data labeling AI

Data labeling: the key to AI success

In this article, Carlos Melendez, COO, Wovenware, discusses best practices for “The Third Mile in AI Development” – the huge market subsector in data labeling companies, as they continue to come up with new ways to monetize this often-considered tedious aspect of AI development. The article addresses this trend and outlines how it is not really a commodity market, but can comprise different strategies for successful outcomes. Wovenware is a Puerto Rico-based design-driven company that delivers customized AI and other digital transformation solutions that create measurable value for government and private business customers across the U.S.

The growth of AI has spawned a huge market subsector and increasing interest among investors in data labeling. In the past year, companies specializing in data labeling have secured millions of dollars in funding and they continue to come up with new ways to monetize this often-considered tedious aspect of AI development. Yet, what can be viewed as the third mile in AI development, data labeling, is also perhaps the most crucial one to effective AI solutions.

In very general terms,  AI development can be broken down into four key phases:

  • Phase 1: The design phase, where the problem is identified, the solution is designed and the success criteria is defined
  • Phase 2: The data collection phase, where all the data needed to train the algorithm is gathered;
  • Phase 3: The development phase, where the data is cleaned and labeled and the algorithm is developed and trained
  • Phase 4: The deployment phase, where the solution is set loose to perform and then continuously updated for improvement

Data Labeling is Not Created Equal

The third mile in AI development is where the action begins. Massive amounts of data is needed to train and refine the AI model – our experience has showed us that a minimum of 10,000 labeled data points are needed –  and it must be in a structured format to test and validate it, and train the model to identify and understand recurring patterns. The labels can be in the form of boxes around objects, tagging items visually or with text labels in images or in a text-based database that accompanies the original data.

Once trained with annotated data, the algorithm can begin to recognize the same patterns in new unstructured data. To get the raw data into the shape it needs to be in, it is cleaned (errors fixed and duplicate information deleted); and labeled with its proper identification.

Much of data labeling is a manual and laborious process. It involves groups of people who must label images as “cars,” or more specifically, “white cars,” or whatever the specifics might be, so that the algorithm can go out and find them. As with many things that can take time, data labeling firms are looking for a quick fix to this process. They’re turning to automated systems to tag and identify data-sets. While automation can expedite part of the process, it needs to be kept in check to ensure that AI solutions making critical decisions are not faulty. Consider the ramifications of an algorithm trained to identify children at the cross-walk of a busy intersection not recognizing those of a certain height because the data set used to train the algorithm didn’t have data about these children.

Since data is the lifeblood to effective AI, it’s no wonder that investors are seeing huge growth opportunities for the market. Effective data labeling firms are in hot demand as companies look to find a faster path to AI transformation. To aggregate and label data not only takes months of time, but effective algorithms get better over time, so it’s a constant process. But when selecting a data labeling firm that automates the process, buyers must beware. Data labeling is not yet a commodity market, and there are many ways to approach it. Consider the following when determining how to accomplish your critical data labeling process:

  • Use custom-data. There is still enormous competitive advantage to owning your own quality private data-sets, so if selecting a partner, make sure the data is quality controlled and know if synthetic data is used to enrich the data-set..
  • Effective data labeling requires expertise.  Many firms will crowd source annotators, or use staff with little-to-no experience, but good data labeling requires really good eyes, as well as skill. A data labeler gets better and faster over time and learns how to avoid false positives because of bad data.
  • Data privacy should remain paramount. Since effective training data requires lots of company information in many cases, those performing your data labeling should be under NDA with your firm or service provider.
  • Data labelers and data scientists should be part of a single team. It’s important that a data scientist building the algorithm is overseeing the data labeling to provide quality assurance and control. They will make sure it is being trained on the best data-sets and addressing the needs specific to the goal of the AI project.
  • Find a long-term partner, not a data labeling factory. Since AI is never one and done, it’s important to constantly train your algorithm to do better.  Selecting a partner who developed the original algorithm, understands it best and can use the same process to improve it is crucial to continuously improving AI.
  • Partially automate when needed. While automated data labeling can be rather fast, it is nowhere as precise or effective as human-led work. Partial automation can point data labelers to where objects are, so that they only need to segment them. Leading with human intelligence, augmented by automation is always best.

As data continues to become the oil that fuels effective AI, it’s critical that getting it into shape for algorithm training is not treated as a commodity, but given the attention it deserves. Data labeling can never be a one-size-fits all task, but requires the expertise, customization, collaboration and strategic approach that results in smarter solutions.

Source: Insidebigdata