Author: Ho Rachel

To keep Taiwan's industry, government, academia, and research institutions ahead of the global technology and industry trends amid this wave of transformation, the Taiwan Science and Technology Hub (Taiwan S&T Hub) has invited internationally renowned AI expert Dr. Fei-Fei Li, current director of Stanford's Human-Centered AI Institute, Chief Scientist and Researcher of ImageNet, and co-founder and chairperson of AI4ALL, to participate in the "What we see and what we value: AI with a human perspective" roundtable forum held on March 23. Together with local experts, they discussed how to make AI a key driving force for the betterment of humanity. The forum was moderated by Erica Lu, a social affairs consultant at the Business Next, and featured guests including the Pegatron Corporation Chairman Tung Tzu-Hsien, PChome CEO Alice Chang, and iKala co-founder and CEO Sega Cheng, among other industry experts.

The following article is based on the insights shared by iKala co-founder and CEO Sega Cheng during the roundtable discussion (Part 2).

Erica Lu：

In the audience, we have a high school student who would like to ask: Nowadays, we encourage everyone to learn about programming and software, and even to write code. In the age of AI, to what extent should we learn AI? Should we focus on learning AI tools, understanding the underlying principles, or actually developing a model?

Sega Cheng：

I have an eight-year-old daughter myself, so I have been considering whether children should learn programming languages at an early age. Let me first share my thoughts on programming languages. I believe that there is no need to invest too early in learning them, and we don't need to turn it into a nationwide movement.

We should look at this issue of children learning programming languages from two perspectives. The first one is from the perspective of AI. There is a research direction focused on redesigning computer systems. Computer systems have been developed with layers of abstraction, which is primarily for human convenience. As a result,it brought the use of many intermediate languages, followed by programming languages and natural languages. However, AI does not require these intermediate parts. So, my expectation is that the future research direction of programming languages will involve AI designing highly efficient programming languages, potentially new ones.  Programming languages will evolve faster than before and the choices we make now will be completely different from those we make ten years from now. AI will likely accelerate the iteration of programming languages, resulting in the most efficient programming language in human history. It won't be too late to start learning it then.

The second perspective is that our interaction with AI has already reached the level of natural language. I often hear language educators say, "Oh no! Language institutions won't be needed anymore since AI can be a teacher.", but I disagree. Natural language skills will only become more critical. Not only do you need to communicate with ChatGPT and design good prompts, but you also need to be able to guide the correct answers and ask the right questions. This skill is not only crucial for communication with humans but also with AI. AI contains vast knowledge waiting to be accessed, but you have to ask the right questions to obtain wisdom. So, I think natural language is even more important. When it comes to education, I believe that natural language is more significant than programming languages. For humans, language is not just a skill; learning an additional language means acquiring a new way of thinking, which is something AI cannot replace. As a result, a person who can speak multiple languages can have more critical thinking, diverse perspectives, and innovative combinations. Innovation essentially involves connecting distant ideas, and humans have this ability due to our language skills. The more languages we learn, the more diverse our thinking frameworks and abilities become.

Returning to the topic of education, should programming languages be included in the curriculum? I think they might eventually become a core subject like English, but one should never learn programming languages just for the sake of it. Moreover, AI might continue to polish the design of programming languages and computer systems. Whatever the case, we should embrace lifelong learning because everything evolves every day. If you are a middle, high school or a college student, I believe the most important thing is to follow the pyramid structure: the base is "self-confidence," the middle is "self-management," and the top is "self-learning." These will remain unchanged regardless of the AI era.

Erica Lu：

How do you view the challenges of limited data faced by startups during their early stages, and what strategies do you suggest for obtaining more data?

Sega Cheng：

First and foremost, AI is already an open community, with numerous pre-trained base models and open datasets available. One strategy is to stand on the shoulders of giants, utilizing their well-trained base models, rather than attempting to train a new model from scratch, which could potentially cost millions of dollars. By fine-tuning the base model, you can add your own desired data and create a more specific model, much like teaching a child's brain a particular skill.. For example, in our influencer search application, we input the influencer data into the AI "brain," which can then answer questions such as "Who is the best influencer in Japan for promoting ramen?" With GPT, it can provide a direct response. Nowadays, every enterprise can start training their own AI "brain" at reduced costs.

As a result, we've transitioned from digital archives to intelligent archives. Previously, the data from vertical industries was the most valuable and held by individuals. By using GPT models to store this data, it becomes an in-house expert within your enterprise or research project. We indeed see a scarcity of vertical data and recognize its value. While AI technology may become widely accessible, data is ultimately the most crucial aspect. We do see the research moving towards small data. In comparison, the human brain is incredibly efficient, consuming only 20-25 watts of power, equivalent to the energy from eating a single hamburger to power a GPT for a whole day. However, running a GPT for an entire day could cost tens of thousands or even millions of dollars, making the human brain much more efficient. The human brain can learn impressive things from very little data, an area in which AI still falls short. Thus, there is no need to worry.

Erica Lu：

How should we contemplate the future AI ecosystem?

Sega Cheng：

Recently, the AI research community's openness has played a crucial role in the rapid development of AI research in the past few years. From Google's Transformer in 2017, to Google's BERT in 2018, and Meta's RoBERTa in 2019, followed by Stanford's Foundation Model, the breakthroughs in AI are actually a result of continuous improvement by the community. However, with the rise of ChatGPT, there are concerns that this openness may be reversed. When the influence of AI has grown to the point of becoming a trade secret, leading AI companies will start protecting their intellectual property. From a pessimistic perspective, it may slow the pace of AI development , especially in the case of AI research coming from these big tech companies. The ecosystem may not continue to develop at the same rapid pace as the past decade, with everything being published. However, it will continue to progress because academic research is still pushing forward. Data will still be a big issue. From the industry perspective, AI will become like water and electricity, and the focus will shift to how to add value to existing business models by finding the right "fields". 

Taiwan's advantage lies in its hardware manufacturing, which is globally renowned, whether in terms of chips or the entire hardware supply chain. These are irreplaceable assets, and the computing power required for AI is still insufficient. Therefore, we can expect Taiwan's semiconductor industry to continue to flourish, as the computing power needed for AI is currently in short supply, with only the largest companies able to access the latest hardware. These shortages will continue for the next few years. On the other hand, in the software industry, it is crucial to think globally from day one, and consider Taiwan as Israel or Singapore, because the software industry seeks scale. While Taiwan's population is relatively small at 23 million, it is definitely not enough to just focus on Taiwan, other markets must be considered as well. Whether it is AI or other software, on the first day, we must look at the world from Taiwan's perspective.

Erica Lu：

As a parent, what kind of AI world would Sega like its future children to live in, and what can we do now?

Sega Cheng：

Expanding the timeline, maybe in 50, 100, or 200 years, human development of digital technology may just be a transitional phase. After 100 years, people may not even be talking about digital technology or AI anymore because they would have matured. At that time, AI will assist in crucial areas like gene editing, protein research, new drugs development, longevity, space exploration, and others. Therefore, I think this period of time is very important because technology is always neutral, human choices about how to use it will determine its impact. This is also a critical moment when AI's influence has reached every corner of society. We must make choices about which areas we really cannot use, which areas we should keep open, and which areas should be used with limitations. These decisions will pose significant challenges in the future.

Every time human society advances, it increases the polarization between people, and as progress continues to widen the gap, the most dangerous issue will be, why does technological progress impact the whole society? The problem lies not in the lack of improvement in human living standards, but in the increase in inequality. Although people today are better off than those  100 years ago, they are not happier or more content. It is because of inequality. If technology continues to widen the gap, then society will collapse. Therefore, I believe that this is a very significant problem that needs to be addressed by AI.

Thus, I think we must look at AI from a societal perspective, not just from a technical standpoint. As we hurtle towards progress, it is crucial to ensure that everyone can board the train and obtain a ticket. This is also a lesson I learned from the industrial revolution because reskilling and upskilling are difficult but necessary to absorb the impact of the entire technological revolution. Therefore, we need to look at AI from a humanistic perspective, not just from an AI perspective.

To keep Taiwan's industry, government, academia, and research institutions ahead of the global technology and industry trends amid this wave of transformation, the Taiwan Science and Technology Hub (Taiwan S&T Hub) has invited internationally renowned AI expert Dr. Fei-Fei Li, current director of Stanford's Human-Centered AI Institute, Chief Scientist and Researcher of ImageNet, and co-founder and chairperson of AI4ALL, to participate in the "What we see and what we value: AI with a human perspective" roundtable forum held on March 23. Together with local experts, they discussed how to make AI a key driving force for the betterment of humanity. The forum was moderated by Erica Lu, a social affairs consultant at the Business Next, and featured guests including the Pegatron Corporation Chairman Tung Tzu-Hsien, PChome CEO Alice Chang, and iKala co-founder and CEO Sega Cheng, among other industry experts.

The following article is based on the insights shared by iKala co-founder and CEO Sega Cheng during the roundtable discussion (Part 1).

Erica Lu：

As someone who comes from a software engineering background and has founded a startup that has been focusing on software development, you've even made AI empowerment an important goal for your company. Could you please share with us your thoughts on how AI is shaping the future of software, industry, and software services?

Sega Cheng：

In fact, when we were doing Vision research at Stanford Computer Science 18 years ago, it was really challenging, because there was no Cloud, no Big Data, no AI, and no iPhone. That was back in 2006. So now when we talk about ABC, which stands for AI, Big Data, and Cloud, it actually developed in reverse order. First, there was Cloud, followed by Big Data, and then AI. Once AI had access to algorithms, computing power, and data, it quickly became effective. This has been the development pattern of AI. That's why iKala initially called itself a Human-Centered AI Company, as we believe AI is an augmentation, not a replacement. In fact, from an industry perspective, whether it's under the fields of healthcare, retail, or even military and defense, AI requires a specific field to be useful; without a field, AI is of no use. Regarding the turning point brought by GPT, we were actually quite surprised. We thought that GPT-like technology would only appear in three years, but OpenAI brought it forward by three years.

For the industry, we see this as a positive development. People have already started talking about AI's Moore's Law, which now seems to double its capabilities or reduce its costs approximately every three months, rather than waiting until every year and a half or two years. Since the release of ChatGPT, the software industry has been trying to shrink the AI "brain". Tesla's former AI director, Andrej, initiated the nanoGPT project, aiming to continually reduce AI in terms of training costs, deployment costs, application costs, etc. Thus, AI has begun to exhibit Moore's Law, and given the openness of the AI research community, we can expect rapid progress. There are a variety of models and datasets, and as soon as a paper is published, its datasets and some source code are openly available, enabling AI advancements to happen very quickly. We expect that under this new Moore's Law, AI will eventually become accessible to everyone, with the cost of obtaining intelligence becoming lower and lower.

That's why we think AI empowerment is so important. In the past, when talking about digital transformation, people were skeptical because they couldn't calculate the return on investment. They were uncertain about the long-term cost benefits. But if the purpose of digital transformation is to gain intelligence, then it will become a new wave for all industries. So, when the cost of gaining intelligence is almost zero, it brings up questions like "Will my existing workflow be disrupted? Will my personal productivity increase tenfold?" All of these are possible. So, when we look at AI empowerment, we treat AI as a utility, like water and electricity. In 10 or 20 years, we may not talk about AI anymore, because it will be as ubiquitous as water or electricity, and just like we don't think about how the entire electrical grid works when we plug in our phones or use a bread machine.

In the future, AI will have reached a point where the cost is low enough and everyone, even non-AI experts, can use it. As Professor Fei-Fei Li mentioned, the world seems to turn over every time we wake up. Now, we must understand that we've always treated AI as a value-added service. When we talk about the impact on industries, I think AI will open up countless new possibilities, such as protein folding, better understanding of consumers, and a multitude of scientific experiments. It has already revolutionized scientific exploration, so AI can even have the potential to become a utility in research. We believe the opportunities brought by GPT and large-scale language models are boundless! In our long-term work with influencer searches, we've seen search evolve from keyword search to the next step of natural language search. What ChatGPT has shown us is that when humans can interact with computers in a comfortable and natural way, it represents a revolution in the software industry brought about by AI. Therefore, we can expect natural language search to become increasingly developed in the future.

As AI continues to advance and integrate into various industries, it will not only transform existing processes but also create new opportunities and potential applications. With the rapid development of AI technologies like GPT, the landscape of software and services is changing at an unprecedented pace. AI is expected to become an indispensable part of our daily lives, much like utilities such as water and electricity. In this future, everyone will be able to utilize AI to enhance their productivity, problem-solving abilities, and overall quality of life, making AI empowerment an integral aspect of our world.

Erica Lu：

When you first started your own business, it was inspired by the birth of the internet and the landscape was uncertain. However, there were many difficulties in embarking on a venture and delving into a technology, and you yourself have gone through several transformations. Now, as we enter a new era of technological advancements, what advice or thoughts do you have for those interested in investing in startups or exploring AI entrepreneurship in the future?

Sega Cheng：

When it comes to generative AI, my primary advice is to avoid starting a generative AI company. The main reason is that the success rate of startups is inherently low, with 90% of companies failing within five years, even in Silicon Valley. As we discussed earlier, AI requires specific fields, so I believe even OpenAI will need broader and deeper moats to succeed. With language models  readily available on GitHub, anyone can deploy their own applications for generating text or images at a low cost.

We realized this in 2018 when we introduced AI-driven MarTech to our clients. At that time, we developed a feature called Picaas, a feature that employed AI  to remove background clutter in images and filled in gaps to create a clean, unaltered photo. This functionality has now been adopted by Google Photos as Magic Eraser. When we launched this feature in 2018, we encountered two challenges. First, we found it challenging to scale the business from a technical standpoint, leading us to realize that startups should prioritize customer needs rather than technology. Although AI has the potential to perform incredible tasks, what do you want it to do for you? How can it be applied to your industry? McKinsey's research shows that 70% of AI's value comes from added services that enhance existing business models, rather than creating entirely new ones. This was a valuable lesson that we learned from our first experience with AI.

The second lesson was centered around an ethical issue that arose after launching the background removal and replacement feature. The design community expressed concerns about the potential for image theft and alteration. In response, we retrained our model to identify and avoid modifying images from paid libraries, preventing copyright issues.  By addressing these concerns, we gained valuable insights into the feasibility of generative AI's business models.

On the other hand, while people worry that generative AI may lead to job loss, AI inherently replaces tasks rather than entire occupations. It leads to a "deconstruction" of job roles rather than the sudden disappearance of jobs. Throughout human history, it's rare for a group of jobs to vanish suddenly. Instead, as technology advances, it replaces certain tasks. For example, ChatGPT is great for summarizing information, but you'll find it takes a lot of time to edit its output. The time saved on ideation, summarization, and drafting still increases productivity, but time spent on certain tasks may increase or decrease. When looking at how AI technology can be popularized and create consumer value, it's essential to deconstruct the entire workflow and determine which tasks AI can and cannot solve.

We've been discussing generative AI and software, but robotics will also experience significant advancements this year due to GPT and multi-model technologies. In Professor Fei-Fei Li's lab, robots can now recognize and execute over a thousand actions. From online to physical applications, we can expect substantial progress in the coming quarters.

Concerning the risk of AI technology running amok, we don't need to worry too much about narrow or weak AI. However, when AI becomes a utility, we can expect governments to regulate it similarly to how they regulate water, electricity, and national defense. As AI transforms into a utility, governments worldwide will intensify their supervision and regulation of this technology.