It wasn’t too long ago that thinking of a “Computer” conjured the image of a person sitting at a desk with a heavy monitor and a noisy processor. But today, that reference is practically non-existent.
Everything is a computer, from wristwatches to handheld devices. Artificial Intelligence is now looking to do the same with the term “Intelligence”, substituting its traditional meaning with one that pertains to machine thinking.
While we are nowhere close to replicating human intelligence, it’s already becoming an inevitable part of people’s lives in conjunction with AI/ML applications. Automated driving, voice assistants, face detection- we have abundant real-life examples of this technology.
This development is made possible by implementing sophisticated model training methodologies like data labeling. Labeling, in particular, is a critical part of training machines to think like humans. It is a technique to define and add meaning to data points in a set.
The resultant dataset enables AI/ML models to make accurate and timely predictions about the data they are fed, helping to reach intended goals while aiding the training of those and other models simultaneously.
Read on to know how data labeling is revolutionizing the future, one data sample at a time. Also, learn about the role data labeling companies have to play in bringing about the transformation that’ll impact everyone’s lives, and businesses in particular, in as yet unimagined ways.
- 1 What Is Data Labeling Exactly?
- 2 How Data Labeling Is Influencing Everything
- 2.1 Security and Safety
- 2.2 Transportation
- 2.3 Manufacturing
- 2.4 Logistics and Supply Chain
- 2.5 Healthcare
- 3 In Conclusion
What Is Data Labeling Exactly?
To understand data labeling, there is a need to understand the way computers work and why artificial Intelligence became a thing. As mentioned earlier, humans were considered the ultimate computers because there was a lack of competent technology to do their tasks.
That changed during the digital age when machines emerged and became powerful enough to do increasingly complex tasks previously possible only by humans. That trend continues to this day.
But computers are objects with no sense of intuition or inherent capability to perform any task. Therefore, they need to be programmed by developers to process the data they are fed.
Computers thus far, called Classical Computers, functioned in this manner, where every new capability has to be introduced into the system through coding. This makes the system less flexible to data variety.
The Rise Of Learning, Thinking Computers
The lack of adaptability of classical computers prompted computer scientists to create new systems that could mimic the human brain’s ability to think and adapt to new situations.
Since these characteristics form the basis of human intelligence, the term “Artificial Intelligence” came to describe the computer’s equivalent.
The new type of computer uses a combination of Neural Networks (hardware circuitry that resembles the setup and function of the human brain cells called neurons) and Machine Learning algorithms that help to train the AI software (called a model).
Machine Learning means just that – machines learn to identify and process target data like humans. Creating AI models that provide accurate and quick analysis needs a lot of ML training with large quantities of data that is relevant to the purpose of the AI.
But as mentioned earlier, computers don’t have the inherent intuition to know what they are looking at in the data and thus need to be shown by some means. Professionals, therefore, demarcate the target subject in the data sample by isolating it using some form of boundary and then adding an identifier or label. This is Data Labeling.
Thus, raw data is transformed into intelligible samples that an ML algorithm can understand. The labeling personnel is also responsible for ensuring the creation of quality training data since the model’s performance depends on it.
While much of the labeling happens manually, large data sets require automated labeling to accomplish successful training.
In such cases, a method called Deep Learning is used where ML algorithms are created in a hierarchy, and one’s result becomes the input for the next until the system learns to label and identify the target by itself accurately.
Data Labeling vs. Data Annotation
You are bound to come across the term Data Annotation along with data labeling, and the similarities between the two may seem confusing. What you need to know about them is that even the industry uses them interchangeably for practical purposes, as both involve marking vital objects in data for ML training purposes.
The minor difference between them is that data annotation is about tagging the objects for identification, while labeling involves tagging to give context and meaning.
Another definition of the difference between the two is annotation, referring to the manual addition of tags, while labeling is annotation done by an ML algorithm during self-training.
How Data Labeling Is Influencing Everything
AI is finding itself in every aspect of modern life, and labeling follows AI anywhere it goes. This way, data labeling sets the stage for future technologies and the world they will create. Here’s a look at some applications where it already is making an impact and how that’ll transpire going ahead.
Security and Safety
Technology is a double-edged sword when it comes to security: it allows nefarious persons to gain an advantage in their crimes while also aiding the security and safety of their potential victims.
The US market for domestic security systems shows this reality, with a survey finding that 75% of Americans purchased a Smart Home device between May 2021 and May 2022, with 65% of those devices being home security and safety systems.
Businesses and government agencies rely on sophisticated security and safety systems to keep their information safe. This means screening people to identify potential threats via facial recognition and behavior monitoring.
AI is necessary to ease the burden of security teams in accomplishing those tasks or in domestic cases, to perform them by itself. It is possible only if the AI is fast and accurate enough for the role. Data labeling is used to train such an AI model.
Facial scans are made of volunteers directly or using multi-angle images and 3D scans. Important features like the eyes, nose, ears, mouth, etc., are labeled to help train the algorithm first.
Deeper labeling even trains the algorithms to recognize finer aspects like face shape, texture, creases, lighting, etc. Over time, the model can identify faces accurately by noting the differences in features between one another.
When applied in the real world, the AI can recognize a wanted person in a crowd after studying that person’s sample image. A future application of this is the hotly contested issue of mass surveillance, where entire populations will be scanned constantly to identify suspects.
Another potential future use made possible by data labeling is the identification of its owner by a self-driving vehicle. Here, the person’s face itself becomes the key, and the vehicle can adjust its interior settings to suit that person’s preferences.
The spate of mass school shootings in the US has intensified the need for better mass security systems to avert further disasters. Schools in the country invested over $3 Billion into mainly tech-focused security systems in 2021. And security companies intend to step up their offerings with deeper AI integration.
Since many of the shootings were done by school students, facial recognition won’t cut it. The AI should recognize the objects they carry instead of stopping them in their tracks.
Detectors and scanners can have AI models trained using sophisticated data labeling to identify objects that could be used as weapons or are dangerous like guns. The same applies to other places like airports and banks.
Combined with facial recognition, the AI can flag unfamiliar people with such objects and ignore those expected to have them, like security personnel and the police.
Suspicious behavior is one of the markers for a crime about to take place. And security AI models can be trained to detect such behaviors in camera footage with labeling.
Labeling enables the AI to differentiate a person from the surroundings and track their movements. It can also be trained to note the usual behavior of people in the vicinity to alert security personnel of untoward motion.
Combining facial and object detection creates a complete and reliable security system that can help thwart a crime before it occurs. The future of such a system also includes adding other data analyses to give a complete picture of the security and safety infrastructure of a location.
An example is adding the known behavior data of a particular criminal to the system to help it detect a similar pattern occurring in its jurisdiction.
Huge changes are sweeping through the automotive sector, disrupting legacy manufacturers and operational mechanisms. One of the most prominent developments in recent years is the introduction of autonomous driving technology, commonly called self-driving technology.
It means a vehicle can steer itself taking inputs from various sensors placed on its outer surface and using an AI model as its brain.
This technology is being pursued because of its potential to save lives besides adding convenience. The US Dept. of Transportation estimates that the tech can help avoid 94% of fatalities that result from accidents. The AI operating such vehicles cannot make errors while judging objects and relative distance to them since lives will be at stake.
However, the road to full self-driving, where the vehicle won’t need any human steering and power-control input, hasn’t been smooth.
Presently, the applied AI in cars has made errors while identifying objects for various reasons, sometimes resulting in fatal accidents, ironically and tragically. This problem illustrates the need to increase the object detection ability of the AI, and labeling is the answer.
Professionals are working on designing more accurate labeling to improve the model’s detection abilities and running real-world field tests to gather feedback. Data labeling companies have a crucial role to play here as they can check the particulars of the errors occurring and suitably resolve them with better labeling efforts.
They can also enhance vehicle safety by using facial recognition, object detection, etc., like in the case of Tesla’s sentry mode.
Another application of labeling for future transportation technologies is autonomous delivery drones. They are slated to carry everything from medicines to toys, with companies like Amazon making a push for them.
The drones will require not just on-ground visual data accuracy to arrive at their destination but also aerial objects like tall buildings, birds, etc. Its AI, too, will need extensive labeling for not just camera data but also GPS and other sensors for coordination.
Factory floors have become the epicenters of innovation in recent times, driven by the adoption of new technologies to improve yield rates, safety, testing procedures, resource management, etc. AI naturally finds extensive applications in this sphere, covering multiple aspects of the manufacturing process and associated operations.
It is helping to cut costs and speed up production while minimizing risks and production bottlenecks. And data labeling is making it happen in the following ways.
Creation Of Automated Production Processes
According to the International Federation Of Robotics (IFR) report, the world will have 2.7 million industrial robots in operation in 2020. This trend is only growing because of the many advantages offered by automated manufacturing processes. These robots need to be able to
Identify what they are working on and how best to go about it should there be a deviation from their standard values. Cameras are used to feed such data into the AI model, and labeling helps it identify the various components and associated parameters of the product.
Detection Of Quality Issues and Predictive Maintenance
Prevention is always better than cure, so it’s better to root out a manufacturing problem before it happens. This requires constant vigilance to identify issues early by looking at their symptoms. You also need exhaustive data gathering and analysis to perform timely predictive maintenance.
AI comes to its own in these cases, buoyed by accurate data labeling. If, for instance, the system detects a minute crack in the product’s body or the factory’s structure, it can automatically alert the concerned personnel with details like size, date and time, probable outcome, etc.
Better Planning With Simulation and Forecasting
Efficiency is the key to profitable manufacturing, and that efficiency is established by implementing a well-thought-out plan. Creating that plan needs data analysis about the production pipeline’s various components. AI can run simulations of possible end-to-end scenarios and forecast the outcome for each user data that’s accurately labeled.
It can be made to identify the right value of the various parameters that go into the pipeline from a list, which helps it forecast respective outcomes. The one with the most favorable outcome can serve as the baseline for plan development.
Logistics and Supply Chain
An extension of the manufacturing sector, logistics and supply chain forms the crucial link between the factory and the customer’s hands. The sector is complex, especially when it’s global. Several interconnected components should function without a hitch to deliver the right products in the right quantity at the right destination and time.
Otherwise, problems like overstocking and understocking will occur and affect the entire chain, leading to losses and wastages.
The best way to prevent untoward incidents from ruining the movement of goods is to incorporate AI in various capacities along the chain. AI will need to be trained by data labeling companies to note the correct data for the smooth functioning of the supply chain.
Depending on the scale of the operation, a supply chain can have a few tens to thousands of vehicles spread across it. They can be land, sea, and air vehicles, and they all need to be tracked in real-time to know the shipment’s status and the vehicle itself.
GPS data of the vehicles is key to making this happen; the AI can be fed GPS data and tasked with identifying the necessary vehicles from the many others moving along that route.
Data labeling can help the AI discern the target vehicles from the rest by focusing on things like the logo used, time, etc. You get automated, accurate tracking in real-time from the point of origin to the destination anywhere in the world.
Automation is also taking over warehouses and transforming them into efficient inventory movers that are accurate and timely no matter the situation. Line tracer robots, full-space monitoring systems, automated conveyors, and other such components are becoming the norm.
A powerful AI with the capacity to manage the numerous aspects of inventory is behind this transformation, bolstered by labeling of the best kind.
Developers should input all sorts of information during labeling, like the size of boxes, bar/QR code information, color coding for line tracing, person identification to prevent the robots from accidentally hitting one, and layout information to know how much of the warehouse’s capacity has been filled, etc.
Person tracking can also be added so that productivity can be determined by monitoring the person’s motions. With warehouse management, data labeling companies have their hands full as they can’t ignore even a single influential parameter if they don’t want the AI to affect the supply chain there adversely.
Scheduling and Documentation Automation
For a supply chain that is spread across the globe, managing the many applicable legal and financial documents can become a hassle. The paperwork will contain multiple crucial factors that determine whether or not a shipment moves through the route in time.
The scheduling of this enormous operation also depends on the smooth flow of approvals and other such documentation clearances. You would need a large team spread everywhere to monitor these aspects of the chain.
Or the alternative is to use an AI that knows what component of the supply chain should be present where at a given instant. It can be used to handle its documentation and scheduling in real-time wherever the logistics company may be operating.
Data labeling helps by letting the AI know what information in the documentation it should look for, what a concerned authority person’s signature looks like, gauge traffic density along the route of various vehicles in transit, etc.
These factors help determine the schedule of the operation accurately while helping deliver the right information to the right personnel for easy clearances.
The healthcare industry is a gigantic one, with a lot of complexities involved. It is also heavily regulated and continuously changing thanks to the advances in medical treatment made all the time.
There are a lot of connecting nodes here, and a failure at any one of them can cause irreparable damage to the patient and the medical facility.
Medical personnel, and administrators, should juggle many things as a part of their daily routine, leading to increased stress and making them prone to committing errors of judgment.
AI is, therefore, finding its way into the healthcare industry’s every aspect, and data labeling is being used extensively to ensure impeccable accuracy and quick delivery of results.
Healthcare’s latest venture is the delivery of services remotely. This requires a medical professional to be present in one location while a robot manages the treatment in another. The professional provides the necessary input to the AI-based system to administer the treatment correctly.
Remote surgery is an example of this, with some minor surgeries already benign performed primarily remotely. This has enormous benefits in places where such professionals are not available. Data labeling is used to help the AI identify the anomaly present in the patient and direct the robot to the right spot while performing the surgery.
Factors like skin color changes, body temperature changes (via thermal imaging), internal organ color changes (for tumor detection), etc., can be checked to perform the procedure accurately.
False and delayed diagnoses cause a lot of deaths annually, all of which can be prevented with the use of AI. Automation can detect the anomalies in test results quickly and accurately and is not prone to factors like fatigue that can lead to misdiagnosis. Data labeling is necessary to help detect anomalies and establish the patient’s condition.
X-rays are a good example of this: the AI is fed labeled data of a healthy person’s X-ray images until the AI understands it. Then, real-world X-rays are provided to the AI to help identify the problem. AI is currently playing an assistant role, with developments in the field vying for complete automation.
Transcription and Billing
A heavily regulated industry like healthcare contains a lot of documentation, including various bills for treatment and medication. And all of it should be for successful compliance and insurance claims processing.
However, transcription can go sideways in the heat of the moment during treatment, leading to processing problems later on. AI can solve this problem by restoring the necessary accuracy. Data labeling is used here to train the AI to recognize medically-relevant phrases and keywords for a given treatment.
Standardized forms for entering such data are scanned and its many sections are labeled so that the AI can fill them with appropriate information. The insurer can use AI to check if the information is for quick claims processing.
Technology is an ever-flowing stream that continues its progression relentlessly. And it’s sweeping everyone into the future with creations like AI that can revolutionize our lives in every way. Accurate data labeling is at the core of this revolution, and your business will benefit greatly if it’s incorporated at the earliest, especially via outsourcing.
The company and its customers will experience a boost in value and convenience while experiencing cost and waste reduction.
Jessica Campbell is a Professional Content Strategist at Data-Entry-india.com, a leading organization providing end-to-end Amazon virtual assistant services. For over 7+ years, she has been writing about best practices, tips, and ways to enhance brand visibility and boost sales on the Amazon marketplace. So far, she has written several articles on Amazon listing optimization, Amazon PPC Management services, Amazon SEO & marketing, Amazon store setup, Amazon product data entry, Data standardization, data enrichment, data annotation, video annotation, and more. She holds 12+ years of copywriting experience and has helped thousands of businesses and Amazon sellers build their presence in the marketplace, reach new customers, and register better sales & conversions through the power of a well-built copy.