Nand Kishor Contributor

Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...

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Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...

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The future of AI in the cloud 'Amazon Web Services': Swami Sivasubramanian

Jun 7, 2017 | 5907 Views

It's pretty clear that the next big battleground for public cloud providers will involve artificial intelligence. Just as companies like Amazon Web Services made it possible for ten-person startups to take advantage of world-class computing infrastructure, so too will the big cloud providers compete to provide artificial intelligence expertise to companies that can't afford to duplicate the advanced machine-learning research already underway.

Swami Sivasubramanian, vice president of Amazon AI, is one of the key drivers of AI research for AWS. Cloud rivals like Google and Microsoft have signaled quite clearly that they will attempt to compete for the cloud workloads of the future by pushing the envelope of AI and machine-learning research and abstracting that effort for their cloud customers, and AWS must at least match those efforts to stay on top.

Sivasubramian will be talking about Amazon's work in this area at our Cloud Tech Summit this Wednesday in Bellevue, and I recently caught up with him to get a preview of his talk.

In your own personal view, what is the single most exciting component of artificial intelligence research that will blossom over the next five years? Why?

We are entering a golden age of AI and machine learning. We believe AI will revolutionize almost all aspects of technology - making it easier to do things that take considerable time and effort today like product fulfillment, logistics, personalization, language understanding, and computer vision, to big forward-looking ideas like self-driving cars.

If you see what is driving this revolution, it is not just the underlying deep learning algorithms that power these AI systems. In fact, some of the classic neural networks have been around for decades. At AWS, we believe the combination of these algorithms, access for cheap ways to store information, process and query data (to train these algorithms), and access to specialized compute infrastructure (e.g., GPU infrastructure, custom ASICs) that can run these algorithms efficiently have spurred the AI revolution.

We believe cloud has spurred a lot of researchers to innovate and experiment on new algorithms in deep learning and you will see more advances in reinforcement learning, auto tuning of models across a wide variety of domains.

What is the greatest obstacle to the widespread adoption of AI research in everyday products?

Today, building these machine learning models for products requires specialized skills with deep Ph.D. level expertise in machine learning. To a large extent, this is one of the primary blockers for broad AI adoption. However, this is changing. There is a broad awareness about the benefits AI can deliver and we have seen various companies making their technologies available in the form of cloud services and open source software to developers.

How do you feel about AI skeptics: not those who deny AI will ever make an impact, but those who believe it will have more of a negative impact on society than a positive impact?

History shows that new technology innovation benefits society and delivers a positive impact to society at large. We believe that AI technology can have a huge positive impact on the world, making jobs less physically demanding and freeing humans to focus on the things that make us unique.

What are the most important infrastructure components that are driving AI research today? What tools (hardware or software) are you lacking that you really wish you had?

AWS is investing in all layers of the stack from core deep learning frameworks (such as Apache MXNet, Caffe, Caffe2, TensorFlow), machine learning platforms, AI application services (such as Amazon Lex, Amazon Polly and Amazon Rekognition). We have heavily invested in optimizing these deep learning frameworks on our compute instance families like GPU and CPU driven instances by working with partners like NVIDIA and Intel.

We are entering the golden age of machine learning that we believe will transform various aspects of technology and products. So, the question is not what we are lacking? It is more "which is the best platform for developers to build these AI models?" This is where we believe AWS with its breadth of offerings in storage, database, analytics, and compute infrastructure - coupled with AI offerings - can nurture and accelerate AI research and enable more developers to build real-world AI applications.

Continue Reading>>

Source: Geekwire
Nand Kishor Contributor

Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...

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Follow on

Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...

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