There was a time when the word “robot” made people think of The Terminator, a futuristic idea from science fiction. However, today, they are becoming an integral part of our living and working spaces. From performing quality checks in factories to assisting surgeons with delicate procedures, they are now equipped with a wide range of advanced capabilities.
As robots take on more complex tasks, their design and intelligence keep improving to meet real-world needs. Thanks to artificial intelligence (AI), robotics engineering, and computer vision, they can now recognize objects, understand their surroundings, and interact with people in more human-like ways.
As a result of these abilities, AI-powered robots are being used in almost every major industry. In fact, there are now more than four million of them working in factories around the world.
In this article, we’ll look at how AI helps robots make smarter decisions in everyday situations. We’ll also explore how combining different sensors, a process known as sensor fusion, helps them better understand the world around them. Let’s get started!
Different Types of Robots
Recent Advancements in Robotics Engineering
Robotics engineers are pushing the boundaries of robotics by improving the design of materials, actuators, control algorithms, and embedded computing systems. An example is how lighter and more agile robotic arms make it easier for machines to operate safely in shared spaces and conserve energy in dynamic setups. Such improvements also make robots more precise and better able to adapt to the conditions they work in.
The ultra-lightweight robotic arm, which was showcased at the tech event CES 2025, is a great representation of this technology. It was built to handle industrial tasks while easily adapting to different environments. Made with lightweight materials and designed for precise, smooth movement, this type of robot is well-suited for manufacturing, service jobs, and can even help around the home.
Choosing the right dataset is one of the most important steps in building an object detection model that performs well. Just like you need a good map to find your way in unfamiliar places, computer vision models need high-quality data to learn how to understand images and videos. A Vision AI model typically works better in the real world when it’s trained on a dataset that’s accurate, diverse, and well-organized.
For example, a core computer vision task is object detection. It’s the process of identifying and locating specific objects within an image. To train models for this task, annotated datasets are used – each image is labeled with the classes of objects it contains and their precise locations, typically in the form of bounding boxes.
These labeled examples help the model learn to detect visual patterns and make accurate predictions. Without accurately labeled examples, most models end up underperforming. In fact, studies show that AI models trained on poor-quality data can cost companies up to 6% of their annual revenue due to inefficiencies.
A reliable dataset has clear labels, a mix of environments, balanced object classes, and a format that fits your model’s needs. In this article, we’ll share five practical tips to help you pick an object detection dataset that gives your model the best chance to succeed.
An Example of an Object Detection Dataset. (Source)
1. Fit Your Object Detection Dataset to Your Project Domain
An object detection model generally will perform best when it’s trained on data that reflects the environment it’s going to be used in. That means using labeled examples from places like busy streets, retail stores, warehouses, or indoor settings – wherever the model needs to make decisions in the real world.
For instance, the MS COCO dataset includes over 300,000 labeled 2D images of everyday objects like people, vehicles, and household items. It’s widely used for general-purpose object detection tasks such as product recognition or pedestrian tracking. However, since it lacks in-depth information or sensor data, it’s not ideal for applications that require spatial understanding.
The nuScenes dataset, on the other hand, is built specifically for autonomous driving. It provides 3D annotations and multi-sensor data from cameras, LiDAR (measures distance using laser pulses), and radar, which uses radio waves to detect object speed and position. These inputs give the model a deeper understanding of depth, motion, and spatial context.
It includes detailed HD maps that provide rich contextual information such as road layouts, traffic signs, and lane boundaries. This makes nuScenes ideal for training models that operate in dynamic environments.
A Look at the nuScenes Dataset for Autonomous Driving. (Source)
2. Consider Bounding Box Types and Label Quality
When it comes to object detection, bounding boxes are used to mark the location of objects within an image. The accuracy and annotation type plays a key role in how well the model learns. Most datasets use 2D bounding boxes, which draw rectangles around visible objects. It works well for many use cases, but in more complex environments, like drone footage or self-driving cars, rectangles may not be enough.
Some applications require more than just simple object location; they also need information about shape, orientation, and spatial positioning. In these cases, alternative bounding box formats like 3D and oriented boxes offer better spatial context.
3D bounding boxes include depth and rotation, helping models understand how far objects are and how they are positioned in space. Oriented boxes capture the angle of objects, which improves precision in aerial imagery or when objects are tilted or off-axis.
Besides the type of box, the quality of your labels is crucial. Things like missing objects, sloppy box placement, or incorrect labels can confuse the model and hurt performance. On the flip side, clean and consistent annotations help your model learn the right patterns and make better predictions.
The KITTI dataset is a good example of high-quality annotations that use 2D and 3D bounding boxes. It includes objects such as cars, pedestrians, and cyclists. Each label contains object dimensions, position, orientation, occlusion level, and truncation status.
Choosing a Reliable Object Detection Dataset
3. Assess the Object Detection Dataset Size
The size of your dataset can have a big impact on your model’s performance and your workflow. Larger datasets usually help models learn more general and flexible patterns because they include a wider variety of objects and scenes. But they also take more time, storage, and computing power to train.
Smaller datasets are easier to manage and faster to work with, which makes them great for early development or when resources are tight. The right size really depends on what your model needs to do and the tools you have to train it.
Large-scale datasets like Objects365 offer over 2 million annotated images across many categories. These datasets are used to train models for high-accuracy detection in varied scenarios. These models often perform well in new categories, even without additional fine-tuning. This makes them useful for open-world detection or zero-shot learning tasks.
Models Trained on Objects365 Can Detect New Objects without Fine-Tuning. (Source)
In contrast, small datasets like the Oxford-IIIT Pet Dataset contain about 7,400 images covering 37 pet breeds. It provides bounding boxes and pixel-level masks with clean annotations, making it a good fit for quick testing, prototypes, or learning projects where you want clean data but don’t need massive scale.
4. Use the Right Type of Data for Your Task
In computer vision, modality refers to the type of data your model processes, such as red, green, and blue (RGB) images, thermal imagery, depth maps, LiDAR, or radar. Each one captures different details about the environment, so it’s important to pick a dataset that matches how your model will be used in the real world.
For many object detection tasks, standard RGB images are enough. But in more specialized situations, like detecting people at night, inspecting equipment in low-light conditions, or helping in search and rescue, RGB alone might not cut it. That’s where thermal or infrared data comes in. It can pick up on details that normal cameras miss.
A great example is the FLIR Thermal Dataset, which has over 26,000 labeled images showing vehicles, pedestrians, and traffic signs, all captured using thermal cameras. It combines thermal and visible data, giving models a better chance to detect objects in tough conditions like darkness, glare, or fog.
5. Check the License, Updates, and Support Around the Dataset
A good dataset isn’t just about the images and labels – it’s also about how it’s licensed, maintained, and supported. These often overlooked details can have a big impact on how easy (and legal) it is to use the data in your project.
Licensing governs how freely you can use and share the data, directly impacting whether a dataset is suitable for commercial use or limited to research. Active maintenance and documentation are equally important; they keep the dataset relevant, accurate, and aligned with evolving technologies and use cases.
Selecting an open-source dataset with clear licensing, active maintenance, and a well-supported ecosystem can accelerate development cycles and keep your model reliable over time.
Choosing a Reliable Object Detection Dataset
How Objectways Helps You Get the Right Dataset
Building a reliable AI model starts with selecting the right dataset, but that’s often where teams get stuck. There’s not always enough time to compare options, check label quality, or sort out licensing and readiness for production.
That’s where we can step in – Objectways works closely with AI teams to remove these obstacles. We help select or create datasets that align with specific project goals and deployment needs.
Every dataset we deliver is carefully sourced, clearly labeled, and tested to make sure it performs well in the real world. We handle everything, from checking annotations and formatting the data to supporting different input types like images, thermal scans, or point clouds.
Beyond data sourcing and annotation, Objectways also supports end-to-end AI development. We help with everything from model training and deployment to more specialized needs like content moderation and generative AI solutions.
The Right Dataset Can Make All the Difference
State-of-the-art model performance results from using the right combination of algorithms and well-matched data. The best datasets are the ones that truly fit your project’s goals, domain, and real-world conditions.
Taking the time to evaluate your options carefully matters because default datasets often aren’t the best fit for every use case. Since no single dataset works for every project, it helps to explore your options or team up with experts who can provide data that’s tailored to your use case. For instance, at Objectways, we create high-quality, well-annotated datasets that are built to support real-world AI applications and empower your models to perform at their best.
Ready to build impactful AI models with the right data? Contact us, and let’s get started today!
Frequently Asked Questions
What is an object detection dataset?
It’s a collection of images or videos where objects are labeled to help AI models learn how to recognize and locate them. These labels typically include bounding boxes that show exactly where each object appears in the image.
What makes the MS COCO dataset popular?
MS COCO includes a large variety of everyday scenes and objects, with 2D bounding boxes and segmentation annotations across 80 categories. It’s great for general object detection tasks, though it doesn’t include 3D data.
How is the nuScenes dataset different from others?
nuScenes is designed for autonomous driving and includes data from cameras, LiDAR, and radar sensors. It provides 3D bounding boxes and sensor fusion, helping models understand complex traffic and urban environments.
Why are bounding box types important in object detection?
Bounding boxes define where objects are in an image. Different types, like 2D, 3D, or oriented boxes, capture different details, such as depth and rotation. Choosing the right type improves accuracy, especially in real-world or 3D tasks.
As humans, we often learn new tasks by building on skills we already have. If you can ride a bicycle, picking up how to ride a scooter isn’t so hard; you’re already familiar with the rhythm of balancing and moving on two wheels.
Similarly, some AI models are now learning to use what they already know to solve tasks they are not specifically trained for. This is called zero-shot learning, and it helps AI models respond to new prompts or tasks without needing labeled examples.
Traditional AI models learn by example. They need many high-quality labeled inputs to recognize patterns and perform well. For instance, a model trained to detect fruits needs hundreds of labeled fruit images to succeed.
Zero-shot learning removes this barrier by helping systems generalize based on language, structure, or other related data. This makes it useful in fields where collecting labeled data is time-consuming or expensive.
In this article, we’ll take a closer look at zero-shot learning, its current importance, how it compares to traditional training methods, and why it’s gaining attention in the AI community.
What is Zero-Shot Learning?
Zero-shot learning is a machine learning method that makes it possible for an AI system to perform a task without being trained on examples specific to that task. Instead of learning from direct samples, the model uses information from related data or previous tasks to understand and complete something new.
This ability relies on generalization. The model connects patterns from one area and applies them to another, often through natural language or contextual clues.
An example of inferring what a zebra looks like using zero-shot learning. (Source)
It’s a bit like trying to build IKEA furniture without the instructions. Even if you’ve never assembled that exact piece before, you can usually make sense of it based on your experience with similar items. In the same way, zero-shot learning enables a model to rely on task descriptions or instructions – rather than example data – to produce results.
This technique is especially useful in domains where labeled data is scarce or where tasks constantly change, such as language understanding or document classification. It helps models stay flexible and adapt to unseen challenges without constant retraining.
Zero-Shot vs Few-Shot Learning
As AI takes on more diverse tasks, choosing the right learning approach becomes key. Two common approaches are zero-shot learning and few-shot learning. Zero-shot learning works with no task-specific examples, while few-shot learning uses just a handful to get started.
While they sound similar, they solve problems in different ways, as shown in the image below:
Zero-Shot Learning vs Few-Shot Learning
How Zero-Shot Prompting Works
Giving a prompt to an AI model is a bit like writing a to-do list for someone new to the job. If the instructions are clear, they can usually figure it out using what they already know. But if the prompt is vague, they might miss the point entirely.
In zero-shot prompting, the model depends completely on the prompt to understand and perform the task without seeing examples during training.
Here’s a look at how zero-shot prompting works, step by step:
1. The model starts with no examples: In zero-shot learning, the AI system has not seen examples of the task it’s being asked to do.
2. A prompt sets the direction: The task is introduced through a written prompt, which acts as a simple instruction instead of training data.
3. The model draws from what it already knows: It uses its general language understanding to make sense of the prompt and generate a response.
4. The clarity of the prompt influences the outcome: Well-written prompts lead to better results, while vague ones can cause confusion or inaccurate answers.
5. The prompt becomes the entire setup: Since there is no task-specific training, the prompt plays a key role in guiding the model’s behavior and output.
Comparing Human and Zero-Shot Learning Approaches (Source)
Zero-Shot Learning is a Game-Changer
Now, let’s explore what makes zero-shot learning so impactful and why it’s a game-changer in the AI space.
Zero-Shot Learning Saves Time and Resources
Building datasets is one of the most resource-intensive stages of any AI project. It requires coordination across labeling teams, quality checks, and domain-specific reviews. Zero-shot learning reduces this load by enabling models to operate based on instructions rather than examples.
In fact, this approach is already working well in real-world tests. Researchers from OpenAI and Meta have shown that large language models can perform a variety of tasks with little to no task-specific training data, simply by following well-crafted prompts.
Zero-shot learning can reduce the cost of development, especially in fields where annotation is expensive or restricted. It also improves scalability. Developers can move from idea to model configuration without pausing to collect new samples. The model’s ability to generalize from prior training helps to explore more tasks with fewer manual steps.
Zero-Shot Learning Speeds Up AI Deployment
AI deployment often slows down due to the need for fresh data and repeated model fine-tuning. Zero-shot learning helps teams skip many of these steps by using task instructions at runtime. It reduces the number of retraining cycles and makes it easier to apply existing models to new problems.
For instance, a recent study found that Natural Language Processing (NLP) models like T5 and PaLM achieved strong performance using zero-shot prompting on unseen benchmarks. Since zero-shot models are task-agnostic, they can be deployed in more settings without manual updates.
This makes it easier to keep AI systems up to date without slowing things down. Teams can just change the prompt instead of rewriting code or gathering new data. It saves time and helps new features go live faster.
Examples of Zero-Shot Tasks Using the PaLM Model (Source)
Zero-Shot Learning Enables Rapid Experimentation
In many AI projects, testing new ideas takes time because each task often needs a separate model with labeled data. Zero-shot learning helps remove these blockers by enabling models to respond to new instructions without retraining.
According to Meta’s 2022 report on Open Pretrained Transformer (OPT) models, prompt design alone can guide the model to complete different tasks without task-specific examples. This makes it easier to explore multiple ideas at once, using the same base model.
If one prompt does not produce a useful result, teams can adjust the wording and try again without restarting the process. Since the model stays the same, the testing process becomes faster and more scalable.
Researchers and developers can test ideas by writing new prompts instead of preparing new datasets. This results in continuous trials and faster feedback cycles during development.
Common Use Cases of Zero-Shot Learning
Now that we have a better understanding of why an AI developer might choose zero-shot learning, let’s take a look at how it’s being used in real-world applications.
Text Classification & Reasoning With Zero-Shot Learning
Classifying text into categories is one of the most common NLP tasks, and zero-shot learning can be very effective here. For example, zero-shot learning has been used to classify financial documents without labeled training data. It can also support more complex tasks, such as answering complex financial questions by prompting large language models to generate and run code.
This is particularly impactful in fields like compliance or law, where data labeling requires domain expertise and time. Zero-shot learning means teams can respond faster to real-world data without needing to rebuild or retrain models from scratch.
Language Translation Driven By Zero-Shot Learning
Zero-shot learning allows language models to translate between language pairs they haven’t been explicitly trained on. Rather than relying on parallel examples for every combination, the model draws on its shared understanding of language to bridge the gap.
A good example of this is Google’s Multilingual Neural Machine Translation (MNMT) system. It supports translation across more than 100 languages, including low-resource ones like Basque and Zulu. These translations are possible even when no direct training data exists for the specific language pair. This method allows developers to launch translation tools in new regions faster, without waiting to gather large bilingual datasets. It also supports global communication by making translation more accessible across a wider set of languages.
Zero-Shot Translation with Google’s Multilingual Neural Machine Translation System (Source)
For organizations that work in multilingual settings, zero-shot learning can reduce costs and make scaling easier. It offers a practical path toward inclusive, language-aware AI systems that serve more users with fewer data requirements.
Zero-Shot Learning for Customer Support Automation
Zero-shot learning is also making it easier for businesses to set up AI-powered customer support without needing large sets of training data. One example is ZeroShotBot, a chatbot platform built for companies that want to launch support tools quickly, without writing code, and with no training data.
The system uses an answer bank, where businesses can add common questions and clear answers. The model then responds to customer queries by matching them with the most relevant answers.
Even if a question has not been asked before, the chatbot can still reply with the help of generative AI. ZeroShotBot supports over 100 languages and works across websites, mobile apps, and social media platforms.
ZeroShotBot’s Answer Bank Interface for Managing Responses (Source)
Limitations of Zero-Shot Learning
While zero-shot learning brings a lot of flexibility, it’s not a silver bullet. Like any other machine learning approach, it has its trade-offs. Here are a few key things to keep in mind when using it:
Prompt quality affects the outcome: The results depend heavily on how clearly the task is written. If the prompt is vague or confusing, the model may not give a helpful answer.
Not always accurate on complex tasks: When a task needs detailed reasoning or expert knowledge, zero-shot learning may not be reliable. The model can miss important context or deliver incomplete responses.
Model size and training still matter: Larger models that are trained on diverse data tend to perform better. Smaller models often struggle with general tasks in zero-shot setups.
Checking answers can be difficult: Without labeled examples, it is harder to know if the output is correct. Manual review is often needed.
Best used as part of a broader approach: Zero-shot learning works well when paired with few-shot or supervised methods. Together, they create a more balanced and reliable AI system.
Wrapping Up
Learning methods like zero-shot learning have opened the door to a new way of building artificial intelligence systems. Instead of training on examples for every task, models can now take on new challenges using only prompts and prior knowledge.
This makes it easier to test ideas, launch faster, and handle changing needs without starting over. However, it isn’t a one-size-fits-all solution. It works best when paired with thoughtful prompt designs and reliable models.
At Objectways, we help teams build better AI solutions by offering a full suite of services, including high-quality data labeling, data sourcing, and AI development support. Whether you’re training models from scratch or working on zero-shot and few-shot learning applications, we provide the clean, scalable data needed to power accurate, reliable systems.
Explore our services to see how Objectways can support your next AI project, and connect with our team to get started today!
Frequently Asked Questions
What is zero-shot learning?
Zero-shot learning is a method where AI models perform tasks without any task-specific training data, relying on general language understanding.
What is zero-shot prompting?
Zero-shot prompting refers to giving an AI model clear instructions or questions to complete a task without prior examples.
What is zero-shot vs few-shot vs chain of thought?
Zero-shot uses no examples, few-shot uses a few examples, and chain of thought adds step-by-step reasoning to improve complex outputs.
What are the disadvantages of zero-shot learning?
Zero-shot learning can struggle with task precision, depends heavily on prompt clarity, and may not match the accuracy of trained models.
Factories used to run with people and machines working together, guided by fixed schedules, checklists, and workers’ experience. When something went wrong, teams would step in and fix it manually – usually after the problem had already caused some damage. Most decisions were based on what had already happened, not what was coming next.
But today, that’s starting to change. More manufacturers are turning to artificial intelligence (AI) to prevent problems before they happen. With the help of Internet of Things (IoT) and edge devices, factory machines are no longer just pieces of equipment – they’ve become connected, intelligent systems that learn from data. These systems can automate tasks, spot issues early, and help keep production running smoothly.
This evolution is part of a broader movement known as Industry 4.0, which brings AI and IoT to factories. By working together, these technologies create safer, faster, and more efficient production environments. The impact of such technologies in manufacturing is substantial. Studies estimate that AI could add up to 15.7 trillion dollars to the world economy by 2030.
In this article, we’ll explore how factories are using AI tools across the production process as part of the shift toward smart manufacturing. We’ll also look at how companies can apply AI to improve operations and stay competitive.
Smarter Assembly Lines with AI in Manufacturing Automation
When it comes to car manufacturing, assembly lines are the heart of production. Even minor issues can lead to major delays and increased costs. In the past, factories relied on routine inspections or waited until equipment failed before taking action. While this approach kept things running, it often resulted in costly downtime and inefficiencies.
Nowadays, many factories are using AI for predictive maintenance, a method that can help identify potential equipment issues before they lead to failure. These systems use machine learning algorithms to analyze data from IoT sensors installed in equipment.
They collect data related to the vibration, temperature, and motor speed of the machines. AI models analyze this data for patterns that may show early signs of wear, damage, or failure, subtle indicators a human might overlook. When the model detects something unusual, maintenance teams are alerted, allowing them to address the issue before it becomes a serious problem.
An interesting example of this can be seen at BMW’s Regensburg plant in Germany. The factory uses an AI system to monitor the conveyor belts that move cars through the production line. These systems analyze the data collected by IoT sensors to recognize patterns that indicate issues that usually happen before a failure.
An Assembly Line at BMW’s Regensburg Plant. (Source)
When it finds something unusual, it sends a warning so that maintenance can be done before a breakdown happens. Over time, the system becomes more accurate as it learns from new data. By using this approach, the BMW plant has avoided more than 500 minutes of downtime each year.
Energy Consumption Monitoring and Optimization
Energy is one of the most overlooked costs in manufacturing, like a dripping faucet, which seems minor but would still add up when the water bill comes around. Small inefficiencies, such as machines running idly, equipment left on during breaks, or power-intensive tasks scheduled at peak times, can silently drain massive amounts of energy. This hidden waste drives up costs and undermines both sustainability and long-term operational efficiency.
To solve this, many factories are using AI and IoT to monitor and manage energy use in smarter ways. IoT devices, such as connected sensors and meters, are placed across machines and systems to track and collect data on energy consumption in real-time. AI models then analyze this data to identify patterns, find areas of waste, and predict future energy needs. Instead of relying on fixed schedules or manual checks, the system can adjust energy use automatically and suggest optimizations.
For instance, Schneider Electric’s smart factory is a good example of this approach. The company uses an IoT platform that connects devices, collects live operational data, and applies AI to manage performance.
Using AI and IoT to analyze real-time energy data from the production line. (Source)
With this platform, they continuously monitor how energy flows through the facility and make real-time adjustments, such as shutting down idle machines or shifting tasks to off-peak hours. The use of AI and IoT in manufacturing has helped the company reduce energy waste, improve uptime, and make steady progress toward its goal of reaching net-zero operations by 2030.
Quality Inspection and Defect Detection with AI
Maintaining high-quality standards is crucial in manufacturing, where even minor defects can drive up costs and lead to negative customer feedback. It’s like building a complex machine with one faulty bolt – no matter how well everything else is made, that single flaw can cause the whole system to fail.
Many factories still rely on human inspectors for quality checks, but even experienced eyes can miss subtle defects, especially during long shifts or when inspecting high volumes. To improve both accuracy and efficiency, many manufacturers are adopting AI-powered inspection systems.
These systems use IoT devices and computer vision to inspect products in detail, detecting issues like scratches, dents, incorrect dimensions, or missing components. Unlike human inspectors, AI systems can operate continuously without fatigue, ensuring consistent, around-the-clock quality control.
Ai Visual Inspection System Identifying Defective Products On The Production Line. (Source)
Smarter 3D Model Labeling for CAD/CAM Integration
Before any component is made in a factory, it typically begins as a 3D model. These models created using Computer-Aided Design (CAD) software, define every detail – holes, edges, and shapes – that the final part will have.
To turn those designs into physical objects, machines like Computer Numerical Control (CNC) tools need to understand the features in the 3D model. That means the features need to be labeled correctly so the machines know where to cut, drill, or shape the material with precision.
Traditionally, engineers have done this labeling manually. However, when components have complex shapes, the process can take hours and is prone to human error. Today, AI is being used to automatically identify and label features in 3D CAD models.
A recent study found that AI models trained on hundreds of designs can learn to recognize common features, like holes and pockets, by analyzing their shape and position. The AI system can interpret the geometry, understand the function of each section, and apply the correct labels faster and more accurately than a human.
Examples of AI labels used by 3D CAD models to recognize features and assign categories. (Source)
This automation makes it easier to prepare models for manufacturing, reduces errors, and shortens the time between design and production. In high-mix or custom manufacturing, where components frequently change, AI-powered labeling saves time and improves consistency across the production process.
Challenges in Implementing AI in Smart Factories
AI has a lot to offer in manufacturing, but using it in real factory settings isn’t always easy. Many companies start with successful pilot projects but run into problems when they try to scale or keep them running. Here are some common challenges manufacturers face:
Limited Data Quality: AI depends on high-quality data to work well, but many machines don’t capture clean or complete information. This makes it harder for the system to learn and deliver accurate results.
Data Labeling Bottlenecks: Training AI models, especially for tasks like detecting defects or analyzing 3D models, requires a lot of labeled data. Creating these labels takes time and often needs experts who understand the factory’s operations.
Connecting with Existing Systems: Integrating AI with current factory systems can be difficult, especially when a mix of old (legacy) and new technologies is involved. Making sure everything communicates smoothly is often more complicated than expected.
Scaling and Maintenance Issues: An AI solution might work well in one area of the factory, but applying it across different lines or locations can be tricky. Plus, AI models need to be updated regularly as equipment, processes, and conditions change.
How Objectways Helps Smart Factories Succeed with AI
Bringing AI into manufacturing isn’t just about using the latest technology – it also requires the right data, careful setup, and proper model training. Many manufacturers face delays because they lack the time, tools, or in-house expertise to manage these steps on their own.
Objectways can help bridge that gap. We specialize in providing high-quality data that AI systems need to perform effectively. With a skilled team of annotators and technical experts, Objectways handles tasks like labeling product images, annotating 3D models, and organizing sensor data. This makes it easier for factories to train AI systems that detect defects, monitor equipment, and support automation.
The best part is our support doesn’t stop at data. Objectways also offers AI development services and works closely with factory teams to ensure AI tools connect smoothly with existing systems. We understand how factory operations run and help reduce the time, cost, and risk of getting new AI solutions up and running.
Smarter Manufacturing Starts with Better Data
AI can help factories reduce downtime, improve product quality, and use energy more efficiently. However, AI solutions are heavily dependent on clear, well-prepared data. That means labeling images, annotating sensor logs, and organizing 3D models in a way machines can interpret and learn from.
Also, different factories have different needs – some require speed, and others demand precision. However, no matter the objective, high-quality data is what powers successful AI projects.
At Objectways, we help manufacturers transform their factories. Our team delivers accurate data labeling and sourcing services that support real-world AI projects on the factory floor. If you’re planning to integrate AI into your operations, contact us to get started today!
Frequently Asked Questions
What is automated manufacturing?
Automated manufacturing involves using machines and software to perform tasks with little human help. With AI, these systems can also learn and adjust to keep production running smoothly.
What does a defect detector do?
A defect detector uses AI and cameras to check products for flaws like cracks, dents, or missing parts. It finds issues in real-time so problems can be fixed early.
What is the scope of smart manufacturing?
Smart manufacturing uses AI, IoT, and automation to improve how factories work. It helps with quality checks, machine upkeep, energy use, and faster decision-making.
How is IoT used in manufacturing?
IoT connects machines and sensors to collect real-time data. This data helps factories track performance, spot problems, and respond faster.
With innovations like ChatGPT gaining in popularity, artificial intelligence (AI) is now a part of daily life for millions of people worldwide. It is used in sectors like healthcare, transportation, customer service, education, and even entertainment. As these systems become more common, they also bring up hard-to-ignore risks. Unfortunately, AI models can make decisions that reflect bias, invade privacy, or create safety concerns.
From interactive chatbots to the warnings seen in films like ‘The Terminator,’ artificial intelligence has always raised questions about control and consequences. These issues have sparked a larger public debate: is AI good or bad, and how do we make sure it helps society rather than harms it? In fact, a recent survey found that 71% of business leaders believe AI cannot be trusted without stronger AI governance in place.
Even when designed with good intentions, AI systems can still produce harmful outcomes. These consequences are real and can affect people in everyday situations. That is why AI governance is no longer a suggestion or recommendation. It is critical to ensure that AI is used responsibly, lawfully, and in ways that protect public interests.
In this article, we’ll explore three key areas of AI governance: creating clear AI policies, building transparent systems, and protecting the public from disinformation. Let’s dive in!
The Three Core Pillars of AI Governance
Why Does AI Governance Matter?
AI has moved quickly from research labs to everyday applications across various industries and services. AI innovations are now helping with tasks like screening job candidates, scoring credit applications, guiding policing systems, and influencing what content people see online.
These tools rely on models trained using large amounts of data. The accuracy and fairness of an AI model depend heavily on the quality of its training data. If the training data includes bias or gaps, those issues often carry over into the system’s decisions.
While AI tools offer convenience and efficiency, they also raise serious ethical concerns. You can think of AI models as a mirror. They reflect what they learn, even when that reflection is unfair. If the data is biased, the outcome will likely be biased too.
For example, in 2018, Amazon shut down its internal AI hiring tool after it showed bias against women. The system had learned from historical data that favored male applicants. Similarly, facial recognition software used in law enforcement has faced issues related to bias.
In 2020, Robert Williams, a Black man from Detroit, was wrongfully arrested after facial recognition software misidentified him as a robbery suspect. Despite clear differences between Williams and the actual suspect, the software’s error led to a traumatic experience for him and his family.
These incidents showcase how unregulated AI solutions can produce real-world harm. They also erode public trust in new technologies and increase skepticism about whether AI is good or bad.
AI Policy: Developing Regulations
AI isn’t so different from a fast-moving vehicle. Without a clear road and working brakes, it can cause harm. Regulations can act as a road map and control system. They help guide AI development toward safe and responsible use that reflects public values and legal standards.
For instance, without regulations, smart home devices powered by AI can pick up private conversations without warning. Likewise, in customer service, automated systems have denied refunds or blocked accounts based on errors in the data. These examples may seem small, but they can affect daily life in serious ways. They are good reminders of why AI systems need proper checks before being widely used.
As AI adoption grows, governments around the world are introducing new laws to guide its development and use. These AI policies are designed to reduce risk while encouraging innovation.
Here are some examples of key AI regulations being put in place around the world:
European Union – AI Act (2021): The EU AI Act sets rules based on system risk. High-risk tools like hiring software must follow strict guidelines, while harmful systems like government scoring are banned.
United States – Take It Down Act (2025): This law targets non-consensual AI deepfakes. It requires platforms to remove harmful content within 48 hours and treats distribution as a crime.
Brazil – Bill 21/2020: Brazil’s bill promotes ethical AI. It highlights transparency, accountability, and the protection of human rights in all AI systems.
China – Algorithm Regulation (2022-23): China’s law controls recommendation systems. It demands user control and stops the spread of harmful or misleading content.
India – AI Oversight Initiatives (2025): India is launching a national board to oversee AI in government. The goal is to ensure systems follow ethical and legal standards.
Another issue associated with AI is transparency. As these systems are used to make important decisions in healthcare, education, and finance, people are asking: How do these systems reach their conclusions, and can we trust the results?
Transparency helps answer those concerns. People are more likely to trust AI innovations when they understand how it works. When the decision-making process is hidden, even accurate results can feel suspicious.
One way to improve trust in AI is through explainable AI (XAI), which is a technology that clearly shows how decisions are made. By revealing the reasoning behind outcomes, XAI facilitates accountability and supports stronger AI compliance.
How Explainable AI Delivers Insights to Users (Source)
In the same way, model cards are impactful tools that can make AI models more transparent. They’re short documents that explain what a model was trained to do, what data it used, and where it might fall short. A good example is the documentation released by OpenAI for its GPT-4 and GPT-4-turbo (o3) models, which outlines their training goals, capabilities, and known limitations.
However, transparency isn’t always easy to achieve. Complex systems, such as large language models, can be difficult to explain – even for experts. Despite these challenges, investing in transparency is vital for building trust and promoting responsible AI.
Combating AI-Driven Disinformation
In recent years, AI has made it easier to create false information that looks and sounds real. Sometimes, AI tools are used in coordinated disinformation campaigns to influence public opinion, spread rumors, and mislead people.
Alongside this, the rise of deepfakes is a growing concern. Deepfakes are fake images, videos, or audio clips that show people doing or saying things they never did. In early 2024, a deepfake audio clip a U.S. presidential candidate went viral just before a major election. It spread quickly across social media before it was flagged and taken down.
Tackling this type of issue isn’t simple. AI-generated content spreads fast, is often convincing, and can be difficult to detect in time to prevent harm.
Emerging Threats Related to Identity Fraud (Source)
In response, developers are building tools that find patterns in speech, image details, and writing styles to identify what is fake. Content moderation plays a crucial role here. These systems help platforms review posts, remove harmful content, and reduce the reach of false information.
Best Practices Associated With AI Governance
Many organizations are taking active steps to manage the risks and responsibilities that come with using AI. Here are some common practices organizations are using to promote responsible AI development:
Use of Ethical AI Guidelines: Most organizations begin with ethical AI principles that guide how models are built and applied.
Internal AI Governance Teams: These principles are enforced by dedicated ethics teams that review projects and advise on sensitive use cases.
Routine Audits of AI Systems: Audits support this process by checking if systems behave fairly and deliver the outcomes they promise.
Clear Documentation Practices: To stay accountable, teams maintain model cards that explain design choices, data sources, and known limits.
Continuous Performance Monitoring: After deployment, systems are monitored for accuracy, bias, and performance changes over time.
Regular Training for AI Teams: Teams receive ongoing training on AI policy, ethical decisions, and AI compliance needs.
Companies like Objectways can help put these best practices into action. If you’re looking for AI experts to support governance, ensure data quality, or improve model transparency, Objectways offers the tools and experience to empower you to build responsible, compliant AI systems.
Whether you’re just getting started or scaling up, partnering with the right team can make all the difference.
Balancing Innovation with Responsibility
While AI regulation is important, it also needs to allow room for innovation. A strong AI policy does both: it protects people while giving researchers and companies the freedom to keep improving and solving real-world problems.
Take Singapore’s Model AI Governance Framework, for example, which provides organizations with clear guidance on ethically implementing AI. It focuses on principles like transparency, accountability, human-centricity, and XAI.
Along the same lines, Canada’s Algorithmic Impact Assessment tool acts as a diagnostic checklist. It supports public sector teams in evaluating the potential risks and impacts of automated decision systems before deployment. By assessing factors like data quality, fairness, and privacy, the tool promotes responsible AI.
Going a step further, Google’s People + AI Research (PAIR) initiative focuses on reducing the gap between complex AI systems and the people who use them. The team builds open-source tools and research to make AI easier to understand, more accurate, and more useful. The goal is to create AI systems that support human values and build trust.
Key Principles From Google’s PAIR Initiative (Source)
AI Compliance Isn’t Optional – It’s the Future
Responsible AI development relies on AI governance that promotes safety, fairness, and public trust. Clear and consistent AI policies make it possible for organizations to create systems that meet ethical standards while solving real-world problems.
However, AI regulation alone isn’t enough. People also need transparency built into AI systems to understand how they work and why they make certain decisions. Accountability should be integrated at every stage of development and deployment.
At Objectways, we support the development of ethical AI through high-quality data practices and collaborative partnerships. If you’re interested in building responsible and ethical AI solutions, book a call with our team today and see how we can build smarter, more efficient innovations together.
Frequently Asked Questions
What is the meaning of AI compliance?
AI compliance means ensuring that artificial intelligence systems follow legal, ethical, and operational standards. It helps reduce risk and builds trust in how AI is used.
Is AI a good thing or bad?
It depends on how it is designed, governed, and applied. With strong rules and oversight, AI can support fair and useful outcomes.
What is an AI policy?
An AI policy is a set of rules and guidelines that direct how AI systems are developed and used. It helps align technology with public interest and ethical principles.
What are the pillars of AI governance?
The pillars of AI governance are regulation, transparency, and disinformation defense, working together to guide responsible AI.
Creating a chatbot goes beyond just making it feel like a real conversation. It’s about truly understanding what users need and helping them find the right solutions. When a chatbot misunderstands someone or responds with off-topic answers, it can be pretty frustrating. Building a great chatbot takes more than just coding skills – it involves understanding how people communicate, picking up on subtle cues, and constantly learning and improving.
Even small mistakes, like biased training data, rigid scripts, or poor testing, can snowball into bigger problems. You can think of a chatbot as the first person a user meets when they visit your website or app. A friendly, helpful, and knowledgeable first impression can encourage them to stick around and explore, while a confused or unhelpful one can quickly drive them away.
In fact, studies show that around 30% of people will walk away from a purchase and go to a competitor if they have a bad experience with a chatbot. That’s why it’s so important to catch common mistakes early and fix them before they turn users away.
Generative AI models, like large language models (LLMs), make it easier to train chatbots that understand and communicate with users in a more natural, human-like way. These models are advanced AI systems trained on massive amounts of text and real-world user prompts. That training helps chatbots grasp context, give better responses, and improve over time. By speeding up how chatbots learn and adapt, generative AI enables companies to build bots that feel more helpful, reliable, and capable of creating real connections with people.
In this article, we’ll look at seven common mistakes to avoid when training a chatbot and explore how generative AI can help you overcome these issues
#1: Train Your AI Chatbot to Recognize Real Intent
One crucial issue in training AI chatbots is relying too much on fixed scripts or predefined responses. While these approaches work well for simple, routine queries, they often fall short when users communicate in more natural, unpredictable ways.
People typically use slang, make typos, ask multiple questions at once, or phrase things differently, and a chatbot needs to be able to handle that. When it can’t, its responses can feel off-topic or unhelpful, missing what the user actually needs.
Generative AI helps overcome this problem by making it easier for chatbots to understand natural language in a more flexible way. Instead of matching exact words or phrases, it analyzes what the user really means, no matter how they say it. This makes it possible for chatbots to give better answers, even when the conversation takes an unexpected turn.
For example, a British Airways chatbot once misunderstood a user input in a way that illustrates a common issue with rigid chatbot design. The user had previously been instructed by the chatbot to enter an airport code. When the user followed those instructions and typed “LHR,” the correct code for London Heathrow Airport, the chatbot didn’t recognize it. Instead, it responded with a generic message asking the user to check for spelling mistakes.
The British Airways Chatbot Misunderstanding a Valid Airport Code. (Source)
This was likely a small glitch or a case of the chatbot not being trained to recognize common abbreviations. But it points to a bigger problem: when chatbots rely too heavily on exact inputs or predefined scripts, they can easily miss valid information or misinterpret what the user is trying to say. These kinds of interactions, even when minor, can break trust and frustrate users.
With better training using generative AI, chatbots can become more flexible – understanding context, handling variations in input, and responding in a way that feels more natural and intelligent.
#2: Using Biased or Unbalanced Data to Train Your AI Chatbot
Chatbots are only as effective as the data they’re trained on. If that data is biased, incomplete, or focused on a specific demography, the chatbot’s responses will reflect those imbalances. The consequences can range from simply irrelevant replies to offensive answers, which makes users not trust the chatbot. For instance, a chatbot trained mainly in American English may have difficulty understanding or responding appropriately to users who speak different dialects or use regional expressions.
Generative AI can help reduce bias when it’s used with carefully chosen data and human guidance. It makes it easier to create more balanced examples and adjust chatbot responses for different languages, cultures, and user needs. With regular updates and feedback, these models keep improving – helping chatbots become more accurate, inclusive, and helpful.
A well-known example of the risks associated with biased or unmoderated training data is Microsoft’s Tay, a chatbot launched in 2016 and taken offline within just 24 hours. Tay was trained using conversations from Twitter – a platform with a wide variety of content, both positive and negative.
Without appropriate safeguards, the bot quickly began mirroring inappropriate language it encountered online. Following widespread public concern, Microsoft decided to retire the project.
#3: Don’t Overlook Conversation Flow When You Train an AI Chatbot
Many chatbots struggle to keep track of context in long and lengthy conversations. They might answer one question well but then lose track of what was said earlier. This leads to repetitive or confusing replies that feel robotic and disconnected. When users are trying to complete tasks that involve multiple steps, it can be frustrating for them.
Consider a situation where someone says, “I want to change my flight,” and then follows up with, “Actually, make it business class.” A chatbot that doesn’t understand the context may not know what “it” refers to, forcing the user to repeat themselves and disrupting the flow of the conversation.
Generative AI models, like LLMs, help solve this problem by keeping track of what was said earlier in the conversation. They can understand pronouns, follow-up questions, and references to previous messages, making interactions feel more natural and connected. Whether a user is booking a service, resolving an issue, or just having a casual chat, generative AI helps chatbots respond with better memory, flow, and relevance – making the experience feel more human.
#4: The Risks of Launching a Chatbot Without Proper Testing
Launching a chatbot without testing it in real-life situations can be a serious risk. It’s easy to assume that if a chatbot performs well in a controlled environment, it will work just as well with real users. But, once it is available to the public, people will ask unexpected questions, use different tones, and interact in ways the bot isn’t trained for. If the testing doesn’t include these real-world challenges, blind spots can happen and cause mistakes and broken conversations.
To avoid this, bots can be trained with synthetic data generated using AI. This data can mimic a wide range of user inputs, edge cases, and conversation styles without depending on thousands of manual test cases. AI-driven testing environments allow developers to fine-tune the bot’s responses, identify and fix logic gaps, and improve its overall performance before being exposed to live users.
DPD’s chatbot, Ruby, ran into this problem when a user asked an unexpected question, and the bot replied with negative comments about the company’s own service. It was unintentional and most likely caused by gaps in testing, but it shows how important it is to prepare chatbots for unusual or tricky inputs. Testing for edge cases like this helps make sure bots respond appropriately and don’t accidentally damage the customer experience or the brand.
DPD’s chatbot responding unexpectedly to a customer query. (Source)
#5: Why Personalization Matters When You Train an AI Chatbot
Nowadays, users expect their online experiences to feel like they’re designed just for them, and chatbots are no different. Sometimes, one-size-fits-all responses often feel impersonal and machine-like.
LLMS can help chatbots create more personalized and engaging conversations. It can analyze a user’s past behavior, preferences, and current actions to respond in a way that feels natural and relevant. Instead of giving the same answer to everyone, it can tailor responses to each user and create responses that understand the situation. This level of personalization improves the user experience and increases the chances they’ll stay engaged with the brand.
The case of Tessa, a chatbot launched by the National Eating Disorders Association (NEDA), showcases the importance of ongoing safety checks and human oversight. Tessa was designed to support individuals dealing with eating disorders, but over time, it began providing guidance that conflicted with its intended purpose – including suggestions related to calorie restriction and weight loss. As a precaution, the chatbot was taken offline.
Tessa’s response raised concerns about health-related guidance. (Source).
#6: What Happens When You Skip Chatbot Security in AI Training?
Chatbots are becoming a normal part of our digital lives, and many of them handle sensitive information – like personal details, bank info, health records, or private messages. Generative AI can help to keep user information safe when it’s used with strong security systems. It can monitor chatbot conversations in real time and warn if sensitive data is being shared by mistake. It also helps spot unusual activities that could lead to a security problem.
In 2024, there was a reported incident where a Telegram-based chatbot was used to access the systems of Star Health Insurance, a major insurance provider in India. With just a phone number and policy ID, the attacker was able to get personal information like names and birthdates of policyholders. It’s a clear reminder that information shared with chatbots needs to be properly protected – just like any other sensitive data.
#7: Keeping Your Chatbot Updated
A vital issue people often make with chatbots is thinking that once they’re up and running, they don’t need any more work or updates. But the truth is, the internet changes all the time, what users need changes, and even the way we talk changes.
If we don’t keep chatbots updated, they slowly stop being helpful. The information they have can get outdated, and they might not understand new slang or the way people ask questions today. Over time, the experience feels less smooth, and even a well-built chatbot can end up falling short of what users expect.
AI makes it easier to keep chatbots learning and improving over time. Unlike old-fashioned chatbots that need someone to manually update them with new information, AI can learn from every new chat and all the new information it sees.
With techniques like reinforcement learning, the chatbot learns from human feedback and adjusts its behavior to meet user needs. It can also spot areas where it’s struggling or where new information is needed. Continuous learning makes the chatbot useful, correct, and impactful in the long run.
Building a successful chatbot revolves around understanding the user’s intentions and delivering helpful responses. Common issues, like misinterpreting user input, losing context, or skipping proper testing, can lead to a frustrating experience and impact user trust. However, with the right tools, including generative AI, many of these challenges can be handled easily.
If you’re looking to build or improve a chatbot that truly connects with users, Objectways is here to help. Our team of experts specializes in creating AI-powered chatbots that enhance user experience and build customer trust. Contact us today to learn more about our AI development services and request a demo of our generative AI solutions.
Frequently Asked Questions
What are the 7 steps to create a chatbot strategy?
Creating a chatbot strategy involves defining its purpose, identifying users, mapping use cases, selecting a platform, designing the conversation flow, testing thoroughly, and continuously improving based on feedback.
Can you train your own AI chatbot?
Yes, you can train your own AI chatbot by preparing training data, applying Natural Language Processing (NLP) models, fine-tuning responses, and working with experts like Objectways for support and development.
How long does it take to train a chatbot?
The time it takes to build and train a chatbot varies with the application and the level of complexity. Advanced AI-powered chatbots may take longer to train, while simple chatbots can be trained relatively quickly.
How to train a conversational AI model?
To train a conversational AI model, you gather and label data, use NLP to understand intent, and then fine-tune the model with real-world interactions and ongoing user feedback.
Choosing a data labeling tool for your AI project is a lot like picking the right equipment for a construction job. You wouldn’t rely on light-duty tools to construct a high-rise building, nor would you bring in industrial machinery for a small home project. Having the right tools makes all the difference.
Similarly, every successful AI model starts with high-quality, accurately labeled data. Data labeling involves tagging raw data so machine learning algorithms can understand it. It lays the groundwork for how innovative, accurate, and reliable your model will be. When data quality falls short, the impact is pretty serious. In fact, studies show that companies can lose up to $12.9 million due to poor data quality.
That’s why choosing the right data labeling tool matters. Organizations often face a tough decision between using flexible, cost-effective open-source tools or investing in proprietary platforms that offer built-in support, scalability, and automation.
Determining the right option can lead to better results and faster innovation. On the other hand, the wrong choice, though, can slow things down and make things more expensive. In this article, we’ll explore the key differences between open-source and proprietary data labeling tools, share real-world examples, and highlight key factors to help you choose the right solution for your project.
Many of these tools use a human-in-the-loop approach, combining automation with human reviews to ensure accuracy. This teamwork helps create high-quality training datasets – the foundation for AI innovations like computer vision models, large language models, and generative AI models.
Data labeling tools are primarily classified into two key categories. Here’s a closer look at each of them:
Open-Source Frameworks: Open-source tools are free to use and provide full access to the platform’s code, so you can customize them to fit your needs. They can be modified and shared under open licenses, but setup and maintenance usually require substantial technical expertise.
Proprietary Platforms: These are commercial, fully managed solutions with built-in automation and analytics. Vendor support makes them easier to deploy and scale, but they offer limited customization flexibility on the user side.
Open-Source Vs. Proprietary Data Labeling Platforms
When you compare open-source and proprietary data labeling platforms, it’s clear that both have their own unique advantages. Open-source tools give you more flexibility and control, but they usually need more technical skills to set up and maintain. Proprietary platforms are easier to use and come with built-in features like automation and support, but they’re less customizable.
As shown in the table below, the best choice depends on what your team needs. That’s why many organizations end up looking for a mix of both.
Differences Between Open-Source and Proprietary Data Labeling Tools
When Open-Source Data Labeling Tools Are the Right Fit
Now that we have a better understanding of what an open-source data labeling tool is, let’s explore some real-world examples where using open-source tools can provide the most value.
Using Open-Source Data Labeling Tools in Academic Settings
Not all AI projects require massive datasets or large teams. In academic or personal projects, especially those using transfer learning and pre-trained models, the amount of annotated data needed can be considerably smaller.
For example, a student working on an object detection task might only need to annotate around 300 images. In these simpler use cases, it makes more sense to use lightweight, open-source tools that are easy to set up and free to use.
Tools like LabelStudio and MakeSense are ideal for this. They support essential annotation types like bounding boxes and polygons, offer user-friendly interfaces, and don’t require heavy technical infrastructure – making them perfect for small-scale projects with limited resources.
Data Labeling Platforms for Early-Stage AI Startups
Early-stage startups often operate with small teams and limited budgets. Their main goal is speed – getting prototypes up and running without added complexity. At this point, datasets are typically small or highly specific, like a few hundred user queries. The perfect data labeling tool should be easy to set up, help teams iterate quickly, and keep the focus on validating ideas.
For instance, a startup developing an AI-powered customer support chatbot might need to annotate text data to detect user intent, extract key information, or flag urgent messages. Open-source tools like Doccano and LightTag handle these tasks well.
An example of using Doccano for text annotations. (Source)
When Proprietary Data Labeling Platforms Are a Better Fit
Academic projects and prototypes usually deal with smaller datasets and focus on learning or experimentation. But real-world, production-grade AI projects need to handle larger volumes of data and deliver reliable, efficient results.
Next, let’s look at a couple of examples where proprietary data labeling platforms might be the better choice.
Data Labeling Tools for AI in Healthcare Projects
Healthcare projects that use AI often require high precision. Let’s say a company is developing a model to detect skin lesions. They might be working with thousands of high-resolution images, where even the smallest details, like asymmetry, slight color changes, or irregular borders, can be crucial.
An example of data labeled for dermatology-related applications. (Source)
In this case, they need an annotation tool that can handle both the scale and sensitivity of the project. On top of that, regulations like HIPAA add strict requirements for data privacy and security.
This is where a platform like TensorAct Studio is especially useful. It supports detailed image annotation for tasks like classification and segmentation, along with secure data handling and customizable workflows.
With AI-assisted labeling, version control, and built-in quality checks, teams can manage complex tasks efficiently – without compromising on accuracy or data compliance. Whether in healthcare or any other industry where precision and security matter, TensorAct Studio provides the control and flexibility needed to deliver reliable, high-quality results.
Proprietary Data Labeling Platforms for E-commerce Workflows
Another area where a proprietary tool like TensorAct Studio proves reliable is e-commerce. Online retail platforms manage millions of product images that must be accurately labeled and continuously updated. This goes far beyond basic tagging – it demands fast, high-precision annotation at scale.
During peak times like seasonal launches or sales events, companies may need to classify tens of thousands of images by attributes such as color, category, material, and brand – often within just a few days. Errors or delays in labeling can disrupt product search, weaken recommendations, and impact sales.
TensorAct Studio is built to handle this level of demand. When accurate product data directly affects customer experience, a dependable proprietary platform delivers the speed, precision, and integration needed to keep everything running smoothly.
Autonomous Vehicle Projects Driven By 3D Data Labeling Tools
So far, we’ve discussed scenarios that involve image or text annotation, but some AI projects rely on more complex data types, like LiDAR. LiDAR (Light Detection and Ranging) uses laser pulses to create detailed 3D maps of the environment. It’s a key technology in autonomous vehicles, helping them detect objects, measure distances, and understand their surroundings with high precision.
Annotating LiDAR data, especially in the form of 3D point clouds, is far more complex than labeling images or text. Generally, developers working on Level 4 autonomous vehicles must label millions of data points to identify features like lane markings, curbs, moving vehicles, and pedestrians, all in 3D space and often aligned with camera footage.
Annotating LiDAR data to train models for deployment in self-driving cars. (Source)
This level of detail is difficult to manage with open-source tools. Proprietary platforms like TensorAct Studio offer the advanced capabilities needed for these tasks – such as 3D annotation tools, sensor fusion, machine learning integration, and version-controlled workflows. For safety-critical applications like autonomous driving, proprietary tools deliver the accuracy, scalability, and control required to build reliable systems.
Blending Open-Source and Proprietary Tools for Smarter Labeling
If your AI project doesn’t fit within the scenarios we’ve walked through so far, a hybrid data labeling approach might be the right fit. This means using both open-source and proprietary tools – switching between them as your project evolves, like a hybrid car switching between electric and gas.
Open-source tools might work well in the early stages. They’re flexible, cost-effective, and great for experimenting or handling large datasets when things are still taking shape.
But as your project moves toward production, the project’s needs can change. You may need faster, more accurate labeling that meets strict quality and industry standards. That’s where proprietary tools can be brought in.
Factors to Consider When Choosing a Data Labeling Tool
Here are some other key factors to consider when choosing a data labeling tool:
Project size and complexity: Big or detailed projects often need robust, scalable tools (usually proprietary). Smaller or more flexible projects might do better with open-source tools that you can customize.
Budget flexibility: Open-source tools are free, but they may take more time and effort to set up. Proprietary tools cost money, but they usually come with built-in features, support, and faster setup.
Data security: If you’re working with sensitive or private data, security is critical. Open-source tools can be hosted on your own servers, giving you full control over where your data goes. Proprietary tools usually offer secure cloud storage and follow strict privacy rules (like GDPR or HIPAA), which can help with compliance.
Tool integration: Open-source tools allow deep customization but take time. Proprietary options offer plug-and-play integrations for faster setup.
Team skills and size: If you have a strong technical team, open-source tools can work well. If your team is small or less technical, proprietary tools are easier to use and manage.
Key Considerations to Make When Selecting a Data Labeling Solution.
The Path to Reliable AI Starts with Data Labeling
Choosing between open-source, proprietary, or hybrid data labeling solutions isn’t a one-size-fits-all decision. It’s a lot like picking the right climbing gear – the gear that works for one terrain might not suit another. Some projects demand complete control and flexibility. Others need speed, built-in support, and structured workflows.
Ultimately, the right solution shapes your AI pipeline’s quality, consistency, and long-term reliability. It’s not just about data labeling; it’s about building trust in your AI solution.
At Objectways, we help teams make these decisions with clarity. Whether starting from scratch or refining a complex deployment, we support your goals with accurate data labeling services and adaptable AI solutions.
A data labeling platform is software that lets users annotate, manage, and organize raw data like images, text, or video for machine learning. It often includes quality control, collaboration tools, and automation features.
What does a data labeling service offer?
A data labeling service combines expert annotators with advanced tools to tag large datasets accurately. These services support AI development across industries like healthcare, automotive, and retail.
Why are machine learning data labeling services essential?
They ensure accurate, consistent annotations so AI models can detect patterns and make reliable predictions. Poor labeling leads to errors and weak model performance.
What are the best tools for data labeling?
It depends on your project needs, but TensorAct Studio is a good all-around option. It supports multiple data types, offers no-code/low-code workflows, and scales well from small projects to production.
One of the most important parts of any AI project, that often gets overlooked, is preparing the data needed to train a model. This process usually starts with data collection and sourcing – simply put, finding the right data from reliable sources or creating it when necessary.
Then comes data labeling, which can be done manually or by using data labeling tools and platforms. Labels or annotated data are essential because they directly impact AI model performance. It’s like building a house on a shaky foundation. If the training data isn’t reliable and well-labeled, everything that comes after is at risk of falling apart.
That’s why these early steps often end up being time-consuming. In fact, a 2020 report found that over 50% of the time spent on machine learning projects goes into labeling and cleaning data – more than the time spent on building or training models.
In AI projects, most of the effort goes into cleaning and labeling data. (Source)
To make that time worthwhile, it’s important to focus on the quality of the labels, not just the quantity of the data. Effective labeling relies on accurate, consistent, and clear labels across all types of data.
Previously, we have explored how data integrity forms the foundation of successful AI solutions. In this article, we will take a closer look at how to evaluate the quality of labeled data. We will walk through key metrics, helpful tools, and best practices to help you build more accurate and reliable AI systems.
What Is Data Labeling and Why Does It Matter?
Data labeling is the process of adding tags or annotations to raw data so that machines can understand it. This can look like using data labeling tools to draw bounding boxes around objects in images, assign categories to text, or mark key sections of audio. These labels enable AI models such as computer vision models and large language models (LLMs) to learn from examples during model training.
Bounding boxes created using predictions from trained computer vision models. (Source)
The model learns from labeled examples like a baby learning to speak – by repeatedly observing patterns in context, it gradually builds an understanding of how to associate inputs with the correct outputs. An AI model can then use that knowledge to make predictions on new, unseen data.
For model training to work well, it must be fueled by accurate and high-quality data. If the labels are wrong, the model learns incorrect information. This can lead to errors and poor performance later in the project. That’s why the saying “garbage in, garbage out” is so relevant – if you feed the model bad data, you can’t expect good results.
Also, consistency is just as important as accuracy. When similar data is labeled in different ways, the model receives mixed signals. This can make it harder for the model to learn and reduce its reliability. Even small mistakes in labeling can add up, leading to confusion during training and weaker results in production.
Essential Metrics to Measure Data Labeling Quality
Now that we have a better understanding of the need for quality data labels, let’s look at how to check if those labels are actually good enough for training AI models. Using the right metrics, and the help of data labeling tools, can make it easier to evaluate the accuracy, consistency, and overall quality of labeled data.
Accuracy: How Many Labels Are Correct
Accuracy is a common metric used to check the quality of labeled data. It tells you how many labels are correct out of the total number of labels.
For example, if you have a set of 1,000 images and 950 are labeled correctly, the accuracy is 95%. Here is a look at the formula:
Accuracy = (Correct Labels ÷ Total Labels) × 100
Accuracy works well when the data is balanced. This means each category, like “cat,” “dog,” or “bird,” has about the same number of examples. In that case, accuracy gives a clear picture of how well the labeling was done.
However, accuracy can be misleading when the data is imbalanced, with one class dominating. For instance, if 900 out of 1,000 images are of cats, someone could label everything as “cat” and still get 90% accuracy. That number might look good, but the incorrect labels for dogs and birds make the dataset less useful for training a model.
In real-world projects, accuracy is often used to evaluate batches of labeled data and to track progress over time. For example, if accuracy improves from 85% to 92%, it suggests the labeling process is becoming more consistent and reliable.
Inter-Annotator Agreement: Are Annotators Being Consistent?
Accuracy tells us how many labels are correct, but it doesn’t showcase whether different annotators are labeling data in the same way. To check consistency between labelers, teams often look at inter-annotator agreement, also known as inter-rater reliability. It gives insights into whether everyone is annotating data in a similar and reliable way.
When agreement is high, it usually means the task is clear and the instructions are easy to follow. If agreement is low, the data might be difficult to label, or the guidelines may need to be improved.
When there are two labelers, a common way to measure agreement is Cohen’s Kappa. You can think of it as two teachers grading the same test. If they often give the same score, that shows strong agreement. This method also considers how often agreement could happen just by chance.
For more than two labelers, Fleiss’ Kappa is often used. It works more like a panel of judges. If most of them give similar scores, it shows good consistency. If their scores vary quite a lot, there may be confusion about the task.
Precision, Recall, and F1 Score: Measuring What Accuracy Misses
We’ve seen how accuracy can give a general sense of performance, but it doesn’t tell the whole story – especially when dealing with imbalanced datasets. For example, if you’re labeling data into categories where one class dominates, accuracy might appear high even if many important cases are mislabeled or missed.
That’s why it’s important to use other metrics, like precision, recall, and F1 score, to evaluate the quality of labeled data itself, not just the model that uses it. These metrics help determine whether the labels are not only correct, but also consistent and comprehensive across the dataset.
Here’s a quick look at how they apply to evaluating labeled data:
Precision: It measures how many of the items labeled as positive are actually correct. High precision means few incorrect positive labels (low false positives).
Recall: This measures how many of the actual positive items were correctly labeled. High recall means few missed positives (low false negatives).
F1 Score: It combines precision and recall into one balanced metric. It’s useful when you want to understand overall label quality, especially if precision and recall differ significantly.
The Difference Between Precision, Recall, and F1 Score.
Quality Assurance Checks
Regular quality assurance checks help ensure that labeled data remains accurate, consistent, and usable for model training. These checks are important for catching mistakes early, before they affect the performance of the model.
A common method used for this is manual spot checking. A reviewer looks through a small sample of the labeled data to find errors, unclear annotations, or anything that goes against the guidelines. This type of review is helpful for identifying obvious issues and giving quick feedback.
Manual reviews are especially useful at the beginning of a project or when working with new annotators. They make it possible to set clear expectations and guide labelers in the right direction from the start.
Meanwhile, automated reviews take a different approach. They use software or rule-based systems to scan large amounts of data quickly. These tools can detect missing labels, flag unusual patterns, or highlight entries that do not follow an expected format.
Each quality assurance method has its own strengths. Manual checks provide human understanding and context, while automated checks offer speed and scale. When both are used together as part of a regular workflow, they can help maintain high labeling quality across an entire project.
Quality Assurance Methods For Data Labeling
Data Labeling Tools and Platforms for Quality Control
Data labeling tools and platforms can make it easier to manage, review, and improve the quality of labeled data throughout the entire process – not just during the annotation step. With the right features, these tools help teams work more efficiently, reduce errors, and maintain high standards at scale.
Here are a few key capabilities to look for when selecting data labeling tools and platforms to use:
Built-In Analytics and Quality Tracking: Use tools that show real-time metrics like accuracy and agreement scores to catch issues early.
Collaboration and Review Features: Choose platforms that support reviewer feedback, role-based reviews, and clear task assignments.
Support for Quality Checks: Look out for tools that allow both manual reviews and automated error detection for better control at scale.
For instance, data labeling platforms like TensorAct Studio are built to support not just the labeling task, but the entire process of measuring and managing quality from start to finish.
TensorAct Studio includes built-in dashboards that track important metrics such as accuracy, inter-annotator agreement, and review completion rates. These features help spot issues early and improve labeling performance over time.
A Look at TensorAct Studio’s Data Labeling Dashboard (Source)
Also, TensorAct Studio makes it easier to manage the labeling process by supporting review steps with clear roles for each team member. This helps reduce mistakes and keeps labeling consistent. Reviewers can follow built-in guidelines made for each project, so quality checks are done the same way every time.
On top of this, the platform also uses active learning to find data that the model is unsure about and sends it to a human for review. This guarantees that time and effort are spent on the things that matter most.
Emerging Trends in AI-Assisted Data Labeling and Validation
As AI continues to evolve, the way data labeling is done is also changing. Here are some recent trends that are enhancing the speed, accuracy, and overall quality of data labeling:
AI-Powered Pre-Labeling: AI is used to create the first round of labels before a person reviews them. This reduces manual work and saves time, especially with large datasets.
Human-in-the-Loop Systems: AI models assist with labeling, but human reviewers remain involved at key steps. This ensures higher accuracy and reliability by combining automation with human judgment.
Active Learning: Instead of labeling everything, AI is used to identify the most uncertain or important data points and sends them to humans for review. This helps teams focus their time and effort on where it has the biggest impact.
Automated Quality Checks: AI tools continuously scan labeled data for errors, inconsistencies, or outliers. These checks help maintain high data quality even when working at scale.
AI-Assisted Labeling with Humans-in-the-Loop (Source)
Best Practices for High-Quality Data Labeling
Using the right tools and staying on top of trends is crucial, but good labeling starts with following the right practices. Here are some best practices to keep in mind when preparing data for your AI project:
Clear Labeling Guidelines: Provide detailed, easy-to-follow instructions before labeling begins. This helps reduce confusion and ensures consistency across the team.
Consistent Training and Onboarding: Train all annotators using the same materials and processes. Consistent onboarding helps maintain labeling quality from the start.
Iterative Review and Feedback Loops: Regularly review labeled data, give timely feedback, and update your guidelines as needed. This continuous improvement cycle helps catch errors early and keeps quality high.
Scalable Workflows for Large Projects: Build processes that work for both small and large datasets. A scalable workflow supports an efficient labeling approach as your project grows.
Collaboration Between Data Experts and Annotators: Maintain open communication between labelers and subject matter experts. This helps resolve edge cases, clarify questions, and keep everyone aligned.
Following all of these best practices can be challenging – and that’s where experts can step in to help. If you’re looking to implement an AI solution, reach out to Objectways. We specialize in building AI innovations, data labeling services, and data sourcing services.
Building Better AI Solutions Starts with Better Labels
Data labeling plays a key role in how well an AI model learns and performs. When labels are accurate and consistent, the model can better understand the data – leading to more reliable results in real-world applications.
A data labeling platform like TensorAct Studio supports this entire process with features like real-time quality tracking, review workflows, and active learning. It helps make sure your labeled data is high-quality from start to finish.
At Objectways, we provide the tools and expert support you need to build a strong foundation for your AI projects. Ready to take the next step? Get in touch with us today.
Frequently Asked Questions
What is data labeling in machine learning?
Data labeling is the process of adding tags or annotations to raw data such as text, images, or audio. It’s a crucial step in training machine learning models, as it helps them learn patterns and make accurate predictions.
Why is data labeling quality important for AI models?
High-quality data labeling helps AI models learn the right patterns. Accurate, consistent labels lead to better predictions, fewer errors, and more reliable performance in real-world applications.
How can I measure the quality of labeled data?
You can measure the quality of labeled data using metrics like accuracy, precision, recall, and F1 score. Regular reviews, audits, and agreement between annotators also help ensure consistency.
What tools help improve data labeling quality?
Data labeling tools that help improve data labeling quality include platforms with built-in QA features, real-time analytics, review workflows, and support for active learning, such as TensorAct Studio.
