EXCLUSIVE - OpenGov Insight session on the adoption of AI in the public sector
On 28 March, 2018, ICT leaders from a range of public sector agencies in Singapore and institutes of higher learning gathered for a discussion on adoption of artificial intelligence (AI), at a Breakfast Insight session organised by OpenGov Asia and Mellanox Technologies.
Mr Mohit Sagar, Editor-in-Chief of OpenGov Asia started the session talking about the imperative of AI adoption in the public sector. He said that even among leaders in the technology and the AI field, there is an entire spectrum of views when it comes where AI is headed and whether and how we can trust AI technologies.
But AI technology as it exists today can be used by governments in healthcare, defence, smart cities, to ultimately provide better, safer lives for citizens.
Mr Gilad Shainer, Vice President, Marketing at Mellanox Technologies, and Chairman of the non-profit HPC-AI Advisory Council, which drives outreach and education in the areas of high performance computing and AI, took the floor next.
He noted that neural networks were designed twenty years ago, but they couldn’t be implemented because there was not enough data. Now the situation has transformed, with huge amounts of data being generated and consumed. In fact, only a small fraction of the data being generated is used. If we were able to use a bigger proportion of the data, we might be able to build even more amazing products and services. This explosion in data volumes and improvement in the ability to collect and use the data is driving the AI revolution today.
He said, “Some people look on AI as a market. I don’t think AI is a market. It is a technology which will impact a lot of markets.”
To take a few examples, applications range from real-time fraud detection, credit/risk analysis, high frequency trading in finance to self-driving cars, image / facial recognition, logistics & mapping in the automotive and transport sectors to drug discovery and diagnostic assistance in medicine.
In order to build those solutions which would take us to the next level, Mr Shainer emphasised that we need more data, better models, as well as better systems that can move the data and analyse it faster. If we cannot move the data, we cannot analyse it. He added that Mellanox provides fast networking solutions which move the data in the most efficient way.
He went on to say that there is change in the data centre architecture which drives the need to analyse the data wherever the data is, enabling faster generation of insights. The network has to become much more than a pipe that moves the data.
Dr Shengen Yan, Research Director at SenseTime Group Limited, a cutting-edge artificial intelligence company from China, talked about some of the work being done by the company.
SenseTime has over 400 partners today, including China Mobile, Wanda Group, Meitu, graphics processor maker Nvidia, China UnionPay, JD Finance, Sina Weibo, China Merchants Bank, and mainland smartphone giants Huawei Technologies, Oppo, Vivo and Xiaomi. It deploys its technology in a range of areas, including Fintech, smart cities, mobile phones, medicine and autonomous driving.
For instance, SenseTime provides a crowd analysis system, which can convert the unstructured data of the video stream to structured data. It detects cars, humans, bicycles, and the technology can even provide the colour of the clothes and the cars, as well as the type of the car. Then the data can be stored for search.
There are several popular frameworks available for AI, such as Caffe2, torch, and TensorFlow. But the existing frameworks have limitations such as poor support of distributed learning (Caffe2), unsatisfactory efficiency and confined technology development and IP issues. To deal with these problems, SenseTime has developed its own deep learning framework from scratch, called Parrots. It is about distributed training and using very complicated models. It is a very scalable platform.
In order to accelerate the training of the AI models, SenseTime also built a deep learning training supercomputer. It has more than 8000 GPUs (graphics processing units) and over 10 GPU clusters.
Polling questions and discussion
When asked about the interconnect speed in their data centre, 47% delegates selected the 10 Gigabit Ethernet option. Mr Shainer said that today there is no price difference between 10 and 25 Gigabits. For the same price and same infrastructure, a 2.5 times improvement can be achieved.
Around 66% attendees responded that they are already using AI or HPC, while another 24% have a plan in place to use those technologies. In terms of applications, 53% of delegates are using AI to drive business intelligence, while 20% are adopting facial recognition or voice recognition technologies and another 13% are exploring computer vision. Several delegates also said that they are using combinations of different AI technologies.
Dr John Kan, Chief Information Officer (CIO) at the Agency for Science, Technology and Research (A*STAR) said that procurement, fraud and demand aggregation are key concerns for the agency as it buys products worth millions of dollars every year for research purposes. So, the agency is using deep learning for procurement analytics. A*STAR has developed an electronic procurement system with random sampling, low-level, mid-level and deep dive analytics using supercomputing.
Ms Samantha Fok, Director, Enterprise Development, Infocomm Media Development Authority (IMDA) said that there is a team at IMDA which develops AI algorithms, to build own internal capabilities, as well as work with some partners to look at specific problem areas. The companies are brought in to co-develop, so that once the algorithm is developed they can commercialise it.
Mr David Toh, Assistant Director, Investment at SGInnovate, said that that the organisation has a talent intelligence team which is using algorithms to better match talents, based on their profile to help develop companies that SGInnovate is investing into.
AI Singapore is working with a number of different institutions and putting in place plans to start their 100 Experiments project, said Mr Maurice Manning, Head, AI Applications at AI Singapore. If any enterprise has a problem statement which they are unable to solve with commodity-off-the-shelf solutions, but for which existing AI technologies can be quickly built with limited research, then AI Singapore will facilitate matching the statement to the work areas of researchers from NUS, NTU, SUTD, SMU and A*STAR.
Majority of delegates (63%) said that there are no hurdles they are facing in the adoption of AI. Around 31% picked lack of knowledge as the major hurdle. Mr Manning said that in his view, the lack of knowledge of AI techniques is not usually the barrier. The problem comes with networks. Researchers today are using very large datasets. But inadequate attention is paid to networks or to questions such as should we be thinking of the network when designing our process and workflow? Very few electrical engineers and almost no computer science people understand networks. That knowledge is not being imparted on the graduate level.
Mr Shainer agreed witht the comment, adding that previously CPU was the most important part, most investment used to go to buying CPUs. Connectivity and storage came lower in priority. Nowadays the network is critical. The network is also becoming a distributed CPU. There are performance bottlenecks cannot be solved at the server or by adding more computing power. In fact, adding more CPUs might make the situation worse, as now there is more information to be combined. Enhanced capabilities on the network itself can be used to solve this problem.
Another highlighted challenge was inadequate annotated data, which is essential as a lot of deep learning today is supervised (supervised machine learning infers a function from labelled training data consisting of a set of training examples, while the objective of unsupervised learning is to find the structure or relationships between different inputs.)
When asked which machine learning/ deep learning framework they are using, nearly 45% of attendees responded that they are using TensorFlow. However, 36% have developed their own framework.
Around 33% of delegates are planning to use existing infrastructure for AI development, 20% plan to build dedicated infrastructure, and 40% are using external resources.
However, the answer sometimes is a mix. As Mr Paul Gagnon, Director, E-Learning, IT Systems and Services, Nanyang Technological University - Lee Kong Chian School of Medicine said, they are working with IBM to develop a virtual tutor designed to assist the learning of medical students and using ‘external’ resources. However, they are also building own dedicated infrastructure. There is also existing infrastructure which is being transformed to support the dedicated infrastructure, which in turn is necessary to use the external resources.
Dr William TJHI, Associate Director, AI Engineering at AI Singapore also expects it to be a mix in the future.
12.50% of delegates are planning to deploy AI infrastructure within the next 6 months, while 38% were aiming for the next 12 months. Another 38% had plans but no definite schedule.
Mr Charlie Foo, Vice President & GM, Mellanox Technologies, Asia-Pacific, concluded the discussion saying that there is a maturity in terms of AI adoption in the public sector. He said that inertia is the biggest threat and that Mellanox can help organisations make their infrastructure more intelligent and robust and future-proof investments.