The global market for AI training datasets is projected to expand at a CAGR of 22.5% during the forecast period 2024 and 2032. AI is gaining prominence in a variety of industries, including manufacturing, IT, BFSI, retail and e-commerce, and healthcare. In addition to creating opportunities for new entrants, the increasing demand for application-specific training data is producing new business opportunities. The importance of Artificial Intelligence (AI) to big data is growing as the technology enables the extraction of high-level and complex abstractions through a hierarchical learning process, necessitating the mining and extraction of meaningful patterns from vast amounts of data.
AI enables machines to learn from experience, perform human-like tasks, and adapt to new inputs. To complete a specific task, these machines are programmed to analyze vast amounts of data and identify patterns. To train these machines, particular data sets are required. The demand for AI training datasets is increasing to meet this demand. The functionality of machines is wholly dependent on the dataset provided. Therefore, the provision of high-quality training datasets becomes essential. This high-quality dataset improves artificial intelligence performance. Additionally, it decreases the time required to prepare data and increases the accuracy of predictions. Consequently, market vendors are also concentrating on acquiring firms that can assist them in enhancing data quality. In March 2020, for instance, Appen Limited, a provider of specialized datasets, announced the acquisition of Figure Eight Inc., a provider of machine learning platforms. The second company transforms unlabeled data into high-quality information using automated tools. This acquisition will help the former company expedite the production of high-quality data sets. Additionally, it will contribute to the enhancement of data quality.
Innovation and technological progress in AI are accelerating the market expansion for AI training datasets. For example, one of the most significant technological advancements is ChatGPT by Open AI, which can reduce the time and resources required to manually construct enormous datasets. ChatGPT can significantly reduce the time and resources required to generate a large training dataset for NLP models. As a large, unsupervised language model that was trained using GPT-3 technology, ChatGPT can generate human-like writing that can be used for NLP training data. This allows it to rapidly and easily construct a vast and diverse dataset without the need for manual curation or the knowledge required to create a dataset that contains a wide variety of scenarios and situations.
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The emergence of big data, which necessitates the recording, storage, and analysis of vast quantities of data, is anticipated to stimulate the growth of the market for artificial intelligence. End-users are more concerned with the need to monitor and enhance computational models related to big data. This emphasis is hastening their adoption of AI solutions. Given that annotated data facilitates the training of AI models and machine learning systems in crucial domains such as speech recognition and image recognition, it is anticipated that the widespread adoption of artificial intelligence will significantly increase the demand for AI training datasets.
AI is strengthened by annotating data with essential information for predicting future outcomes and making decisions. Numerous public and private entities collect domain-specific data, including data from a variety of applications such as national intelligence, fraud detection, marketing, medical informatics, and cybersecurity. Annotation of data enables the labeling of unstructured and unsupervised data by continuously enhancing the accuracy of each data item.
Significant restrictions on the protection of personal information are anticipated to restrict data collection in the Asia-Pacific region. In Japan, for instance, the Personal Information Protection Act prohibits the transmission of sensitive personal information to unapproved entities or locations. Inaccurate data classification hinders the expansion of the market.
The main problem with data annotation tools is output precision. Concerns regarding the quality of the output, such as inaccurate data, should be minimized. In some cases, manual labeling is performed incorrectly, and locating these labels can be time-consuming, thereby increasing the cost to the business. It is anticipated that with the development of advanced algorithms, the accuracy of automated AI data training dataset tools will increase, reducing the need for manual annotation and tool costs.
Due to digital capturing devices, particularly smartphone cameras, the amount of digital content in the form of photographs and videos has increased exponentially. Numerous applications, websites, social networks, and other digital channels collect and distribute vast quantities of visual and digital data. Several businesses have utilized this freely accessible web content with data annotation to provide innovative and superior services to their clients. Unstructured text records collected due to the increasing use of Electronic Health Record (EHR) systems are now one of the most valuable resources for clinical research. These factors are anticipated to generate enormous growth opportunities for the market over the forecast period.
The text segment will account for 30% of the market in 2023. This is due to the extensive use of text datasets in the IT industry for various automation processes, including speech recognition, text classification, and caption generation. Due to the wide availability of audio datasets, it is anticipated that the audio segment will have a moderate share. Music datasets, speech datasets, a speech commands dataset, the Multimodal Emotion Lines Dataset (MELD), and environmental audio datasets are among these.
It is anticipated that the image/video segment will experience the highest CAGR over the forecast period. This is due to the fact that key players are focusing more on releasing new datasets with an increasing number of applications. In May 2020, for instance, Google LLC, a multinational technology company, announced the launch of a new artificial intelligence training dataset titled Google-Landmarks-v2 that contains millions of images and thousands of landmarks. In addition, the company issued two Kaggle challenges: landmark retrieval 2020 and landmark recognition 2020. These datasets were developed for image retrieval, instant recognition, and the training of more robust and efficient systems.
In 2023, the IT market segment will account for 33% of the market. Vertical segments of the market include IT, automotive, government, healthcare, BFSI, retail & e-commerce, and others. AI in healthcare offers numerous opportunities in therapy areas such as lifestyle and wellness management, diagnostics, virtual assistants, and wearables. AI is also employed in voice-enabled symptom checkers and to improve organizational workflow. All of these applications require a sizable dataset to generate accurate results. Therefore, the utilization of datasets will increase, resulting in a high CAGR over the forecast period.
Various technology companies on the market employ machine learning to improve the user experience and create innovative products. Effective machine learning technology necessitates high-quality training data to ensure that ML algorithms are continually optimized. Moreover, high-quality datasets enable IT companies to enhance a variety of solutions, such as computer vision, crowdsourcing, data analytics, and virtual assistants. These factors contribute to the extensive use of training datasets in this industry. Amazon, for instance, released the Amazon Berkeley Objects dataset in June 2021 to facilitate the development of new AI models for image-based shopping.
North America will account for 35% of the market share in 2023. To expedite the adoption of artificial intelligence technology in emerging industries, North American market vendors are focusing on the release of new data sets. Waymo LLC, a subsidiary of Google LLC, released a new dataset for autonomous vehicles in September 2020. This dataset contains sensor data collected from camera sensors and LiDAR under various driving conditions, including cyclists, pedestrians, and road signs. Such advancements are driving the market's adoption of datasets, thereby catering to a significant market share.
Asia-Pacific is the largest contributor to the global market for AI training datasets and is anticipated to grow at a CAGR of 21.5% during the forecast period. In order to modernize their businesses, companies in developing nations such as India are adopting innovative technologies at a much higher rate. Additionally, a number of major players are concentrating on expanding their influence in Asia-Pacific. Microsoft, for example, created the Indoor Location Dataset to collect a variety of data from buildings in Chinese cities, including the geomagnetic field and indoor Wi-Fi signature. These datasets advance the study and development of localization, indoor environments, and navigation. Microsoft and other major companies are also expanding their presence in this religion. It is anticipated that these factors will lead to a substantial increase in the use of datasets in the region over the forecast period. As Indian businesses strategize to transform their operations, the rate of adoption of emerging technologies continues to rise. In addition, many major players are focusing on expanding their presence in the Asia-Pacific region. Microsoft, for instance, released a dataset titled Indoor Location Dataset in July 2020 in order to collect various information, such as the geomagnetic field, indoor signature of wi-fi, etc., in Chinese city buildings. These datasets are intended to facilitate research and development on navigation, indoor space, and localization. In addition to Microsoft, a large number of other prominent corporations are expanding their presence in this region. It is anticipated that these factors will increase the utilization of datasets in the region, resulting in a high growth rate over the forecast period. It is anticipated that the European market will experience moderate growth with a sizeable market share.
As a result of strategic initiatives such as mergers, partnerships, and acquisitions, market consolidation is growing. Key market participants are also focused on the publication of new datasets. Vector Space AI, a provider of datasets, collaborated with Elasticsearch B.V., a search company, in January 2021, for instance. The first company will provide its users with AI datasets developed in partnership with the second. Vectorspace AI has developed datasets for AI, ML, and data engineering. In a similar manner, Comet ML Inc. has developed a machine learning platform that assists data scientists in tracking, comparing, deriving meaning from, and optimizing experiments and models throughout the entire model's lifecycle, from training to production. Data scientists can register code modifications, datasets, experimentation models, and history for experiment tracking. Companies such as Google LLC (Kaggle), Appen Limited, Cogito Tech LLC, Lionbridge Technologies, Inc., Amazon.com, Inc., Microsoft Corporation, Scale AI, Inc., Samasource, Inc., Alegion, and Deep Vision Data dominate the market for AI training datasets.
