Oliver Schabenberger: The rise of AI during the last decade is due to the convergence of big data and big compute power with decades-old neural network technology.
AI systems based on deep neural networks require large amounts of data to train deep networks well.
Improving technological infrastructure and cloud processes is a requirement for any business involved in digital transformation, whether business problems are solved through AI, machine learning, or other technologies.
We will see continued investment in cloud infrastructure as a result of it.
Ultimately however, the question of whether AI-driven insights are created via cloud-based or on-premises systems is secondary.
It is central that smarter and more effective data analysis by AI systems addresses real-world challenges for businesses and governments.
insideBIGDATA: How will AI-optimized hardware solve important compute and storage requirements for AI, machine learning, and deep learning?.Oliver Schabenberger: This is an exciting area.
For many years, analytics and data processing has followed behind advances in computing.
The importance of AI has now changed the equation.
Hardware is being designed and optimized for AI workloads.
One avenue is to increase the performance and throughput of the systems to speed up training and to enable the training of more complex models.
Graphics Processing Units (GPUs) are playing an important role to accelerate training and inference because of their high degree of parallelism and because they can be optimized for neural network operations.
So do FPGAs, ASICs, and chip designs optimized for tensor operations.
New persistent memory technology moves the data closer to the processor.
A second exciting route is the development of computing architectures that enable constant training with low power consumption, for example neuromorphic chips.
This is an important step to bring learning and adaptability to edge devices.
insideBIGDATA: What’s the most important role AI plays for your company’s mission statement?.How will you cultivate that role in 2019?.Oliver Schabenberger: SAS’ mission is to transform a world of data into a world of intelligence.
We help customers solve their most critical business issues, as well as tackling humanitarian issues related to natural disasters, opioid abuse, child welfare and more.
Our investment in honing AI technologies takes that mission to the next level.
By making AI more transparent and accessible, we’re able to lead more organizations from data to discovery, enabling decision makers with powerful advanced analytics to automate as many of their decisions as possible.
Our approach to AI is multi-fold.
By embedding artificial intelligence and machine learning into our products (tools and solutions) we empower others to benefit from AI without having to develop AI.
By providing AI tooling such as deep learning, natural language processing and computer vision tools, we enable others to build powerful AI applications.
By providing tools that govern, monitor, and explain AI models, we add transparency.
Finally, we provide services to our customers to solve business problems, sometimes those solutions involve AI.
In my role as SAS COO and CTO, I am tasked with the execution of this vision from both a business and technology standpoint.
As COO, I am responsible for ensuring SAS data scientists, marketers and salespeople remain curious about what’s next for our business, and how we can continue to help our customers solve new and emerging challenges.
And as CTO, I am responsible for making sure these customers have access to innovative SAS technologies, including a host of AI and machine learning capabilities.
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