Countries accelerate growth with AI
Artificial Intelligence (AI) in conjunction with machine learning, is transforming every aspect of our lives. It is a tool to help businesses consider ways of reintegrating information and using data to inform strategic decision-making. This advanced form of technology enables robots to take charge of a broad scope of tasks such as proactive healthcare management, automated financial investing and social media monitoring.
With many businesses recognizing its intrinsic value, countries are more determined to scale up plans to implement and utilize AI across all sectors, including Malaysia. The country hopes to transform into an “AI nation” with the support of leading tech companies.
Skymind is the world’s first dedicated AI ecosystem builder, enabling businesses and organizations to develop their own AI applications, providing them with the advanced tools to be innovators in their industry.
The ecosystem builder intends to do its part in transforming Malaysia into an ‘AI nation’ with RM3.4 billion (US$800 million) fund to fuel artificial intelligence (AI) innovation.
With the aim of building an AI ecosystem that will accelerate the digital transformation of enterprise, they provide clients with world-class support and access to Eclipse Deeplearning4j and other tools, as well as global capital funding for AI innovation and talent development.
Skymind vice president (growth division) Rafe Azsnal said, “The company is passionate about unlocking and developing Malaysia’s talent, which will play a pivotal role in boosting the nation firmly into the global digital arena,” explaining the company’s commitment to AI in Malaysia.
According to Azsnal, more than 350 of Fortune 500 companies are currently using Skymind technologies.
While the AI industry could touch $2.9 trillion globally by 2021 and $15 trillion by 2030, it is important for Malaysia to be at the forefront of AI technology.
How does Skymind’s platform Deeplearning4j help? It helps developers and large organizations build deep learning applications, covering the entire deep learning workflow from data pre-processing through distributed training and hyper-parameter optimization and production-grade deployment.