Data Annotation

“Check all the images that contain traffic lights.”

For some, these increasingly difficult CAPTCHAs are a source of endless frustration. But they give us something interesting to consider. If we prove that we are human by correctly identifying objects, how can a computer check our work? The answer lies in a domain of artificial intelligence called machine learning (ML).

Before CAPTCHA pictures get to you, data scientists train computers to recognize objects by providing lots of examples (training sets). If you’re wondering where those training sets come from, you’re right on the money! They come from a process called data annotation or data labeling.

Then, a model is developed to recognize specific objects. If the model is good, the computer can use it to identify the same objects in new pictures.

Artificial intelligence can’t create working models without well-trained data sets—garbage in, garbage out – this has always been the rule of thumb.

1. We Get the Big Picture

Imagine that you could talk to a computer to teach it new things. If you wanted to teach this computer to recognize a pest that is disrupting your crop yield, how might you approach this?

Chances are, you’d show it some pictures of pests you are interested in spotting and say, “Hey computer, look for these!”.

Machine learning works in the same way. Data annotation is like gathering the pictures you would like to show the computer and circling the important parts.

Unlike the computer, we understand the end goal of the model. We’ve likely defined, or at least have an understanding of its use case. As humans, understanding how the entire process works gives us an advantage when developing a data annotation strategy.

For instance, you can use your judgment to pick out a picture that wouldn’t be the best to include in the set. In this way, you’re telling the computer, “This isn’t a great example; let’s move on to a different one.”

This type of human logic is what artificial intelligence cannot yet replicate. The human side of understanding what the data means offers greater flexibility and understanding that create more substantial outcomes. Outcomes are not as strong with automated training set preparation.

2. We are Natural Language Processors

Natural Language Processing, or NLP, is the branch of artificial intelligence working to make computers understand human speech. We interact with NLP almost every day through “smart” devices.

“Hey Alexa, tell me more about Natural Language Processing.”

Like other areas of machine learning, NLP requires large training data sets. One type of data set consists of transcribed audio to train AI to turn speech into text. Another data set contains large amounts of text with annotations to highlight specific areas.

Both need humans to curate and pre-process the data before moving forward. As humans, we have an obvious advantage: we create and use language constantly. Human-powered data annotation for NLP is a great way to optimize model development.

The applications of NLP are endless. Sentiment analysis helps companies mine affective states or moods from customer messages/feedback. NLP can break down language barriers in unprecedented ways. This means people can communicate about weather patterns or pest attacks in real-time using different languages!

3. The Promise of Innovation

With so many advances in artificial intelligence and machine learning, we can be sure that our work is only getting started. AI won’t innovate itself, and researchers in computer science are the ones moving the field forward.

Of course, thinking about the importance of humans in the data preparation process does not diminish the role of technology—new software solutions to machine learning enter the market daily. Human innovation is needed to translate theoretical advances into practice.

An essential part of assembling a data annotation strategy is determining which tools to use and when to use them. Experienced professionals draw from experience to select the right tools for specific situations.

With so much raw data available in the agricultural tech industry, companies realize that the best solution is often a combination of software. Check out how machine learning has use cases across industries.

4. Data Annotation Professionals See the Process Through

Data can be messy. And let’s be honest: humans can be messy too! In the case of machine learning, this shared characteristic works to our advantage.

We need workers to clean data, address inconsistencies, and format data in a way that works for training AI. We use the term “data wrangling” to describe this process. Although “wrangling” may seem like a harsh term, it captures the actual amount of effort needed to prep data before use.

Part of the benefit of using a data annotation provider is that they can help you through the entire process. This includes:

• data creation or collection

• data cleaning and curation

• data labeling or annotation

 Consider using artificial intelligence to detect potential disease in a large field of crops by periodically analyzing photos of crops. This is likely a massive undertaking for an organization. First, enough data to compile a training data set is needed.

 Once you’ve created a clean training data set for supervised learning, the story isn’t over.

Human intervention is needed to assess how well the AI can correctly identify diseased crops in the future. In situations where the machine cannot perform accurately, people need to determine the parameters of a new training set. Then, the process repeats, once again under human supervision.

Harness the Power of Data Annotation

With machine learning driving global industries forward, organizations need access to high-quality training sets. Organizations might not have in-house resources to handle data annotation at scale.

Fortunately, Digital Divide Data offers across-the-board support to get companies to the finish line, no matter where they start. As a non-profit organization, DDD is challenging the industry’s status-quo with impact sourcing, youth outreach, and more.

To get started, see how DDD’s suite of fully managed services (CV, NLP, Data and Content) can exceed your expectations.

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Artificial Intelligence (AI) is growing in popularity as a tool to provide everything from better customer care to translation services, driverless cars, smart technology, and more. Consisting of several different technologies that work together to deliver the end result, AI is computer-based programming that mimics human behavior.

Although AI has advanced enormously over the past decade, involving humans in its development is still essential if premium results are required.

Here we take a look at how AI is trained using test data and how human-powered data annotation and data labeling adds significant value to the outcomes that AI delivers.

What is Data Annotation Software?

Data annotation software is software that is written to annotate production-grade training data. AI isn’t created in a fully formed state. To provide a human-like response to data, AI has to “learn”. As an example, when AI picks up an image of a tree, it doesn’t know that it’s an image of a tree. The ability to recognize that a particular configuration of pixels is a tree is only obtained after AI has had access to millions of tree images.

The process by which the AI learns to recognize a tree (as an example) is known as machine learning (ML). For effective machine learning to take place, the AI needs access to a large volume of training datasets – data that can be used to help develop the algorithms (mathematical models) needed to develop a human-like response. Using the data, AI can develop a prediction model on the basis of its learning.

For example, if an AI program has been given access to millions of tree images, it can use mathematical modeling to build a picture of what arrangement of pixels, statistically speaking, is most likely to be a tree. With this information, when the AI is given access to another tree picture, it can assess the probability of it being a tree and label it accordingly. Obviously, AI is capable of interpreting millions (if not billions) of different pieces of data, but to do so accurately, it needs access to enormous amounts of test data that provides the material needed to create accurate algorithms (mathematical models).

To assist in the process, the test data needs to be annotated – labeled in such a way that AI can interpret it effectively and developing a high quality training dataset, depends on many things. You can use platform providers or managed services with specialists. In the context of recognizing a tree, for example, data annotation might be used to enable the AI machine to interpret the data you’ve provided as a tree.

Due to the enormous amount of trained data, or training datasets that are needed for successful machine learning, data annotation software has been developed to try to reduce the time needed for annotation to take place. Data annotation software does make machine learning faster, but it also has some significant drawbacks, some of which are highlighted below.

What are the Limitations of Data Annotation Software?

• Exceptions. Every set of data is likely to have exceptions – outliers that are likely to confound the boundaries set up as part of the algorithmic modeling that AI completes. If the data annotation software can’t recognize these outliers and label them correctly (which is likely if the data doesn’t conform to the usual parameters), this limits the level of machine learning that can take place.

• Limited annotation labeling. Particularly when diverse data is being deployed, the software may not be able to cope with the large variety of labels that are needed for effective machine learning.

• Quality control. Data annotation software is usually equipped with features that identify where there are quality control issues. Unfortunately, the issues identified are those that are beyond the capability of the annotation software to resolve. Without additional input, those quality issues will remain.

• Limited sorting. Data annotation software can play a valuable role in sorting data, and flagging data that it can’t easily sort and label. Unfortunately, the software can’t correct the issues it flags – which is where human intervention comes in.

What Role do Humans Play in Data Annotation Software?

Humans can resolve issues with test data that data annotation software can’t. Although the goal of machine learning is to create AI that can “think” in the same way as a human (but without the risk of human error), it’s still not as advanced as the human brain. Particularly when it comes to making judgments that involve subjectivity, data that involves an understanding of intent is vital to get the best results. For example: a surgeon clutching a scalpel, could be considered interchangeable with a knife-wielding criminal, without the benefit of understanding intent.

What are the Advantages That Humans Bring to Data Annotation Software?

The advantages that humans bring to data annotation software mainly relate to our ability to process data that falls outside the machine-learned parameters.

Humans are essential when it comes to developing the training datasets that can’t be successfully cataloged by the annotation software. More sophisticated decision-making, particularly that which is based on subjective criteria, needs human input.

When annotation software presents a quality control issue, it’s humans that are required to decide on a suitable course of action.

Similarly, diverse, complex data will need human intervention for it to be correctly labeled so that machine learning can take place effectively.

Why are Optimal Results Dependent on Human Input?

Ultimately, AI algorithms are only as good as their test data. The higher the caliber of the datasets (including accurate, clear labeling), the more effective the AI is going to be in meeting its outcomes.

As humans are the machines that control machine learning, their input is essential for the process to deliver optimal outcomes.