#Lidar Annotation

[priyanshilspl]

ADVANTAGES OF DATA ANNOTATION

Data annotation is essential for training AI models effectively. Precise labeling ensures accurate predictions, while scalability handles large datasets efficiently. Contextual understanding enhances model comprehension, and adaptability caters to diverse needs. Quality assurance processes maintain data integrity, while collaboration fosters synergy among annotators, driving innovation in AI technologies.

[sofiapra]

At Learning Spiral, get the best image annotation services for a variety of sectors. By using these image annotation services, businesses can save time and money, improve the accuracy of their machine-learning models, and scale their operations as needed. For more details visit: https://learningspiral.ai/

[datalabelingcompany]

Visit: https://learningspiral.ai/

[objectwaysblog]

The Future is Lidar: Unlocking Possibilities with Precise Labeling 

Lidar (which stands for Light Detection and Ranging) is a type of 3D sensing technology that uses laser light to measure distances and create detailed 3D maps of objects and environments. Lidar has a wide range of applications, including self-driving cars, robotics, virtual reality, and geospatial mapping.

Lidar works by emitting laser light pulses and measuring the time it takes for the light to bounce back after hitting an object or surface. This data is then used to create a precise 3D map of the surrounding environment, with detailed information about the size, shape, and distance of objects within the map. Lidar sensors can vary in their range and resolution, with some sensors able to measure distances up to several hundred meters, and others able to detect fine details as small as a few centimeters.

One of the primary applications of lidar is in autonomous vehicles, where lidar sensors can be used to create a detailed 3D map of the surrounding environment, allowing the vehicle to detect and avoid obstacles in real-time. Lidar is also used in robotics for navigation and obstacle avoidance, and in virtual reality for creating immersive, 3D environments. https://www.youtube.com/embed/BVRMh9NO9Cs

In addition, lidar is used in geospatial mapping and surveying, where it can be used to create highly accurate 3D maps of terrain and buildings for a variety of applications, including urban planning, disaster response, and natural resource management.

Overall, lidar is a powerful and versatile technology that is driving advances in a wide range of fields, from autonomous vehicles to robotics to geospatial mapping.

Choosing your labeling modality

Object Tracking

Computer vision techniques are often used in combination with lidar 3D point cloud data to extract information and insights from the data. Here are some of the different computer vision techniques that can be used with lidar 3D point cloud data:

Object detection and recognition:-This involves using computer vision algorithms to detect and identify objects within the point cloud data, such as cars, buildings, and trees. Object detection and recognition can be useful for a wide range of applications, from urban planning to autonomous driving.

Object tracking:-This involves tracking the movement and trajectory of objects within the point cloud data over time. Object tracking can be used for applications such as crowd monitoring or autonomous vehicle navigation.

Segmentation:-This involves dividing the point cloud data into different segments based on the properties of the points, such as color or reflectivity. Segmentation can be used to identify regions of interest within the data, such as road surfaces or building facades.

Overall, these computer vision techniques can help to extract valuable information and insights from lidar 3D point cloud data and enable a wide range of applications and use cases. By combining lidar data with computer vision techniques, it is possible to create highly accurate and detailed 3D models of objects and environments, with a wide range of potential applications.

Why High-Quality labeling is important

High-quality lidar 3D point cloud labels are important for creating accurate and reliable 3D models of objects and environments. Here are some reasons why high-quality lidar 3D point cloud labels matter for a high-quality model:

Accuracy:-High-quality lidar 3D point cloud labels ensure that objects are accurately and consistently identified and labeled within the data. This is important for applications such as autonomous vehicles or robotics, where accurate and reliable information about objects in the environment is critical.

Consistency:-High-quality lidar 3D point cloud labels ensure that objects are labeled consistently across the data, with no variations in naming or identification. This is important for applications such as object detection or segmentation, where consistent labels are necessary for accurate and reliable results.

Completeness:-High-quality lidar 3D point cloud labels ensure that all objects within the data are identified and labeled, with no missing or incomplete information. This is important for applications such as geospatial mapping or urban planning, where complete and comprehensive information about objects in the environment is necessary.

Cost-effectiveness:-While high-quality lidar 3D point cloud labels may be more expensive to acquire or process initially, they can actually be more cost-effective in the long run, as they may require fewer resources to process or analyze and may be more suitable for a wider range of applications.

Overall, high-quality lidar 3D point cloud labels are critical for creating accurate and reliable 3D models of objects and environments, with a wide range of potential applications and benefits. By investing in high-quality labeling and processing, it is possible to create more accurate, detailed, and valuable 3D models that can be used in a wide range of applications, from autonomous vehicles to urban planning to robotics.

Best Practices to Manage Large Scale Lidar 3D Point Cloud Labeling Projects

Planning your lidar 3D point cloud labeling project is an important step in ensuring that the project is completed on time, on budget, and with the desired level of quality. Here are some key considerations to keep in mind when planning your lidar 3D point cloud labeling project:

Project scope:-Define the scope of the project, including the size of the dataset, the types of objects to be labeled, and the level of detail required. This will help to ensure that the project is focused and well-defined.

Labeling requirements:-Define the labeling requirements for the project, including the labeling schema, the labeling accuracy, and the level of detail required. This will help to ensure that the labeling process is consistent and produces high-quality results.

Labeling team:-Define the size and composition of the labeling team, including the number of labelers, their expertise, and their availability. Objectways can provide 1000s of trained Lidar labeling experts to match the throughput demand.

Tools and resources:-Identify the tools and resources required for the labeling project, including software, hardware, and data storage. This will help to ensure that the labeling process is efficient and that the results are consistent and of high quality.

Timeline and budget:-Define the timeline and budget for the labeling project, including the estimated time required for labeling, the cost of resources, and the expected deliverables. This will help to ensure that the project is completed on time and within budget.

Use Pre-labeling or downsampling to Save Cost:-There are many SOTA models available for pre-labeling that can provide starting point for labeling to save human labeling cost. Other approach in object tracking is to downsample frames at lower rate to reduce labeling effort

Overall, careful planning is key to the success of any lidar 3D point cloud labeling project. By taking the time to define the scope, requirements, team, tools, timeline, and budget for the project, it is possible to ensure that the project is completed with the desired level of quality and within the allotted resources.

At Objectways, we have worked on 100s of Lidar 3D point Cloud labeling projects across Autonomous Vehicles, Robotics, Agriculture and Geospatial domains. Contact Objectways for planning your next Lidar 3D point cloud labeling project

Lidar 3D Object Detection

Computer Vision

Autonomous Vehicle

Self Driving Cars

Tesla

[cogitotech]

Cogito — A Reliable LiDAR Annotation Expert to Partner with

[tagx01]

Expanding Your Data Labeling Process for Machine Learning

The victory of machine learning models depends intensely on the quality and amount of labeled information they are prepared on. Organizations are hooking with gigantic volumes of unstructured, unlabeled information, making a vigorous information labeling preparation significant. At TagX, we get the significant part information labeling plays in ML victory. Our multi-tiered approach starts with understanding clients' interesting needs to advise custom fitted workflows driving extended accomplishments.

Machine learning has changed problem-solving in computer vision and common dialect preparation. By leveraging endless information, calculations learn designs and make profitable forecasts without express programming. From protest acknowledgment to voice collaborators, ML models are vital however depend on high-quality labeled preparing information. Information labeling fastidiously structures crude information for machine comprehension - a basic, frequently ignored movement supporting ML venture success.

What is Data Labeling?

Data labeling is the process of assigning contextual meaning or annotations to raw data, enabling machine learning algorithms to learn from these labeled examples and achieve desired outcomes. At TagX, we understand the pivotal role data labeling plays in the success of any machine learning endeavor.

This process involves categorizing, classifying, and annotating various forms of data, such as images, text, audio, or video, according to predefined rules or guidelines. Tasks can include object detection and segmentation in images, sentiment analysis and named entity recognition in text, or speech recognition and transcription in audio data.

The labeled data is then used to train machine learning models, allowing them to recognize patterns, make predictions, and perform tasks with increasing accuracy and efficiency. Our team of skilled data annotators meticulously label vast amounts of data, ensuring the models our clients rely on are trained on high-quality, accurately labeled datasets.

Types of Data Labeling

Data labeling is a crucial process for various types of data, each requiring specific approaches and techniques. We have extensive experience in labeling diverse data formats, ensuring our clients' machine learning models are trained on accurate and high-quality labeled datasets. Here are some of the common types of data labeling we handle:

Image Labeling: This involves annotating image data with labels or bounding boxes to identify objects, classify scenes, or segment specific regions. Common tasks include object detection, instance segmentation, and pixel-level semantic segmentation.

Video Labeling: Similar to image labeling, video data is annotated frame by frame to identify and track objects, actions, or events across multiple frames. This type of labeling is essential for applications like autonomous vehicles, surveillance systems, and activity recognition.

3D Data Labeling: LiDAR (Light Detection and Ranging) and Radar data provide depth information and are labeled to create precise 3D representations of scenes. This data is crucial for applications like autonomous navigation, robotics, and environmental mapping.

Audio Labeling: Audio data, such as speech recordings or environmental sounds, is labeled for tasks like speech recognition, speaker identification, and audio event detection. This involves transcribing speech, annotating sound events, and identifying speakers.

Text Labeling: Text data is labeled for various natural language processing tasks, including sentiment analysis, named entity recognition, intent classification, and language translation. This involves annotating text with relevant labels, such as entities, sentiments, or intents.

Our team of skilled data annotators is well-versed in handling these diverse data types, ensuring that the labeled data adheres to industry-standard guidelines and meets the specific requirements of our clients' machine learning projects.

Importance of Data Labeling

Information labeling is the basic establishment that empowers machine learning models to learn and make exact expectations. Without high-quality labeled information, these models would be incapable of recognizing designs and extracting important insights.

Labeled information acts as the ground truth, giving the administered direction that machine learning calculations require to get it and generalize from illustrations amid the preparation. The quality and exactness of this labeled information straightforwardly impacts the execution of the coming about model.

Data labeling is especially pivotal for complex errands like computer vision, characteristic dialect preparing, and discourse acknowledgment. Clarifying information with objects, content substances, estimations, and other significant names permits models to learn modern concepts and relationships.

As datasets develop bigger and utilize cases that end up more complicated, the significance of a strong and versatile information labeling preparation escalates. Effective information labeling operations empower organizations to emphasize and refine their models quickly, driving development and keeping up a competitive edge.

At TagX, we recognize information labeling as a mission-critical component of effective machine learning activities. Our mastery in this space guarantees our clients have access to high-quality, precisely labeled datasets custom-made to their particular needs, engaging their models to accomplish ideal performance.

What is Data Labeling for Machine Learning?

Data labeling, also known as data annotation, is a critical process in the realm of machine learning, particularly for computer vision applications. It involves assigning labels or annotations to raw, unlabeled data, such as images, videos, text, or audio, to create high-quality training datasets for artificial intelligence models.

We understand the pivotal role that accurate data labeling plays in the success of machine learning endeavors. For computer vision use cases, data labeling encompasses tasks like applying bounding boxes or polygon annotations to identify objects, segmenting specific regions, or annotating intricate details like microcellular structures in healthcare projects. Regardless of the complexity, meticulous accuracy is essential in the labeling process to ensure optimal model performance.

Top 6 Tips for Better Data Labeling in Machine Learning

1. Define Clear Annotation Guidelines

Establish precise instructions and examples for annotators to follow. Clearly define label categories, annotation types (bounding boxes, polygons, etc.), and provide visual references. Consistent guidelines are crucial for creating high-quality, coherent datasets.

2. Implement Robust Quality Assurance

Data quality is paramount for model performance. Implement processes like manual reviews, automated checks, and consensus scoring to identify and correct labeling errors. Regular audits and providing annotator feedback helps maintain high standards.

3. Leverage Domain Expertise

For complex domains like healthcare or specialized tasks, involve subject matter experts in the labeling process. Their deep domain knowledge ensures accurate and meaningful annotations, reducing errors.

4. Choose Appropriate Annotation Tools

Select user-friendly annotation tools tailored to your data types and labeling needs. Tools with customizable workflows can significantly improve annotator efficiency and accuracy. Seamless integration with machine learning pipelines is a plus.

5. Prioritize Data Security and Privacy

When dealing with sensitive data like personal information or medical records, implement robust security measures. This includes access controls, encryption, anonymization, and adhering to data protection regulations.

6. Plan for Scalability

As your machine learning projects grow, so will the demand for labeled data. Implement processes and infrastructure to efficiently scale your data labeling operations. This may involve outsourcing, automating workflows, or building dedicated in-house teams.

We follow these best practices to deliver high-quality, accurately labeled datasets optimized for our clients' machine learning needs. Our expertise enables us to scale labeling operations while maintaining stringent quality standards, fueling the success of your AI initiatives.

Challenges of Data Labeling in Machine Learning

Volume and Variety of Data

Machine learning models require vast amounts of labeled data to achieve high accuracy. As datasets grow larger and more diverse, encompassing different data types (images, videos, text, audio), the labeling process becomes increasingly complex and time-consuming.

Quality and Consistency

Inaccurate or inconsistent labels can significantly degrade a model's performance. Ensuring high-quality, consistent labeling across large datasets is a major challenge, especially when involving multiple annotators for crowd-sourced labeling.

Domain Complexity

Certain domains like healthcare, finance, or highly specialized industries require a deep understanding of the subject matter to accurately label data. Finding annotators with the necessary expertise can be difficult and costly.

Scalability and Efficiency

As machine learning projects scale, the demand for labeled data increases exponentially. Scaling data labeling operations efficiently while maintaining quality and consistency is a significant challenge, often requiring robust processes, tools, and infrastructure.

Data Privacy and Security

When dealing with sensitive data, such as personal information or proprietary data, ensuring data privacy and security during the labeling process is crucial. Implementing robust security measures and adhering to data protection regulations can be complex.

Ambiguity and Edge Cases

Some data samples can be ambiguous or contain edge cases that are difficult to label consistently. Developing comprehensive guidelines and protocols to handle these situations is essential but can be time-consuming.

Cost and Resource Management

Data labeling is a labor-intensive and often expensive process. Managing costs and allocating resources efficiently while balancing quality, speed, and scalability requirements can be challenging, especially for small or medium-sized organizations.

We specialize in addressing these challenges head-on, enabling our clients to develop highly accurate machine learning models with efficiently labeled, high-quality datasets. Our expertise, processes, and tools are designed to tackle the complexities of data labeling, ensuring successful and scalable machine learning initiatives.

Final Thoughts

In conclusion, expanding your data labeling process for machine learning is not just about increasing the quantity of labeled data, but also about ensuring its quality, diversity, and relevance to the task at hand. By embracing innovative labeling techniques, leveraging domain expertise, and harnessing the power of crowdsourcing or automation where applicable, organizations can enhance the effectiveness and efficiency of their machine learning models, ultimately driving better decision-making and outcomes in various fields and industries. TagX is at the forefront of this transformation, bringing innovation and change by providing top-notch data labeling services. Our expertise ensures that your data is accurately labeled, diverse, and relevant, empowering your machine learning models to perform at their best. With us, you can achieve superior results and stay ahead in the competitive landscape.

Visit us, www.tagxdata.com

Original Source, https://www.tagxdata.com/expanding-your-data-labeling-process-for-machine-learning

[ai-news]

#AI #ML #Automation

[digitalmbn]

The Greatest MATLAB Introduction to Automated Driving Toolbox

With the introduction of autonomous vehicles, the automobile industry is undergoing a dramatic transition in today's quickly evolving technology landscape. These cars have the power to completely transform transportation, making them safer, more effective, and more convenient because of their cutting-edge sensors, computers, and algorithms. The creation, testing, and implementation of autonomous driving systems are made easier by the Automated Driving Toolbox provided by MATLAB, a robust computational software platform that is utilised in many different sectors.

 Understanding Automated Driving Toolbox

MATLAB's Automated Driving Toolbox provides a comprehensive set of tools for designing and simulating autonomous driving algorithms. Whether you're a researcher, engineer, or student, this toolbox offers a streamlined workflow for developing and testing perception, planning, and control algorithms in a simulated environment.

Perception

Perception is crucial for an autonomous vehicle to understand its surroundings accurately. The toolbox offers algorithms for sensor fusion, object detection, and tracking, allowing the vehicle to detect and recognize pedestrians, vehicles, signs, and other relevant objects in its environment.

Planning and Control

Planning and control algorithms enable the vehicle to make intelligent decisions and navigate safely through various scenarios. The toolbox provides tools for path planning, trajectory generation, and vehicle control, ensuring smooth and efficient motion planning while adhering to traffic rules and safety constraints.

Simulation and Validation

Simulation is a key component in developing and testing autonomous driving systems. MATLAB's Automated Driving Toolbox includes a high-fidelity simulation environment that enables users to create realistic scenarios, simulate sensor data, and evaluate the performance of their algorithms under various conditions.

Key Features and Capabilities

1. Sensor Simulation

The toolbox allows users to simulate various sensors such as cameras, lidar, and radar, enabling realistic sensor data generation for algorithm development and testing.

2. Scenario Generation 

Users can create complex driving scenarios including urban, highway, and off-road environments, allowing for thorough testing of autonomous driving algorithms in diverse conditions.

3. Deep Learning Integration

MATLAB's deep learning capabilities seamlessly integrate with the Automated Driving Toolbox, enabling the development of advanced perception algorithms using convolutional neural networks (CNNs) and other deep learning techniques.

4. Hardware-in-the-Loop (HIL) Simulation

The toolbox supports HIL simulation, allowing users to test their algorithms in real-time with hardware components such as vehicle dynamics models and electronic control units (ECUs).

5. Data Labeling and Annotation

 Efficient tools for data labelling and annotation are provided, facilitating the creation of labelled datasets for training perception algorithms.

 Getting Started with Automated Driving Toolbox

Getting started with MATLAB's Automated Driving Toolbox is straightforward, thanks to its user-friendly interface and extensive documentation. Whether you're a beginner or an experienced developer, MATLAB offers resources such as tutorials, examples, and online forums to support your learning journey.

1. Installation

Ensure you have MATLAB installed on your system, along with the Automated Driving Toolbox.

2. Explore Examples 

MATLAB provides numerous examples covering various autonomous driving tasks, from simple lane following to complex intersection navigation. Explore these examples to gain insights into the capabilities of the toolbox.

3. Experiment and Iterate

 Start experimenting with the toolbox by designing and testing your autonomous driving algorithms. Iterate your designs based on the results obtained from simulation and validation.

4. Engage with the Community

 Join online forums and communities dedicated to MATLAB and autonomous driving to connect with experts and enthusiasts, share ideas, and seek assistance when needed.

 Conclusion

MATLAB's Automated Driving Toolbox empowers developers to accelerate the development and deployment of autonomous driving systems through its comprehensive set of tools and intuitive workflow. By leveraging this toolbox, researchers, engineers, and students can contribute to the advancement of autonomous vehicle technology, paving the way for a safer, more efficient, and more sustainable future of transportation. Whether you're exploring the possibilities of autonomous driving or working on cutting-edge research projects, MATLAB provides the tools you need to navigate the road ahead.

[datalabeler]

How Does Data Annotation Assure Safety in Autonomous Vehicles?

To contrast a human-driven car with one operated by a computer is to contrast viewpoints. Over six million car crashes occur each year, according to the US National Highway Traffic Safety Administration. These crashes claim the lives of about 36,000 Americans, while another 2.5 million are treated in hospital emergency departments. Even more startling are the figures on a worldwide scale. 

One could wonder if these numbers would drop significantly if AVs were to become the norm. Thus, data annotation is contributing significantly to the increased safety and convenience of Autonomous Vehicles. To enable the car to make safe judgments and navigate, its machine-learning algorithms need to be trained on accurate and well-annotated data.

Here are some important features of data annotation for autonomous vehicles to ensure safety:

Semantic Segmentation: Annotating lanes, pedestrians, cars, and traffic signs, as well as their borders, in photos or sensor data, is known as semantic segmentation. The car needs accurate segmentation to comprehend its environment.

Object Detection: It is the process of locating and classifying items, such as vehicles, bicycles, pedestrians, and obstructions, in pictures or sensor data.

Lane Marking Annotation: Road boundaries and lane lines can be annotated to assist a vehicle in staying in its lane and navigating safely.

Depth Estimation: Giving the vehicle depth data to assist it in gauging how far away objects are in its path. This is essential for preventing collisions.

Path Planning: Annotating potential routes or trajectories for the car to follow while accounting for safety concerns and traffic laws is known as path planning.

Traffic Sign Recognition: Marking signs, signals, and their interpretations to make sure the car abides by the law.

Behaviour Prediction: By providing annotations for the expected actions of other drivers (e.g., determining if a pedestrian will cross the street), the car can make more educated decisions.

Map and Localization Data: By adding annotations to high-definition maps and localization data, the car will be able to navigate and position itself more precisely.

Weather and Lighting Conditions: Data collected in a variety of weather and lighting circumstances (such as rain, snow, fog, and darkness) should be annotated to aid the vehicle’s learning process.

Anomaly Detection: Noting unusual circumstances or possible dangers, like roadblocks, collisions, or sudden pedestrian movements.

Diverse Scenarios: To train the autonomous car for various contexts, make sure the dataset includes a wide range of driving scenarios, such as suburban, urban, and highway driving.

Sensor Fusion: Adding annotations to data from several sensors, such as cameras, radar, LiDAR, and ultrasonics, to assist the car in combining information from several sources and arriving at precise conclusions.

Continual Data Updating: Adding annotations to the data regularly to reflect shifting traffic patterns, construction zones, and road conditions.

Quality Assurance: Applying quality control techniques, such as human annotation verification and the use of quality metrics, to guarantee precise and consistent annotations.

Machine Learning Feedback Loop: Creating a feedback loop based on real-world data and user interactions to continuously enhance the vehicle’s performance.

Ethical Considerations: Make sure that privacy laws and ethical issues, like anonymizing sensitive material, are taken into account during the data annotation process.

Conclusion:

An important but frequently disregarded component in the development of autonomous vehicles is data annotation. Self-driving cars would remain an unattainable dream if it weren’t for the diligent efforts of data annotators. Data Labeler provides extensive support with annotating data for several kinds of AI models. For any further queries, you can visit our website. Alternatively, we are reachable at sales@DataLabeler.com.

[dorleco]

Machine Learning for Autonomous Vehicles

October 19, 2023

 by dorleco

with no comment

 Autonomous Vehicle Technology

Introduction

Machine learning has greatly aided in developing and operating autonomous vehicles (AVs). Autonomous vehicles, also known as self-driving cars, can navigate and make decisions about how to drive on their own thanks to sensors, cameras, radar, and other equipment. The massive amount of data generated by these sensors is processed by machine learning algorithms to guarantee that the automobile is driven safely and effectively. This article provides a summary of machine learning’s use in autonomous vehicles.

1. Data gathering and sensors

Various sensors, including LiDAR, radar, cameras, and ultrasonic sensors, are included in autonomous cars.

These sensors gather information about the environment around the car, including the state of the roads, the presence of other vehicles, pedestrians, and traffic lights.

2. Data Preparation

Redundancy and noise are frequently present in raw sensor data. The data is cleaned and pre-processed using machine learning algorithms.

This could entail operations like data filtering, data alignment, and sensor fusion to merge data from several sensors.

3. Observation

For activities requiring perception, machine learning models are utilized to comprehend the surroundings of the vehicle.

Algorithms for object detection and recognition locate and categorize nearby items like other cars, people, and traffic signs.

Semantic segmentation classifies each pixel in an image or point cloud to help understand the road scene.

4. Regionalization

Orientation and position must be precisely determined by autonomous vehicles.

Utilizing methods like SLAM (Simultaneous Localization and Mapping), machine learning can aid in localization when combined with sensor data.

5. Planning and managing the path

The path and motion of the vehicle are planned using machine learning.

Algorithms for path planning assist the vehicle in deciding where to go and how to get there while avoiding hazards and obeying traffic regulations.

Control algorithms guarantee that the vehicle follows the intended path effectively and safely.

6. Reinforcement Learning:

For autonomous vehicles to learn from their interactions with the environment, reinforcement learning can be used.

7. Human-AI Interaction:

In autonomous vehicles, machine learning models can also be utilized to communicate with humans by comprehending their requests and explaining AI choices.

8. Data annotation and labeling:

For the purpose of training ML models in autonomous vehicles, high-quality labeled data is crucial.

The process of human annotators marking specific objects and events in sensor data is known to be labor-intensive and time-consuming.

Advantages of Machine Learning for Autonomous Vehicles

Autonomous vehicles (AVs) can benefit greatly from machine learning, which increases their capabilities, productivity, and safety. Some of the main benefits of applying machine learning to autonomous vehicles are as follows:

Enhanced Object Detection and Perception:

Large volumes of sensor data may be processed by ML algorithms, improving the detection and identification of items like pedestrians, cars, and barriers by AVs.

These algorithms improve the vehicle’s ability to perceive by adjusting to different lighting and weather situations.

Making decisions instantly:

Using historical data and their sense of the environment, AVs are able to make decisions in real-time.

When unexpected things happen, such as sudden stops or the sudden presence of pedestrians, they can respond fast.

Increased Safety:

Machine learning systems in autonomous vehicles allow them to anticipate potential dangers and take preventative action to avoid collisions.

Additionally, they can keep an eye on their surroundings constantly, lowering the possibility of driver inattention and fatigue.

Planning a path efficiently:

Algorithms for path planning based on machine learning can optimize routes to increase fuel efficiency, cut down on travel time, and lessen wear and tear on the vehicle.

AVs have the ability to dynamically change their routes based on the current flow of traffic.

Adaptive Learning:

AVs can adapt to their driving environments and learn from them thanks to machine learning. Based on facts from the real world, they may continuously enhance their performance and decision-making.

Reduced Human Error:

Human errors, which are a major factor in traffic accidents, such as distracted driving, fatigue, or poor judgment, are not common in autonomous vehicles.

Traffic Management:

By coordinating AVs and enhancing general traffic management, machine learning can be utilized to improve traffic flow.

To ease congestion, AVs can communicate with one other and the traffic infrastructure.

Reduced Fuel Consumption and Emissions:

Disadvantages of Machine Learning for Autonomous Vehicles:

While machine learning has many benefits for autonomous vehicles (AVs), there are also a number of serious drawbacks and difficulties that come with its application in this setting:

Safety Concerns:

ML models are unreliable and susceptible to errors, raising questions about AV safety. A machine learning algorithm’s poor choice could have fatal repercussions.

Lack of Common Sense:

Lacking common sense reasoning, ML models may find it difficult to comprehend complicated, unstructured circumstances when driving.

Data Quality and Diversity:

High-quality and varied training data are essential to machine learning models. It might be difficult to ensure that data adequately depicts all conceivable circumstances, including uncommon and edge cases.

Data Annotation Costs:

Given the enormous amount of data needed for AV development, labeling and annotating training data for ML models can be expensive and time-consuming.

Data Privacy Concerns:

Adversarial Attacks:

Adversarial attacks, in which hostile actors try to trick or manipulate the algorithms by giving false sensor data, can affect machine learning models in AVs.

Limited Robustness:

ML models may not generalize well, and as a result, they may not perform well in unexpected or uncommon circumstances that differ from their training data.

Regulatory Challenges:

Machine learning-based AV development and deployment require navigating complicated regulatory environments, some of which may not yet be fully responsive to this cutting-edge technology.

Conclusion:

In conclusion, ML is a transformative technology that plays a central role in the development and operation of autonomous vehicles (AVs). Its integration brings a multitude of benefits and challenges to the world of transportation.

As the industry continues to evolve, it is essential to address these challenges and harness the advantages of ML in autonomous vehicles responsibly. Collaboration between industry stakeholders, regulators, researchers, and the public is crucial to ensure that AVs become a safe, efficient, and accessible mode of transportation that benefits society as a whole. While there are hurdles to overcome, the potential for ML in autonomous vehicles remains promising, with the prospect of revolutionizing the way we travel and enhancing road safety.

[haivoai]

Every Detail About the Data Annotation Service

An essential stage in the development of artificial intelligence (AI) is now data annotation. Data annotation is the practice of labeling and categorizing data to make data understandable and helpful for AI models. Among the many different forms of data annotation services available, Audio Annotation Services are crucial for assisting AI systems in handling and comprehending audio data.

The Divisions Of Data Annotation:

Audio Section:

The practice of labeling or describing audio recordings to classify and organize the data is known as audio Annotation. Professional businesses provide simple audio annotation services to assist organizations in accurately and quickly annotating their audio files. By outsourcing audio Annotation, it is possible to provide useful audio data for analysis rapidly and precisely.

Geospatial Service:

Datasets that are acceptable for AI are incorporated with suitable satellite and aerial imagery through geospatial Annotation. An internal real-time dataset is produced as a result, which may be utilized to assess and provide businesses with essential, actionable data. Mapping expansive fields, construction sites, mines, real estate projects, disaster recovery scenarios, and geographical characteristics are a few instances of geospatial imagery commonly annotated. Geospatial Annotation is a priceless source of input data for machine learning tools regarding algorithms. That allows efficient access and retrieval of images from large geographical datasets.

Polygon Annotation:

A set of coordinates is drawn around a picture using the exact approach of polygon annotation. These coordinates are intended to encircle a particular object in an image closely.

Lidar Annotation:

Labeling the scene’s elements, such as the vehicles, people, and traffic signs, is required. Lidar mainly relies on machine learning algorithms to deliver real-time interpretations of point cloud data.

Keypoint Annotation:

By identifying the locations of key points, keypoint Annotation is a more thorough method of picture annotation used to find small objects and form variations. Keypoint annotations describe an object’s shape by labeling a single pixel in the image.

Data Validation:

Data Validation for AI is crucial to ensure that data from various sources will adhere to business standards and not become damaged owing to inconsistencies in type or context while moving and combining data. To avoid data loss and errors during migration, the objective is to create consistent, accurate, and complete data.

Waste Management:

The Waste Annotation technique aids in training AI models to identify waste materials and properly handle them. Waste management AI firms can achieve the accurate semantic segmentation of datasets using data annotation technologies.

Conclusion:

It is an essential step in developing and refining a versatile and practical ML algorithm. It can be skipped when only a small portion of the algorithm is required. Data Annotation Services, however, becomes vital in the age of huge data and intense competition because it trains machines to see, hear, and write as people do.

[sofiapra]

Learning Spiral is a leading provider of data annotation services in India. The company offers a wide range of data labeling services in different sectors, including automobile, healthcare, education, cybersecurity, e-commerce, etc. For more details visit: https://learningspiral.ai/

[metalporsiempre]

A few months after graduating from college in Nairobi, a 30-year-old I’ll call Joe got a job as an annotator — the tedious work of processing the raw information used to train artificial intelligence. AI learns by finding patterns in enormous quantities of data, but first that data has to be sorted and tagged by people, a vast workforce mostly hidden behind the machines. (..) It’s difficult and repetitive work. A several-second blip of footage took eight hours to annotate, for which Joe was paid about $10.

Then, in 2019, an opportunity arose: Joe could make four times as much running an annotation boot camp for a new company that was hungry for labelers. Every two weeks, 50 new recruits would file into an office building in Nairobi to begin their apprenticeships. There seemed to be limitless demand for the work. They would be asked to categorize clothing seen in mirror selfies, look through the eyes of robot vacuum cleaners to determine which rooms they were in, and draw squares around lidar scans of motorcycles. Over half of Joe’s students usually dropped out before the boot camp was finished. (..)

After boot camp, they went home to work alone in their bedrooms and kitchens, forbidden from telling anyone what they were working on, which wasn’t really a problem because they rarely knew themselves. (..) Each project was such a small component of some larger process that it was difficult to say what they were actually training AI to do. Nor did the names of the projects offer any clues: Crab Generation, Whale Segment, Woodland Gyro, and Pillbox Bratwurst. They were non sequitur code names for non sequitur work.

As for the company employing them, most knew it only as Remotasks, a website offering work to anyone fluent in English. Like most of the annotators I spoke with, Joe was unaware until I told him that Remotasks is the worker-facing subsidiary of a company called Scale AI, a multibillion-dollar Silicon Valley data vendor that counts OpenAI and the U.S. military among its customers. Neither Remotasks’ or Scale’s website mentions the other.

Much of the public response to language models like OpenAI’s ChatGPT has focused on all the jobs they appear poised to automate. But behind even the most impressive AI system are people — huge numbers of people labeling data to train it and clarifying data when it gets confused. Only the companies that can afford to buy this data can compete, and those that get it are highly motivated to keep it secret. The result is that, with few exceptions, little is known about the information shaping these systems’ behavior, and even less is known about the people doing the shaping.

For Joe’s students, it was work stripped of all its normal trappings: a schedule, colleagues, knowledge of what they were working on or whom they were working for. In fact, they rarely called it work at all — just “tasking.” They were taskers.

The anthropologist David Graeber defines “bullshit jobs” as employment without meaning or purpose, work that should be automated but for reasons of bureaucracy or status or inertia is not. These AI jobs are their bizarro twin: work that people want to automate, and often think is already automated, yet still requires a human stand-in. The jobs have a purpose; it’s just that workers often have no idea what it is.

The current AI boom (..) began with an unprecedented feat of tedious and repetitive labor.

In 2007, the AI researcher Fei-Fei Li, then a professor at Princeton, suspected the key to improving image-recognition neural networks, a method of machine learning that had been languishing for years, was training on more data — millions of labeled images rather than tens of thousands. The problem was that it would take decades and millions of dollars for her team of undergrads to label that many photos.

Li found thousands of workers on Mechanical Turk, Amazon’s crowdsourcing platform where people around the world complete small tasks for cheap. The resulting annotated dataset, called ImageNet, enabled breakthroughs in machine learning that revitalized the field and ushered in a decade of progress.

Annotation remains a foundational part of making AI, but there is often a sense among engineers that it’s a passing, inconvenient prerequisite to the more glamorous work of building models. You collect as much labeled data as you can get as cheaply as possible to train your model, and if it works, at least in theory, you no longer need the annotators. But annotation is never really finished. Machine-learning systems are what researchers call “brittle,” prone to fail when encountering something that isn’t well represented in their training data. These failures, called “edge cases,” can have serious consequences. In 2018, an Uber self-driving test car killed a woman because, though it was programmed to avoid cyclists and pedestrians, it didn’t know what to make of someone walking a bike across the street. (..)

Over the past six months, I spoke with more than two dozen annotators from around the world, and while many of them were training cutting-edge chatbots, just as many were doing the mundane manual labor required to keep AI running. There are people classifying the emotional content of TikTok videos, new variants of email spam, and the precise sexual provocativeness of online ads. Others are looking at credit-card transactions and figuring out what sort of purchase they relate to or checking e-commerce recommendations and deciding whether that shirt is really something you might like after buying that other shirt. Humans are correcting customer-service chatbots, listening to Alexa requests, and categorizing the emotions of people on video calls. They are labeling food so that smart refrigerators don’t get confused by new packaging, checking automated security cameras before sounding alarms, and identifying corn for baffled autonomous tractors. (..)

The data vendors behind familiar names like OpenAI, Google, and Microsoft come in different forms. There are private outsourcing companies with call-center-like offices, such as the Kenya- and Nepal-based CloudFactory, where Joe annotated for $1.20 an hour before switching to Remotasks. There are also “crowdworking” sites like Mechanical Turk and Clickworker where anyone can sign up to perform tasks. In the middle are services like Scale AI. Anyone can sign up, but everyone has to pass qualification exams and training courses and undergo performance monitoring. Annotation is big business. (..)

This tangled supply chain is deliberately hard to map. According to people in the industry, the companies buying the data demand strict confidentiality. (This is the reason Scale cited to explain why Remotasks has a different name.) Annotation reveals too much about the systems being developed, and the huge number of workers required makes leaks difficult to prevent. Annotators are warned repeatedly not to tell anyone about their jobs, not even their friends and co-workers, but corporate aliases, project code names, and, crucially, the extreme division of labor ensure they don’t have enough information about them to talk even if they wanted to. (Most workers requested pseudonyms for fear of being booted from the platforms.) Consequently, there are no granular estimates of the number of people who work in annotation, but it is a lot, and it is growing. A recent Google Research paper gave an order-of-magnitude figure of “millions” with the potential to become “billions.”

(..) Erik Duhaime, CEO of medical-data-annotation company Centaur Labs, recalled how, several years ago, prominent machine-learning engineers were predicting AI would make the job of radiologist obsolete. When that didn’t happen, conventional wisdom shifted to radiologists using AI as a tool. Neither of those is quite what he sees occurring. AI is very good at specific tasks, Duhaime said, and that leads work to be broken up and distributed across a system of specialized algorithms and to equally specialized humans. (..)

Worries about AI-driven disruption are often countered with the argument that AI automates tasks, not jobs, and that these tasks will be the dull ones, leaving people to pursue more fulfilling and human work. But just as likely, the rise of AI will look like past labor-saving technologies, maybe like the telephone or typewriter, which vanquished the drudgery of message delivering and handwriting but generated so much new correspondence, commerce, and paperwork that new offices staffed by new types of workers — clerks, accountants, typists — were required to manage it. When AI comes for your job, you may not lose it, but it might become more alien, more isolating, more tedious.

Earlier this year, I signed up for Scale AI’s Remotasks. The process was straightforward. After entering my computer specs, internet speed, and some basic contact information, I found myself in the “training center.” To access a paying task, I first had to complete an associated (unpaid) intro course.

The training center displayed a range of courses with inscrutable names like Glue Swimsuit and Poster Macadamia. I clicked on something called GFD Chunking, which revealed itself to be labeling clothing in social-media photos.

The instructions, however, were odd. For one, they basically consisted of the same direction reiterated in the idiosyncratically colored and capitalized typography of a collaged bomb threat. (..)

I skimmed to the bottom of the manual, where the instructor had written in the large bright-red font equivalent of grabbing someone by the shoulders and shaking them, “THE FOLLOWING ITEMS SHOULD NOT BE LABELED because a human could not actually put wear any of these items!” above a photo of C-3PO, Princess Jasmine from Aladdin, and a cartoon shoe with eyeballs.

Feeling confident in my ability to distinguish between real clothes that can be worn by real people and not-real clothes that cannot, I proceeded to the test. Right away, it threw an ontological curveball: a picture of a magazine depicting photos of women in dresses. Is a photograph of clothing real clothing? No, I thought, because a human cannot wear a photograph of clothing. Wrong! As far as AI is concerned, photos of real clothes are real clothes. Next came a photo of a woman in a dimly lit bedroom taking a selfie before a full-length mirror. The blouse and shorts she’s wearing are real. What about their reflection? Also real! Reflections of real clothes are also real clothes.

After an embarrassing amount of trial and error, I made it to the actual work, only to make the horrifying discovery that the instructions I’d been struggling to follow had been updated and clarified so many times that they were now a full 43 printed pages of directives: Do NOT label open suitcases full of clothes; DO label shoes but do NOT label flippers; DO label leggings but do NOT label tights; do NOT label towels even if someone is wearing it; label costumes but do NOT label armor. And so on.

There has been general instruction disarray across the industry, according to Milagros Miceli, a researcher at the Weizenbaum Institute in Germany who studies data work. It is in part a product of the way machine-learning systems learn. Where a human would get the concept of “shirt” with a few examples, machine-learning programs need thousands, and they need to be categorized with perfect consistency yet varied enough that the very literal system can handle the diversity of the real world. (..)

The act of simplifying reality for a machine results in a great deal of complexity for the human. Instruction writers must come up with rules that will get humans to categorize the world with perfect consistency. To do so, they often create categories no human would use. (..)

The job of the annotator often involves putting human understanding aside and following instructions very, very literally. (..) Annotators invariably end up confronted with confounding questions like, Is that a red shirt with white stripes or a white shirt with red stripes? Is a wicker bowl a “decorative bowl” if it’s full of apples? What color is leopard print? When instructors said to label traffic-control directors, did they also mean to label traffic-control directors eating lunch on the sidewalk? Every question must be answered, and a wrong guess could get you banned and booted to a new, totally different task with its own baffling rules.

Most of the work on Remotasks is paid at a piece rate with a single task earning anywhere from a few cents to several dollars. Because tasks can take seconds or hours, wages are hard to predict. When Remotasks first arrived in Kenya, annotators said it paid relatively well — averaging about $5 to $10 per hour depending on the task — but the amount fell as time went on.

Scale AI spokesperson Anna Franko said that the company’s economists analyze the specifics of a project, the skills required, the regional cost of living, and other factors “to ensure fair and competitive compensation.” Former Scale employees also said pay is determined through a surge-pricing-like mechanism that adjusts for how many annotators are available and how quickly the data is needed.

(..) The most common complaint about Remotasks work is its variability; it’s steady enough to be a full-time job for long stretches but too unpredictable to rely on. Annotators spend hours reading instructions and completing unpaid trainings only to do a dozen tasks and then have the project end. There might be nothing new for days, then, without warning, a totally different task appears and could last anywhere from a few hours to weeks. (..)

This boom-and-bust cycle results from the cadence of AI development, according to engineers and data vendors. Training a large model requires an enormous amount of annotation followed by more iterative updates, and engineers want it all as fast as possible so they can hit their target launch date. There may be monthslong demand for thousands of annotators, then for only a few hundred, then for a dozen specialists of a certain type, and then thousands again. (..)

To succeed, annotators work together. (..) Like a lot of annotators, Victor uses unofficial WhatsApp groups to spread the word when a good task drops. When he figures out a new one, he starts impromptu Google Meets to show others how it’s done. Anyone can join and work together for a time, sharing tips. (..)

Because work appears and vanishes without warning, taskers always need to be on alert. Victor has found that projects pop up very late at night, so he is in the habit of waking every three hours or so to check his queue. When a task is there, he’ll stay awake as long as he can to work. (..)

Identifying clothing and labeling customer-service conversations are just some of the annotation gigs available. Lately, the hottest on the market has been chatbot trainer. Because it demands specific areas of expertise or language fluency and wages are often adjusted regionally, this job tends to pay better. Certain types of specialist annotation can go for $50 or more per hour.

A woman I’ll call Anna was searching for a job in Texas when she stumbled across a generic listing for online work and applied. It was Remotasks, and after passing an introductory exam, she was brought into a Slack room of 1,500 people who were training a project code-named Dolphin, which she later discovered to be Google DeepMind’s chatbot, Sparrow, one of the many bots competing with ChatGPT. Her job is to talk with it all day. At about $14 an hour, plus bonuses for high productivity. (..)

Each time Anna prompts Sparrow, it delivers two responses and she picks the best one, thereby creating something called “human-feedback data.” When ChatGPT debuted late last year, its impressively natural-seeming conversational style was credited to its having been trained on troves of internet data. But the language that fuels ChatGPT and its competitors is filtered through several rounds of human annotation. One group of contractors writes examples of how the engineers want the bot to behave, creating questions followed by correct answers, descriptions of computer programs followed by functional code, and requests for tips on committing crimes followed by polite refusals. After the model is trained on these examples, yet more contractors are brought in to prompt it and rank its responses. This is what Anna is doing with Sparrow. Exactly which criteria the raters are told to use varies — honesty, or helpfulness, or just personal preference. The point is that they are creating data on human taste, and once there’s enough of it, engineers can train a second model to mimic their preferences at scale, automating the ranking process and training their AI to act in ways humans approve of. The result is a remarkably human-seeming bot that mostly declines harmful requests and explains its AI nature with seeming self-awareness.

Put another way, ChatGPT seems so human because it was trained by an AI that was mimicking humans who were rating an AI that was mimicking humans who were pretending to be a better version of an AI that was trained on human writing.

This circuitous technique is called “reinforcement learning from human feedback,” or RLHF, and it’s so effective that it’s worth pausing to fully register what it doesn’t do. When annotators teach a model to be accurate, the model isn’t learning to check answers against logic or external sources or about what accuracy as a concept even is. The model is still a text-prediction machine mimicking patterns in human writing, but now its training corpus has been supplemented with bespoke examples, and the model has been weighted to favor them. Maybe this results in the model extracting patterns from the part of its linguistic map labeled as accurate and producing text that happens to align with the truth, but it can also result in it mimicking the confident style and expert jargon of the accurate text while writing things that are totally wrong. There is no guarantee that the text the labelers marked as accurate is in fact accurate, and when it is, there is no guarantee that the model learns the right patterns from it. (..)

When Anna rates Sparrow’s responses, she’s supposed to be looking at their accuracy, helpfulness, and harmlessness while also checking that the model isn’t giving medical or financial advice or anthropomorphizing itself or running afoul of other criteria. (..) According to Geoffrey Irving, one of DeepMind’s research scientists, the company’s researchers hold weekly annotation meetings in which they rerate data themselves and discuss ambiguous cases, consulting with ethical or subject-matter experts when a case is particularly tricky.

Because feedback data is difficult to collect, it fetches a higher price. Basic preferences of the sort Anna is producing sell for about $1 each, according to people with knowledge of the industry. But if you want to train a model to do legal research, you need someone with training in law, and this gets expensive. Everyone involved is reluctant to say how much they’re spending, but in general, specialized written examples can go for hundreds of dollars, while expert ratings can cost $50 or more. One engineer told me about buying examples of Socratic dialogues for up to $300 a pop. Another told me about paying $15 for a “darkly funny limerick about a goldfish.”

OpenAI, Microsoft, Meta, and Anthropic did not comment about how many people contribute annotations to their models, how much they are paid, or where in the world they are located. Irving of DeepMind, which is a subsidiary of Google, said the annotators working on Sparrow are paid “at least the hourly living wage” based on their location. Anna knows “absolutely nothing” about Remotasks, but Sparrow has been more open. She wasn’t the only annotator I spoke with who got more information from the AI they were training than from their employer; several others learned whom they were working for by asking their AI for its company’s terms of service. (..)

Until recently, it was relatively easy to spot bad output from a language model. It looked like gibberish. But this gets harder as the models get better — a problem called “scalable oversight.” (..) This trajectory means annotation increasingly requires specific skills and expertise.

Last year, someone I’ll call Lewis was working on Mechanical Turk when, after completing a task, he received a message inviting him to apply for a platform he hadn’t heard of. It was called Taskup.ai, and its website was remarkably basic: just a navy background with text reading GET PAID FOR TASKS ON DEMAND. He applied.

The work paid far better than anything he had tried before, often around $30 an hour. It was more challenging, too: devising complex scenarios to trick chatbots into giving dangerous advice, testing a model’s ability to stay in character, and having detailed conversations about scientific topics so technical they required extensive research. (..) While checking one model’s attempts to code in Python, Lewis was learning too. He couldn’t work for more than four hours at a stretch, lest he risk becoming mentally drained and making mistakes, and he wanted to keep the job. (..)

I spoke with eight other workers, most based in the U.S., who had similar experiences of answering surveys or completing tasks on other platforms and finding themselves recruited for Taskup.ai or several similarly generic sites, such as DataAnnotation.tech or Gethybrid.io. Often their work involved training chatbots, though with higher-quality expectations and more specialized purposes than other sites they had worked for. One was demonstrating spreadsheet macros. Another was just supposed to have conversations and rate responses according to whatever criteria she wanted. (..)

Taskup.ai, DataAnnotation.tech, and Gethybrid.io all appear to be owned by the same company: Surge AI. Its CEO, Edwin Chen, would neither confirm nor deny the connection, but he was willing to talk about his company and how he sees annotation evolving.

“We want AI to tell jokes or write really good marketing copy or help me out when I need therapy or whatnot,” Chen said. “You can’t ask five people to independently come up with a joke and combine it into a majority answer. Not everybody can tell a joke or solve a Python program. The annotation landscape needs to shift from this low-quality, low-skill mind-set to something that’s much richer and captures the range of human skills and creativity and values that we want AI systems to possess.”

Last year, Surge relabeled Google’s dataset classifying Reddit posts by emotion. Google had stripped each post of context and sent them to workers in India for labeling. Surge employees familiar with American internet culture found that 30 percent of the labels were wrong. (..)

Surge claims to vet its workers for qualifications (..) but exactly how Surge finds workers is “proprietary,” Chen said. As with Remotasks, workers often have to complete training courses, though unlike Remotasks, they are paid for it, according to the annotators I spoke with. Having fewer, better-trained workers producing higher-quality data allows Surge to compensate better than its peers, Chen said, though he declined to elaborate, saying only that people are paid “fair and ethical wages.” The workers I spoke with earned between $15 and $30 per hour, but they are a small sample of all the annotators, a group Chen said now consists of 100,000 people. The secrecy, he explained, stems from clients’ demands for confidentiality.

Surge’s customers include OpenAI, Google, Microsoft, Meta, and Anthropic. Surge specializes in feedback and language annotation, and after ChatGPT launched, it got an influx of requests. (..)

The new models are so impressive they’ve inspired another round of predictions that annotation is about to be automated. Given the costs involved, there is significant financial pressure to do so. Anthropic, Meta, and other companies have recently made strides in using AI to drastically reduce the amount of human annotation needed to guide models (..). However, a recent paper found that GPT-4-trained models may be learning to mimic GPT’s authoritative style with even less accuracy, and so far, when improvements in AI have made one form of annotation obsolete, demand for other, more sophisticated types of labeling has gone up.

“I think you always need a human to monitor what AIs are doing just because they are this kind of alien entity,” Chen said. Machine-learning systems are just too strange ever to fully trust. The most impressive models today have what, to a human, seems like bizarre weaknesses, he added, pointing out that though GPT-4 can generate complex and convincing prose, it can’t pick out which words are adjectives: “Either that or models get so good that they’re better than humans at all things, in which case, you reach your utopia and who cares?” (..)

One way the AI industry differs from manufacturers of phones and cars is in its fluidity. The work is constantly changing, constantly getting automated away and replaced with new needs for new types of data. It’s an assembly line but one that can be endlessly and instantly reconfigured, moving to wherever there is the right combination of skills, bandwidth, and wages.

Lately, the best-paying work is in the U.S. In May, Scale started listing annotation jobs on its own website, soliciting people with experience in practically every field AI is predicted to conquer. (..) You can make $45 an hour teaching robots law or make $25 an hour teaching them poetry. There were also listings for people with security clearance, presumably to help train military AI. Scale recently launched a defense-oriented language model called Donovan, which Wang called “ammunition in the AI war,” and won a contract to work on the Army’s robotic-combat-vehicle program.

(When Remotasks first arrived in Kenya, Joe thought annotation could be a good career. Even after the work moved elsewhere, he was determined to make it one. (..)

Rather than let their skills go to waste, other taskers decided to chase the work wherever it went. They rented proxy servers to disguise their locations and bought fake IDs to pass security checks so they could pretend to work from Singapore, the Netherlands, Mississippi, or wherever the tasks were flowing. It’s a risky business. Scale has become increasingly aggressive about suspending accounts caught disguising their location, according to multiple taskers. It was during one of these crackdowns that my account got banned, presumably because I had been using a VPN to see what workers in other countries were seeing, and all $1.50 or so of my earnings were seized. (..)

Another Kenyan annotator said that after his account got suspended for mysterious reasons, he decided to stop playing by the rules. Now, he runs multiple accounts in multiple countries, tasking wherever the pay is best. He works fast and gets high marks for quality, he said, thanks to ChatGPT. The bot is wonderful, he said, letting him speed through $10 tasks in a matter of minutes. When we spoke, he was having it rate another chatbot’s responses according to seven different criteria, one AI training the other.

[objectways1]

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How to Annotate 100 LIDAR Scans in 1 Hour: A Step-by-Step Guide

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