Artificial intelligence holds the potential to be a transformative tool, capable of continually learning and providing rich, actionable insights to drive the growth and success of your business. Due to their rigid structure, traditional machine learning models are ill-equipped to handle the dynamic nature of our rapidly changing world, particularly with the influx of data generated by the IoT and autonomous vehicles. The inability of these models to adapt to new data streams often limits the effectiveness of even cutting-edge machine learning methods. But with the advent of adaptive AI, this issue is being addressed. Adaptive AI, with its capability for continuous learning, provides a more flexible approach to machine learning by enabling models to evolve and adapt to changing data in real time. This helps to overcome the limitations of traditional machine learning and effectively tackle the challenges posed by our constantly evolving data landscape.
Continuous learning is a powerful form of AI that continually builds knowledge and improves over time. This makes it a game-changer for a multitude of industries, as it represents a move away from static learning towards a more adaptive approach. Gartner has identified adaptive AI as one of the top 10 emerging trends for 2023, poised to take AI to new heights by enabling it to learn as it develops. It is predicted that businesses utilizing adaptive AI will outperform their competitors by 25%. In a note, Gartner also mentioned “Flexibility and adaptability are now vital, as many businesses have learned during a recent health and climate crises,” says Gartner distinguished VP Analyst Erick Brethenoux. “Adaptive AI systems aim to continuously retrain models or apply other mechanisms to adapt and learn within runtime and development environments — making them more adaptive and resilient to change.”
This article provides an overview of adaptive AI, possible business areas where you can use it and the steps to implement it in real-life business.
What is adaptive AI?
To understand the concept better, let’s compare adaptive AI with the traditional machine learning model.
Consider an object detection system to drive a self-driving vehicle. The car must be able to recognize pedestrians and cyclists to ensure safe operation. It does an amazing job by training its neural network with large numbers of samples. However, as new object categories are constantly emerging, including hoverboards and electric steps, the car is also expected to detect hoverboards and electric steps if we update our detection system with a sufficient number of representative images. However, there is a catch! With traditional machine learning models, our system would forget pedestrians and cyclists from the old task if we update the system with new data and leave them unidentified. This phenomenon is known as catastrophic forgetting within neural networks.
There comes adaptive AI with continuous learning, a sequential method that does not involve catastrophic forgetting. In this case, the neural network retains all concepts learned over time, so it is easy to recall what you have learned using data. You can store and revisit the information when you are ready to learn something new. Given that these data streams may not have a definitive endpoint, it is crucial to remember that they may never end, thereby avoiding the risk of catastrophic forgetting within our systems.
Here is a brief comparison between adaptive AI and traditional AI:
How does adaptive AI work?
AutoML plays an essential component in the continuous learning process of adaptive AI. AutoML (Automated Machine Learning) refers to the process of automating the entire machine learning pipeline, from data preparation to model selection and deployment. The goal of AutoML is to remove the need for manual intervention, reduce the time and resources required for training models and improve the accuracy of machine learning models. AutoML does not have to be complicated meta-learning; instead, it is easy to use frameworks, hyperparameter optimization and open-source algorithms with AutoML. Transfer-learning might be a good option if you are working on a computer vision problem that allows you to use a variety of pre-trained models, which you can then use to retain the last layer of your network and then have your model deployed. For continuous learning, you can choose one of the pre-built models, such as VGG, Inception or ResNet. Also, specifying parameters for each model is essential.
After the training, you will do model validations to verify that all models function properly. You can also choose the best model and then deploy it to production. In this case, the pipeline appears to be like a closed traditional machine learning pipeline where monitoring is added to the pipeline to apply continuous learning while connecting it back to the data.
Monitoring will take place for predictions collected within the model deployment area. After the data has been monitored, you can clean it and label it as needed. However, closing the loop with no human labeling for things like forecasting or recommender systems will be possible. After clearing and labeling the data, we will move it back to the data for validation and training. The loop is closed in this case.
Your models will learn continuously and adapt to new trends and data while improving the model’s accuracy. Consequently, your application will perform better overall.
Why is adaptive AI critical for business growth?
Adaptive AI combines agent-based design and AI techniques like reinforcement learning, allowing systems to adapt their learning patterns and behaviors to real-world changes while in production. It learns from past human and machine experiences and delivers better results in runtime environments. For example, the U.S. Army has developed a system that adapts to each learner’s strengths using its learning system. It can tell what to teach, when to test, and how to measure progress while acting as a tutor and tailoring the learning to each student.
Adaptive AI systems will ultimately open the door to new business models, products, services, and channels, breaking down silos, and eventually, they will be able to enable new ways of doing things.
In a nutshell, adaptive AI helps business growth in the following ways:
Decision-making is an essential but more complex task for any company, making it necessary to have decision-intelligence systems that can exercise greater autonomy. However, adaptive AI will require that decision-making processes be reengineered to make them more efficient. This could have significant implications for existing process architectures and will require business stakeholders to ensure the ethical use of AI for compliance with regulations and laws.
Adaptive AI use cases
Adaptive AI has many potential applications across various industries. Here are some of the most common use cases for adaptive AI:
Customer service
Adaptive AI can be integrated into chatbots to provide customers with personalized and efficient support as part of customer service. Let’s focus on how adaptive AI can be integrated to fulfill the purpose of seamless customer service.
Healthcare
Adaptive AI is a new dimension on the horizon of improving the diagnosis and treatment of diseases. Here are the technical details of how adaptive AI can be applied in healthcare:
Marketing
Adaptive AI helps companies personalize their marketing efforts and target their ads more effectively in the following ways:
Finance
There is a surge of adaptive AI implementation in the financial sector due to increased risks of fraudulent activities. However, adaptive AI is not limited to fraud detection only; there is wide use in the following financial activities to improve overall financial decisions:
Education
Adaptive AI plays a crucial role in personalizing learning and improving student engagement. For example, it can provide customized recommendations for educational content based on a student’s learning style and progress and adjust its approach as the student’s needs change.
Here is an overview of how adaptive AI can enhance the education industry:
Supply chain management
Adaptive AI helps companies optimize supply chain processes in various aspects, as described below:
Retail
Adaptive AI can improve the customer experience in retail by providing personalized product recommendations, optimizing prices, and managing inventory.
Here are some of the areas of retail that adaptive AI can enhance:
Energy
Adaptive AI can be used to optimize energy consumption and improve energy efficiency, as well as to monitor and predict energy demand.
Here are some of the ways how adaptive AI can be applied in the energy sector:
Transportation
Adaptive AI help optimize routes, reduce fuel consumption, predict maintenance needs, and improve safety in transportation systems.
Here are some of the details of how adaptive AI can be applied in transportation:
How to implement adaptive AI?
Step 1: Define the system’s objectives
Defining the system’s objectives is a critical step while implementing adaptive AI, which should provide a clear direction for the system’s development and help determine the desired outcome. When defining the system’s objectives, the following key aspects need to be considered:
Step 2: Data collection
Data works as the foundation for training machine learning models and making informed decisions in building any AI model. Here are the key aspects to consider when collecting data for adaptive AI:
Step 3: Model training
Model training is the process of training a machine learning model on a dataset to make predictions. This critical step of adaptive AI implementation provides the foundation for decision-making. Here are the key aspects to consider when training a model for adaptive AI:
Step 4: Contextual analysis
Contextual analysis is the process of analyzing the current context and making informed decisions based on it, allowing the system to respond in real-time. Here are the key aspects to consider when performing contextual analysis for an adaptive AI system:
Step 5: Evaluate and fine-tune the model
Fine-tuning an AI model involves parameters or architecture adjusting for performance improvement depending on the model type and its targetted problem.
Some common techniques include:
Step 6: Deploy the model
Deploying a model in an adaptive AI implementation means making the model available for use in a real-world setting or production environment, which typically involves the following steps:
Step 7: Continuous monitoring and improvement
Monitoring and maintaining an adaptive AI system after implementation ensures that the system continues to function correctly and effectively over time. It involves the following steps:
Best practices for implementing adaptive AI
In conclusion
Adaptive AI transforms the landscape of ML with its complex system approach, allowing the AI to seek its goals while simultaneously learning the optimum path and adapting its learning methods to align with real-world scenarios in the most effective way. With its flexibility and continuous learning abilities, adaptive AI surpasses traditional AI while its usability in multiple areas fits into any business application seamlessly and transforms how businesses operate and compete. From increased efficiency and automation to better decision-making and personalization, the benefits of adaptive AI are far-reaching. By adopting this innovative technology, companies can gain a competitive edge,enhance customer satisfaction, achieve cost savings, and improve risk management and decision-making processes all at once. To conclude, in a world that is constantly advancing technologically, implementing adaptive AI is not just a choice but a necessity for growth and success. Don’t fall behind, tap into the potential of adaptive AI today.
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