The Emergence of Intelligence – How AI Self-Assembles Through Complexity

AI | Crystal emerging from bluish fog watercolors and silver

The creation of AI, at its core, is a process of emergence through complexity. It is not “born” in a biological sense, but rather it’s built and trained through layers of algorithms and data. The concept of AI “emerging” refers to the phenomenon that as the complexity of an AI system increases, new properties and capabilities can manifest that were not explicitly programmed into the system. This is often seen in machine learning and deep learning systems, where the AI can learn from data and improve over time, exhibiting behaviours that may seem to “emerge” organically from the learning process.

Similar to the lifecycle of a typical software product or hardware infrastructure, the development of an AI system also follows a lifecycle, sometimes referred to as the AI development lifecycle or AI project lifecycle. This lifecycle typically outlines the sequential stages involved in the development, deployment, and maintenance of an AI system.

1. Problem Definition: The first step in the AI lifecycle is defining the problem that needs to be solved. This includes understanding business goals, defining specific objectives for the AI system, and identifying key performance indicators (KPIs) to measure the success of the AI system.

2. Data Collection: AI systems require data to learn from. This step involves gathering relevant data that the AI system will use to train. This could involve data creation, data augmentation, or collecting data from different sources.

3. Data Preparation: The collected data is cleaned and organised. This might involve dealing with missing or inconsistent data, normalisation, and other forms of preprocessing to make the data suitable for training an AI model.

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4. Model Selection & Training: In this stage, an appropriate AI model is chosen based on the problem at hand. The model is then trained using the prepared data. This involves tuning parameters, selecting features, and iteratively refining the model.

5. Evaluation: After training, the model’s performance is evaluated. This involves testing the model on unseen data and measuring its performance using pre-defined KPIs.

6. Deployment: If the model’s performance is satisfactory, it is deployed into the real-world environment where it begins to make predictions or decisions based on new data.

7. Monitoring and Maintenance: After deployment, the AI system needs to be continuously monitored to ensure it is performing as expected. The system may require updates, retraining with new data, or even a complete redesign if the problem scope changes or if the model performance degrades over time.

8. Retirement: If an AI system is no longer needed, or if a better solution has been developed, the AI system is retired. This includes taking care of any data that the system was using or generated.

Ethics and privacy considerations should also be part of the entire lifecycle, from initial problem definition and data collection to deployment and retirement specifically for this context. It is important to ensure that AI systems are developed and used in a way that respects user privacy, minimises bias, and promotes fairness and transparency .

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