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The Latest Innovations in Artificial Intelligence – Part 2

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According to IDC, the artificial intelligence market is expected to break the $500 billion mark by 2024, with a five-year CAGR of 17.5%. What are the top AI innovations in 2021? When AI is revolutionizing and transforming human society, how can we respond to the challenge and benefit from AI? 

Here are some highlights from iKala Co-founder & CEO Sega Cheng's sharing.

Privacy & Security

An online questionnaire conducted by MIT in 2018 raised a question that if an autonomous vehicle is about to hit a pedestrian on the way, whether it should save the young or the old? The result of this survey showed the cultural differences between the East and the West. It is clear that the eastern countries are mostly on the right of the chart. On the left are western countries which emphasize independence, and the importance of young people and the future of newborns. In the same moral choice of AI, people in different countries have completely different opinions. That's why AI faces so many moral issues.

There is one more local example. The year before last, Taiwan Railway introduced a smart surveillance camera, mainly to prevent crime, which caused a very serious human-rights controversy at the time. The main technology used was AI facial recognition. There is a group of people who argue that facial recognition is very good to deter crime, so it must be used unconditionally, but some feel that privacy is infringed, others feel that there should firstly be a balance between privacy and crime detection, then the technology can be widely adopted. What we are facing is the contradictory of "individual's right to privacy" and "public's right to know". These two things are constantly raising controversy due to the development of technology.

Back to the industry, the tracking of digital ads is widely adopted by advertisers, but some users have negative feelings about it. Therefore, several digital giants have responded to and improved on this. For example, Google promises to gradually phase out third-party cookies, the new Android system will start to allow users to block advertiser's tracking ID to prevent it from tracking specific customers, and Apple updates its privacy policy that tracking ID on users' phones are blocked by default. 

In this privacy-conscious world, how are we going to continue to develop in machine learning and AI? One of them is called Federated Learning. Its main concept is that I can see the whole picture without looking at an individual. In other words, I can still understand what the users' interests may be, without identifying who he is. What we did in the past is that we have to detect individuals and groups. The privacy-based machine learning technology is about behaviors of a group of people instead of a single person, and Google is the first company to unveil the technology, and it is now using this technology to train some of their models. This is how people develop AI in a world where privacy rights are rising.

Looking at the responses of various countries, the European Union is the most active player. Starting from the GDPR, California, Brazil, and South Africa have all proposed their own data protection and privacy rights regulations. Now companies in various countries who want to do cross-border online business are actually adapting to the laws and regulations of various governments. Therefore, the laws and regulations of various governments and the response of technology giants to privacy rights, have shaped the future business world.

Deep Fake

Take a look at the four pictures on this screen. Can you suspect anything unusual in these four pictures? I think most people think it's photos of dogs, landscapes, butterflies, and burgers. In fact, what is important is that these four pictures do not exist. They are not photos taken by real cameras, but completely synthesized by the computer. The technology was developed by DeepMind in 2018. This highlights a very important issue, especially in 2021. Seeing is not believing. When you see anything on the Internet, or in any other places, you have to be skeptical to find out whether it is true. This is a very big impact of AI. But we can still give an example here, of the same technology being used in the business world, for good.

Picaas is a technology developed by iKala, mainly used for image editing. The problem it solves is very simple. When we get a product image with some logos or taglines on it, some covering the product itself, most of us will ask the designer to retouch the image. But now, AI can help. The left is the image before editing, and the right is the picture retouched by Picaas. It improves the productivity of designers. Originally, it took about ten minutes to process a picture, but now with AI, it can be processed in 2.2 seconds, and there is little work to be done by designers. It greatly reduces some of our repetitive work in image editing.

Data

How does Tesla train its autonomous car? In fact, it is a model of machine learning that collects a large amount of data when people are driving, sends all the data back to their data center, and deploys the data back to every car to make self-driving safer. It can be considered as a decentralized training model. When you are driving a Tesla, you are actually training it at the same time, and you can help other Tesla owners. However, there is an issue in it. Whether the data is Tesla's or mine? This will bring a lot of controversy. Why not use an even simpler example to explain why the issue is not easy to solve.

The farmer and the beekeeper are actually in an interesting cooperative relationship, because the farmer needs his crops to be pollinated by bees; on the other hand, the beekeeper needs to sell his honey. They have different needs, but are dependent on each other. Now the problem comes, under such a relationship, who is going to pay? The answer is that the farmer has to pay the beekeeper because the farmer is more dependent on him. 

Why can they make such a transaction? There are two main reasons why the pollination market can operate like this? The first one is that the transaction cost is low. The beekeeper provides pollination services and gets more honey, then crops of the farmer grow better. It is easy for them to reach a consensus. The second is that they have a clear ownership of their assets. Beekeepers own bees and what they need is honey. What farmers want is for his crops to grow vigorously. Therefore, they are clear about the ownership of these assets, which caused their transactions to be completed. In Economics, this is called the Coase Theorem, firstly introduced in 1966. 

One more example that may be easier for everyone to understand recently is the matter of vaccination. If I am a person who should get a vaccination, do I have to pay others for not getting vaccinated, or people have to pay to get me vaccinated, in order to prevent me from causing more damage to society? The externality that the Coase Theorem is talking about is that both options work, but still controversies exist.

The problem of data is quite similar. Whether enterprises can freely use the data because of human goods, or the data should be treated as a personal asset, and can be transacted? From the example of Tesla, we have seen such a problem of data ownership. Whether the data is yours will be an important issue to be discussed in the coming years.

Explainable AI

When AI makes more and more decisions for us,inevitably many people will be curious about the way it makes decisions. The problems can be big and small. For example, when we buy books on Amazon, AI will recommend some books we want to read. At this time, we may accept it with pleasure, and then take a look at what it recommends. But we don't care too much about why AI knows our preferences. We consider it as a normal customer journey, with AI helping us, and recommending the books to us. This is a relatively positive application of AI.

In some cases, such as bank loans, there will be some controversies that humans hope AI can explain how the machine makes decisions. In 2018, the European Union proposed a regulation of "the right to explanation", empowering its people to ask companies to explain how the machine makes decisions, especially when facing some automation decisions. As you can imagine, when we humans make decisions, it is already difficult to explain what the context of our thinking may be, and that's the same for machines. This is the so-called "Black box AI" in the field of AI. Many scholars and developers are trying to respond to the governments' protection of human rights and hope to strike a balance between AI development and human life.

 

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The Latest Innovations in Artificial Intelligence – Part 1

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According to IDC, the artificial intelligence market is expected to break the $500 billion mark by 2024, with a five-year CAGR of 17.5%. What are the top AI innovations in 2021? When AI is revolutionizing and transforming human society, how can we respond to the challenge and benefit from AI? 

Here are some highlights from iKala Co-founder & CEO Sega Cheng's sharing.

Challenges of Adopting AI

From our previous experience, one of the pain points of people adopting AI is that they spend more than half of the time processing data. In the first half of the project, people should spend a lot of time and effort on cleansing, labeling or even augmentation of data; while AI models and algorithms only account for a small proportion of time. We find that there are still entry barriers for many enterprises to adopt AI. 

AI came out around 1960s. As you can see from this curve, the time and cost we spend in training AI systems have been increasing. You can find that after the boost of deep learning in 2010, the whole curve is reversed. We once doubled our computing power to train better AI systems every two years, but it turned out to double every three to four months after 2010. That is why "cost" has been a major concern for the industry when adopting AI and making further breakthroughs on it.

Key Developments in AI

Stanford University  released the "2021 AI Index Report" that excerpted highlights of AI's development around the world.

  1. AI investment in 2020 is 4.5 times higher than 2019, probably because of COVID-19, people invest in AI research focusing on pharmaceutical and biomedicine. Investment on drugs, drug discovery and molecular research in 2020 sees a massive increase.
  2. From 2019, 65% of graduating PhDs in AI went into the industry, compared to 44% a decade ago, when deep learning just began to emerge.
  3. AI has a certain creative ability that can compose text, audio, and images.
  4. AI is now facing a diversity challenge in researchers and industry workers. In the United States, 45% of resident AI PhD graduates were white.
  5. The AI industry in China has been developing rapidly in recent years due to the large amount of data from its government. China has surpassed the United States in the total number of journal publications for the first time in 2020. However, the United States has consistently more AI journal citations and conference papers than China over the last decade. We can tell that China and the United States are firing on all cylinders for AI.
  6. In the United States, international students in the AI field rise to 64.3%, and this proportion continues to increase.
  7. Surveillance technologies have evolved quickly in the past two years, mainly because the accuracy of computer vision has exceeded that of human eyes in 2017. Governments are also adopting different kinds of surveillance technologies, and this raises some ethics problems and social issues.
  8. As AI is under rapid development, the discussion of public policies and personal privacy just began in the past few years, so that AI ethics lacks benchmarks and consensus.
  9. AI finally gained the attention of the US Congress. The 116th Congress is the most AI-focused congressional session in history, paying great attention to the international development of the US internet giant companies. In addition, many congressional hearings related to AI and digital technology were raised.

AI's Impact on Jobs

Looking back to the last two decades, some scholars began to investigate on how susceptible are jobs to computerization as computers and artificial intelligence become more and more mature in 2013. The left side in this picture stands for relatively unaffected work and the right for high-risk ones. We can see that those on the left are jobs related to management, computer skills, education, and health care, which require some soft skills so that it would be hard for them to be replaced by computers. However, for these red areas on the right, it may be jobs of manufacturing, transportation, or even agriculture, forestry and fisheries, which machines can easily involve in their work, and they are classified as high-risk jobs.

On the left of this chart, we found that like telemarketers may be at high risk of being replaced by machines, but if you look upwards, like the entertainment industry, even clergy and religious personnel will probably never be replaced. Therefore, automation plays different roles in different job functions. AI can almost do what humans can do to a certain extent, so the key point is whether the work is repetitive, and the automation brought about by repetition.

Besides replacing jobs, AI also creates new jobs. A PwC study in 2018 indicates that in the UK over the next two decades, in the area of health care, there will be more jobs created than replaced. 

A Double-Disruption Scenario for Workers

The World Economic Forum actually released a report last year. The impact of the epidemic is actually a double blow. Business owners will start to consider reducing manpower, and consider office automation to further cut costs and survive the pandemic. The overall environment is ever changing due to the pandemic, and there is a high degree of uncertainty, so at this time, 41.8% of business owners will begin to increase the use of contractors.

Vital Skills in the Future

When Harvard Business Review asked managers what the most important skills in the future are, most of them answered "digital technology", but in fact, the real situation is that social skills, the ability to interact with people, guide, teach, and cooperate with each other is underestimated.

Even in the field of mathematics, for mathematicians with social skills and those without social skills, there's a gap of up to 15% between the average salary they get. This is an example of why we should pay more attention to social skills in the AI era.

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Future Talents

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While innovative talents are considered the driving force for the economy, how can we resolve Taiwan's brain drain problem? How can schools, enterprises and the government work together to cultivate innovative talents?

In iKala Future Talks this week, we invite Dr. Robin Bing-Yu Chen, Director of D-School (School of Design and Innovation) at National Taiwan University, and Neil Huang, CDO of iKala, to share their ideas of cultivating future talents. 

Here are some highlights from their conversations.

Closing the Talent Gap in Contemporary Society

Neil: A survey indicates that in recent years, due to the declining birthrate, the expanding production capacity of Taiwan's local technology manufacturers and competition in the global talent market, it is estimated that by 2030, in the Information and Communication Technology industry, there will be a talent gap of 83,000 people. The Minister of Science and Technology mentioned this problem in an interview before, saying that the solution to the talent gap problem is to cultivate cross-disciplinary talents. People who major in humanities and social sciences should know more about information technology, and enhance their digital skills. On the other hand, those with engineering and manufacturing background also need to understand humanities and society, so that innovation may occur. 

Robin: I think cultivating cross-disciplinary talents, or the so-called T-shaped talents is indeed a trend. What we encounter in modern society has become more complicated. When we faced problems in the past, we may be able to disassemble it into a very simple problem, and it can be solved by a single engineer. We are now facing a world of the unknown. Instead of a talent who is good at problem-solving, we need more that can define problems.

Neil: This reminds me of The Law of Raspberry Jam by Gerald Weinberg. Imagine that you dig a pile of jam and spread it on toast. The larger the area of toast you have to spread, the thinner the jam would be. In the end, it becomes so thin that the jam disappears. When we are pursuing the cultivation of cross-disciplinary talents, we may accidentally fall into this myth—Jacks of all trades and masters of none. What is your point of view to this contradiction of the breadth and depth of learning?

Robin: We are actually pursuing a diversified way of education in the future. In other words, all things in their being are good for something. There's no need to encourage every student to become a T-shaped talent. If we can make the education system more flexible and diversified, life will always find its way. I think this so-called T-shaped, instead of saying that you need to know a lot of different things, we would rather say that the horizontal bar of the T stands for the changing mindset, or the ability to communicate.

The Cultivation of Talents in Schools and Enterprises

Neil: According to the statistics from the Ministry of Education, in the past five years, the rate of drop-out has actually been at a record high, and in the past two years, about one out of every four students will take a suspension or termination of studies. The main reason for this is "Incompatible interest". Does it mean that we now have some urgent problems in higher education that need to be solved? Or should it be considered a positive phenomenon, because everyone is taking his career plan seriously, rather than blindly pursuing a diploma?

Robin: I have mixed feelings about this issue. From a positive point of view, more and more young people have their own ideas. It can be attributed to, or you can say blamed on, the current development of the media and information. They now know what the outside world looks like, so their goal is no longer "graduation", but something bigger. However, there are quite a lot of negative phenomena. Our current university education, environment, or institution cannot fit young people now and in the future. When we are asking our talents to adapt to the environment, whether our institution can keep pace with the times would be a question we need to think about when facing university education.

Neil: In terms of the industry, take iKala for example, we implement internship programs. Our first internship program last year was actually very interesting. The youngest intern is a high school third-grader, and there are also some interns from Stanford University and MIT. During their internship, they completed projects such as automatic crawling and social analysis. I think they all did a good job. In addition to internship programs, what can enterprises do to bridge the gap between industry, university and institute, and make the environment in Taiwan more friendly for our future talents?

Robin: What we talked about frequently is the university-industry gap. We hope students have more connections with the industry. There are two major benefits to this: One is shortening the so-called learning-doing gap, so that students can understand what they are learning for, or whether they can apply what they learn to their work. Another is that many students, before entering university or in the process of studying at a university, are actually very confused about their future. Therefore, we encourage them to do career exploration during university or even before university. I think this is what enterprises can help with.

2020 iKala Internship Program

Challenges of Talent Cultivation

Robin: The biggest challenge would be motivation in learning. We often say that you can simply think of one thing you want to do. It may not necessarily be related to traditional learning. It may be just a self-practice, or a self-exploration. Instead of saying what you should actually learn in university, I feel that if you can find your motivation to study, cultivate the ability to learn, and even develop a habit to learn independently, it would be helpful in the future. 

Neil: What you mentioned just now is similar to the real situation in the industry. If a company has so many regulations, and an employee can only do things after the boss's approval, then he is likely to lose his motivation. At iKala, we value our people. We think the people here shape the company's culture, and the culture affects the result of our work, our business decisions, and whether we can be innovative. We also care about diversity in the workplace, including a diverse professional background, and people with different nationalities. We also want to create a free and flexible working environment, so we always talk about "freedom and responsibility" at iKala. We hope that everyone can be responsible for his work, at the same time having enough space to make decisions on their own in the front line, and the flexibility to grow and develop in the company.

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Innovative Application of Data

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Data has become central to how we run our businesses today. How can we get useful insights from data instead of taking it at face value? Why is it important to build data literacy? We invited Irene Chen, AI Team Lead at iKala to talk about the innovative application of data with Co-founder of Re-lab, Yu-Hsuan Liu.

Here are some highlights from their conversations:

 

What are the differences between Data Visualization and Information Design?

Yu-Hsuan: Data itself has a wide definition. It may include words, numbers, or professional field knowledge. Generally speaking, Information Design is about supplementing the transmission of information with design, to make it effective and efficient. On the other hand, the definition of Data Visualization is relatively clear. Since we can't deal with so much data in a short period of time, we need to visualize the data. It is like an interface, so that we don't have to memorize a lot of information, but can focus more on the relationship between data.  

When we are talking about Data Visualization, the most important part is "data". We should be as objective as possible. If we are preconceptional, or keep thinking about the conclusion, we might ignore the most valuable part of the data, or the hidden story in it. On the other hand, in Information Design, you have to think from a human perspective. You have to put the problems to overcome and the value to create in mind, with a specific purpose.

Share the innovative application of data in both companies.

Irene: There are a lot of data-driven products at iKala. One interesting example, you might have heard about before, is KOL Radar. It analyzes massive amounts of real-time data from mainstream social platforms like Facebook, YouTube, Instagram and TikTok. In the past, when marketers were looking for KOLs, they usually decided with their experience and intuition, and there was no way to deal with so many cases at a time. With KOL Radar, we help them to build up precise and outstanding performance influencer marketing with data.

Yu-Hsuan: People come to us with very different challenges of communication. Sometimes it may be a brand new product that does not exist in the market yet, so they may need to start from scratch to think where their customers are. Based on some of their previous information, or the work they've done with other teams, we then start with making hypotheses. Then design the materials based on these assumptions. How can we communicate with this group of people? What stories, copies and images are more likely to attract them? Through at least two stages of analysis, we can figure out what kind of person will be attracted? What are the reasons they are attracted? If possible, we'll also tap into some of the reasons that might be psychologically relevant, and use these insights to build up further communication strategies.

 

Why is Data Literacy important?

Irene: I find the topic of Data Literacy really interesting. I've been facing it in my work. I firmly believe that not only we working with data should have data literacy, but those who raise the questions and who we need to communicate with in the final stages, should possess data literacy. 

Here are some important steps of data science projects. I actually have to spend a lot of time doing user interviews and defining business problems. Without data literacy, you may keep talking about something big and unrealistic, and this might lead to more problems. 

Nowadays, everyone is talking about digital transformation, but if enterprises really want a successful digital transformation, everyone should have these mindsets. You have to frequently observe indicators and trend reports in the industry, then try to understand it, criticize it, and dig into the reasons. 

Yu-Hsuan: Speaking of non-professional people, I think two things are very important. First, stay curious. If you have your own preconceived ideas, and are not willing to explore the origin of the problem, or the reasons behind it, data then loses its value. Second, critical thinking,  constantly questioning, and exploring the limit of data. Whether the customer is working with us or with iKala, I think if he only wants us to provide an answer, and does not want to embark on this journey together, it would be a great pity.

Thus, when we are working with new clients, specially for cross-departmental projects, we will arrange a start-up meeting to let everyone understand the reasons, goals, and what everyone should be responsible for. We actually found that optimizing the start-up meeting will greatly improve customer's feedback to the project.

What are the key factors in digital transformation?

Irene: At iKala, we help enterprises implement a DAA flywheel framework, combining "Digitalization", "Analytics" and "Application". 

In the first step, Digitalization, enterprises need to know which data should be sorted, and what information might be missed when collecting the user-behavior trajectories. Customer data might come from websites, membership systems, EDMs and so on, so how we build a complete user journey is the key to collecting data. For Analytics, not only AI modules, but many statistical reports or analytical methods play important roles in this stage. The last step, Application, is where we export data, by sending text messages, emails, or even some interactive ways to engage customers. The reason why we emphasize DAA as a flywheel is that the data generated in the final step, Application, should be returned to the step of Digitalization, so that people can understand the entire user journey and the user profile. This is the so-called omni-channel integration, in which we find the user's behavior and profile in a human-centered way.

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Social Commerce for Good

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A 2019 report from Econsultancy found 85% of the SEA social media shoppers said they would buy more on social media over the next few years. With Facebook and Instagram launching their commerce tools, how can we find a new way to connect with our customers through social commerce? What are the future prospects of social commerce?

We invited Nadia Tan, Director of APAC Marketing Partnerships at Facebook Inc. and Kimmy Chen, General Manager of Southeast Asia at iKala, to talk about their thoughts on the development of social commerce, and their expectations for "social commerce for good".

Here are some highlights from their conversations:

What are the key drivers of social commerce development?

Nadia: One of the drivers is the sense of intimacy. It's like talking to a storekeeper without having to go to the store offline. You have an opportunity to find out more about the product, the brand, or even the story of the brand itself. For a lot of us in Asia, it is very entertaining, too. We love chatting with the person that we are shopping from, and buy from the person that we trust. I love it when it's not a bot talking to me, when it's really another person at the end of the line.

 

What are the differences between social commerce and other types of e-commerce?

Kimmy: The core design is so different. For e-commerce platforms, they want shoppers to make their purchase decision right away, but for social commerce, or conversational commerce, people value the interactions and the comments, and treat it as a way for customers to learn about the product, or the story behind the products.

 

What are the country differences in social commerce? 

Nadia: We actually worked together with BCG to understand what are the differences in customers behavior around, especially in Southeast Asia. In the five markets surveyed, the BCG survey highlights the ease and access to additional product information is the key reason for them to use chat while purchasing. The one country that is slightly different is Indonesia, where product customization was their key decision motive. 

In terms of the maturity of the market, Vietnam and Thailand are advanced markets, demonstrating the strongest awareness of this trend and setting a lot of the trends as well. From the survey, one third of the customers said that they prefer to use this method to purchase. Three markets that we're seeing in Southeast Asia are Malaysia, Indonesia and the Philippines, where conversational commerce is prospering rapidly, and is definitely set to expand further.

Kimmy: Last year, we also did a social commerce trend report. Within the survey of 12,000 consumers and more than 1,000 social sellers across Thailand, Vietnam, Singapore and the Philippines last year. The favorite feature of technology they choose is very different. For example, in Singapore, the No.1 feature is AI chatbots, due to the maturity of conversational commerce and social commerce. People and the shoppers are more comfortable interacting with the chatbots. In Thailand, their favorite feature is the order management system, because social commerce is so popular in Thailand, even small businesses have a huge volume of orders, so order management system brings a lot of benefits. In Vietnam and the Philippines, it is interesting that we see people love payment reminders, especially the shoppers. After delivering some quantitative interviews, we know the shoppers love to be reminded because they usually buy the product during the discount, so if they forget to pay, they then miss the chance to have this special discount. 

Source: The Rise of Social Commerce in Southeast Asia, iKala

 

Why is social commerce having growing importance to big brands?

Nadia: We believe that the future is in messaging, as a way to communicate. It's a session between business and consumers. It is immediate, intimate and private. Brands, large or small, should seriously consider investing in learning about this way of communication, whether it is for transactions, or it is for customer care. Ultimately, you and I, and a lot of people, want brands to respect our time, and deliver interactions of utility and value. The demand for consumer service today is something that is interactive, immediate, personalized and frictionless. Especially to commerce, I think conversational commerce offers untapped growth opportunities for businesses. We have a study, something around 45% of respondents in this study from BCG, reported that they never shop online until they began a conversation with sellers via a chat. These new chat-preferred shoppers account for close to half of the purchases made by a chat.

Kimmy: Last year, because of COVID-19, we worked with Unilever Philippines to empower the retailers who had been suffering from the COVID-19 lockdown. With Shoplus, we helped a local supermarket chain store, NCCC Supermarket, to deliver a full e-commerce shopping experience on Facebook Shop and Facebook Messenger. In the first week, there were 17,000 new Messenger connections to their online shop. What's more, the average value of the orders was 5 times than offline bucket size and the return on ad spend was 4.9 times. 

Kimmy

What are your vision when we're talking about "Social Commerce for Good"?

Nadia: Back to my passion about small and medium businesses, I feel so connected to the company, because we are in the business of businesses, big or small. We're here to give every individual, every entrepreneur, every small business access to the same kind of tool that historically only big companies have access to. The way I talked about it is we "democratized" marketing. We invested in a lot of features and programs to support economic recovery of small business brands in this region, and to also help people through the crisis. 

Kimmy: iKala's mission is to "enable AI competencies" of our customers. We are eager to assist brands and entrepreneurs, especially SMBs, to capture the wave of social commerce, and we're trying to create a new era of social shopping with our AI technology. 

 

Can you provide some tips and tricks for new entrants to social commerce or conversational commerce?

Nadia: First, understand what you are trying to achieve. I believe that every business needs to communicate with their customers, so understand the role that messaging should play in the communication. Second up would be assembling a team that tries to understand the messaging experience. I tell my team who works for the messaging business, "Go buy something on conversational commerce, go attend a live shopping event, then you understand the problem." The third one, start experimenting. Start with a hypothesis and try it out. If it doesn't work, change the approach, or figure out the problems in the hypothesis. In this truly unprecedented time, there's no tradition right now. It's all about innovation and trying something new.

 

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