Do you know The Secret behind AI & ML Algorithms?

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It is the 20th Century when people believe in smart tools and thoughtful lifestyles. The moment you open your eye in the morning, your brain gets surrounded by the blazing threads of AI-ML algorithms- starting from the weather forecast, checking the best deals on a fashion website, getting driving directions, booking a safe ride to the office, or playing your favorite song in the streaming queue. The list must go on.

Let me tell you the unspoken truth, aka the hint–AI enablement services!

Wait! Don’t you familiar with the concept of data annotation or AI-ML algorithm? This blog post will shed light on AI enablement services and explain why AI and ML technologies are the biggest secret. Let me explain to you. 

What is annotation & why annotation?

Data annotation labels data in multiple formats, such as video, images, speech, or text, so that machines can sense it. For supervised machine learning, labeled datasets are crucial because ML models require understanding input patterns to process them and produce accurate results.

For example, training machine learning models of self-driving cars involve annotated video data. Individual video objects are annotated, allowing machines to predict objects’ movements. Data annotation is called data labeling, tagging, classification, or machine learning (ML) to train data generation.

Annotated data is the bottom line of managed learning models since the implementation and accuracy of such models revolve around the quality and quantity of annotated data. Annotated data matters because

  • Machine learning (ML) models have a broader range of critical applications
  • Identifying high-quality annotated data is one of the initial challenges of developing machine learning models
  • Data collection is a prerequisite of data annotation, and we must do it before initiating labeling

Visit our AI-enablement offering to learn more about our data annotation, collection, and moderation. 

What are the Advantages of Data Annotation?

Data annotation instantly contributes to the machine learning algorithm’s training with a directed learning process for the correct forecast.

Enhances the Accuracy of Output

As much as image annotated data is applied to focus the machine learning model, the accuracy will be higher. It is because the various data sets used to prepare the machine learning algorithm will learn different factors that will help the model utilize its database to give the most relevant results in various scenarios.

More Enhanced Knowledge for End-users

Machine learning-based equipped AI models to deliver wholly different and seamless knowledge for end-users. Moreover, virtual assistant equipment or chatbots assist the users instantly as per their necessities to solve their questions.

Similarly, web search engines like Google, Bing, Amazon, and others use machine learning technology that provides the most meaningful results using search relevance technology to improve the resulting quality as per the past searching behavior of the end-users.

Similarly, in speech recognition technology, virtual assistance is used with the benefit of natural language processes to comprehend human terminology and communication.

Text annotation and NLP annotation are part of data annotation, developing the training data sets to formulate such models delivering more enhanced and user-friendly understanding to various people globally through numerous devices.

Analytics is delivering full-fledged data annotation assistance for AI and machine learning. It is implicated in video, text, and image annotation using all categories of techniques per the consumers’ provision. Moreover, it works with competent annotators to deliver consistent quality training data sets at the lowest cost to AI customers.

Twist between AI-ML and data annotation

Though, Data labeling and annotation are words used interchangeably to represent the art of tagging or labeling the contents available in various formats. Both techniques make the object or text of interest recognizable to machines through computer vision.

Labeling is done with valuable tags or added metadata to make the texts more meaningful and informative, making them understandable to machines. On the other hand, data annotation comprises text annotation, image annotation, and video annotation using various techniques per the project requirements and the compatibility of machine learning algorithms. Data annotation is done to create the training data sets for AI and ML.

AI-enablement services support machines to learn specific patterns, link the results, and manage the data sets to observe similar patterns in the future to forecast the results. As much as training is used to train such models, the propensity will be accurate making AI possible in real-life through data annotation services.

How Cognitive Technology Is Taking Giant Strides for Data Annotation

According to Gartner, 70% of customer communications will involve emerging technologies such as machine learning (ML) applications, chatbots, and mobile messaging.

According to a study, it has been observed that if you spend 1% on third-party data labeling or annotating, you must spend five times more for internal data labeling efforts. Nowadays, Organizations are using the bulk of dollars on hiring data scientists and ML engineers to annotate oceans of data manually.

That is where cognitive technology can play the role of a game changer. First, it can organize and label all the data to kick off the ML development strategy. Then, once the ML algorithm gets prepared, it can discover identical patterns, encounter problems, and recommend suitable solutions. This allows your data scientists to manage the situation in complex use cases, guiding them to cut costs and adequate scalability.

Some cognitive platforms take data annotation to the next level by leveraging it for personalization, efficiency, and expertise. Let’s find out how.

  • First, we can study the nature of computer vision in the automotive industry. Computer vision cameras capture and share video from diverse angles with the input. Computer vision can manage this for classification and recognition. Then the system will recognize objects close to the car in real-time, such as traffic lights, pedestrians, and road maps. If you are considering where to find such vehicles, then Tesla’s advanced vehicles that offer autopilot are the perfect example. Such advanced technologies can enhance road safety and open new business opportunities for companies from related industries, such as insurance, car-sharing, and driver training. It means those who spend on such developments can take the lead in the era of the spread of innovative technologies.
  • Second, Beyond the hype, cognitive technology in healthcare. By combining individual medical information with larger-scale statistics and scientific data, these applications allow doctors to identify targeted treatment by immediately accessing all the available information about similar cases. Cognitive technology in healthcare offers a range of benefits that support a Personalized Medicine approach. The solution integrates structured data with unstructured details about patients, treatments, drugs, etc.
  • Third, the champion is an intelligent search. Annotating data supports ML algorithms to examine a treasure of data and meet detailed information. It further supports innovative cognitive platforms to conduct annotation with ML-generated metadata fields to manage any taxonomy situations which may crop up because of erratic tagging.

People Tech AI-enablement services beyond boundaries

People Tech is one of the best data labeling and annotation companies providing world-class AI-enablement services for machine learning and AI. Knowing the actual value of data & data management, People Tech Group establishes expert data annotation & niche data labeling services for machines by humans applying innovative aggregation techniques to not only think faster but act smarter. 

Our in-house global annotators and niche resources can perform end-to-end data collection, annotation, and moderation techniques to bring optimum accuracy in hyper-intelligent automation and build an AI-enablement future for an organization. 

We have extensive expertise in different industries, from medical AI to Autonomous to eCommerce. Our customized workflow management process helps clients fulfill their requirements and meet all project needs. Moreover, we always secure end-to-end data with our robust governance model and GDPR policies that can always help you stay in compliance.

We can guide you on the right track if you are looking for a great partner to get help with data collection, labeling, and moderation. 

Feel free to talk to our experts today and let us collaborate with you!

Posted by Satya Maharajan

Director of Data Services Group at PeopleTech Group

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