Automated data labeling holds the key to AI implementation
The importance of many items in history, material or notional, has been sought to be denoted by equating it with money. Who has not heard of the phrase ‘time is money?’ Similar reverence today is reserved for data, which is viewed as a key resource for companies to leverage in their quest for success and domination in the marketplace.
But you cannot just walk to the nearest bank with your data and get money in exchange. It is not that simple. While companies are generating huge amounts of data, using it for drawing conclusions and as a decision-support system is a challenge.
Why should it be a challenge?
The reason is that data exists in what we call a ‘raw’ format. In other words, it exists as it is received and collected. While a human being may be able to draw conclusions out of it, a computer program cannot, unless it is taught how to.
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Why are computer programs needed for drawing conclusions from data? Why do human beings not do it?
With the volumes of data being generated, if we are to harbor any hopes of harnessing it, computers have to play a role. This is because a human being has physical limitations because of which he/she can only read/understand/peruse so much data and draw conclusions. A computer, on the other hand, does not have these human limitations. It can process data much faster. Hence their need in the process.
The process of making data suitable for the understanding of computers is referred to as data labeling. With the help of labeling, software programs are able to create associations and extrapolate those associations to actions.
As an example, if a certain image has been identified as a firearm repeatedly, if a video monitor comes across a person carrying an object that resembles a firearm, it can identify it and raise an alarm to the police for further investigation.
This is also known as Artificial Intelligence or AI, models for which are developed through a process known as Machine learning, or ML. Data labeling has widespread application in the field of AI.
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Automation of data labeling
How is labeling done?
The starting point is usually manual. This is the step at which data is identified and tagged in a language or manner that a software program can understand and create associations
If it is to be done manually, what is the use? We may as well do the analysis manually.
Yes and no.
Firstly, even if labeling is to be done manually, though a voluminous exercise, it is a limited one. The volume can be chosen based on the repercussions of an incorrect decision based on the data. The benefit thereafter can be everlasting; in other words, be substantially greater than the input effort.
Secondly, techniques are available for automation of the labeling process which, if used, can significantly reduce the manual effort in the process. Though human intervention and oversight seem unavoidable, through automation techniques they can be limited to involvement at the broader level rather than at the level of each transaction.
What does that mean?
Analysts and data scientists responsible for the process should make an effort to create functions that reflect the rationale behind the activity, instead of label-level identification, for it to be successful. That being said, each project is likely to be unique and vary in terms of the level of automation possible from the next.
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Benefits of automated data labeling
At the generic level, the benefits are similar to any automation exercise. There are, however, some specific advantages that need to be called out.
Flexibility in terms of scale
Manual activities are limited in scale by human availability and the capacity of each individual. Computer activities are much less so. Once made capable, a software program can label vast volumes of information, that is way beyond the scope of a human. Moreover, through a process that is called pre-annotation, the requirement of human labeling can be drastically reduced. In other words, humans may need to focus only on parts that a computer was unable to label.
With oWorkers, you get a reliable, experienced set of resources that will give you the ability to label data at scale and develop your AI models, for any type of unstructured data. With their known ability to provide additional resources for handling unforeseen requirements, you can trust them with ongoing assignments as well as project-based contracts, which is how data labeling is handled in many cases.
Creation of better models
By transferring the heavy lifting to computers, human beings can be freed up for processes that add greater value. Improving the quality and accuracy of the automated labeling algorithms could be an example of such an activity. Humans will also be better placed to monitor the exceptions and introduce them in upgraded versions of the algorithm for better results.
With their established relationships with technology companies, oWorkers is in a position to deploy the latest innovations in technologies for use in client projects. This adds speed and quality to their work.
Reduces rework and keeps labels current
Labeling is done based on context, as well as human understanding. The context can change as we discover more information about an object or entity and its context. Each time new information is discovered, a re-labeling exercise may need to be initiated in order to ensure accuracy and changing needs of the project. A manual labeling exercise will need to be re-labeled manually. By the same token, automated labeling can also be updated automatically, after the functions and parameters, based on which labeling is done, have been updated. As we know, at computer speed.
Each of the delivery centers is geared to operate round the clock, 365 days a year. Clients working with oWorkers can rest assured that there will be no delays to the updates and upgrades that may need to be made from time to time. This flexibility is over and above the already advantageous position they have since they are in time zones that are vastly different, allowing for work to be done overnight and be ready the following morning.
Reduced variability in results
The thinking ability of humans, while it distinguishes them from all other living beings, can also be a hindrance in exercises like labeling. As the information they are handling is unstructured and open to interpretation, which is the reason for labeling it in the first place, two different operators could have differences in the way they view the object that is to be labeled. This variability can compromise the efficacy of the model that is generated as a result of the labeling exercise, and allow biases to creep in. With automated labeling, such issues are obviated. There is a logic behind every label that has been attached and that logic will be consistently followed across the database.
While oWorkers reduces the variability in outcomes, it provides a huge benefit to clients in terms of savings. The oWorkers model of offering a choice to clients between a pricing model based on input variables and one that is based on output variables is already a hit with most clients. Additionally, many clients claim savings of up to 80% being delivered as a result of their outsourcing work to oWorkers.
Automated labeling, since it produces consistent output, lends itself to smarter human intervention by identifying areas where it would add greater value, such as through the assignment of confidence labels to the data, based on, again, an algorithm. Human reviewers can manually update the dataset that needs their inputs based on a pre-defined threshold level of the confidence score.
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Automation of data labeling delivers is not without some attendant challenges. There is always a flip side to the story. While a software program will do exactly what it is programmed to do, accuracy in handling unstructured information, which is what most information that needs to be labeled, is, remains an issue, requiring human intervention more often than not. Of course, as technologies develop, it is to be expected that the accuracy rates will go up and human involvement occasions down.
Automation of this key step towards creating AI models, builds the digital mindset of an organization and opens it up to searching for, and implementing, digitization solutions and business excellence. It creates openness to the deployment of digital solutions in finding answers to problems. It creates the ability to respond with speed when something in the environment changes and forces a responsive change upon an organization.
And, when implemented, organizations get the ability to respond with speed to developments requiring real-time solutions on a large scale as well as a logic-based decision-making ability. The solution is designed to enhance efficiency as well as the competitiveness of an organization.
With each delivery unit registered as a local company, the payment of all local and social taxes, oWorkers is the partner that allows you to focus on business. It works with employees, and not freelancers, which enables it to invest in people and build skills, that create value for clients. It is not a surprise that both past and present employees rate them in excess of 4.6 (out of 5) on external platforms like Glassdoor.
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