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.

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How to implement adaptive AI?
leewayhertz.com/how-to-implement-adaptive-ai
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?
Adaptive AI, which combines artificial intelligence with an advanced and responsive
autonomous learning method, is the next evolutionary step. Systems that use adaptive AI
can quickly adapt to changing circumstances, which was not possible during initial AI
development. This is achieved by continuously retraining and learning models in runtime
based on new information. This type of AI is designed to improve its performance over
time by modifying its algorithms, decision-making processes, and actions based on the
information it receives from its environment. This allows it to respond better to changes
and challenges and to achieve goals more effectively.

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Static ML model vs Adaptive AI model
Static ML Adaptive ML
Learn once
Data
Deploy once
Learn continually
Deploy continually
New Data
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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:
Feature Adaptive AI Traditional AI

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Feature Adaptive AI Traditional AI
Maintenance Requires ongoing monitoring
and maintenance to ensure the
system continues learning and
adapting to new information.
Requires less maintenance, as the
system does not change or adapt
once it has been deployed.
Human
Interaction
Required Optional or not required
Learning Online learning or continuous
learning involves feeding data
to the system and updating its
model in real-time.
Batch learning, where the system is
trained on a fixed dataset and then
deployed, with no further updates
to its model.
Performance Improves over time Fixed or degrades over time
Real-World
Application
Ideal for dynamic and rapidly
changing environments where
the system must evolve and
continuously provide value.
Suitable for environments with
stable and well-defined conditions,
where the system’s performance
does not need to change over time.
Adaptability High, able to adapt to new
information and changing
conditions.
Low, limited to the performance
capabilities determined by the fixed
training dataset.
Scalability High Low
Implementation Dynamic and flexible Static and inflexible
Definition AI systems that can adapt and
improve their performance over
time through continuous
learning.
AI systems that are trained on a
fixed dataset and do not adapt to
new information or changing
conditions.
How does adaptive AI work?
Adaptive AI works based on continuous learning (CL), an essential step toward achieving
AI. Continuous learning refers to the ability of a model to learn continuously using the
stream of data, which means that a model can autonomously learn from new data and
adapt to production. This is why it is also known as continuous AutoML or auto-adaptive
learning. CL is a method that mimics human’s ability to continuously acquire, refine and
transfer knowledge and skill throughout their lives. Machine learning aims to create
models in a production environment, whereas continuous learning allows us to use the
data coming into the production environment and retrain the model using the new
activity. For example, we are familiar with Netflix’s highly-acclaimed recommender
system for “Up Next”. The Netflix recommender system suggests a show right after the
last episode ends. This type of model is necessary to be retrained as new movies,
preferences and market trends change. Continuous learning is key to gaining high

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accuracy and retaining high-performing models. Data scientists find continuous learning
is a great way to improve models’ accuracy and performance. It also saves time in
retraining by making models auto-adaptive.
Adaptive AI working mechanism
Data validation
Cleaning & Labeling
AutoML & HPO
Monitoring
Model validations
Predictions
Model development
DATA
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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?

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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:
Increased efficiency and automation: Adaptive AI helps automate routine
tasks and processes, freeing up employees’ time and increasing productivity.
Improved decision-making: Adaptive AI provides real-time insights and data-
driven decision-making, eliminating the chances of human error.
Personalization and customization: Trained adaptive AI models understand
individual customer preferences, enabling companies to offer personalized
experiences and products.
Competitive advantage: Companies that adopt adaptive AI technology can gain a
competitive edge by staying ahead of their competitors in terms of efficiency and
innovation.
Increased customer satisfaction: Adaptive AI can help companies provide
faster and more effective customer service, increasing customer satisfaction and
loyalty.
Cost savings: Automation and improved decision-making can lead to cost savings,
freeing up resources that can be invested in other business areas.
Better risk management: Adaptive AI can analyze data and predict potential
risks, allowing companies to avoid potential problems proactively.
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

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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.
Use of natural language processing (NLP) and machine
learning: Adaptive AI chatbots use NLP and machine learning algorithms to
understand and interpret customer inquiries by analyzing and categorizing the text
into specific topics to determine the best response. This can also enable them to
personalize user preferences based on the customer’s history and preferences. For
example, they can recommend products or services based on previous purchases or
provide answers tailored to the customer’s specific needs.
Multi-channel integration: Adaptive AI enables chatbots to be integrated with
multiple channels, such as websites, mobile apps, and social media, to provide
customers with seamless support across all touchpoints.
Continuous improvement: Machine learning algorithms allow adaptive AI
chatbots to improve their performance over time continually by analyzing customer
interactions, identifying areas for improvement, and updating their knowledge base
to provide better support in the future.
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:
Medical image analysis: Adaptive AI algorithms can analyze medical images,
such as X-rays, MRIs, and CT scans, to assist with the diagnosis of diseases. For
example, it can detect patterns and anomalies that may indicate a specific condition
and provide doctors with more accurate diagnoses.
Predictive analytics: Adaptive AI algorithms can use patient data, such as
medical history, lab results, and demographic information, to predict future health
outcomes. For example, it can predict the likelihood of a patient developing a
certain disease or the probability of a patient’s condition improving or deteriorating.
Personalized treatment: Adaptive AI algorithms can personalize treatment
plans based on patient needs and progress. For example, it can recommend specific
medications or treatments based on a patient’s unique medical history or adjust the
plan as the patient’s condition changes.
Real-time monitoring: Adaptive AI algorithms can monitor patients in real time
and detect any changes in their condition. For example, it can monitor vital signs,
such as heart rate and blood pressure, to detect changes and alert medical staff to
potential problems.
Clinical decision support: Adaptive AI algorithms can provide doctors with
clinical decision support by analyzing large amounts of medical data and providing
recommendations for diagnosis and treatment.
Marketing

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Adaptive AI helps companies personalize their marketing efforts and target their ads
more effectively in the following ways:
Customer segmentation: Using adaptive AI algorithms, marketers can analyze
customer data, such as demographics, purchasing behavior, and online activity, to
divide customers into segments allowing marketers to target specific groups with
tailored messages and offers.
Predictive analytics and personalized marketing: Adaptive AI algorithms
use customer data to predict customers’ purchasing behavior by identifying the
segment of customers and their likelihood of the particular products.
Multi-channel integration: You can integrate adaptive AI algorithms with
multiple marketing channels, such as email, social media, and websites, to provide
customers with a seamless experience across all touchpoints.
Real-time optimization: Adaptive AI algorithms can monitor and analyze
customer interactions in real-time to optimize marketing campaigns based on
previous emails’ open rates and click-through rates.
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:
Credit scoring: Adaptive AI algorithms analyze large amounts of data, such as
credit history, employment information, and payment behavior of customers,
predicting the likelihood of a borrower repaying a loan. This lets lenders make more
informed decisions about loan approvals and interest rates of the same.
Fraud detection: Adaptive AI algorithms help identify patterns and anomalies in
financial transactions, allowing them to detect and prevent fraudulent activities. For
example, it can detect unusual transactions, such as large withdrawals from a
customer’s account, and alert financial institutions about potential fraud.
Risk management: Adaptive AI algorithms analyze market trends and historical
data and, by comparing both, can predict market behavior and help financial
institutions manage risk. For example, it can analyze stock prices and market
indicators to predict market trends while allowing financial institutions to make
more informed investment decisions.
Customer segmentation: Adaptive AI algorithms analyze customer data based
on common characteristics, such as their financial behavior and risk profiles for
customer segmentation. Financial institutions can use the same method to target
specific groups with tailored products and services.
Portfolio optimization: By analyzing market trends, historical data, and
financial projections, adaptive AI algorithms can optimize investment portfolios to
predict future performance.
Education

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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:
Personalized learning: Adaptive AI algorithms can analyze student data, such as
performance history and learning style, to personalize learning content and
activities allowing students to receive tailored instruction based on their individual
needs and preferences.
Skill assessment: Adaptive AI algorithms can assess student proficiency in
specific skills, such as reading comprehension, math, or science. This allows
educators to monitor student progress and adjust instruction accordingly.
Adaptive testing: Adaptive AI algorithms can be used to create adaptive tests,
which adjust the difficulty of questions based on a student’s responses. This allows
for a more accurate assessment of a student’s abilities and helps to identify areas of
strengths and weaknesses.
Student engagement: Adaptive AI algorithms can monitor student engagement
and identify students at risk of falling behind. For example, it can analyze student
interactions with educational content to identify students needing additional
support or interventions.
Content recommendation: Adaptive AI algorithms can recommend educational
content and activities to students based on their interests and performance history.
This can help students find content that is more engaging and relevant to them.
Supply chain management
Adaptive AI helps companies optimize supply chain processes in various aspects, as
described below:
Demand forecasting: Adaptive AI algorithms analyze sales data and market
trends to predict future product demand allowing supply chain managers to make
informed production, inventory and distribution decisions.
Inventory optimization: Adaptive AI algorithms optimize inventory levels based
on demand forecasts and supplier lead times, helping supply chain managers to
avoid overstocking or stock shortages.
Route optimization: Adaptive AI algorithms can be used to optimize delivery
routes based on factors such as traffic, delivery time windows, and driver schedules.
This helps to reduce delivery times and increase efficiency.
Supply chain risk management: Adaptive AI algorithms analyze data on
supplier performance, delivery times, and economic indicators to identify potential
risks in the supply chain, allowing supply chain managers to proactively manage risk
and reduce disruptions’ impact.

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Predictive maintenance: Adaptive AI algorithms can predict when equipment in
the supply chain will likely fail, allowing maintenance to be scheduled proactively
while minimizing downtime and reducing the impact of equipment failures.
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:
Customer segmentation: Adaptive AI algorithms analyze customer data, such as
purchase history and behavior, to segment customers into groups with similar
characteristics allowing retailers to tailor their marketing and customer service to
meet each group’s specific needs and preferences.
Personalized product recommendations: Adaptive AI algorithms analyze
customer data for personalized product recommendations. For example, they can
recommend products based on a customer’s purchase history, browsing behavior, or
interests.
Enhanced customer service: Adaptive AI algorithms provide customer service
through chatbots or virtual assistants who can respond to customer queries and
provide information about products, prices, and shipping effectively and in real
time.
Inventory management: Adaptive AI algorithms optimize inventory levels based
on demand forecasts and sales data, helping retailers to avoid overstocking or stock
shortages.
Predictive maintenance: Adaptive AI algorithms predict when equipment, such
as cash registers or self-service kiosks, will likely fail, allowing maintenance to be
scheduled proactively. This helps to minimize downtime and reduce the impact of
equipment failures.
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:
Predictive maintenance: Adaptive AI algorithms predict when energy
equipment, such as turbines or generators, is likely to fail, allowing maintenance to
be scheduled proactively. This helps to minimize downtime reducing the impact of
equipment failures.
Demand forecasting: Adaptive AI algorithms help analyze energy consumption
data and market trends to predict future demand for energy allowing energy
providers to make informed production, distribution, and pricing decisions.

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Portfolio optimization: Adaptive AI algorithms optimize energy portfolios by
balancing the risk and returns of different energy sources. This helps energy
providers to make informed investment decisions and manage risk.
Grid management: Adaptive AI algorithms help manage the energy grid by
optimizing energy production and distribution, helping to minimize energy waste
and improve the overall efficiency of the grid.
Risk management: Adaptive AI algorithms analyze data on market trends,
production capacity, and weather patterns to identify potential risks in the energy
sector. This allows energy providers to manage risk and reduce disruptions’ impact
proactively.
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:
Predictive maintenance: Adaptive AI algorithms predict when vehicles like cars,
buses, or trains will likely need maintenance, allowing maintenance to be scheduled
proactively. This helps to minimize downtime and reduce the impact of equipment
failures.
Route optimization: Adaptive AI algorithms help optimize transportation routes
based on real-time traffic data and demand patterns, which helps to minimize travel
time, reduce fuel consumption, and improve overall efficiency.
Safety monitoring: Adaptive AI algorithms help monitor vehicle performance,
traffic patterns, and weather conditions to identify potential safety risks allowing
transportation providers to proactively manage safety and reduce the impact of
accidents or incidents.
Demand forecasting: Adaptive AI algorithms help analyze transportation
demand patterns and predict future demand for transportation services. This allows
transportation providers to make informed decisions about vehicle deployment and
pricing.
Fraud detection: Adaptive AI algorithms can identify fraudulent activity in
transportation systems, such as ticket fraud or unauthorized vehicle use, helping
transportation providers reduce losses and improve overall security.
Adaptive AI system needs to be built and implemented in a way so that they can modify
their behavior based on changes in the environment and context. Here are the detailed
steps for implementing adaptive AI.

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How to implement Adaptive AI for business
LeewayHertz
1
Define the system’s
objectives
2
Data collection
7
Continuous monitoring
and improvement
3
Model training
6
Deploy the model
4
Contextual analysis
5
Evaluate and fine-tune
the model
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:
Identifying the desired outcome: The first step is to determine what the system is
supposed to achieve, which could include classifying images, predicting user
behavior, or optimizing resource allocation.
Setting performance metrics: To measure the system’s success, performance metrics
should be aligned with the desired outcome providing a clear way to evaluate the
system’s performance. Some of the well-known performance metrics of a machine
learning model are accuracy, recall, precision, F1-score, and AUC, which evaluates
the system performance.
Target audience: Understanding the target audience is crucial for defining any
system’s objectives. The data type used for system modeling and decision models
depends highly on the target audience. For example, an adaptive AI system for
financial advisors will have a different target audience and desired outcome than an
adaptive AI system for health care providers.
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:

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Relevance: The data should be relevant to the system’s objectives, which means it
should be directly related to the targetted problem and the desired outcome.
Diversity: It’s important to collect diverse data representing the target audience
which will help the system to generalize and make informed decisions in different
situations widening the scope of intense testing.
Timeliness: The data should be up-to-date and reflective of the current context
while being updated continuously so that the system’s behavior should be adapted
to the changing context.
Storage: The collected data should be stored in a centralized database for easy
access and analysis. The database should be secure and scalable to accommodate the
growth of the data over time.
Privacy: It’s important to consider privacy when collecting data. Personal
information should be protected, and appropriate measures should be taken to
ensure that the data is not used for any malicious purposes.
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:
Algorithm selection: A machine learning algorithm’s choice depends on the
problem’s nature, the type of data, and the desired outcome. Popular algorithms
used to train an adaptive AI model are supervised, unsupervised, and reinforcement
learning.
Data preparation: The data used to train the model undergo intense preprocessing
and transformation steps to convert it into a format the machine learning algorithm
can use. This process includes cleaning, normalizing, and splitting the data into
training and validation sets.
Hyperparameter tuning: Hyperparameters can significantly impact the model’s
performance which is why the optimal value of it should be identified and set before
training the model using the tuning process.
Model evaluation: The trained model should be evaluated on a validation set to
measure its performance. You must align the performance metrics with the system’s
objectives, reflecting the desired outcome.
Model improvement: Based on the model evaluation results, there should be scope
for continuous improvement by retraining the model on a larger dataset, changing
the algorithm, or adjusting the hyperparameters.
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:

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Data sources: The system should collect data from relevant sources, including
sensors, logs, and other sources that provide contextual information about the
environment and target audience, helping the system to understand the current
context comprehensively.
Data processing: The collected data should be processed and transformed into a
machine learning model format by cleaning, normalizing, and transforming the data
for making predictions.
Model prediction: The trained machine learning models should be able to predict
based on the current context, where predictions should align with the system’s
objectives, providing a basis for making informed decisions.
Feedback loop: The decisions made by the system should be monitored and
evaluated to create a feedback loop allowing the system to adapt continuously to
environmental changes and improve its performance over time.
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:
Hyperparameter tuning: This is done to adjust parameters not learned during
training, such as the learning rate, number of hidden units in a neural network, or
regularization strength. Hyperparameter tuning can be done either manually by
trying different values or automatically using grid or random search techniques.
Feature engineering: This technique involves adding or removing features from the
model training data to improve the model’s overall performance.
Model architecture: This involves changing the model structure itself, such as
adding or removing layers or changing the type of activation function used. You can
adjust the model architecture to improve the model’s ability to capture complex
relationships in the data.
Ensemble methods: Through these methods, you can combine multiple models to
create a more robust and accurate adaptive AI model, which can improve the
model’s performance by combining the strengths or output of multiple models to
make a final decision.
Transfer learning: This learning process uses a pre-trained model as a starting point
and fine-tunes it for a specific task. For example, you can use transfer learning to
improve the model’s performance by leveraging knowledge learned from related
tasks.
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:

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Model preparation: This involves making the model production ready, such as
converting it into a TensorFlow SavedModel or a PyTorch script.
Infrastructure setup: Next, the necessary infrastructure must be set up to support
the deployment of the model, which may involve setting up a server or cloud
environment to host the model or deploying the model to a mobile device or edge
device.
Deployment: Once the infrastructure is set up, the model can be deployed, which
may involve uploading the model to a server or cloud environment or installing the
model on a mobile device or edge device.
Model management: After the model has been deployed, it is important to manage it
effectively, which may involve monitoring its performance, updating the model as
necessary, and ensuring that it is available and accessible to users.
Integration: The final step is integrating the deployed model into the overall system,
which may involve integrating the model with other system components, such as a
user interface, a database, or other models.
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:
Performance monitoring: This step is crucial to ensure the system functions
correctly and effectively. It may involve monitoring the accuracy and reliability of
the model and its overall performance.
Data collection and analysis: Continuous data feed to the system can provide
valuable information about the system, whether it is functioning properly and what
changes may be necessary to improve its performance enhancement. This may
involve collecting data about the model’s predictions, the input data, and the overall
system performance.
Model retraining: Over time, the system may need to be retrained to account for
data changes or improve its performance. This may involve updating the training
data, fine-tuning the model, or using transfer learning to adapt the model to new
tasks.
System updates: The system may need to be updated to address any issues or
incorporate new features or functionality. This may involve updating the software,
hardware, or other system components.
User feedback: Gathering user feedback can provide valuable information about the
system’s use and what changes may be necessary to improve its performance. This
may involve conducting surveys or interviews with users or collecting feedback
through user testing or other means.
Best practices for implementing adaptive AI

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A clear understanding of the problem allows you to identify the relevant
information and training data to train your adaptive AI system. It also helps you to
determine the appropriate algorithmic approach for your system and to establish
the performance metrics that will be used to evaluate its effectiveness. Defining the
goals for your adaptive AI system provides a clear target for the team to work
towards. It helps to focus the project while ensuring the effective allocation of
resources. Having SMART (specific, measurable, achievable, relevant, and time-
bound) goals will help you evaluate your adaptive AI system progress and make
necessary adjustments.
Acquiring high-quality data free from errors, inconsistencies, and duplicates is
essential to establish a robust adaptive AI system that will learn from this data and
make predictions based on it. If the training data is of poor quality, the system
cannot model the problem, leading to suboptimal performance. Besides, diversity in
the training data is also important to ensure the system can learn from a wide range
of examples while remaining generalized to new cases. This is particularly important
in adaptive AI systems because they are designed to adapt to changes in the problem
space in a real-time manner. Also, ensuring diverse training data enables the system
to handle new and unexpected situations effectively.
Selecting the right algorithms is essential for achieving effective results with
adaptive AI. Several algorithms are particularly well-suited to adaptive systems,
including reinforcement learning and online learning algorithms. However, the
selection should be problem specific and depends on the data type used for training.
For example, for streaming data, online learning algorithms are suitable, whereas
reinforcement learning algorithms are often used in decision-making problems
where the system must make a series of decisions over time.
Regular performance monitoring and using metrics relevant to your goals is
essential for evaluating the effectiveness of an adaptive AI system. This is
particularly important in adaptive AI systems, as they are supposed to perform in
real time. Through monitoring, you can track the progress of your system, ensuring
the desired outcomes. Besides, it helps identify any issues or problems with your
system early on and make necessary adjustments to improve its performance.
Concept drift occurs when the data distribution changes over time, causing the
system’s performance to deteriorate. For example, concept drift could occur when
customer preferences change in a behavior prediction system. Unless the AI system
identifies these changes, its predictions become less accurate, resulting in
performance deterioration. To handle this issue, it’s important to implement
detection methods for data distribution and update the system accordingly. This
can be done using techniques such as online learning algorithms, which can
continuously learn and adapt to new data as it becomes available. Another approach
is regularly retraining the system on the latest data to ensure it is up-to-date,
which could be combined with online learning algorithms to balance real-time
adaptation while improving overall performance.

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Implementing an effective testing and validation framework is crucial for
ensuring the accuracy and reliability of an adaptive AI system. This is essential to
validate the performance of your system and identify any issues or bugs that may
affect its accuracy and reliability. Hence, when implementing a testing and
validation framework, it is important to use various testing methods, including unit,
integration, and performance testing. While unit testing allows you to test individual
system components to ensure they function correctly, integration testing validates
the interaction between different system components. On the other hand,
performance testing measures the system’s efficiency and scalability. Using diverse
test data that accurately represents the problem space is also important, including
both normal and edge cases and any unexpected scenarios. Alongside, using a
diverse range of test data allows you to validate the performance of your system
under different conditions and identify any areas that need improvement.
Monitoring the fairness and ethics of an adaptive AI system is critical to avoid
any unforeseen harm or discrimination. For example, a biased AI system might
discriminate against certain groups of people based on race, gender, or age resulting
in unfair treatment or outcomes, such as denial of access to services or employment
opportunities. To ensure the fairness and ethics of an adaptive AI system, it’s
important to monitor its decisions and outcomes to identify the signs of bias or
discrimination. This can be done using fairness metrics, which measure how much
the system treats different groups equally. In addition, it’s important to regularly
review the training data and algorithms used in the system to ensure they are free
from biases and if necessary, remove any data that may contain biases. You can
apply the algorithms designed to reduce the impact of biases in the training data.
Finally, it’s important to have clear and transparent policies aligning with how the
system is designed, along with required measures to ensure its fairness and ethics.
This allows stakeholders to understand the system’s decision-making processes and
provides accountability for any unfair or unethical outcomes.
One of the challenges of adaptive AI systems is that they are difficult to understand
and interpret, as they constantly evolve and change in response to new data, leading
to difficulties for stakeholders to understand the reasoning behind the system’s
decisions, resulting in a lack of trust and accountability. To address
this, explainable AI (XAI) techniques are used to provide a clear and concise
explanation of the system’s decisions. XAI is a subfield of AI that focuses on
developing transparent, understandable, and interpretable systems. For example,
this can include visualizations of the system’s internal workings, such as decision
trees or heatmaps, or methods for generating natural language explanations of the
system’s decisions. Using XAI techniques helps build trust in the system, as
stakeholders can see how it arrived at its decisions and understand its reasoning
while providing greater transparency and accountability, as the system’s decision-
making processes can be audited and reviewed to ensure they are fair and ethical.

17. 17/17
With the increasing use of AI systems, security becomes an alarming issue as they
are prone to potential threats raised by malicious actors. For example, an attacker
could try to manipulate the system’s inputs causing it to make incorrect decisions or
access sensitive information stored in the system. So, ensuring the robustness
and security of an adaptive AI system is the need of the hour. So, it’s important to
assess the system thoroughly and implement security measures by applying security
audits, penetration testing, and other security assessments.
Endnote
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.
Want to transform the way you do business with cutting-edge adaptive AI
solutions? Contact LeewayHertz AI experts to integrate adaptive AI
solutions into your business!