Ever since the advent of artificial intelligence (AI), it has become an integral part of real-life systems. There is barely any space or industry left which stands unaffected by AI in today's era.
Businesses are benefitting from artificial intelligence and many business owners are now, willing to adopt AI/ML operations to gain a competitive advantage. Although before businesses venture deep into the sci-fi movie-inspired possibilities of machine learning, it is important to be aware of its underlying principles and what makes it work. At the heart of every machine learning initiative, data annotation & labeling forms a key foundation of this disruptive technology. AI implementation in all industries requires an expert-level understanding of data collection, filtering, categorizing, labeling, and annotation techniques.
Let us walk through data annotation and why is it important for your AI projects.
As mentioned, AI is an integral part of real-life systems with its real-world applications. From weather forecasting, and medical imaging to smart assistants like Alexa, Siri, Google, and autonomous driving, there are a lot of use cases out there. All of this includes human-made decisions, through inputs they receive. These inputs are in different formats such as images, videos, audio, or simply text. As humans learn and label colors, places, animals, things, etc., AI replicates the learning process by feeding the machine learning algorithms with curated data sets in large quantities to train the models.
For example, self-driving vehicles, are trained to identify people, speed breakers, traffic signals, and other necessary objects. All of this is possible, with Data annotation & labeling. Relevant data points are required to be labeled so that the ML model can precisely identify the said objects.
Data annotation encompasses many different aspects of the AI process. Fundamentally, though, data annotation is one of the first processes in the development of any machine learning project. Data annotators respond to the need for data, sometimes setting out on an initial dataset search based on a specific need.
After wrangling the data, data scientists clean the datasets to ensure they can be easily digested and are fed to the training model. Once that is complete, annotators and data scientists collaborate to determine which annotation method(s) they should use moving forward. There are many forms of annotations and it is important to decide the right type for the collected data and scope of the project. Most importantly, annotations should be of a certain quality so that when used for training and testing models, they present good results. This is key to having high precision in your model.
Two important fields of AI cover most annotation tasks; Computer Vision & NLP.
1. Computer vision deals with images & videos, the visual format of data. Various tasks can be performed within computer vision such as facial recognition, movement detection, autonomous driving, etc.
2. NLP focuses on image-based text recognition with an exception of text data. NLP helps ML algorithms to understand language and trains them to understand human speech.
A few other data types are also used to annotate freestanding data, viz., LiDar uses lasers, and Radar uses waves. An algorithm sees a surrounding environment by using the 3D cloud of data points.
To annotate and curate the data sets to be used in any of the said fields, the quantity & quality of data is highly important. Poor quality of training data can compromise the ability of artificial intelligence to make accurate decisions. The after-effects can vary. For example, poor-quality data in chatbots can lead to sub-par customer experience. This can lead to comprised brand loyalty and in many cases, your customers can opt for rival businesses offering smarter data. In cases of autonomous vehicles, poor-quality datasets can lead to fatal accidents. This is why companies like DesiCrew are important in the current market, where the skepticism towards the evolution of AI grows and where developers are aware of the consequences of poorly annotated data.
The demand for highly customized data sets curated by trained professionals will continue to perform the bulk of the industry’s curation tasks. Data annotation companies have a valuable role to play in the evolution of AI as they provide the foundation of AI.
Getting professional help to manage your data annotation & labeling needs is the best way to unlock the full potential of data and content to improve your business results. Higher quality data and content fuels faster training of machine learning models and directly impacts the success of your AI systems. Most importantly, all of this effort can help deliver a more positive customer and employee experience for your brand.
DesiCrew is an experienced provider of customized data annotation services across various sectors such as healthcare, retail, agriculture, Edutech, construction, and many more. To know more, reach out to us on firstname.lastname@example.org