What is happened after my projects has been finished.

The following article [pdf] in a professional magazin gives an overview of logistic solutions with proALPHA (sorry, only in German): Artikel-im-Sonderheft_Logistik-Heute_02-2018_Seite 38-39 [GER]

Cognitive abilities in AI, such as learning, reasoning, problem-solving, and perception, are crucial for driving technological advancement and fostering a deeper understanding of AI. Here’s why: AI can be a sparring partner or personal assistant with deep understandig e.g. of excel or your specific software after learning procedures. This can lead to software improvements.

AI’s cognitive abilities will drive technological progress and lead to a better understanding of users. This is an an example of an AI assistant observing software usage:

• Enhanced Problem-Solving: Cognitive AI can tackle complex problems that are difficult or impossible for traditional algorithms to solve. By learning from data and experience, it can identify patterns, make predictions, and adapt to changing circumstances. This leads to more effective solutions in various domains, from healthcare and finance to manufacturing and transportation.
• Improved Human-Computer Interaction: Cognitive AI enables more natural and intuitive interactions between humans and computers. By understanding natural language, recognizing facial expressions, and interpreting human behavior, AI can create more user-friendly interfaces and personalized experiences.
• Deeper Understanding of the World: By processing and analyzing vast amounts of data from various sources, cognitive AI can uncover hidden patterns and relationships in the world around us. This can lead to new scientific discoveries, better understanding of social phenomena, and more effective solutions to global challenges.
• Increased Transparency and Explainability: As AI becomes more sophisticated, it’s crucial to understand how it makes decisions. Research in explainable AI (XAI) focuses on developing methods to make AI’s reasoning processes more transparent and understandable to humans. This builds trust and allows users to better understand the capabilities and limitations of AI.

Example: An AI Assistant Observing Software Usage:

Imagine an AI assistant that observes how users interact with a specific software application. Instead of solely relying on documentation or pre-programmed rules, this AI learns by watching real users.

Here’s how this approach benefits both the user and the software itself:

• Learning from Real-World Usage: The AI observes how users navigate the software, which features they use most often, and where they encounter difficulties. This provides a much richer understanding of actual software usage than static documentation can provide.
• Assisting with User Errors: When a user makes a mistake, the AI can recognize the error pattern based on its observations and offer targeted assistance. This could involve providing helpful tips, suggesting alternative approaches, or even automatically correcting the error.
• Automating Tasks: The AI can learn to perform repetitive tasks on behalf of the user, based on observed patterns of behavior. For example, if a user always performs a specific sequence of actions, the AI can offer to automate this sequence.
• Improving Software Usability: By analyzing user interactions, the AI can identify areas where the software is confusing or difficult to use. This information can be used to redesign the interface, improve workflows, and make the software more user-friendly. This feedback loop is invaluable for ongoing software development.

Specific Examples of AI Assistance:

• Predictive Actions: The AI predicts the user’s next action based on their past behavior and offers suggestions or shortcuts.
• Contextual Help: The AI provides help and guidance that is specific to the current context within the software.
• Personalized Tutorials: The AI generates personalized tutorials based on the user’s individual learning style and needs.
• Automated Troubleshooting: The AI automatically detects and resolves common software problems based on observed error patterns.

By observing real-world usage, this type of cognitive AI assistant can bridge the gap between software developers’ intentions and actual user behavior, leading to more user-friendly and effective software. This approach also demystifies the AI itself, as users directly experience its helpful capabilities.