Joint Learning of Multiple Tasks, also recognized as Multi-task Learning (MTL), proposes a potent paradigm to concurrently realize and optimize numerous interconnected assignments. By leveraging pertinent information across related applications, joint learning facilitates accelerated learning, improved generalization and progression of the model's applicability to multi-task situations.

- **Connectivity:** MTL capitalizes on the connectivity between associated tasks. This interconnectedness can enable improved performance and precision in learning compared to individual task learning.

• Shared Representation: Joint learning often utilizes a shared representation to extract common features beneficial to all tasks. This shared representation can enhance the efficiency of the learning process.

• Task-specific Components: Along with shared representation, each task in joint learning typically possesses task-specific components. These tailor-made components cater to the unique characteristics and requirements of individual tasks.

• Balance: Striking a balanced focus on all tasks is critical in joint learning. An extreme inclination towards one task over the other can impede the accomplishment of the weaker task, ultimately affecting the model's overall performance.

• Boost in Performance: As it capitalizes on the innately interconnected relationships between tasks, joint learning often results in heightened performance compared to individual task learning.

• Improvement in Generalization: Joint learning procures commonalities across tasks enhancing the model's ability to generalise its knowledge, making it a more flexible and adaptable solution.

• Improved Performance: By sharing representations across tasks, joint learning can boost the overall performance of the model.

• Efficiency: Joint learning facilitates efficient learning as it not only learns from individual tasks, but supplements this learning with knowledge obtained from other tasks.

• Scalability: It provides scalability by effectively managing multiple tasks simultaneously. As such, it can readily adapt to growing business needs.

• Access to Proprietary Features: A multi-task learning model allows instantaneous access to advanced features, eliminating the hassle of extended development time.

• Task Imbalance: Managing multiple tasks requires equal importance to all tasks. Ignoring or overlooking any task could lead to subpar learning outcomes.

• Dependence on Common Representations: While common representations facilitate learning, an over-reliance could potentially mask the nuances of individual tasks.

• Integration Difficulties: Often, a challenge with joint learning aficionados is designing a model that can efficiently integrate information across tasks.

• Forcing Unrelated Tasks: One of the inherent assumptions in joint learning is the existence of a relationship between tasks. Forcing unrelated tasks could lead to detrimental learning outcomes.

The integration of joint learning necessitates a precise understanding of the problem domain and an astute comprehension of the relationships between tasks. Optimal usage of joint learning should follow a rigorous process of problem identification, analysis of relatedness between tasks, selection of suitable features for shared representation and proper handling of task-specific components.

In summary, joint learning of multiple tasks presents a promising avenue propelling machine learning advancements. While it brings certain challenges that require skillful management, the benefits in terms of generalization, performance, and efficiency are substantive. As the digital age progresses, joint learning continues to mark its ground as a critical player in machine learning and artificial intelligence.