Why is Data Labeling so Important in AI Machine Learning?
Data, Algorithms, and Processing are Three indispensable Elements of AI
The question is: which matters most?
Some argue that data is like artificial intelligence gasoline, some say that it is meaningless to have data without context.
Let’s explore these AI elements concisely to discover the advantages of each perspective.
Data is the starting point.
Data is like a knowledge carrier, and using that knowledge will benefit those who are good at studying it.
It makes sense for AI to start with data and take advantage of learning itself. In scalable data and the high-speed era, it is very convenient to use data to train artificial intelligence.
Companies that have a long history of business intelligence conduct lots of works around data. It’s the same for artificial intelligence.
The original data is generally acquired through data collection, and the subsequent data cleaning and data annotation are equivalent to processing the data, and then transferred to the ARTIFICIAL intelligence algorithm and model for invocation.
If the data used in artificial intelligence training is not sufficiently diverse and unbiased, problems such as artificial “AI bias” may arise.
It is important to understand the advantages of algorithms in the natural environment, compared to static data.
Organizations can gain an advantage by optimizing their business algorithms. Finding the right formula, statistical model or prediction is true business art.
These algorithms are protected by organizations and are often considered the secret weapon of success. While they rely on clean data, the rules implicit in mathematics or logic are the real differences in many industries.
What would be the future of the insurance industry without actuaries and precious algorithms? Artificial intelligence makes no exception.
Common algorithms used in machine learning include decision tree, random forest algorithm, logistic regression, SVM, Naive Bayes, K-nearest neighbor algorithm, K-mean algorithm, Adaboost algorithm, neural network, Markov, etc.
Artificial intelligence algorithms can be divided into several categories according to model training methods and different solving tasks, among which, the attributes include the quantity, quality, characteristics of the data itself, problems in specific business scenarios, calculation time and accuracy requirements, etc.
The right steps and the appropriate approach are critical to the quality of the results. It makes no difference whether a process is static, repeatable, dynamic, or emergent. Good processing is about using the right data and algorithms at the right time. The business results are certainly accurate and can be appropriately adjusted by using transparent feedback cycles in various forms of monitoring.
You can’t have one without the others.
In order to be successful in the long-term competition, you need all three. However, people can start with one element and then add up the others.
As machine learning begins to show its power, many AI projects start with no bias data. With the development of artificial intelligence, algorithm and process will appear vital as problems grow in complexity and extension.
Just as a triangle needs three sides to stabilize its shape, artificial intelligence will also need all three elements to perfect itself.
ByteBridge — A Human-Powered Data Labeling SAAS Platform
There is a SAAS labeling platform. The biggest advantage is clients can individually decide when to start their projects and get their results back instantly.
- Set labeling rules, iterate data features, attributes, and task flows, scale up or down, make changes.
- Monitor the labeling progress and get the results in real-time on our dashboard
- These labeling tools are available on the dashboard: Image Classification, 2D boxing, Polygon, Cuboid
Once task flow well settled, the project can start in 24h. A medium-level project with 10,000 image labeling will take less than 1 business day.
If you need data labeling and collection services, please have a look at bytebridge.io,the clear pricing is available.
Please feel free to contact us: firstname.lastname@example.org