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AI Singapore and PwC Singapore Collaborate to Enhance Digital Trust Competencies through Responsible AI

Singapore, 15 March 2018 – Today, AI Singapore (AISG) and PwC Singapore are joining hands to collaborate on Artificial Intelligence (AI) projects and to enhance digital trust competencies in both the public and private sector. This will be through dedicated research projects relating to data analytics; AI research & development; and risk management and governance to ensure responsible AI adoption. This will be pivotal to ensure trusted digital ecosystems as Singapore develops as a Smart Nation.

Greg Unsworth, Digital Business and Risk Assurance Leader, PwC Singapore says:

“As the demand for automation and use of AI increases, designing in appropriate levels of risk management and governance will be essential for the trusted application of AI adoption. PwC Singapore advocates responsible AI adoption and is well positioned to help embed the right level of risk assessment, controls and governance frameworks to enable organisations to innovate with confidence. We are glad to be the first professional services firm to work with AISG in support of Singapore’s AI initiatives in line with Singapore’s Smart Nation ambitions.”

Lawrence Liew, Director, Industry Innovation, AI Singapore concludes:

“We are excited to collaborate with PwC to accelerate the use of AI in the finance, audit and risk markets. The 100Experiments and AI Apprenticeship Programme, which PwC will participate in, will accelerate the adoption of AI in these areas and train new AI engineers for PwC and the Singapore ecosystem.”

ACCA, NTUC, AI Singapore Pledged to Help Accelerate Capability Development in the Finance and Accounting Functions of Small and Medium Enterprises’ (SME) and Help Them Go Global

Singapore, 24 May 2018 – Association of Chartered Certified Accountants (ACCA), the largest global professional accounting body in the world, together with the National Trades Union Congress (NTUC) and AI Singapore, pledged to support capability development for finance and accounting professionals in small and medium-sized businesses (SMBs) and for small and medium-sized professional services firms (finance and accounting) [SMPs]. This took place on the sideline of the ACCA Annual Conference 2018 at the Marina Bay Sands Convention Centre earlier today.

To illustrate their support, the organisations will be partnering on a new ACC(X)ELERATE program to help professionals in SMBs and SMPs in the areas of talent development, digitalisation and internationalisation. Insights were drawn from the following studies to formulate the program:

  • ACCA’s global research ‘Professional accountants – the future: Generation Next’ highlighted the skills finance and accounting professionals need to enhance their employability.
  • ACCA’s report ‘Market Demand for Professional Business and Advisory Services’ revealed a growing demand for non-regulated professional services ranging from IT solutions advisory to risk advisory and process improvements.
  • NTUC’s Future Jobs, Skills and Training (FJST) Forum 2018 report highlighted the key enablers of success for companies to transform and stay relevant in a changing world of work
  • From a NTUC roundtable discussion with Chief Financial Officers, 76 per cent of them indicated technology as the key driver of change for jobs and skills in the accountancy sector, while 20 per cent felt that the change came from changes to business models and the remaining four per cent felt that it was due to globalisation.

ACCA will engage SMBs and SMPs to take the lead in leveraging technology to transform their businesses and take active steps to develop their workforce’s skillsets. To enrich accountants and finance professionals’ digital skills and knowledge, it will also provide training and workshops focused on technology education that enhances productivity and facilitates internationalisation, including cloud technology. Tapping on NTUC’s suite of progression and placement programs, workers will also be guided through this transformative process, enabling and ensuring that they stay competent to take on the jobs of tomorrow.

The program draws on the digital expertise of AI Singapore and will work with technology solution partners that operate in the cloud and small business space, to accelerate the digital awareness and transformation in the finance function. ACCA and AI Singapore will also explore robotic process automation (RPA) as a potential industry solution and AI talent recruitment program for accounting and audit firms.

Reuter Chua, Country Head, ACCA Singapore, said, “ACCA’s global insights present the future of the profession and what global business leaders need from their finance functions to guide the direction of our program.

We are thrilled to pledge this new initiative with the NTUC and AI Singapore today, and we are also progressing the opportunity with other technology solution partners within their domains of progressive training and technology expertise. ACCA is confident that this partnership will help transform small and medium businesses and to go global.”

Patrick Tay, Assistant Secretary-General, NTUC, shared, “As highlighted in various studies including the one done by NTUC’s FJST, our finance and accountancy professionals will increasingly need to incorporate technology into their daily work as businesses and business operations transform. 

NTUC is glad to partner ACCA and AI Singapore as well as other future cloud technology partners in this program to support capability development of our finance and accounting professionals. We urge employers in this sector, especially our SMEs, to leverage the program to develop their talents, digitalise their businesses and work with NTUC on the continued progression of their workforce. Collective action on the part of all stakeholders is a key driver to enable deep, sustained and meaningful change, and we look forward to partnering our industry partners, our employers and our workers to transform the sector for the future economy.”  

More details on the ACC(X)ELERATOR program will be released end of the year.

AI Apprenticeship Programme Batch #2

A new week…a new batch. On 12 Nov, we welcome on board our Batch #2 AI apprentices from the AI Apprenticeship Programme (AIAP). This batch comprises an interesting combination of 26 apprentices/senior apprentices as the programme now takes in graduates with more than 3 years of working experience. We are confident that there will be good exchange and interaction amongst them and that they will tap on each other’s experience and know-how in the 9-month AIAP journey

AI for Industry (AI4I) Inaugural Launch

AI for Industry (AI4I) was one of two initiatives announced by AI Singapore in Aug’18 to enable and educate 2,000 people with basic AI competency to enhance their competitiveness in a digital economy. Targeted at technically inclined industry professionals, participants will learn to apply AI technologies to increase their productivity and their firms’ competitiveness.

AI Singapore has since received close to 460 applications for the first intake. The inaugural AI4I programme kicked off on 8 Nov with a face-to-face workshop, attended by 300+ participants. This initiative is supported by IMDA, Microsoft and Intel, together with DataCamp. The second intake will be for Feb – Apr’19. For more information, please visit aisingapore.org/ai4i

How people keep learning – the role of intrinsic and extrinsic motivators and when behavioural economists need to come in

AI Singapore has a strong focus on learning and growth. We want our internal engineers and apprentices to develop and improve their craft. This means we don’t only think about how to build machine learning systems. We also spend time thinking about how to engineer productive learning environments for our people. The following are a few thoughts about a key feature of any learning environment – sustained motivation

Daphne Koller is a Stanford Professor who founded Coursera, a platform that offers Massive Open Online Courses. In a lecture she gave at Carnegie Mellon university, she joked that many people in the audience must have started a course on Coursera. Whether or not they had finished the course, however, was another matter. In response, the audience laughed sheepishly.

Daphne’s contemporary, Peter Norvig, who founded the a similar MOOC platform Udacity, has also shared how he experimented with different course delivery styles meant to slow down how fast students were dropping out of courses. They sent email reminders to students. They built features to facilitate more peer-to-peer interactions so students felt a sense of community. These measures did appear to work, and indeed Daphne and Peter’s experiences show that any discussion around learning is incomplete without a discussion around how to engineer and sustain motivation.

In my own experience, motivation falls along a spectrum ranging from intrinsic to extrinsic. At the intrinsic end, internal emotions like curiosity or an appetite for learning something can be enough to drive someone to start learning something new. Learning might look like playing with Raspberry Pis over a weekend of signing up for an online course with friends. At the opposite end, external motivators, like a problem at work or a company mandate, are what pushes a student to hone a new skill. These people may then petition their boss to send them for a training course or attend a conference. In these two instances, the motivators are strong, and so learning is largely effective. In fact, there is sometimes double effectiveness if there is a tangible outcome at the end, for example switching careers, making new friends, or reaching a new work milestone.

The question is what to do about the masses of people who fall in the middle of these two extremes. These are people who might hear rumblings like “the jobs landscape is changing” and “upskilling is important in a modern world”. Yet, because there isn’t a concrete push or pull factor, they fall through the cracks. They may sign up for a Coursera account, attend a few courses, then drop out. They may even complete the courses and videos, but miss out on a crucial next step – applying what they have learnt to a real-world problem.

It is in this middle space that behavioural economists come in with their tools: “nudges” and default options that push people towards desired behaviour, or competitions and points systems to keep people motivated. These measures work, and although it’s tempting to think that people who rely on these are more “weak-willed”, the fact is that even the strongly motivated sometimes also need these interventions to keep them on track.

I would suggest though, that having a strong intrinsic/extrinsic motivator needs to exist first. Get that right, and the need for a lot of the behavioural checkpoints like assignment deadlines and automated email reminders falls away.  

Dispatches from Jupyter Con

Dispatches from Jupyter Con

Jupyter Notebook is the tool of choice for many data workers. Data visualisation experts use it to build dashboards , data scientists use it to test algorithms, and computational scientists use it to study the stars in the sky and the genes in the human body. We use Jupyter a lot in our day to day as well. Usually, we use it on a laptop as a quick and easy way to build prototypes and explore data. Once we have a good idea of what we’re working with, we can then write scripts to to automate our analysis or train our model on a more powerful virtual machine. But we are only one type of engineer using Jupyter in only one of many ways. Jupyter Con this August gave us the chance to widen our knowledge by meeting other users and gathering more ideas from them.

As a former animal biologist, I observed roughly three breeds of people at Jupyter Con. There were the data workers use Notebooks to visualize and analyze data, there were the engineers who set up Jupyter for data workers, sometimes building on powerful big data frameworks and serving hundreds of people, and there were the educators who use Jupyter helps cultivate data literacy not only in scientists, but literature majors and high schoolers as well.

The data workers came from many fields, which really showed how different industries are being enhanced with a data-centric approach. There was Mark Hansen from the Columbia Journalism School, who talked about using Notebooks to investigate fake profiles on Twitter and the behaviour of the bots behind these accounts. He and his students eventually published the analyses as a longform article The Follower Factory in The New York Times, and the piece is a pioneering work on how statistics and data can be mixed with traditional investigative journalism to inform and to tell a great story. While Mark talked about journalism and data, Michelle Ufford from Netflix looked at how data fuels well, almost everything at her company, from business decisions to their legendary movie recommendation system. My favourite part of her talk was when she showed the company’s organisational chart – there were engineers for algorithms, visualisations, business analytics and compute infrastructure, just to name a few. All of them use data as their raw material, and Jupyter is their data tool.

The infrastructure engineers talked a lot about how Jupyter was not only a stand-alone web-browser application, but also a powerful extension to their existing compute infrastructure. There was CERN, who used Jupyter as a user-friendly interface that extended the functionality of their existing big-data processing system SWAN. Netflix improved their job scheduling system with parameterized Jupyter Notebooks. These teams took what was good about Notebooks – the interactivity and ease of use – to improve, not replace, what they had already built.

The educators were inspiring. One highlight was a talk from the UC Berkeley Data Sciences division, where a small team worked with student volunteers to craft Notebooks that applied machine learning to all sorts of undergraduate fields of study. There were Notebooks to analyze text data for English Literature classes; there were Notebooks that used data to teach fundamental theories in Economics. Despite only being in action for a short time, the team has managed to reach many students and many faculty, giving members of the university a taste of what data means for their field.

One last thing

Throughout the event, what was remarkable was how these three breeds didn’t move in silos. An educator was just as comfortable talking about hosting Jupyter Notebooks on the cloud as she was talking about working with Economics professors to use Jupyter to teach a class. An infrastructure engineer was clear about the multiple ways data analysts use Jupyter to build dashboards and visualize data. The atmosphere was interdisciplinary and open and the fast exchange of ideas was invigorating. If software and data are equal parts technical tooling and community, that feeling of community is one more thing to learn and emulate from the conference.

HPC to Deep Learning from an Asian Perspective

Big data, data science, machine learning, and now deep learning are all the rage and have tons of hype, for better—and in some ways, for worse. Advancements in AI such as language understanding, self-driving cars, automated claims, legal text processing, and even automated medical diagnostics are already here or will be here soon.

In Asia, several countries have made significant advancements and investments into AI, leveraging their historical work in HPC.

China now owns the top three positions in the Top500 with Sunway TaihuLight, Tianhe-2, and Tianhe, and while Tianhe-2 and Tianhe were designed for HPC style workloads, TaihuLight is expected to run deep learning frameworks very efficiently. In addition, Baidu of China probably has one of the largest AI teams in this part of the world, and it would not be surprising to learn that these large Internet companies are working closely with the likes of TaihuLight and the Tianhe team to develop their own AI supercomputers.

Japan is no stranger to AI and robotics, and has been leading the way in consumer-style AI systems for a long time. Remember that Fuzzy Logic washing machine? Japan’s car industry is probably one of the largest investors into AI technology in Japan today, with multiple self-driving projects within Japan and globally.

RIKEN is deploying the country’s largest “Deep learning system” based on 24 NVIDIA DGX-1 and 32 Fujitsu servers this year. Tokyo Tech and the National Institute of Advanced Industrial Science and Technology (AIST) have also announced their joint “Open Innovation Laboratory” (OIL), which will have the innovative TSUBAME3.0 AI supercomputer this year and an upcoming massive AI supercomputer named “ABCI” in 2018.

South Korea announced a whopping US $863M investment into AI in 2016 after AlphaGo’s defeat of grandmaster Lee Sedol, and this is an additional investment on top of existing investments made since early 2013 (Exobrain and Deep view projects). It will establish a new high profile, public/private research center with participation from several Korean conglomerates, including Samsung, LG, telecom giant KT, SK Telecom, Hyundai Motor, and Internet portal Naver.

Closer to home, Singapore has recently announced a modest US $110M (SGD $150M) national effort over five years to build its capabilities in Artificial Intelligence called AI@SG. Funded by the National Research Foundation of Singapore and hosted by the National University of Singapore, this is a multi-agency effort comprising government ministries, institutes of higher learning, and industry to tackle specific industry problems in Singapore. Besides a grand challenge problem (to be identified by end of the year), a major focus is on partnering with local industry to drive the adoption of AI technology to significantly improve productivity and competitiveness.

In particular, an effort called SG100 — for 100 industry “experiments” over five years — will work closely with industry partners to help solve their problems using AI and HPC with the combined efforts of all the government agencies and institutes of higher learning and research centers. As is typical of Singapore style, three big bets for AI have been identified in Finance, Healthcare, and Smart City projects. The compute backbone of AI@SG is expected to ride on new AI HPC systems and also leverage various HPC systems existing in Singapore, including the newly established National Supercomputing Centre.

AI being powered on HPC-style clusters is not an accident. It has been and always was a workload that HPC folks have been running — it’s just that it was not sexy to be associated with AI back then. Now, we can all come out of the closet.

About the Author

Laurence Liew is currently the Director for AI Industry Innovation in AI Singapore.

Prior to AI Singapore, Laurence led Revolution Analytics’ Asia business and R&D until its acquisition by Microsoft in 2015. Laurence and his team were core contributors to the open source San Diego Supercomputer Centre’s Rocks Cluster distribution for HPC systems from 2002-2006 and contributed the integration of SGE, PVSF, Lustre, Intel, and PGI compilers into Rocks.

Newly Launched – “AI for Students” Programme

AI Singapore has launched a new programme “AI for Students” as part of our efforts to enable the next generation of AI talents. This was announced today in a MoU signed with DataCamp, a leading online learning platform for Data Science.

As part of the MoU, DataCamp will provide access to their online data learning platform to all full time students who are currently enrolled in government / government-aided secondary schools and Post-Secondary Education Institutes (PSEI) of Singapore at no cost. Educators can use DataCamp to conduct training for full time students (up to undergraduates) only. AI Singapore will facilitate and coordinate the access of the DataCamp platform under this initiative.

The event was attended by representatives (educators) from Sec/JC/ITE/Poly as well Universities respectively.

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