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Consider you've deployed a machine-learning model in a live environment. The results look good, but you must trust the model's predictions. Your gut tells you that lurking beneath the surface is some hidden bias that eventually leads to trouble. This is a common scenario for those building AI systems, and as the pressure mounts to improve performance, few take the time to enhance their machine-learning models before moving on to the next phase. This guide will show you how to improve machine learning models so that you can build trustworthy AI agents.
OpenSesame's AI agent infrastructure can help you achieve your objectives by creating a structured, efficient framework for your AI agents. This solution enables you to understand better and improve the performance of your machine learning models so you can build reliable AI systems.
What Is A Machine Learning Model?
A machine learning model is computer software that discovers patterns or makes choices based on previously unseen data. For instance, in natural language processing, machine learning models can break down and accurately identify the intent behind previously unheard sentences or combinations of words. In image recognition, a machine-learning model can be trained to detect objects—like cars or dogs. A machine learning model accomplishes these tasks by being trained with a large dataset.
During training, the machine learning algorithm optimizes to identify specific patterns or outputs from the dataset, depending on the task. The outcome of this process—often a computer program with specific rules and data structures—is called a machine learning model.
The Three Main Types of Machine Learning Models
There are three primary types of machine learning models: supervised, unsupervised, and reinforcement. Each class of learning model has distinct characteristics and is suitable for different types of tasks.
1. Supervised Learning: The Most Common Approach
Supervised learning is the most common machine learning model. The process gets its name because the algorithm learns from a “supervisor” or training dataset that contains labeled examples. In other words, supervised learning uses input-output pairs to train a model. First, a function is created based on the training data, and then the algorithm applies this function to predict outputs for unknown data. Supervised learning is task-oriented and gets tested on labeled datasets.
2. Unsupervised Learning: The Mysterious Stranger
Unsupervised machine learning models implement the learning process opposite to supervised learning. Instead of starting with labeled training data, these algorithms learn from an unlabeled dataset. Based on the information in the dataset, the model predicts the output. Using unsupervised learning, the model learns hidden patterns from the dataset without supervision.
3. Reinforcement Learning: The Game Player
In reinforcement learning, the algorithm learns actions for a given set of states that lead to a goal state. A feedback-based learning model takes feedback signals after each state or action by interacting with the environment. This feedback works as a reward (positive for each good action and negative for each nasty action), and the agent’s goal is to maximize the positive rewards to improve their performance. The model's behavior in reinforcement learning is similar to human knowledge, as humans learn things through experiences as feedback and by interacting with the environment.
Applications of Machine Learning Models
1. Image Recognition: Machines That See and Understand Data
One of the most common uses of machine learning is image recognition. To do this, data professionals train machine learning algorithms on data sets to produce models capable of recognizing and categorizing specific images. These models are used for various purposes, including identifying particular plants, landmarks, and even individuals from photographs. Examples of machine learning applications for image recognition include Instagram, Facebook, and TikTok.
2. Translation: The AI Potential of Language Conversion
Translation is a natural fit for machine learning. The large amount of written material available in digital formats effectively amounts to a massive data set that can be used to create machine learning models capable of translating texts from one language to another. Known as machine translation, there are many ways that AI professionals can develop models capable of translation, including rule-based, statistical, and syntax-based models, as well as neural networks and hybrid approaches. Some famous examples of machine translation include Google Translate, Amazon Translate, and Microsoft Translator.
3. Fraud Detection: Protecting Your Money with AI
Financial institutions process millions of transactions daily. So, it can be difficult for them to know which are legitimate and which are fraudulent. As more and more people use online banking services and cashless payment methods, the number of fraudulent transactions has similarly risen. In fact, according to a 2023 report from TransUnion, the number of digital fraud attempts in the US rose a staggering 122 percent between 2019 and 2022.
AI can help financial institutions detect potentially fraudulent transactions and save consumers from false charges by flagging those that seem suspicious or out of the ordinary. Mastercard, for example, uses AI to flag potential scams in real time and even predict some before they happen to protect consumers from theft in certain situations.
4. Chatbots: AI for Customer Support
Effective communication is a crucial requirement of almost all businesses operating today. Whether helping customers troubleshoot problems or identifying the best products for their unique needs, many organizations rely on customer support to ensure their clients get the help they need. The costliness of supporting a well-trained workforce of customer support specialists, however, can make it difficult for many organizations to provide their customers with the resources they require. As a result, many customer support specialists may find their schedules inefficiently packed with customers who face a wide range of needs – from those that can be easily in a matter of minutes to those that require additional time.
AI-powered chatbots can provide organizations the extra support they need by assisting customers with their most basic needs. Using natural language processing, these chatbots can respond to consumers' unique queries and direct them to the appropriate resources so that customer support specialists can assist those with the trickiest needs.
5. Generate Text, Images, and Videos: The Creative Side of AI
With simple prompts, generative AI can quickly produce original content, such as text, images, and video. Many organizations and individuals use generative AI like ChatGPT and DALL-E for various reasons, including creating web copy, designing visuals, or even producing promotional videos. Yet, while generative AI can produce many impressive results, it also has the potential to produce material with false or misleading claims. If you’re using generative AI for your work, it’s advised that you provide an appropriate level of scrutiny before releasing it to the broader public.
6. Speech Recognition: Machines That Can Hear
Whether driving a car, kneading dough, or going for a long run, it’s sometimes easier to operate a smart device with your voice than to stop and use your hands to input commands. Machine learning makes it possible for many smart devices to recognize speech when prompted by users so that they can complete tasks without directly interacting with the device, such as calling a friend, setting a time, or searching for a specific show on a streaming service. Today, speech recognition is a relatively common feature of many widely available smart devices like Google's Nest speakers and Amazon’s Blink home security system.
7. Self-Driving Cars: The Future of Transportation
Perhaps one of the more “futuristic” technological advancements in recent years has been the development of self-driving cars. While such a concept was once considered science fiction, today, there are several commercially available cars with semi-autonomous driving features, such as Tesla’s Model S and BMW’s X5. Manufacturers are working hard to make fully autonomous vehicles a reality for commuters over the next decade.
The dynamics of creating a self-driving car are complex – and indeed still being developed – but they primarily rely on machine learning and computer vision. As the car drives from one place to another, it uses computer vision to survey its environment and machine learning algorithms to make decisions.
8. AI Personal Assistants: The Help You Didn’t Know You Needed
Everyone could use extra help. That’s why many smart devices come equipped with AI personal assistants to assist users with everyday tasks like scheduling appointments, calling a contact, or taking notes. Whether people realize it or not, when they use Siri, Alexa, or Google Assistant to complete these tasks, they use machine learning-powered software.
9. Recommendations: How AI Personalizes Your Online Shopping Experience
Businesses and marketers spend significant resources connecting consumers with the right products at the right time. After all, if they can show customers the kinds of products or content that meet their needs at the precise moment they need them, they’re more likely to purchase – or simply stay on their platform. In the past, sales representatives at brick-and-mortar stores would be best equipped to match consumers with the kinds of products they’d be interested in.
However, as online and digital shopping become the norm, organizations need to be able to provide the same level of guidance for Internet users. To do it, modern online retailers and streaming platforms use recommendation engines that produce personalized consumer results based on their geographic location and previous purchases. Some common platforms that use machine learning-based recommendation engines include Amazon, Netflix, and Instagram.
10. Detect Medical Conditions: How AI Diagnoses Health Issues
The healthcare industry is awash in data. From electronic health records to diagnostic images, health facilities are repositories of valuable medical data that can be used to train machine learning algorithms to diagnose medical conditions. While some researchers are already using machine learning to identify cancerous growths in medical scans, others are using it to create software that can help healthcare professionals make more accurate diagnoses.
OpenSesame: The Hallucination-Busting AI Agent Platform
OpenSesame offers innovative AI agent infrastructure software that grounds AI models in reality. Our platform reduces hallucinations, enhances reliability, and saves hours of manual checking. Key features include real-time hallucination reports, business data integration, multimodal AI expansion, and open-source frameworks.
We provide ungrounded truth recognition, prompt template extraction, accuracy scoring, and a hallucination dashboard. OpenSesame allows businesses to confidently build trustworthy AI systems, offering real-time insights without latency for high-performing, reality-grounded AI solutions. Try our AI agent infrastructure management software for free today!
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How To Improve Machine Learning Models In 12 Practical Ways
1. Improve Machine Learning Models by Using OpenSesame
OpenSesame offers innovative AI agent infrastructure software that grounds AI models in reality. Our platform reduces hallucinations, enhances reliability, and saves hours of manual checking. Key features include real-time hallucination reports, business data integration, multimodal AI expansion, and open-source frameworks.
We provide ungrounded truth recognition, prompt template extraction, accuracy scoring, and a hallucination dashboard. OpenSesame allows businesses to confidently build trustworthy AI systems, offering real-time insights without latency for high-performing, reality-grounded AI solutions. Try our AI agent infrastructure management software for free today!
2. Clean the Data
Cleaning the data is the most essential part of the machine learning ecosystem. You must fill in missing values, deal with outliers, standardize the data, and ensure data validity. Sometimes, cleaning through a Python script doesn't work. You have to look at every sample individually to ensure no issues. It will take a lot of your time, but cleaning the data is essential.
For example, when training an Automatic Speech Recognition model, I found multiple issues in the dataset that could not be solved by simply removing characters. I had to listen to the audio and rewrite the accurate transcription. Some transcriptions could have been more specific and made sense.
3. Add More Data
Increasing the volume of data often improves model performance. Adding more relevant and diverse data to the training set can help the model learn more patterns and make better predictions. If your model lacks diversity, it may perform well in the majority class but poorly in the minority class.
Many data scientists now use Generative Adversarial Networks (GAN) to generate more diverse datasets. They achieve this by training the GAN model on existing data and then using it to develop a synthetic dataset.
4. Feature Engineering
Feature engineering involves creating new features from existing data and removing unnecessary features that contribute less to the model's decision-making. This provides the model with more relevant information to make predictions.
You need to perform SHAP analysis, look at feature importance analysis, and determine which features are essential to the decision-making process. Then, they can be used to create new features and remove irrelevant ones from the dataset. This process requires a thorough, detailed understanding of each feature's business use case. If you need help understanding the features and how they are helpful for the business, you will be walking down the road blindly.
5. Cross-Validation
Cross-validation is a technique for assessing a model's performance across multiple subsets of data. It reduces overfitting risks and provides a more reliable estimate of its generalization ability. This will tell you whether your model is stable enough.
Calculating the accuracy of the entire testing set may provide incomplete information about your model's performance. For instance, the first fifth of the testing set might show 100% accuracy, while the second fifth could perform poorly with only 50% accuracy. Despite this, the overall accuracy might still be around 85%. This discrepancy indicates that the model is unstable and requires more clean and diverse data for retraining.
So, instead of performing a simple model evaluation, I recommend using cross-validation and providing it with various metrics on which you want to test the model.
6. Hyperparameter Optimization
Training the model with default parameters might seem simple and fast, but you are missing out on improved performance, as in most cases, your model needs to be optimized. To increase your model's performance during testing, it is highly recommended to thoroughly perform hyperparameter optimization on machine learning algorithms and save those parameters so that you can use them for training or retraining your models next time.
Hyperparameter tuning involves adjusting external configurations to optimize model performance. Finding the right balance between overfitting and underfitting is crucial for improving the model's accuracy and reliability. It can sometimes improve the model's accuracy from 85% to 92%, which is significant in machine learning.
7. Experiment with Different Algorithms
Model selection and experimenting with various algorithms are crucial to finding the best fit for the given data. Do not restrict yourself to only simple algorithms for tabular data. You should consider neural networks if your data has multiple features and 10,000 samples. Sometimes, even logistic regression can provide excellent results for text classification that cannot be achieved through deep learning models like LSTM.
Start with simple algorithms and then slowly experiment with advanced algorithms to achieve even better performance.
8. Ensembling
Ensemble learning involves combining multiple models to improve overall predictive performance. Building an ensemble of models, each with its strengths, can lead to more stable and accurate models.
Ensembling the models has often improved my results, sometimes leading to a top-10 position in machine learning competitions. Keep low-performing models; combine them with a group of high-performing models, and your overall accuracy will increase.
Ensembling, cleaning the dataset, and feature engineering have been my three best strategies for winning competitions and achieving high performance, even on unseen datasets.
9. Treat Missing and Outlier Values
The unwanted presence of missing and outlier values in machine learning training data often reduces the accuracy of a trained model or leads to a biased model. It leads to inaccurate predictions. This is because we don’t analyze the behavior and relationship with other variables correctly. So, treating missing and outlier values well is essential for a more reliable and naturally improved machine learning model.
10. Feature Transformation
There are various scenarios where feature transformation is required:
Changing a variable's scale from the original scale to a scale between zero and one is a common practice in machine learning, known as data normalization. For example, suppose a dataset includes variables measured in different units, such as meters, centimeters, and kilometers. Before applying any machine learning algorithm, it is essential to normalize these variables on the same scale to ensure fair and accurate comparisons.
Normalization in machine learning contributes to better model performance and unbiased results across diverse variables. Some algorithms work well with normally distributed data. Therefore, we must remove the skewness of variable(s). There are methods like a log, square root, or inverse of the values to remove skewness.
Sometimes, creating bins of numeric data works well since it also handles outlier values. Numeric data can be made discrete by grouping values into bins, a process known as data discretization.
11. Feature Creation
Deriving new variable(s) from existing variables is known as feature creation. It helps equip a data set's hidden relationships. Let’s say we want to predict the number of transactions in a store based on transaction dates. Transaction dates may not directly correlate with the number of transactions, but if we look at the day of the week, it may have a higher correlation.
In this case, the information about the day of the week is hidden. We need to extract it to improve the model's accuracy. This might not be the case every time you create new features, and it can also lead to a decrease in the accuracy or performance of the trained model. So, every time you create a new feature, you must check the feature's importance to see how that feature will affect the training process.
12. Feature Selection
Feature Selection is finding the best attribute subset that better explains the relationship between independent and target variables.
You can select the valuable features based on various metrics like:
Domain Knowledge
Based on domain experience, we select feature(s) that may impact the target variable more.
Visualization
As the name suggests, it helps visualize the relationship between variables, making the variable selection process more manageable.
Statistical Parameters
We also consider the p-values, information values, and other statistical metrics to select the right features.
PCA
It helps to represent training data in lower-dimensional spaces but still characterizes the inherent relationships in the data. It is a type of dimensionality reduction technique. Various methods reduce training data’s dimensions (features), including factor analysis, low variance, higher correlation, backward/ forward feature selection, etc.
How Do You Create A Good Machine Learning Model
1. Optimize AI Agent Construction with OpenSesame
OpenSesame is your go-to resource for a reliable AI agent infrastructure that helps you create trustworthy machine-learning models. With OpenSesame, you can ground your AI agents in reality and optimize their performance for specific use cases. This means fewer hallucinations, less bias, and greater accuracy.
Key features of the OpenSesame platform include real-time hallucination reports, multimodal AI expansion, business data integration, and open-source frameworks. Our software allows organizations to build machine learning models confidently and transparently. Get started with OpenSesame for free today!
2. Establish Clear Objectives for Your Machine Learning Project
Every machine learning project needs a solid foundation. The first phase of this process is developing an understanding of the business requirements. You need to know what problem you're trying to solve before attempting to solve it. To start, work with the project owner to establish the project's objectives and requirements. The goal is to convert this knowledge into a suitable problem definition for the machine learning project and devise a preliminary plan to achieve the project's objectives.
Key questions include the following:
What's the business objective, and which parts of achieving that goal require a machine-learning approach?
What is the heuristic option- the quick-and-dirty approach that doesn't require machine learning- and how much better than the heuristic does the model need to be?
What algorithm best fits the problem, for example, classification, regression, or clustering?
Have the relevant teams addressed the technical, business, and deployment issues?
What are the project's success criteria, and how will the organization measure the model's benefits?
How can teams stage the project in iterative sprints?
Are there requirements for transparency, explainability, or bias reduction?
What are the ethical considerations?
What are the acceptable parameters for accuracy, precision, and confusion matrix values?
What are the expected inputs and outputs?
Setting specific, quantifiable goals will help you realize measurable ROI from your machine learning project rather than implementing a proof of concept that will be tossed aside later.
3. Identify Data Needs Early in the Process
After establishing the business case for your machine learning project, the next step is to determine what data is necessary to build the model. Machine learning models generalize from their training data, applying the knowledge acquired in the training process to new data to make predictions. A lack of data will prevent you from building the model, but more than access to data is needed: Useful data must be clean, relevant, and well-structured.
Focus on data identification, initial collection, requirements, quality identification, insights, and aspects worth further investigation to determine your data needs and whether the data is in proper shape for model ingestion.
To get a handle on the quantity, quality, and types of data you'll need, consider these key questions:
What type and quantity of data is necessary for the machine learning project?
What are the required data sources and locations?
What is the current quantity and quality of training data?
How will you split the data collected into test and training sets?
How will you label data when working on a supervised learning task?
Can you use a pre-trained machine-learning model?
Are there special requirements like accessing real-time data on edge devices or other difficult-to-reach places?
4. Collect, Clean, and Prepare Data for Model Training
After identifying the appropriate data, the next step is to shape it so that it can be used to train the model. Data preparation tasks include data collection, cleansing, aggregation, augmentation, labeling, normalization, transformation, and other activities for structured, unstructured, and semi-structured data. Data preparation and cleansing tasks can take a substantial amount of time, but because machine learning models depend on data, it's well worth the effort.
Steps you undertake during data preparation, collection, and cleansing include the following:
Collect data from various sources.
Standardize data formats and normalize data across different sources.
Replace incorrect or missing data.
Enhance and augment data using third-party data or multiplying image-based data sets if the core data set is insufficient for training.
Add dimensions with precalculated amounts and aggregate information as needed.
Remove extraneous and redundant information, a process known as deduplication, and remove irrelevant data.
Reduce noise and remove ambiguity.
Consider anonymizing personal or otherwise sensitive data.
Sample data from large data sets.
Select features that identify the most critical dimensions and, if necessary, reduce dimensions using various techniques.
Split data into training, test, and validation sets.
5. Determine the Model's Features and Train It
Once the data is usable and you know the problem you're trying to solve, it's time to train the model to learn from the quality data by applying various techniques and algorithms. This phase requires selecting and applying model techniques and algorithms, setting and adjusting hyperparameters, training and validating the model, developing and testing ensemble models if needed, and optimizing the model.
To accomplish all that, this stage often includes the following actions:
Select the correct algorithm for your learning objective and data requirements. Linear regression is famous for mapping correlations between two variables in a data set.
Configure and tune hyperparameters for optimal performance and determine a method of iteration, such as learning rate, to attain the best hyperparameters.
Identify features that provide the best results.
Determine whether model explainability or interpretability is required.
Develop ensemble models for improved performance.
Compare the performance of different model versions. Identify requirements for the model's operation and deployment.
6. Model Evaluation and Benchmarking
Evaluating a model's performance encompasses confusion matrix calculations, business KPIs, machine learning metrics, model quality measurements, and determining whether the model can meet the established business goals.
Perform the following assessments during the model evaluation process:
Evaluate the model using a validation data set.
Determine confusion matrix values for classification problems.
Identify methods for K-fold cross-validation if you are using that approach.
Further, tune hyperparameters for optimal performance.
Compare the machine learning model to the baseline model or heuristic.
Model evaluation should be considered as the quality assurance of machine learning.
Adequately evaluating model performance against metrics and requirements helps you understand how the model will work in the real world.
7. Deploy the Model and Monitor Performance in Production
When you're confident that the machine learning model can work in the real world, it's time to see how it operates.
This process, known as operationalizing the model, includes the following steps:
Deploy the model by continually measuring and monitoring its performance.
Develop a baseline or benchmark against which you can measure future iterations of the model.
Continuously iterate on different aspects of the model to improve overall performance.
Operationalization considerations include model versioning, iteration, deployment, monitoring, and staging in development and production environments.
Model operationalization might include deployment scenarios in a cloud environment, at the edge, in an on-premises or closed environment, or within a closed, controlled group.
Model operationalization can range from generating a report to a more complex, multi-endpoint deployment, depending on the requirements.
8. Iterate and Adjust the Model in Production
The formula for success when implementing technologies is often said to start small, think big, and iterate frequently. You're not done even after a machine learning model is in production; you're continuously monitoring its performance. Business requirements, technology capabilities, and real-world data change unexpectedly, potentially creating new requirements for deploying the model onto different endpoints or in new systems. Repeat the process and make improvements in time for the next iteration.
When evaluating and adjusting a machine learning model in production, consider the following:
Incorporate the following requirements for the model's functionality.
Expand model training to encompass more excellent capabilities.
Improve model performance and accuracy, including operational performance.
Determine operational requirements for different deployments.
Address model or data drift, which can cause changes in performance due to real-world data changes.
Reflect on what has worked in your model, what needs work, and what's in progress.
The surefire way to succeed when building a machine learning model is to continuously look for improvements and better ways to meet evolving business requirements.
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Importance of Improving Machine Learning Models
1. Healthcare – The Risk of Misdiagnosis in AI Systems
IBM Watson for Oncology was designed to assist doctors in diagnosing cancer and recommending treatments. However, the model was found to provide incorrect treatment suggestions in some cases. This occurred because the model had not been sufficiently updated with real-world data or fine-tuned to reflect the complexity of medical cases. A machine learning model that is not continually updated with new medical research and patient data may provide incorrect recommendations, putting patients' health at risk.
2. Finance – The Danger of Fraud Detection Failures
Many banks rely on machine learning algorithms to detect fraud. These systems learn from historical fraud patterns, but tactics evolve. If the models are not continuously improved with new fraud patterns, they become less effective. Outdated fraud detection models may fail to identify emerging types of fraud, leading to substantial financial losses for companies and customers.
3. Retail – The Challenge of Customer Segmentation
Amazon used a machine learning model to automate recruitment. It was later discovered that the model favored male candidates over female ones because it was trained on resumes submitted over 10 years, predominantly from men. The model needed to be updated to reflect current diversity standards and became biased. An ML model that is not regularly updated to reflect changing social standards or customer demographics can perpetuate bias or discrimination, harming the company’s reputation and resulting in lost talent.
4. Transportation – The Dangers of Safety Concerns in Autonomous Vehicles
In 2018, an Uber self-driving car struck and killed a pedestrian because its machine learning system failed to classify the pedestrian as a hazard correctly. The model needed to be fully trained to handle unexpected or rare scenarios. In the case of self-driving cars, failing to update the machine learning model with new data on unusual or rare driving conditions can result in fatal accidents, as the system may not recognize or respond to real-world dangers in time.
5. Social Media – The Consequences of Content Moderation Failures
Facebook’s content moderation algorithms identify misinformation, hate speech, and harmful content. However, as misinformation evolves rapidly, outdated models can fail to detect new types of false information or subtle manipulation tactics. If content moderation models are not improved, harmful misinformation can spread unchecked, leading to societal consequences, such as political unrest or health crises, as seen during the COVID-19 pandemic.
OpenSesame: The Hallucination-Busting AI Agent Platform
OpenSesame offers innovative AI agent infrastructure software that grounds AI models in reality. Our platform reduces hallucinations, enhances reliability, and saves hours of manual checking. Key features include real-time hallucination reports, business data integration, multimodal AI expansion, and open-source frameworks.
We provide ungrounded truth recognition, prompt template extraction, accuracy scoring, and a hallucination dashboard. OpenSesame allows businesses to confidently build trustworthy AI systems, offering real-time insights without latency for high-performing, reality-grounded AI solutions. Try our AI agent infrastructure management software for free today!
9 Real-Life Examples of Machine Learning Models
1. Recommendation Systems: The Personalized Shopping Experience
Recommendation engines are among the most popular applications of machine learning. Using machine learning models, e-commerce websites track your behavior to recognize patterns in your browsing history, previous purchases, and shopping cart activity. This data collection is used for pattern recognition to predict user preferences.
Companies like Spotify and Netflix use similar machine-learning algorithms to recommend music or TV shows based on your previous listening and viewing history. Over time and with training, these algorithms aim to understand your preferences and accurately predict which artists or films you may enjoy.
2. Social Media Connection Suggestions: Getting You to Make New Friends
Another example of a similar training algorithm is the “people you may know” feature on social media platforms like LinkedIn, Instagram, Facebook, and X (formerly known as Twitter.) Based on your contacts, comments, likes, or existing connections, the algorithm suggests familiar faces from your real-life network that you might want to connect with or follow.
3. Image Recognition: Teaching Machines to See
Image recognition is another machine learning technique that appears in our day-to-day lives. With the use of ML, programs can identify an object or person in an image based on the intensity of the pixels. This type of facial recognition is used for password protection methods like Face ID and law enforcement. By filtering through a database of people to identify commonalities and matching them to faces, police officers and investigators can narrow down a list of crime suspects.
4. Natural Language Processing (NLP): Enabling Machines to Understand Human Language
Like ML can recognize images, language models can also support and manipulate speech signals into commands and text. Software applications coded with AI can convert recorded and live speech into text files. Voice-based technologies can be used in medical applications, such as helping doctors extract important medical terminology from a conversation with a patient. While this tool isn't advanced enough to make trustworthy clinical decisions, other speech recognition services remind patients to “take their medication” as if they have a home health aide.
5. Virtual Personal Assistants: The AI Bots Living in Our Devices
Virtual personal assistants are devices you might have in your homes, such as Amazon’s Alexa, Google Home, or the Apple iPhone’s Siri. These devices use speech recognition technology and machine learning to capture data on your request and how often the device delivers accurately. They detect what you say when you speak and deliver on the command. For example, when you say, “Siri, what is the weather like today?” Siri searches the web for weather forecasts in your location and provides detailed information.
6. Stock Market Predictions: Forecasting Financial Trends with Machine Learning
Predictive analytics and algorithmic trading are typical machine learning applications in finance, real estate, and product development industries. Machine learning classifies data into groups and then defines them with rules set by data analysts. After classification, analysts can calculate the probability of an action. These machine-learning methods help predict how the stock market will perform based on year-to-year analysis. Analysts can use predictive analytics and machine learning models to predict the stock price for 2025 and beyond.
7. Credit Card Fraud Detection: Protecting Consumers from Cyber Crime
Predictive analytics can help determine whether a credit card transaction is fraudulent or legitimate. Fraud examiners use AI and machine learning to monitor variables involved in past fraud events. They use these training examples to measure the likelihood that a specific event was fraudulent activity.
8. Traffic Predictions: How AI Knows The Best Routes
Using Google Maps to map your commute to work or a new restaurant in town provides an estimated arrival time. Google uses machine learning to build models of how long trips will take based on historical traffic data (gleaned from satellites). It then uses that data, based on your current trip and traffic levels, to predict the best route according to these factors.
9. Self-Driving Car Technology: The Future of Transportation
A frequently used type of machine learning is reinforcement learning, which is used to power self-driving car technology. Self-driving vehicle company Waymo uses machine learning sensors to collect real-time data on the car's surrounding environment. This data helps guide the car's response in different situations, whether a human crossing the street, a red light, or another car on the highway.
Try Our AI Agent Infrastructure Management Software for Free Today
OpenSesame offers innovative AI agent infrastructure software that grounds AI models in reality. Our platform reduces hallucinations, enhances reliability, and saves hours of manual checking. Key features include real-time hallucination reports, business data integration, multimodal AI expansion, and open-source frameworks.
We provide ungrounded truth recognition, prompt template extraction, accuracy scoring, and a hallucination dashboard. OpenSesame allows businesses to confidently build trustworthy AI systems, offering real-time insights without latency for high-performing, reality-grounded AI solutions. Try our AI agent infrastructure management software for free today!
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