Deep Learning to Be Key Driver for Expansion and Adoption of AI in Asia-Pacific, Says GlobalData
Deep learning, a subset of machine learning and artificial intelligence (AI), is predicted to provide formidable momentum for the adoption and growth of artificial intelligence in the Asia-Pacific (APAC) region. The next few years will see deep learning become part of main-stream deployments, bringing commendable changes to businesses in the region, says GlobalData, a leading data and analytics company.
GlobalData estimates the APAC region to account for approximately 30% of the global AI platforms’ revenue (around US$97.5bn) by 2024. However, the share is expected to significantly go up, given the incumbent technology companies and the increasing number of start-ups that specialize in this field.
Furthermore, the technological enhancements supporting higher computation capabilities (CPU and GPU), and the huge amount of data, which is predicted to grow multiple folds due to the growth of connected devices ecosystem, are expected to contribute to this growth.
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Digital assistants like Cortana, Siri, GoogleNow and Alexa leverage deep learning to some extent for natural language processing (NLP) as well as speech recognition. Some of the other key usage areas of deep learning include multi-lingual chatbots, voice and image recognition, data processing, surveillance, fraud detection and diagnostics.
Sunil Kumar Verma, Lead ICT analyst at GlobalData, comments: “The APAC market is proactively deploying deep learning-based AI solutions to bring increased offline automation, safety and security to businesses and their assets. In addition, AI hardware optimization with increased computing speed on small devices will result in the cost reduction and drive deep learning adoption across the region.”
In APAC, deep learning is increasingly being adopted for various applications, driven by product launches and technical enhancements by regional technology vendors.
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For instance, China-based SenseTime leverages its deep learning platform to power image recognition, intelligent video analytic and medical image recognition to its customers, through its facial recognition technology called DeepID. Similarly, DeepSight AI Labs, an India-based start-up (which also operates in the US), also uses deep learning to develop SuperSecure – Platform, a smart retrofit video surveillance solution that works on any CCTV to provide a contextualized AI solution to detect objects and behaviors.
Australia-based Daisee too offers an algorithm called Lisa, which leverages a speech-to-text engine to identify key conversational elements, determine its meaning and derive its context. Similarly, Cognitive Software Group is using deep learning / machine learning for the tagging of unstructured data to enhance natural language understanding.
Verma concludes: “Although still in its infancy, deep learning is proving to be a stepping stone for technology landscape evolution in APAC. However, with the lack of skilled professionals and the fact that only a handful of technology companies are focussing on investing, hiring and training their workforce specifically for Deep Learning, there would be some initial roadblocks before witnessing success in adoption rates.”
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