The Challenges of Large Scale Data Annotation
Creating a great artificial intelligence program to leverage the advancements in machine learning that have been made in recent years requires enormous data sets. Sometimes, billions of parameters are needed to do the job. The fast computer storage that is required goes far beyond the few terabytes on desktop computers and can fill large data centers. This information overload can be overwhelming for humans to contemplate.
Effort Consuming Nature
It takes an incredible amount of information to train a truly genius-level AI. Whether that data is in the form of pictures and videos, text, or other things. All that data must also be correctly identified. Then it must all be properly annotated, correctly labeled, and tagged to be useful for machine learning algorithms.
It requires human labor with attention to fine details to create the best possible algorithms. Unfortunately, the devil is in the details, and those fine details are all in large data sets. Each must be correctly identified, tagged, labeled, and annotated.
Bias in Data
A considerable amount of data is also required to overcome algorithm bias. This is also important to address various ethical concerns. Otherwise, programs have a way of revealing and exaggerating ugly stereotypes. That can bring a new meaning to phrases like “systemic racism.”
The future of data annotation and artificial intelligence will always need human intelligence. This work has a very low margin of error that is constantly reduced over time. This is because any public-facing algorithm often magnifies small errors. This makes errors very visible.
The future role of artificial intelligence in society and industry is not to replace human labor with more capital. Instead, we will work with AIs in a far more interconnected world. As a result, we are collaborating with other humans and machines.
To achieve that utopian vision, artificial intelligence must be trained on diverse and inclusive datasets. That’s true even when the purpose of your next genius AI is very specific and singular. To achieve your next big breakthrough in AI, automation, or machine learning, you would do well to outsource to an experienced data annotation service provider for solutions.
Different Types of Data Annotation for Different Challenges
There are many different kinds of data and also many different kinds of data annotation. Each comes with various challenges. Additionally, various use cases and project goals provide their own unique problems. Unique problems that may require customized solutions. For example, the image and video annotation required for computer vision and machine learning comes in different forms to provide solutions for different applications.
The different kinds of data annotation and labeling services include:
- Bounding Boxes and Rotating bounding boxes
- Cuboid Annotation, for when 2d boxes are not enough
- Polygon Annotation
- Semantic Segmentation, to provide context for dense prediction
- Skeletal and Key Points Annotation, used to track human and animal motion
- Lane Annotation, for traffic analysis and autonomous vehicles
- Instance Segmentation, to detect and delineate objects of interest
- Bitmask Annotation, to enhance and provide for bitwise operations
- Video Annotation
- Object tracking through video frames
- Object and Facial Recognition
- Custom Annotation Solutions
On top of all that, mass data collection and creation can be challenging to do responsibly, especially when there are ethical concerns the public has about privacy and various laws that demand compliance. Of course, this must be done and done right. Care must be taken to avoid abuse and corruption. People are uneasy when governments and corporations play Big Brother.
All that data must also be safely stored and accessible to humans and machines. There is a lot of labor and resources to do all that data annotation, tagging, and labeling at a large enough scale to be helpful.
For Every Problem, There is a Solution
The technical and ethical challenges of creating your breakthrough AI can be overcome. What may seem like science fiction today is tomorrow’s scientific reality. Your company does not have to do it all alone. There is data annotation for startups available. Outsourcing your data annotation project is one great solution. It can free your staff and resources to work on your top priorities.
A data annotation service provider with a great platform can provide custom solutions to your unique problems. You don’t have to suffer from information overload. Let others handle gathering, securely storing, and processing that immense amount of data is okay.
That tremendous amount of data is required to solve the problems of algorithm bias. The bias problem can be solved for AI, even if it seems impossible to solve.
The best data annotation companies are full service and can provide solutions for ethical mass data collection. That also helps with legal compliance and public relations. The best companies can even generate the valuable data you need to achieve an exciting breakthrough. That frees your company to work on creating the best possible artificial intelligence.
Every problem has a solution. Every computer vision problem has image labeling solutions available. The goals that are most worth achieving are usually also the most difficult. Technical challenges can be broken up and outsourced. Nothing is impossible. Ethical problems may keep you up at night. They may create public relations problems and expensive legal nightmares.
Fortunately, you do not have to solve the problems of bias in society. You can just solve the problems of data and algorithm bias. That adds value to your product, your company, and the world. It is enough. With your next exciting breakthrough, it can be more than enough. With the right help, every problem can be solved. Let’s solve your problems together with a great image annotation outsourcing service.