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10 common challenges in building high-quality ai training data

Artificial Intelligence is a wonderful computer science that creates intelligent machines to interact with humans. These machines play an analytical role in learning, planning as well as problem-solving. The technical and specialized aspects that AI data covers, can give an advantage over the conceptual designs.

AI was founded in the year 1956, motivated the transfer of human intelligence to machines that can work on specified goals. This led to the development of 3 types of artificial intelligence.

Types of AI

  1. Artificial Narrow Intelligence – ANI 
  2. Artificial General Intelligence – AGI 
  3. Artificial Super Intelligence – ASI 

Speech recognition and voice assistants are ANI, general-purpose tasks handled the way a human would is AGI while ASI is powerful than human intelligence. 

Why AI is Important?

AI performs the frequent and high-volume tasks with precision and the same level of efficiency every time. It adds capabilities to the existing products. This technology revolves around large data sets to perform faster and better.

The science and engineering of making intelligent machines is flourishing on technology. 

The ultimate aim is to make computer programs that can conveniently solve problems with the same ease as humans do. 

According to Market and Markets, the global autonomous data platform is predicted to become a USD 2,210 billion industry and AI market size to reach USD 2,800 million by the year 2024. The data analysis, storage, and management market in life sciences are projected to reach USD 41.1 billion by the year 2024.

The growth of artificial intelligence is due to ongoing research activities in the field. 

AI Models: The top 10 AI models based on their algorithms understand and solve the problems. 

  1. Linear regression
  2. Logistic regression
  3. Linear Discriminant Analysis – LDA
  4. Decision Trees
  5. Naive Bayes
  6. K-Nearest Neighbors
  7. Learning Vector Quantization – LVQ
  8. Support Vector Machines
  9. Bagging & Random Forest
  10. Deep Neural Networks

AI can accustom to gradually developing learning algorithms that let the data do the programming. The right model can classify and predict data. AI can find and define structures and identify regularities in data to help the algorithm acquire new skills. The models can adapt to the new data fed during training. It can use new techniques when the suggested solutions are not satisfactory and the user demands more solutions.

AI-powered models help in development and advancements that cater to the business requirements. The selection of a model depends on parameters that affect the solutions you are about to design. These models can enhance business operations and improve existing business processes.

AI models help in resourcefully delivering innovative solutions.  

AI Training Data

Human intelligence is achievable by assembling vast knowledge with facts and establishing data relations.

According to the survey of dataconomy, nearly 81% of 225 data scientists found the process of AI training difficult than expected even with the data they had. Around 76% were struggling to label and interpret the training data.

We require a lot of data to train deep learning models as they learn directly from the data. Accuracy of output and analysis depends on the input of adequate data.

AI training data

AI can achieve an unbelievable level of accuracy through training data. It is an integral part based on which the accurate results or predictions are projected.

Data can improve the interactions of machines with humans. Healthcare-related activities are dependent on data accuracy. The AI techniques can improve the routine medical checks, image classification or object recognition that otherwise would have required humans to accompany the machines.

AI data is the intellectual property that has high value and weight for the algorithms to begin self-learning. Ultimately, the solutions to queries are lying somewhere in the data, AI finds them for you, and helps in interpreting the application data. Data can give a competitive advantage over other industry players even when similar AI models and techniques are used the winner will be best and accurate data. 

Industries that need AI training data

  • Automotive: AI can improve productivity and help in decision making for vehicle manufacturing.
  • Agriculture: AI can track every stage of agriculture from seeding to final production.
  • Banking & Financial Services: AI facilitates financial transactions, investments, and taxation services.
  • FMCG: AI can keep the customers informed of the latest FMCG products and their offers.
  • Energy: AI can forecast in renewable energy generation, making it more affordable and reliable.
  • Education: Using AI technology and the student data helps the universities to communicate for the exams, syllabus, results and suggesting other courses. 
  • Healthcare: AI eases patient care, laboratory, and testing activities, as well as report generation after analyzing the complex data.

(Read here: 9 Ways AI is Transforming Healthcare Industry)

  • Industrial Manufacturing: The procedural precautions in manufacturing and the standardization is what AI can deliver.
  • Information Technology: AI can detect the security threat and the data they have can prepare companies in advance for the threat.
  • Insurance: AI bridges the gaps in insurance renewals and benefits the customers and companies both.
  • Media & Entertainment: AI can initiate notifications relating to the news and entertainment as per the data preferences stored.
  • Sales & Marketing: AI can smoothen and automate the process of ordering or promoting the products.
  • Telecom: AI can personalize recommendations about telecom services.
  • Travel: AI can facilitate travel decisions, booking tickets and check-in at airports.
  • Transport & Warehousing: AI can track, notify, and crosscheck the in transit and warehousing details.
  • Retail: AI can remind the frequent buyers of the list of products to the customers who prefer to buy from retail outlets.
  • Pharmaceuticals: The medicine formulation and new inventions are where AI can be helpful.

All functions in the industry’s improvement are possible only based on historic and ground-level data. The data dependency can add to challenges as the relational database and its implementation only make AI effective. AI training data is useful to companies; for automation of customer care, production, and operational activities. AI technology helps in cost reduction once implemented.

Read here: 8 Industries AI is transforming

Common AI Training Data Challenges

AI is programmed to perform selective tasks, assigning new tasks can be challenging. The limited experience and data can create obstacles in training the machines for new and creative methods of using the accumulated data. The costs of implementing AI technology are higher restricting many from using it. Machines are likely to replace human jobs but on the other hand, we can expect quality work assigned to humans. Ultimately the induced thought process cannot replace what humans can do hence the machine cannot innovatively perform tasks.

AI can take immediate actions but the accuracy is related directly to the quality of data stored. If the algorithms suit the type of task you want the machines to perform, the results will be satisfactory else, dissatisfaction will mount.

Ten most common challenges companies face in AI training data:

  1. Volumes of Data: Repetitive learning is possible with the use of existing data, which means that a lot of data, is required for training. 
  2. Data Presentation: The computational intelligence, statistical insights, processing, and presentation of data are of utmost importance for establishing a relationship with data. Limited data and faulty presentation can interrupt the predictive analysis for which AI data is built.
  3. Proper use of Data: Automation based on the data, the base that improves many technologies. This data is useful in creating conversational platforms, bots, and smart machines.
  4. Variety of Data: AI needs data that is comprehensive to perform automated tasks. Data from computer science, engineering, healthcare, psychology, philosophy, mathematics, finance, food industry, manufacturing, linguistics, and many more areas are useful.
  5. AI Mechanics: We need to understand the mechanisms of artificial intelligence to generate, collect, and process data; for the computational procedures, we want to handle smartly. 
  6. Data Accuracy: Data itself is a challenge especially if erroneous, biased, or insufficient. Even unusable formats of data, improper labeling of data or the tools used in data labeling can affect the accuracy. Data collected vary in formats and quality as collected from diverse sources such as e-mails, data-entry forms, surveys, or company website. Consider the pre-processing requisites for bringing all the attributes to proper structures for making data usable. 
  7. Additional Efforts on Data: Nearly 63% of enterprises have to build automation technology for labeling and annotation. Data integration requires extra attention even before we start labeling.
  8. Data Costs: Data generation for AI is costly but implementing it in projects can result in cost reduction. Missing links of data can add to the costs of data correction. The initial investment is huge hence; the process and strategies require proper planning and implementation.
  9. Procuring Data: Obtaining large data sets requires a lot of effort for companies. Other than that de-duplication, removing inconsistencies are some of the major and time-consuming activities. Transferring the learning from one set of data to another is not simple. The practical use of AI data in training is complex than it looks due to a variety of data sets on industries.
  10. Data Permissions: Personal data, if collected without permission, can create legal issues. Data theft and identity theft are some allegations, which no company would like to face. Choose the right data for representing that criteria or population. 

With a lack of training data or quality issues, can stall AI projects or be the principal reason for project failure. AI technology is reliable but the human capabilities are restricted with the dependencies they create. 

Read here: 7 Best Practices for creating High-quality Training Data

Another viewpoint is something humans already know cannot be erased. With the help of AI technology, enhance the speed, and accuracy of tasks. Human has superiority in terms of thinking, getting the tasks done and even automating them with AI. Human life is precious and in risky situations, while experimenting, the AI machines are worth considering.

Like all the technologies, AI comes with its own set of pros and cons and we need to adapt it wisely.