How to create differentiating products in the face of AI democratization
I have the privilege of hosting Avi (VP — Data Sciences, AI & ML, InMobi), Hong Ting (CEO and Cofounder, Botbot.AI) and Kenny (Head of R&D Singapore, DataRobot) during the recent Tech in Asia Singapore 2018 conference. Here are the key takeaways.
Q: What is AI and does the definition matter?
- From computer science angle, what we deem as “AI” in recent news is just Narrow AI — machines that can do a single task well like self-driving cars, AlphaGo, Google Home and so on. We are still very far away from General AI — the likes of J.A.R.V.I.S. from Ironman movies that can think, communicate and act in a wide range of activities, just like an average person. Artificial Superintelligence — AI that goes beyond the limits of human thoughts is just a pipe dream at the moment. Ultimately, the definition might not matter as much as the fantastic technologies we build in pursuit of next level developments in AI.
Q: Are AI tools only for the big companies with big budgets? Why should startups or SMEs care about AI?
- As noted by Hong Ting, “The advantage that we have, as SMEs and start-ups, is that we can zoom in on specific areas and get really, really good at it. We can also use all the tools that the big players have built and add on the datasets to create something unique for us”. Staying focus, moving fast, making use of public open data and open source tools are an essential competitive advantage of smaller firms.
- It is difficult to hire good AI talents, especially for startups who might not be able to compete on pay alone. A definite purpose of the company and showing up at a hospital with flowers and balloons for your potential AI engineer might be edge needed to secure the talent you need.
Q: Will self-service AI tools like DataRobot, Google Cloud AI, AWS ML replace the need for data scientists?
- Computers are like a bicycle for the mind — they allow us to go further and do more with our lives. Photoshop did not kill the photography industry; it created new possibilities in image editing. Similarly, self-service AI tools will help automate a lot of the repetitive, structured expects of machine learning, freeing up the human mind to focus on analysis, design and productionize their domain knowledge. By using standardized, well-tested methodologies, it also lowers the risk of development and makes brings the powerful tools to the public. It is far more important to push for AI adoption in companies at this stage.
Q: Lots of companies are going with the mobile-first strategy, what are some of the unique opportunities and challenges that mobile offers to building AI products?
- Asia, especially Southeast Asia, will witness an entire generation of people coming online via mobile devices within the next few years. Even mobile device makers are producing AI chips. Mobile-first provides companies with huge opportunities for personalization and instant gratification, though privacy is a growing concern.
- The biggest challenge in developing AI products is usually in scoping the problem and gathering the right data. Language localization is a huge challenge in Southeast Asia due to the large number of spoken languages. Building an AI startup in Southeast Asia that can handle the mix of English, Chinese, Bahasa, Thai, Vietnamese, Tagalog and Singlish is both science and art.