Adaptive AI: What is it exactly?
Adaptive AI (Autonomous Intelligence) is the advanced and responsive version of traditional autonomous intelligence with independent learning methods. Adaptive AI incorporates a decision-making framework that helps in faster decision-making while remaining flexible to adjust as issues arise. The Adaptive nature is achieved by continuously re-training and learning models while performing based on new data.
This kind of AI is developed to enhance real-time performance by updating its algorithms, decision-making methods, and actions based on the data it receives from its environment. Adaptive AI enables the system to reply better to changes and challenges and achieve goals more effectively.
For example, let’s compare the learning model of both traditional AI and adaptive AI. In the case of an object detection system in self-driving cars, the car should be able to detect different objects, such as pedestrians. Hence, the system should be trained using a large number of samples to ensure safety. As new things keep emerging, such as cyclists, electric steps, hoverboards, etc., the system should be regularly updated with new data for identification. However, in the case of traditional AI, if the system is updated with new data, the system will forget the earlier objects, such as pedestrians. This phenomenon is referred to as catastrophic forgetting with neural networks.
Hence, to overcome this issue, the concept of adaptive AI was invented. The neural network retains all concepts learned over time, making it easy to recall what was learned using the information.
How does Adaptive AI matter to your business?
Adaptive AI offers a set of processes and AI techniques to allow systems to change or alter their learning techniques and behaviors. Adaptive AI makes it possible to adapt to changing real-world conditions while in production. It can alter its code to adapt to the modifications occurring in the real world that weren’t identified or known at the time when the code was first written.
According to Garter, businesses and companies that have utilized AI engineering techniques to develop and execute adaptive AI systems will achieve at least 25% greater operationalization speed and quantity than their rivals by 2026. By learning the past behavioral patterns of human and machine experience, adaptive AI provides faster and better outcomes.
For example, the U.S. Army and U.S. Air Force have developed a learning system that adapts its lessons to the learner utilizing their strengths. The program acts like a tutor that tailors the learning to the student. It knows what to teach, when to test, and how to measure progress.
How does Adaptive AI work?
Adaptive AI operates on the concept of continuous learning (CL), which defines a significant aspect of attaining AI capabilities. A continuous learning model can adapt in real-time to new data as it comes in and learns autonomously. However, this method, also called continuous AutoML or auto-adaptive learning is capable of mimicking human intelligence to learn and refine knowledge throughout life. It serves as an extension of traditional machine learning by allowing models to push real-time information into production environments and constrain them accordingly.
For example, Spotify is one of the most popular music streaming applications with adaptive AI algorithms. Spotify curates user-specific music recommendations. Based on the user’s history of songs, Spotify analyzes the user’s song preferences and real-time trends to produce the most suitable recommendations. Further, to ensure relevance, Spotify utilizes an adaptive AI algorithm that continuously retrains and changes preferences. This dynamic learning method allows Spotify to provide a seamless and personalized music experience, helping users discover new songs, genres, and artists that suit their tastes.
AutoML (Automated Machine Learning) is one of the essential components of the continuous learning (CL) process of adaptive AI. AutoML refers to automating the complete machine learning (ML) pipeline, including data preparation, model selection, and deployment. AutoML aims to eliminate the requirements for training models and increase the accuracy of models with auto-detection. AutoML is an easy-to-use framework, open-source algorithm, and hyperparameter optimization.
After training, model validation is performed to verify the functionality of the models. Further, monitoring is implemented for predictions gathered within the model deployment area. Once the data is monitored, it can be cleaned and tagged as required. Once the data has been cleaned and tagged, we re-enter it into the data for validation and training. In this case, the cycle is closed.
The models constantly learn and adapt to new trends and data while improving accuracy. This gives the application better overall performance.
How to implement Adaptive AI?
Step 1: Determine the objective of the system
When executing adaptive AI, it is important to set the objectives of the system, as it guides its development and determines the desired outcome. Defining the system’s objectives involves considering the factors, such as determining the required outcome, setting performance metrics, and target audience.
Step 2: Data collection
When developing AI models, data acts as the primary building block for training machine learning models and allowing informed decision-making. The important factors to keep in mind when gathering data for adaptive AI are relevance to the objective of the system, diversity of the collected data, updated data, storage, and privacy.
Step 3: Model training
Training a machine learning model on a dataset to make predictions is known as model training. This vital phase in implementing adaptive AI establishes the groundwork for decision-making. The essential factors to take into account when training a model for adaptive AI are algorithm selection, hyperparameter tuning, data preparation, model evaluation, and model improvement.
Step 4: Contextual analysis
Contextual analysis involves examining the present context and utilizing that information to make well-informed decisions, enabling real-time responsiveness of the system. When performing contextual analysis for an adaptive AI system, the most important factors are data sources, model prediction, data processing, and the feedback loop.
Step 5: Evaluate and fine-tune the model
The process of fine-tuning an AI model includes making adjustments to its parameters or architecture to enhance its performance, depending on the specific model type and the problem it aims to solve. The commonly utilized techniques for fine-tuning include hyperparameter tuning, model architecture, feature engineering, ensemble methods, and transfer learning.
Step 6: Deploy the model
In the context of adaptive AI, deploying a model refers to creating a model accessible and operational in a production or real-world environment. This process generally encompasses the following steps:
- Model preparation: This involves preparing the model for production by transforming it into a TensorFlow SavedModel or a PyTorch script.
- Infrastructure setup: The required infrastructure is set up to support model deployment, including cloud environments, servers, or mobile devices.
- Deployment: The model is deployed by uploading it to a server or cloud environment or installing it on a mobile device.
- Model Management: Effective management of the deployed model includes performance monitoring, necessary updates, and ensuring accessibility to users.
- Integration: The deployed model is integrated into the overall system by connecting it with other components such as user interfaces, databases, or additional models.
Step 7: Continuous monitoring and improvement
After implementation, monitoring, and maintenance are required to ensure the continued proper functioning and effectiveness of an adaptive AI system. This involves performance monitoring, model retraining, data collection and analysis, system updates, and user feedback.
The best practices for implementing Adaptive AI
- Understand the problem:
Gaining a thorough understanding of the problem at hand is crucial for the effective training of adaptive AI systems. This understanding aids in identifying relevant information and training data, selecting appropriate algorithms, and establishing performance metrics to evaluate the system’s effectiveness. Defining precise goals for an adaptive AI system sets a specific target and enhances focus, optimizing the allocation of resources. Setting SMART goals (specific, measurable, achievable, relevant, and time-bound) enables progress evaluation and facilitates necessary adjustments. - Gather high-quality data:
Acquiring high-quality data is of utmost importance when striving to build a robust adaptive AI system capable of learning from the data and making accurate predictions. Insufficient quality in the training data adversely affects the system’s ability to model the problem, leading to suboptimal performance. Furthermore, diversity within the training data is critical to enable the system to learn from a wide array of examples while maintaining the ability to generalize to novel cases. This aspect holds particular significance in adaptive AI systems, which must adapt to real-time changes within the problem domain. Moreover, ensuring diverse training data empowers the system to handle new and unexpected situations effectively. - Select the right algorithm:
Making the right algorithm selection plays a key role in attaining optimal results in adaptive AI. While algorithms such as reinforcement learning and online learning are the most suitable choices for adaptive systems, the decision should be tailored to the particular problem and the type of training data involved. For example, online learning algorithms are well-suited for streaming data, whereas reinforcement learning algorithms excel in decision-making scenarios that necessitate a sequence of decisions made over time. - Performance monitoring:
Regularly monitoring performance and employing learning metrics are essential for evaluating the effectiveness of an adaptive AI system, particularly due to its real-time nature. Monitoring allows for tracking the system’s advancement towards desired outcomes, early identification of potential issues, and making necessary adjustments to enhance performance. - Implement effective testing and validation framework:
Implementing the right testing and validation framework is critical to ensuring the accuracy and reliability of an adaptive AI system. It is imperative to test system performance and identify any issues or errors that may affect accuracy and reliability. Various testing methods should be used to achieve this, including unit, integration, and performance testing.
In addition to using different test methods, it is important to use different test information that accurately reflects the problem space. This includes normal and extreme cases as well as unexpected scenarios. By including different test data, developers can test system performance under different conditions and identify opportunities for improvement.
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