Commentary

2020 was an unprecedented year for everybody, startups to established enterprises, to humanity. Now that we are nearly halfway through 2021, truly little has changed. Lockdowns and lack of mobility as well as congregations continue, which is the new normal for businesses. For marketers specifically, new challenges emerged that drove them back to the drawing board. As business priorities get rejigged, this has been a testing time. With tighter budgets, and limited resources, they are having to think out of the box to reach their audience through newer ways, formats, and platforms.

<p>The rise of artificial intelligence has naturally seen people applying it (or attempting to apply it) in countless ways, with varying degrees of success. While artificial intelligence can be a powerful tool- in the right hands, in the right situation- it is not as easy as implementing a system and tapping a few keys. This is perhaps especially true for environments that deal with human behavior, like marketing. As the saying goes, &ldquo;with great power, comes great responsibility&rdquo;: and marketing managers must be aware of its potential pitfalls to avoid problems. Equally important is the need to know how to properly deploy their AI tools to avoid squandering both its potential and their company efforts and resources. By understanding AI&rsquo;s pitfalls, marketing managers can make the most of its opportunities.</p> <p>So far, AI&rsquo;s biggest advancements in the business world have been related to deep learning, referring to complex, multilayered (i.e., deep) neural networks, solving difficult problems with predictive analytics. The more layers in a neural network, the more complex it is, and more &ldquo;layered&rdquo; networks can identify and learn more complex relationships between variables.&nbsp; This means artificial intelligence can learn to uncover relationships that existing statistical techniques cannot detect and that it can learn to do so autonomously. This is the main selling point of contemporary AI algorithms.</p> <p>While the ability of AI algorithms to autonomously create models is its strength, it is not without its challenges when it comes to putting it in action. These challenges are: a lack of common sense, objective functions, a safe and realistic learning environment, biased algorithms, understandable and controllable artificial intelligence, the paradox of automation, and knowledge transfer.</p> <p><strong>Lack of common sense</strong></p> <p>What do we mean by a lack of common sense? It is not an insult to its programmers or to those operating it; no, we mean that the algorithm itself lacks what we humans call &ldquo;common sense&rdquo;. We know that emotional intelligence is important, and indeed AI systems are increasingly able to recognize people&rsquo;s emotions, through image recognition, voice analysis, or text analysis. But recognizing emotions is a far cry from understanding and feeling them. An AI system could learn that the words &ldquo;queen&rdquo; and &ldquo;crown&rdquo; are linked, and could even use them appropriately in a sentence, but the meaning of the words and sentences would be lost on it. Anything they have approaching common sense must be programmed into them by a person, which becomes a problem when it comes to objective functions.</p> <p><strong>Objective functions</strong></p> <p>An objective function is one that specifies the result that the AI algorithm aims to optimize (Sutton and Barto, 2018). In a marketing context, this could look like profit maximization or customer retention. The &ldquo;freedom&rdquo; of AI from common sense hinders its ability to define an objective function. It might be that humans understand something implicitly, but then have a hard time translating this for the algorithm. This might go awry: an autonomous car directed to &ldquo;get to the airport ASAP!&rdquo; might get there in record time, but having mowed down pedestrians and sped through red lights on its way. While the previous example is obviously extreme, we have already seen consequences of this play out in real life, with gender- or racially-biased systems. An outcome like profit maximization cannot be considered without allowing for the legal, moral, and ethical implications, which marketing stakeholders need to keep in mind when building and implementing their systems.</p> <p><strong>Safe and realistic learning environment</strong></p> <p>As you can imagine, all this is easier said than done. Knowledge transfer from the expert to the algorithm and vice versa is one of the biggest problems facing AI today, and the potential for costly mistakes is enormous. To avoid the fallout, it is important for AI algorithms to learn in a safe, realistic environment. Safe, in that if they do make mistakes, there is less impact on the business, and they avoid the marketing equivalent of running a red light. Realistic, in that the data resembles what they would receive in a real-life situation. This presents a challenge in marketing, because customers can be unpredictable, and a new factor (like, say, COVID-19) can throw a wrench into the best-laid marketing campaigns. While it might be tempting to think that AI reduces or even eliminates our need to understand customer behavior, it is the opposite: we need detailed customer behavior theory more than ever, as this will help us better configure our AI algorithms.</p> <p><strong>Biased algorithms</strong></p> <p>This brings us to another limitation to AI&rsquo;s use in marketing: its potential to be biased. Of course, the algorithm itself is not prejudiced, but if it is powerful enough, it could identify a characteristic like race or gender on its own and make biased predictions. How so? It might pick up on other information that acts as a proxy to the factor in question, like education or income, thereby unintentionally replicating the biases that are found in the data. In a marketing context, this could lead to outcomes like a price-optimization algorithm that aims to charge women more or an advertising algorithm that targets a vulnerable population. This has legal implications as well as the obvious ethical ones. Complicating the problem is the fact that adding the sociodemographic variable in question to the model in an attempt to clarify it could just make it easier for the algorithm to make prejudiced predictions. If marketing stakeholders do not properly understand the algorithms they are using, they might not know to challenge these troubling predictions.</p> <p><strong>Understandable artificial intelligence</strong></p> <p>The ability to understand and explain the model is another factor in the uptake of AI. If you are going to use an AI model, you need to understand why it makes the predictions it does, and to be able to interpret what the model is doing. More specifically, an AI&rsquo;s human &ldquo;handlers&rdquo; need to be able to explain: 1) the purpose of the model, 2) the data it is using, and 3) how the inputs relate to the outputs. By understanding this, it is also possible to know why the AI system is preferable to a non-AI system.</p> <p><strong>Controllable artificial intelligence</strong></p> <p>Using the term &ldquo;handlers&rdquo; above was intentional: an AI system must be able to be controlled and overridden. This might conjure up images of I, Robot and killer robots, and while the reality is rather less lethal, it is still serious. One recent example is that Uber&rsquo;s pricing algorithm responded to the crush of people fleeing the scene of the June 2017 terrorist attack in London by adapting (read: increasing) the ride prices to more than double the typical fare. Anyone who has taken Uber is unfortunately familiar with their surge pricing system, but in the aftermath of a terrorist attack, it made Uber seem like ruthless profiteers. However, Uber&rsquo;s monitoring system quickly flagged the problem, and they had mechanisms established that allowed them to override the algorithm within minutes. They were also quick to communicate about what was going on, made rides free in that area, and reimburse those affected. Alas, the damage was done. This situation left a black mark on their reputation and serves as a warning to marketing managers that any algorithm they implement needs to be constantly monitored and have the possibility to be overridden built in.</p> <p><strong>The Paradox of Automation</strong></p> <p>The purpose of automation is to replace the role of humans, aiming to make tasks faster and more accurate and leaving people free to do more complex work. The downside to this is that then people don&rsquo;t have experience with those simpler tasks and don&rsquo;t have the opportunity to gradually build up their expertise and skills. In marketing, this could mean that those in marketing, from customer service agents to market research analysts, miss the opportunity to hone their skills on simpler and more repetitive tasks that allow them to better understand customers and their needs, and are left dealing with only the most complicated and unique cases. It remains to be seen what implications this would have for the quality of service and work.</p> <p><strong>The next frontier of AI and marketing: transferring and creating knowledge</strong></p> <p>What sets AI apart from traditional statistics is its ability to execute higher-order learning, like uncovering relationships between indicators to predict the likelihood that an Internet user will click on an ad, and to do so autonomously. Being able to create knowledge like this is a huge advantage of AI. However, the transfer of knowledge from the AI model to the expert and vice versa is a major weakness of AI. Since marketing deals with human behavior, this requires a lot of common sense, which, as we now know, is not the forte of AI models. Since this kind of knowledge is often more implicit, dealing with social codes and norms, it is also harder to program into an AI model. The machine will also be able to pick up on links that it needs to transfer back to the human expert, especially so that the experts can identify flaws in the system and understand how it is operating. An AI system that is able to create and transfer knowledge back to the human expert is thus the Holy Grail of AI technology.</p> <p><strong>Takeaways</strong></p> <ol>     <li>         So what is a marketing manager who wants to use AI to do? There are a few key points to keep in mind:</li>     <li>         Understand the purpose of implementing the AI system. What are you aiming to accomplish?</li>     <li>         Identify the added value of the AI system. What does it add over and above human capabilities?</li>     <li>         Understand what your AI system is doing. What data is it analyzing? How is it producing the results?</li>     <li>         Examine the system for bias. Does your system have any built-in biases?</li> </ol> <p>Communicate: ensure that relevant stakeholders (consumers, employees) have the possibility to observe and interact with the AI system, to build trust, ensure reciprocal knowledge transfer, and practice.<br />     &nbsp;</p>  

Trade finance is generally seen as sound finance, underwritten by long-standing practices and procedures. The World Trade Organization (“WTO”) estimates that some 80 to 90 per cent of world trade relies on trade finance. These estimates are made with the acknowledgement of the absence of comprehensive and reliable data on trade finance flows.

Digital banking has been an innovation darling of 2020 as payments increasingly moved to digital channels and economies went cashless during COVID-related lockdowns.

The term VUCA (volatile, uncertain, complex and ambiguous) had become somewhat overused and watered down in recent years until 2020 threw a spanner and greeted us with the unprecedented COVID-19 pandemic. Now, VUCA has never been truer, as we witnessed the upending of lives, businesses and economies, and the rapid changes governments had to make to ensure survival and recovery of their countries.

Distributed ledger technology (DLT) and cryptocurrencies have continued to develop and mature in Singapore and around the world over the past 12 months. Prominently, the market value of Bitcoin recently surged past its previous all-time high to a new high of USD 41,940, and other prominent coins and tokens like Ether have also seen significant price growth over past weeks. Such developments cast an increasingly bright spotlight on cryptocurrencies and the technology upon which they are based, and other recent developments also help demonstrate what many stakeholders describe as DLT and cryptocurrencies 'going mainstream'.

By Malina Platon, Managing Director, Strategic Accounts, APAC, UiPath

By Marc Remond, vice president, meeting and learning experience solutions, Barco APAC

Before COVID-19, the rise of open banking had already set off a race around the globe to transform the financial services industry. Catalyzed by regulations in Europe and commercially embraced in Asia, open banking allows third-party developers and financial technology companies to access customer information and transaction data held by banks – at the request of the customer – for use in new applications and services.

When COVID-19 struck, people were looking for masks, gloves and toilet paper. But for enterprises, they were demanding data network, and lots of it – to ensure their connectivity remains strong for remote work collaboration. Telstra’s business is to connect the world to Asia, and Asia to the world; our network – including the largest subsea cable network in the Asia- Pacific region – was the very first to see these data demands. Our international network sees a dizzying amount of traffic on any given day, but the increase in data being sent back-and-forth during COVID-19 is truly massive. The demands for data have spiked significantly, seemingly overnight, by up to 50%.

Disruption is not new to Singapore. Long has the country recognised the necessity to upskill its workforce and keep abreast of the latest technological developments in order to maintain relevance in a highly dynamic global economy. It has witnessed a growth of approximately 92,000 ICT professionals in 1999 to over 210,000 in 2017, with that number only set to grow as many new initiatives have been launched to train a digitally ready workforce.

Whilst property markets in Asia are still reeling from the effects of the global pandemic, UK commercial properties are already showing signs of life.

Cities around the world including Singapore were laid bare by the pandemic that has forced businesses to start working from home on a massive scale. But it doesn’t just end here. This new model has dramatically broadened the cyberattack surface. Remote workers are now easy conduits to corporate resources, and most organisations are unprepared to spot unusual network activities generated by these remote users.

The impact of COVID-19 has been felt in every part of the world. It has upended everyday lives, forcing millions of people to reset their routine and adapt to a new normal. As people worldwide hunkered down and spent more time at home, they also began looking for creative ways to establish a sense of normalcy.

The pre-2020 concept of working at the office has been turned on its head. COVID-19 is forcing businesses to rethink workplace design and consider remote working as part of the long-term solution. According to a recent study, only 40 percent of employees in Singapore feel safe returning to the office, whilst 75% of employees want social distancing to be observed for at least the next six months.

One of the things we take for granted these days is the humble smartphone - it’s easy to forget the full range of its capabilities and features, which only continue to expand every year. Think of the number of separate items that have been packed into a single svelte device. To name just a few: a camera, watch, calendar, notebook, miniature TV, speakers, mp3 player, gaming console, calculator… and more! Even the computing capabilities of a modern smartphone would be considered to be on par with a supercomputer of the past.