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).
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?
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.
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?
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.