Effective prescriptive maintenance only becomes possible after the accumulation and integration of multiple data sources over an extended period. Inference models should understand both normal and abnormal equipment performance in various conditions, such as extreme weather, during incorrect operation, or when adjacent parts are degraded. For many smaller organisations or those deploying new equipment, the necessary volume of data will not be available without the assistance of equipment manufacturers. Moreover, even manufacturers will not have sufficient data on interaction with complementary equipment. This provides an opportunity for large operators to sell their own inference models as a new revenue stream. For example, an electrical grid operator in North America can partner with a similar, but smaller organisation in Europe to provide operational data and maintenance recommendations. Similarly, telecom providers, regional transportation providers, logistics companies, and smart cities will find industry players in other geographies that they do not naturally compete with.
Employing multiple sensors. Baseline conditions and failure signatures are improved using machine learning based on feeds from multiple sensors, such as those that monitor vibration, sound, temperature, pressure, and humidity. The use of multiple sensors makes it possible to not only identify potential failure but also the reason for it and can therefore more accurately prescribe a solution to prevent an outage.
Data assessment and integration. Prescriptive maintenance is most effective when multiple data sources are unified as inputs. Identify the location of these sources, such as ERP systems, time series on site, environmental data provided externally, or even in emails or on paper. A data fabric should be considered to ensure insights can be extracted from data no matter the environment it resides in.
Automated action. Reduce the potential for human error or delay by automatically generating alerts and work orders for resource managers and service staff in the event of anomaly detection. Criticality measures should be adopted to help prioritise maintenance tasks and reduce alert noise.
There are significant considerations for banks in offering these types of capabilities, such as:
Privacy. While the technology operates on non-identifiable information, the perception of clients being ‘stalked’ by their bank in order to drive business to a merchant is one that would need to be managed carefully.
Consumer opt-out. The ability for customers to opt out of this type of service is critical.
Consumer financial wellbeing. It may be in the best interests of some consumer to not receive merchant offers, for instance where they are managing to a strict budget. These considerations can be baked into the overall customer journey (eg. prompts when the consumer is nearing their self-imposed monthly budget for a category), but care will need to be taken to keep customers’ best interests at heart.
While there are multiple challenges to overcome, the fact remains that personalisation is quickly becoming a core expectation for consumers. How will banks respond, and will we see AI use cases like Crayon Data become more prominent?
Artificial Intelligence is real and has started becoming mainstream – chatbots using AI to answer queries are everywhere. AI is being used in stock trades, contact centre applications, bank loans processing, crop harvests, self-driving vehicles, and streaming entertainment. It is now part of boardroom discussions and strategic initiatives of CEOs. McKinsey predicts AI will add USD 13 trillion to the global economy by 2030.
Hype vs Reality
So much to like – but why then do we often find leaders shrugging their shoulders? Despite all the good news above there is also another side to AI. For all the green indicators, there are also some red flags (Figure 1). In fact, if one googles “Hype vs reality” the majority of the results returned are to do with AI!!!!
Our experience shows that broad swaths of executives are skeptical of AI. Leaders in a variety of businesses from large multinational banks, consumer packaged goods companies to appliance makers have privately expressed their disappointment at not being able to make AI work for them. They cannot bridge the gap between the AI hype and reality in their businesses.
The data available also bears this out – VentureBeat estimates that 87% of ML projects never make it into production. Ecosystm research suggests that only 7% of organisations have an AI centre of excellence (CoE) – while the remaining depend on ad-hoc implementations. There are several challenges that organisations face in procuring and implementing a successful AI solution – both technology and business (Figure 2).
Visible Patterns Emerge from Successful AI Use Cases
If we look back to figure 2 and analyse the challenges, we will see certain common themes – many of which are now commonplace wisdom, if not trite. Leadership alignment around AI strategy is the most common one. Getting clean data, aligning strategy with execution, and building the capabilities to use AI are all touted as critical requirements for successful execution. These themes all point to the insight that it is the human element that is more critical – not the technology.
As practitioners we have come across numerous examples of AI projects which go off-track because of human issues. Let’s take the example of an organisation that had enhancing call centre capabilities and capacity using RPA tools, as a key business mandate. There was strong leadership support and enthusiasm. It was clear that a large number of basic level tickets raised by the centre could be resolved using digital agents. This would result in substantial gains in customer experience, through faster ticket resolution and higher employee productivity – it was estimated to be above 30%. However, after two months of launching the pilot only a very small percentage of cases were identified for migration to digital agents.
Very soon, it became clear that these tools were being perceived as a replacement for human skills, rather than to augment their capabilities. The most vocal proponent of the initiative – the head of the customer experience team – became its critic, as he felt that the small savings were not worth the risk of higher agent turnover rates due to perceived job insecurity.
This was turned around by a three-day workshop focused on demonstrating how the job responsibility of agents could be enhanced as portions of their job got automated. The processes were redesigned to isolate parts which could be fully automated and to club non-automated components together driving more responsibility and discretion for agents. Once enhanced responsibility of the call centre staff was identified, managers felt more comfortable and were willing to support the initiative. In the end, the goals set at the start of the project were all met.
In my next blog I will share with you what we consider the winning formula for a successful AI deployment. In the meantime, share with us your AI stories – both of your challenges and successes.
Reconciling these seemingly conflicting requirements is possible. But it requires serious commitment from business and data/ analytics leaders – not (just) because regulators demand it, but because it is good for their customers and their business, and the only way to start capturing the full value from AI/ML.
1. ‘Heart’, not just ‘Head’
It is relatively easy to get people excited about experimenting with AI/ML. But when it comes to actually trusting the model to make decisions for us, we humans are likely to put up our defences. Convincing a loan approver, insurance under-writer, medical doctor or front-line sales-person to trust an AI/ML model – over their own knowledge or intuition – is as much about the ‘heart’ as the ‘head’. Helping them understand, on their own terms, how the alternative is at least as good as their current way of doing things, is crucial.
2. A Broad Church
Even in industries/ organisations that recognise the importance of governing AI/ML, there is a tendency to define it narrowly. For example, in Financial Services, one might argue that “an ML model is just another model” and expect existing Model Risk teams to deal with any incremental risks from AI/ML.
There are two issues with this approach:
First, AI/ML models tend to require a greater focus on model quality (e.g., with respect to stability, overfitting and unjust bias) than their traditional alternatives. The pace at which such models are expected to be introduced and re-calibrated is also much higher, stretching traditional model risk management approaches.
Second, poorly designed AI/ML models create second order risks. While not unique to AI/ML, these risks become accentuated due to model complexity, greater dependence on (high-volume, often non-traditional) data and ubiquitous adoption. One example is poor customer experience (e.g., badly communicated decisions) and unfair treatment (e.g., unfair denial of service, discrimination, misselling, inappropriate investment recommendations). Another is around the stability, integrity and competitiveness of financial markets (e.g., unintended collusion with other market players). Obligations under data privacy, sovereignty and security requirements could also become more challenging.
The only way to respond holistically is to bring together a broad coalition – of data managers and scientists, technologists, specialists from risk, compliance, operations and cyber-security, and business leaders.
3. Automate, Automate, Automate
A key driver for the adoption and effectiveness of AI/ ML is scalability. The techniques used to manage traditional models are often inadequate in the face of more data-hungry, widely used and rapidly refreshed AI/ML models. Whether it is during the development and testing phase, formal assessment/ validation or ongoing post-production monitoring, it is impossible to govern AI/ML at scale using manual processes alone.
o, somewhat counter-intuitively, we need more automation if we are to build and sustain trust in AI/ML. As humans are accountable for the outcomes of AI/ ML models, we can only be ‘in charge’ if we have the tools to provide us reliable intelligence on them – before and after they go into production. As the recent experience with model performance during COVID-19 suggests, maintaining trust in AI/ML models is an ongoing task.
I have heard people say “AI is too important to be left to the experts”. Perhaps. But I am yet to come across an AI/ML practitioner who is not keenly aware of the importance of making their models reliable and safe. What I have noticed is that they often lack suitable tools – to support them in analysing and monitoring models, and to enable conversations to build trust with stakeholders. If AI is to be adopted at scale, that must change.
Shameek Kundu is Chief Strategy Officer and Head of Financial Services at TruEra Inc. TruEra helps enterprises analyse, improve and monitor quality of machine
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Most government projects involve several stakeholders and are complex in terms of the data, infrastructure and investments required. To take better decisions in terms of project complexity, risks and investments, public sector agencies need to have a structured project management framework, using an optimum mix of physical. technical, financial and human resources. In an environment where citizens expect more accountability and transparency, and where projects are often funded by citizens’ taxes, running these projects become even more complicated. Government agencies struggle to get funding, optimise costs (especially in projects that run over multiple years and political environments), and demonstrate some form of ROI. There is also an overwhelming requirement to detect and prevent frauds.
The global Ecosystm AI study reveals the top priorities for public sector, that are focused on adopting emerging technologies (Figure 1). It is very clear that the key areas of focus are cost optimisation (including fraud detection and project performance management) and having access to better data to provide improved citizen services (such as public safety and predicting citizen behaviour).
Technology as an Enabler of Public Sector transformation
Several emerging technologies are being used by government agencies as they look towards DX in the public sector.
The Push to Adopt Cloud
To prepare for the data surge that governments are facing and will continue to face, there is a push towards replacing legacy systems and obsolete infrastructure. The adoption of cloud services for data processing and storage is helping governments to provide efficient services, improve productivity, and reduce maintenance costs. Moreover, cloud infrastructure and services help governments provide open citizen services. The Government of India has built MeghRaj, India’s national cloud initiative to host government services and applications including local government services to promote eGovernance and better citizen services. The New Zealand Government has sent a clear directive to public sector organisations that public cloud services are preferred over traditional IT systems, in order to enhance customer experiences, streamline operations and create new delivery models. The objective is to use public cloud services for Blockchain, IoT, AI and data analytics.
Transparency through Communication & Collaboration technologies
Since the 1990s, the concept of eGovernment has required agencies to not only digitise citizen services but also work on how they communicate better with their citizens. While earlier modes of communication with citizens were restricted to print, radio or television, digital government initiatives have introduced more active communication using mobile applications, discussion forums, online feedback forms, eLearning, social media, and so on. Australia’s Just Ask Once allows citizens to access information on various government services at one place for better accessibility. More and more government agencies are implementing an omnichannel communication platform, which allows them to disseminate information across channels such as web, mobile apps, social media and so on. In the blog The Use of Technology in Singapore’s COVID-19 Response, Ecosystm analysts spoke about the daily updates shared by the Government through mobile phones. Demonstrating cross-agency collaboration, the information disseminated comes from multiple government agencies – the same channel is also used to drip-feed hygiene guidelines and the evolving government policies on travel, trade and so on.
AI & Automation for Process Efficiency and Actionable Intelligence
Governments are focusing on leveraging centralised resources and making processes smarter through the adoption of AI platforms. Initiatives such as the Singapore Government’s concept of Single Sources of Truth (SSOT), where all decision-making agencies have access to the same data, is the first step in efficient AI adoption. Singapore’s government agencies also have three data aggregators – Trusted Centers (TCs). This enables initiatives such as Vault-Gov.SG which allows government officials to browse a metadata catalogue and download sample data to run exploratory analytics. To push the adoption of AI, several governments are focusing on roadmaps and strategies such as Singapore’s National AI Strategies to transform the country by 2030, and the Government of Australia’s AI Roadmap and framework to help in the field of industry, science, energy, and education.
The first step of AI adoption is often through automation tools, such as virtual assistants and chatbots. The US Citizen and Immigration Service (USCIS) introduced an AI powered chatbot Emma to better support citizens through self-service options and reduce the workload of their customer service agents. The department of Human Services in Australia rolled out various chatbots named Roxy, Sam, Oliver, Charles and the most latest in progress PIPA (Platform Independent Personal Assistant) to provide information on various services and assist on queries.
Real-time data access with IoT
Governments have the responsibility of enforcing law and order, infrastructure management and disaster management. Real-time information data access is key to these initiatives. IoT sensors are being used in various government applications in object detection, and risk assessment in cities as well as remote areas. For example, IoT-enabled traffic monitoring and surveillance systems are embedded to provide real-time updates and continuous monitoring that can be used to solve issues, as well as provide real-time information to citizens. In a futuristic step, the US Department of Transportation (USDOT) is working with auto manufacturers on embedding vehicle to vehicle communication capabilities in all vehicles to avoid collision with emergency braking and vehicle speed monitoring. In an effort to promoting smart city initiatives and for infrastructure maintenance, New Zealand has installed smart cameras with automated processing capabilities, and IoT based street lighting system. IoT has tremendously benefited the supply chain and logistics sector. The US Army’s Logistics Support Activity (LOGSA) is using IoT for one of the Government’s biggest logistics systems. and military hardware with on-board sensors to analyse data directly from the vehicles for better asset maintenance. Again like in AI, there is a need for a clear roadmap for government adoption of emerging technologies, especially considering the safety and ethics angle. The Government of UK has introduced IoTUK, a program to help the public sector and private enterprises to come together and develop IoT technologies considering aspects such as privacy, security, and reliability.
Blockchain enabled Traceability & Transparency
Moving paper-based systems to digitised systems makes processes efficient to a degree. However, more is required for full traceability and transparency. Managing the data flow and safeguarding the information is vital for government organisations, especially as there is an increase in cross-agency collaboration. Government agencies and departments across the globe are increasingly collaborating using Blockchain technology, while at the same time maintaining the security of the data. For instance, in Georgia, the government department of Land, Property and Housing Management is using Blockchain to maintain land and property records. The blockchain-based land registry allows speedier approvals with no involvement of paperwork or multi-party signatures on physical documents. This is enhancing service quality while offering better security measures as the data is digitally stored in the National Agency of Public Registry’s land title database. Estonia is using Blockchain to protect their digital services such as electronic health records, legal records, police records, banking information, covering data and devices from attacks, misuse, and corruption.
Technology-led digital transformation has become the norm for public sector organisations across both emerging and mature economies. However, agencies need to create clear roadmaps and frameworks, including RoI considerations (which may not only be financial but should include citizen experience) and avoid ad-hoc implementations. The key consideration that government agencies should keep in mind is citizen security and ethics when adopting emerging technologies.
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Yes and no. If we look at the history of the ERP solution, as an example, we find that it was initially meant for and deeply entrenched in Manufacturing organisations. In fact, the precursor to modern-day ERP is the Manufacturing Resource Planning (MRP II) software of the 1980s. Now, we primarily look at ERP as a cross-industry solution. Every business has taken lessons on inventory and supply chain management from the Manufacturing industry and has an enterprise-wide system. However, there are industries such as Hospitality and Healthcare that have their niche vendors who bundle in ERP features with their industry-specific solutions. This will be the general pattern that all tech solutions will follow: a) an industry use case will become popular; b) other industries will try to incorporate that solution, and in the process; c) create their own industry-specific customisations. It is important, therefore, for those who are evaluating emerging technologies to cast their net wide to identify use cases from other industries.
AI and automation is one such tech area where organisations should look to leverage cross-industry expertise. They should ask their vendors about their implementations in other allied industries and, in some cases, in industries that are not allied.
For cybersecurity, their approach should be entirely different. As companies move on from network security to more specific areas such as data security and emerging areas such as GRC communication, it will be important to evaluate industry experience. Data protection and compliance laws are often specific to industries – for example, while customer-focused industries are mandated on how to handle customer data, the Banking, Insurance, Healthcare and Public Sector industries have the need to store more sensitive data than other industries. They should look at solutions that have in-built checks and balances in place, incorporating their GRC requirements.
So, the answer to whether organisations should look for industry expertise in their vendors is that they should for more mature tech areas. An eCommerce company should look for industry experience when choosing a web hosting partner, but should look for experience in other industries such as Banking, when they are looking to invest in virtual assistants.
Are some industries more focused on industry experience than others?
Ecosystm research also sought to find out which industries look for industry expertise more than others (Figure 2). Surprisingly, there are no clear differences across industries. The Services, Healthcare and Public Sector industries emphasise marginally more on industry expertise – but the differences are almost negligible.
There are some differences when we look at specific tech areas, however. For example, industries that may be considered early adopters of IoT – Transportation, Manufacturing and Healthcare – tend to give more credit to industry experience because there are previous use cases that they can leverage. There are industries that are still formulating standards when it comes to IoT and they will be more open to evaluating vendors that have a successful solution for their requirement – irrespective of the industry.
The Healthcare Industry Example
Ecosystm Principal Analyst, Sash Mukherjee says, “In today’s fast-evolving technology market, it is important to go beyond use cases in only your industries and look for vendors that have a demonstrated history of innovation and experience in delivering measurable results, irrespective of the industry.” Mukherjee takes the example of the Healthcare industry. “No one vendor can provide the entire gamut of functionalities required for patient lifecycle management. In spite of recent trends of multi-capability vendors, hospitals need multiple vendors for the hospital information systems (HIS), ERP, HR systems, document management systems, auxiliary department systems and so on. For some areas such as electronic health records (EHR) systems, obviously industry expertise is paramount. However, if healthcare organisations continue to look for industry expertise and partner with the same vendors, they miss out on important learnings from other industries.”
Talking about industries that have influenced and will influence the Healthcare industry in the very near future, Mukherjee says, “Healthcare providers have learnt a lot from the Manufacturing industry – and several organisations have evaluated and implemented Lean Healthcare and Six Sigma to improve clinical outcomes. The industry has also learnt from the Retail and Hospitality industries on how to be customer focused. In the Top 5 Healthtech trends for 2020, I had pointed out the similarities between the Financial and Healthcare industries (stringent regulations, process-based legacy systems and so on). As the Healthcare industry focuses on value-based outcomes, governments introduce more regulations around accountability and transparency, and people expect the experience that they get out of their retail interactions, Healthtech start-ups will become as mainstream as Fintech start-ups.”
It is time for tech buyers to re-evaluate whether they are restricting themselves by looking at industry use cases, especially for emerging technologies. While less industry customisations mean easier deployments, it may also hamper innovation.
RPA has grown out of Business Process Automation (BPA) and refers to the use of AI to automate workflow and business processes. The advantages of RPA demonstrate it to be a solid tool in attaining higher quality output at lower costs which is much quicker than traditional methods. RPA can be used in IT support processes, back-office work, and workflow processes. The rules are programmed, and bots extract structured inputs from applications like Excel and enter them into other software such as CRM, SCM or accounting. A good example is the use of NLP to scan incoming emails and undertake the appropriate action, such as generating an invoice or flagging a complaint in an automated manner.
Machine learning is an application of artificial intelligence (AI) which involves a combination of raw computing power and logic-based models to simulate the human learning process. Machine Learning is proving to be a successful approach to AI. When humans learn, they alter the way they relate information and the world, similarly when machines learn, they alter the data and form it into a piece of information.
An example, Image recognition is a popular application of machine learning in which images are fed into an algorithm, which attempts to recognise the contents of the image based on patterns. For instance, Yelp’s machine learning algorithms help the company’s human staff deal with tens of millions of photos to compile, categorise, and label the images more efficiently.
Chatbots and virtual assistants
Chatbots are robotic processes which simulate human conversation and automate functions. The technology is also used for so-called ‘virtual assistants’, which uses AI to interact with humans and aid with specific queries. They are increasingly being used to handle simple conversation and tasks in B2B and B2C environments. The addition of chatbots reduces human assistants and they can work throughout the clock. Chatbots and Virtual assistants improve with AI and can be trained to review conversations, past transactions and to draft a response based on context. If the user interacts with the bot through voice, then the chatbot requires a speech recognition engine.
Chatbots have been used in instant messaging (IM) applications and online interactive platforms. To exemplify, chatbots are deployed to assist online shoppers by answering noncomplex product questions, pricing, FAQ’s, order processing steps or forwarding information to human agents on complicated questions such as shipping delays or faults.
With AI technology evolving and improving so rapidly, many organisations are looking to use AI in their business, but there are still many questions to which adopters are seeking answers such as how to integrate AI into their existing systems, how to get access to data that will enable AI as well as the persistent technology concerns around cybersecurity and cost. The goal of many AI providers is to reach a stage where AI will support humans, control machines for us and automate repetitive tasks and processes.
Here are the Top 5 Artificial Intelligence trends for 2020 that we believe will impact both businesses and consumers in 2020.
Digital Transformation puts Analytics Back on Top of the Tech Priority List
In an effort to help the business operate faster, IT teams are looking to better analytics to drive functions and decisions more accurately. While many business teams deploy their own technologies and systems – only the IT team is in a higher position to gather data from multiple systems of record in order to create the detailed insights that business users demand. Getting a view across the entire customer journey means analysing data across many systems – both front and back-end. Business teams struggle to get these types of insights on their own, which is why IT excels at providing great analytics to help make better and faster decisions.
Just like in previous generations of BI, the analytics market is starting to consolidate. While the ability to display data visually will always be important, it is the analytics that drives automated decisions that will often be of the most business value.
Automation will Lead Organisations to AI
RPA is increasingly moving beyond the usual task and process automation, to now being a business transformation lever. Additionally, there is an immense focus on incorporating AI/machine learning within RPA to make automation smart and intelligent. This allows software robots to mimic human behaviour and handle complex use cases, which was earlier not possible without human intervention.
Businesses will spend more money on their simple automation activities (RPA and analytics applications that do not learn) – but those that have already invested in automation are likely to want to take the next steps to AI.
AI will Start to be Embedded in Most Business Applications
To date AI has been an overlay to most applications – data is extracted from processes, learnings are made, and then the process is altered based on those learnings. In 2020 we will see mass availability of self-learning intelligent applications. The standard ERP, CRM, SCM, knowledge management solution and other business applications will have embedded intelligence. This will make it easier and faster for businesses to get the benefits of machine learning and AI without the need to hire expensive data scientists, or the requirement to learn the tools and platforms required for creating smart applications.
2020 will see the Democratisation of AI
Typically organisations required data scientists, AI coders, AI platforms and so on to do well in AI but with the increasing availability of AI in business applications, typical business users will begin to get a glimpse of what will be available at their fingertips in the next few years.
We expect templatised approaches to machine learning and associated technologies. Business users and data owners will be able to create algorithms that will improve business and customer outcomes. In some cases, we even expect AI to be available to consumers. We will start to see banking and finance applications that help better money management through learning – not just basic analytics, we will see more intelligent services in the market in 2020.
More Businesses Will Require AI on the Edge
In the next decade or two, it is estimated that there will be 100 billion IoT devices generating and exchanging data into the cloud, without any human intervention. With so many IoT devices generating a huge quantum of data, decisions will need to be made in real-time and the current cloud environments will be a bottleneck in data processing due to latency rates, network speed and traditional data architectures. To overcome this, Edge Computing solutions will be essential to work with a variety of sensor and data input devices, information processing and decisions driven by machine learning and AI, and additionally work with cloud for the next level of analytics, decisions and management.
Ecosystm in partnership with SGInnovate, the government-backed organisation that promotes Deep Tech in Singapore, released a series of four reports covering areas of mutual interest: Cybersecurity, Artificial Intelligence, Cities of the Future and Healthtech. ‘Ecosystm Predicts: The top 5 Artificial Intelligence trends for 2020’ report is a part of this collaboration and is available for download from Ecosystm and SGInnovate websites.
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Commenting on Singapore’s AI adoption and implementation strategies, Ecosystm Principal Advisor, Tim Sheedy said “being a smaller country, Singapore is one of the few around the globe that can make investments into AI at a ‘country level’. Singapore has the luxury of having a progressive public sector that operates at the same speed as the private sector, and has the opportunity to become one of the leading economies in the world for both the deployment of AI in everyday services AND in ensuring the economy has the required skills.”
To begin with, the SNDGO will be initially working on key high-value sectors:
Transport and Logistics: Intelligent Freight Planning
Smart Cities and Municipal Services: Infra management and smart services
Healthcare: Chronic Disease Prediction and Management
Education: Personalised Education
Safety and Security: Seamless border clearance operations
Speaking on AI strategies devised for transportation, smart cities, healthcare, education and safety and security in Singapore, Sheedy said “all of these investments are ones that you would make as an economy if you had the opportunity. They are all logical and will all benefit the lives of citizens and the success of the overall economy.”
Source: Singapore National AI Strategy document (SNDGO) Graphic by: Ecosystm
Transport and Logistics: To optimise the freight and delivery services, a common data platform will be built. The aim is to bring in efficient transportation and logistics with intelligent AI systems in place by 2022 and further AI developments will scale deployment, enable optimised delivery processes and routing of services for freight planning.
Smart Cities and Estates: The country aims to launch AI powered chatbots by 2022 to record municipal issues and allocate them to the responsible authorities. AI powered services will be introduced to better serve the residents. By 2025, efforts will be established to optimise estate maintenance through AI and sensors and by the year 2030, data driven insights will be used to improve the infrastructure and living environment in Singapore.
Healthcare: An all-new AI system known as SELENA+ will be in place to screen and detect diabetic eye disease. SELENA+ is a first artificial intelligence algorithm which performs automated retinal photo analysis to detect retinopathy and systemic complications in diabetics. In addition to this, AI capabilities will also be used to predict cardiovascular diseases and create personalised chronic risk scores. All of these will help to detect diseases and take early preventive measures by the healthcare teams for the welfare of country and betterment of the economy.
“This could detect chronic issues early, and reduce the impact of them on individuals, families and the economy. Being able to make investments at a macro level – like this happening in Singapore – will make sure everyone benefits” said Sheedy.
Education: AI in education will bring the benefits of automated marking systems for English language in primary and secondary education. Further, AI-enabled learning systems will help students on learning and mastery of topics. The government has planned to expand automated AI and adaptive learning systems to more subjects at a later stage.
Sheedy said, “with the government being able to influence and change the syllabus in schools and universities, as well as the skills in the public and private sector, Singapore is uniquely positioned to drive real economic benefit from their investment into AI.”
Safety and Security: AI systems will help in border security and clearance procedures. AI will enhance travel experience with automated immigration clearance systems involving face and iris scans. The immigration processes will develop into seamless self-clearance systems and become faster.
“A few countries have the ability to drive this level of planning – most countries have many levels of government which make this planning – or execution of plans – difficult.” said Sheedy. “Many are also leaving the investment to the private sector, which means it will happen eventually, but may see many competing initiatives or different capabilities emerge that only benefit a single company, not an entire economy.”
According to Sheedy, there are many benefits of having deep investments in AI and AI capabilities in an economy.
It will make Singapore a more attractive investment location. If access to government and other services are seamless, then the barriers to entry to starting a new business or creating a new business capability will be much lower. It will mean that it is easier to build a business case for businesses to move to Singapore or start in Singapore – attracting investment funds and employment into the island state.
It will boost export capabilities – both for the skills that will be in demand through technology and business service providers and for the intelligent products and services that will likely emerge from the early AI investments. If Singapore can make more of the products and services that they produce “smart” – then these products and services will see increased demand – both locally and outside of Singapore.
It will make Singapore a better place to live and visit. With seamless government services, easier travel into and out of the country, and a government that anticipates the needs of its citizens, the quality of life for residents will increase.
The country can get ahead of the challenges and downsides of AI – and legislate or plan for these challenges, to ensure these challenges are understood and managed before they become problems.
In the escalating initiatives to become an AI superpower, Singapore has clearly indicated they are fully committed to leveraging AI to drive growth and citizen services.
An AI roadmap report was published by the Australian Government in November 2019, co-developed by CSIRO’s Data61 and the Department of Industry, Innovation and Science. The report identifies the opportunities and benefits of AI that Australia could capture.
The report classifies the strategies to help develop AI capabilities to boost the productivity of industry, generate jobs, bring economic growth, and enhance the quality of citizens’ life. To drive this, Australia has identified 3 key areas where it has the best opportunity to create new value-
Health, Ageing, and Disability – To develop AI to improve healthcare, aged care, and disability services while reducing healthcare costs.
Cities, Towns, and Infrastructure – To develop an AI system for the cities and infrastructure to provide better services, safety efficiency in a smart and cost-effective way.
Natural resource and environment management – Develop AI for better natural resource management and improve the productivity of agriculture, mining, fisheries, forestry, and environmental management.
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