Artificial Intelligence is not an isolated, monolithic discipline. Advances in AI do not necessarily translate into usable solutions for real-world problems. Additionally, the evolving nature of AI implies that many AI solutions are far from being perfect. Consequently, the deficiencies in AI often need to be addressed through modern software engineering.
Consider the following:
- Certain classes of AI applications (for example, Computer Vision) can be extremely resource intensive in terms of compute, storage and network.
- Enterprise AI applications are often part of larger enterprise software systems, and/or need to be integrated with other software applications.
- AI applications often need to be designed to self-adapt and self-evolve over time. As such, their operational requirements may be different from those of traditional software applications.
The above factors necessitate optimizing the overall AI application and the hosting infrastructure to fulfil the requirements of run-time execution, particularly in large-scale enterprise production environments with high performance needs. Course5 Intelligence addresses these by focusing on AI-specific software engineering R&D.
Our Key Differentiation
Development of hybrid systems that comprise of probabilistic (AI) sub-systems and deterministic (rules-based software engineering) sub-systems to overcome the deficiencies of pure AI systems
Address the Black-box nature of AI algorithms through other software algorithms
Focus on building distributed AI systems that can be easily integrated with Big Data systems (like Hadoop or Spark) so that massive amounts of data can be leveraged to create robust AI solutions
Course5 Software R&D Framework
Optimizes cycle time and maximizes efficiency while designing, developing, testing and deploying new proof-of-concepts and prototypes in Software Engineering