
Agile, application-oriented development
AI models are built as business solutions in mind.
Simulation platform
AI and simulation are related capabilities. AI can accelerate and augment simulation systems by increasing realism and scenario flexibility.
Do you have business needs that may require Artificial Intelligence? Contact us to query about our solutions and models and request a demo today.