In this thought leadership piece by David Tan, Chief Technology Officer, Electronics, ST Engineering, we will take a look at the Artificial Intelligence (AI) technology trend and how it can advance the way we live, work and play.
David spent 30 years in Singapore’s Air Force and a decade in developing future concepts with MINDEF as Director of Ops-Tech in the then Future System Directorate and DSO National Lab before joining ST Engineering as Chief Technology Officer in 2011. In his current role, he exploits emerging technology and advance new ideas for how emerging technology can be used for the betterment of society.
A Data-Driven Organisation
Human beings have long had an ambivalent relationship with AI. Through the years, our popular media have expressed our fears and awe of AI through shows such as Westworld with futuristic park looked after by robotic hosts that allow its visitors to live out their fantasies through artificial consciousness.
AI indeed is fast becoming a possible reality. With the development of AI programs, it is key for businesses to focus and harness the potential for AI in innovative and practical areas such as public safety and security, transportation in both road and metro and in growing sectors such as aviation. AI in machine is no longer a pipe dream. Google’s AlphaGo beats world champion Go player Lee Sedol by a score of 4-1 in 2016. Driverless care is making headways and is predicted to grow considerably in the coming years which wouldn’t have a problem with drivers texting or dozing whilst driving or driving under the influence of alcohol or medication. In health care, AI machines are surpassing the capabilities of human radiologists which interprets results with surpassing accuracy faster than a human can cope.
AI is the Next Technology Trend
AI has left the lab and is now found in our homes, offices and within our neighbourhood in our estates. It is pervading all the institutions that drive our economy. From Amazon to Netflix and Uber to Waze, we are now augmented by smart machines running on incredibly powerful and self-learning software platforms to aid our choices in our daily living. With AI, Amazon recommends you the right gift whilst Netflix suggests the desirable choice of film for your weekend escapades and Waze suggests to you the way to navigate and avoid traffic congestion and toll charges. AI indeed is transitioning from being our little augmented brother helper to something much powerful and disruptive impacting our daily lives and business decisions.
Indeed, the fourth Industrial Revolution has arrived. It shall be powered by machines that are always learning and constantly thinking. Whether we are managing a large enterprise or just starting up, this new concoction of AI, algorithms, bots and big data with machine learning algorithms towards AI will be the single and biggest determinant of our future success or imminent failures.
Key Challenges for Organisations to Process Data
Data is the lifeblood of companies intending to survive and grow. It has been said, the largest hoteling company; AirBnB owns no hotel rooms, the largest taxi company (Uber) owns no taxis. And yet at their core, they are the best data-driven companies that operationalise data. They don’t treat data as a retrospective report. They use data for their business survival and growth.
Let me begin with a story on business disruption. Four years after Uber was founded, its San Francisco revenues totaled more than three times all the revenues of all the taxi cab companies in Fresco. Two years later, the Yellow Cab Cooperative which has operated the largest fleet of taxis for decades was disrupted and filed for bankruptcy. In essence, Uber brought data to the taxi industry. It advises drivers to be where they should be in certain hotspots at different times of the day driven by demands in order to maximise revenue. Uber matches the closest driver with the customer to minimise wait time and maximise driver utilisation and earnings.
We are now in the era of instant data and the demand for them will increase inexorably.
The first challenge with organisation is that of inefficient supply chain of data. Data supply chains are key but it suffers from a fundamental lack of the 3Cs. In essence, the flaw in one’s organisational architecture and its data platform is the inability to continuously COLLECT, CURATE and COMPUTE data for insights.
The second challenge of most organisations is the problem of being data-rich but insights poor. In essence, business organisations lack data enlightenment for business insights.
We have and are investing heavily on becoming what I called a data-driven company. We see the importance of unifying silos, digitising our manual human intensive logs and inculcating a data-driven culture across our business verticals and line of businesses. We don’t want to use data in retrospect and to merely understand history. We want to crunch data for insights in order to drive decisions in an anticipatory manner. When I talk about being data-driven, I am talking about operationalising data and using data to tune our actions, manage our businesses, develop competitive advantages in our product lines and sustaining our core businesses whilst seeking new growths.
An interesting example: Unmanned Surface Vehicle (USV)
We have seen early successes in developing Unmanned surface vehicle (USV) as a business for maritime surveillance. Unmanned Surface Vehicle was developed with indigenous capabilities and collaboration for persistent and pervasive maritime capabilities. USV as a force projection and force enforcement capability is potentially a lucrative business proposition for prolonged operations which require intensive manpower deployment.
We are developing data analytics capabilities to predict potential USV engine failures of up to 12 hours in advance. The central idea is that if we can predict USV related failures, we will not have to worry about unmanned vessels out there at sea having engine failures and hence becoming a danger and hazard to seafarers and sea traffics. Our data scientist’s team are developing the data model with algorithms to design and build the data analytics engine for USV predictive maintenance capability. Part of our techniques is to look at maintenance logs and matching them to sensor data in order to discover insights on trends and patterns of failures. We want to be data-driven in our operations for USV with real-time machine learning solutions to better predict failures.
Data Analytics is Key to Business Success
We are developing machine learning contextual algorithms with AI technology to develop Electronic Management Advisory System (EMAS) in our public roads for maintenance, with innovative idea as an option such as EMASpedia – ie, developing an AI-based Knowledge Corpus which is capable of monitoring the health of the EMAS systems to help better predict impending breakdowns within or across systems that will contribute to a Smart Intelligent Traffic System. EMASpedia embraces innovation in the heart of our solution on offer to the customer. We want to reduce maintenance effort on our EMAS system by predicting the cause and effects of failures ahead of time.
Three Technologies that will Impact AI
We are into cybersecurity and developing services on a cloud platform that are meant to differentiate with unique capabilities on offer.
Cyber threat is new and has no physical boundaries. AI-in-Cyber is one new trajectory that is worth developing. Cyber is not just being defensive. Our SOCs (Cyber Security Operations Centre) are not meant just to identify and isolate threats. It must have the ability to fight cyber threats with AI-in-Cyber to lure and trap perpetrators and to deceive them in order to trace out the sources, linkages and depth content of attackers to determine whether the attack is a lone wolf act or organisational an/or state-owned sponsored attacks. Building a Knowledge Corpus of the threats in its levels of connectivity allows for cyber warriors to better prepare themselves in the cyberwarfare arena.
Another technological area of importance for business growth is the idea of an AI-in-the-Cloud capability and developing it as part of our enterprise cloud platform business for scaling. Cloud platform will continue to scale from native to enterprise cloud and those with the ability to build a robust and resilient platform for XaaS as a business proposition will prosper and survive. Many companies are already entering into XaaS. However, the ability to analyse patterns of needs, transactional profiling and forward anticipation of demands are what differentiate between strong service providers and traditional providers.
Pervasive AI-in-the-Cloud with edge AI computing can influence a new generation of cloud computing infrastructure for business disruption. AI-in-the-Cloud provides for anticipatory capability in pushing for value-add and differentiated services to clients.
- First wave of cloud computing is attributed to platforms such as Google App Engine, Azure PaaS to developers.
- Second wave and big thing in the cloud was Infrastructure as a Service where customers could provision for virtual machines and storage all by themselves.
- Third wave of cloud has been centered on data in the cloud from relational databases to big data. The next wave would be the drive and growth of cloud with AI. It is already catching on and shall provide AI and Machine Learning computation services for business insights and growth.
Data has to make sense and this is where AI-in-IoT (Internet of Things) will provide the sense-making. Since IoT devices generate large scale of data, the AI is imperative to handle such volume in order to generate meaningful insights out of the data. This will help to increase operation efficiency, enhance risk management, reduce unplanned downtime and generate new development of products and services. AI-in-IoT will be able to detect patterns, speech and pitch recognition and anomaly detection such as temperature, pressure, humidity, sound and colours.
Infinite Possibilities through Digital Transformation
There are infinite possibilities that digital transformation can have on our lives if we work together. It is a continuous arduous process of discovering, developing and breaking out of existing mental barriers. Such a role can be very satisfying when making a difference to others.
Being put in this position also inspires me as it is a position that I can leverage on to serve others in the One ST Engineering approach through technologies that are designed to solve future problems. With AI and Machine Learning, we will need to be future ready by constantly casting our vision and placing our investment into the future bets, both for growing new businesses whilst strengthening our core businesses with AI in the bags.
Finally something on brain-inspired chip. With the trilogy of neuroscience, supercomputing and Nanotechnology, Neuromorphic engineering is in the watch list. The Brain-on-the-Chip Board that will spike up the human neurons in the brain to have perception beyond detection, reasoning beyond surveillance and doing so at very low power in very small form factor – addressing the challenge of smart unmanned and thinking robotics in terms of size, weight and power.
The concept of Augmented Cognition and Rapid Cognition may be the two possibilities that can aid decision making in both businesses and operations in the future.
Learn more about Digital Transformation here.