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So it’s only fitting that Kim, cofounder of Superb AI, has been selected as the featured honoree for the Enterprise Technology category of this year’s Forbes 30 Under 30 Asia list, leading a pack of several fellow honorees who founded startups based on AI. Since launching Superb AI in April 2018 with four cofounders, Kim has grown his startup to $2 million in revenues last year and 21 employees, fueled by increasing demand for AI. Profits are still in the future, but Superb AI also managed last year to join Y Combinator, a prominent Silicon Valley startup accelerator. So far, it has raised $2 million in funding from Y Combinator, Duke University and VC firms in Silicon Valley, Seoul and Dubai, giving it a valuation of $12 million as of March 2019.

Superb AI helps companies create and manage the huge amounts of customized datasets they need to build algorithms. “We wanted to solve these problems and lower the hurdle for industries to adopt machine-learning technology,” says Kim.

The inspiration for Superb AI came to Kim while he was pursuing a Ph.D. in robotics and AI at Duke University. When companies work on a machine-learning project, data must be manually labeled in order to train a computer in the algorithms—an expensive, laborious and error-prone process. “This is partly because building a deep learning system requires extreme amounts of labeled data that involve labor-intensive manual work and because a standalone AI system is not accurate enough to be fully trusted in most situations,” says Kim, who dropped out of the Ph.D. program to focus on Superb AI.

Superb AI uses deep learning AI to label and analyze images and videos up to 10 times faster than manual processes can, Kim says. About 30 companies already use Superb AI’s platform, mostly small businesses but also Samsung, LG, Qualcomm and Pokémon Go maker Niantic.

Now Kim is looking to expand further in North America and enter Europe. “We believe that AI should be widely adopted and used as a commodity across all industries to truly empower humans,” says Kim. “And we will make it happen.”

According to a McKinsey & Company report, about half of the 2,135 business leaders it surveyed in 2018 across various sectors said their companies deployed at least one AI-based system into its business. Another McKinsey report in 2018 estimates that AI could add around $13 trillion by 2030 to the current global economic output.

Hong Kong-based Gerardo Salandra is one of the business leaders contributing to the AI-driven economic growth. Salandra is cofounder and CEO of Rocketbots, an AI-powered chat automation platform for customer engagement. The AI element allows Rocketbots to learn messages in more than 15 languages, including English, Spanish and Chinese, then suggest a response to speed up conversations with customers.

According to Rocketbots, more than 10,000 companies use the platform, including Accenture, AXA and PricewaterhouseCoopers. Salandra is also the president of the Artificial Intelligence Society of Hong Kong, an association with more than 4,000 members dedicated to the development of AI and adoption of the technology.

Transforming Industries

One of the industries where AI is playing a big tangible role and driving transformation is finance. Last year, financial-services firms invested an estimated $5.6 billion on AI, according to research firm IDC, second only to the retail industry, which is estimated to have spent $5.9 billion.

Founded by Ashish Airon, CogniTensor is an example of how AI is being utilized in finance. The startup uses AI mainly to predict prices of commodities and energy. CogniTensor is working with India’s largest power trader, the state-owned Power Trading Corp., to help it with its power procurement strategies. In commodities, it started off with predicting prices for aluminum. Airon is also a member of the WHO – ITU Focus Group working on standardizing AI for health solutions.

AI technology is not just being used to improve business operations, but also for sustainable development. 30 Under 30 Asia list honorees Ayushi Mishra and Utkarsh Singh are doing just that.

The duo cofounded DronaMaps, a startup operating an AI-powered platform that creates and analyzes 3D maps to develop cities, villages and neighborhoods with pipeline planning, precision agriculture and flood mitigation, among others. Since its launch in 2016, it has mapped 600 square kilometers across six states in India. DronaMaps has worked with governments, universities and companies, including Reliance Industries, SAP and Johns Hopkins University. […]

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But Mordatch is different. He’s not a linguist. He doesn’t deal in the AI techniques that typically reach for language. He’s a roboticist who began his career as an animator. He spent time at Pixar and worked on Toy Story 3, in between stints as an academic at places like Stanford and the University of Washington, where he taught robots to move like humans. “Creating movement from scratch is what I was always interested in,” he says. Now, all this expertise is coming together in an unexpected way.

Bots teach themselves a new language

Born in Ukraine and raised in Toronto, the 31-year-old is now a visiting researcher at OpenAI, the artificial intelligence lab started by Tesla founder Elon Musk and Y combinator president Sam Altman. There, Mordatch is exploring a new path to machines that can not only converse with humans, but with each other. He’s building virtual worlds where software bots learn to create their own language out of necessity. As detailed in a research paper published by OpenAI this week, Mordatch and his collaborators created a world where bots are charged with completing certain tasks, like moving themselves to a particular landmark. The world is simple, just a big white square—all of two dimensions—and the bots are colored shapes: a green, red, or blue circle. But the point of this universe is more complex. The world allows the bots to create their own language as a way collaborating, helping each other complete those tasks.

Reinforcement Learning

All this happens through what’s called reinforcement learning, the same fundamental technique that underpinned AlphaGo, the machine from Google’s DeepMind AI lab that cracked the ancient game of Go. Basically, the bots navigate their world through extreme trial and error, carefully keeping track of what works and what doesn’t as they reach for a reward, like arriving at a landmark. If a particular action helps them achieve that reward, they know to keep doing it. In this same way, they learn to build their own language. Telling each other where to go helps them all get places more quickly. As Mordatch says: “We can reduce the success of dialogue to: Did you end up getting to the green can or not?”

Successful Language Building

To build their language, the bots assign random abstract characters to simple concepts they learn as they navigate their virtual world. They assign characters to each other, to locations or objects in the virtual world, and to actions like “go to” or “look at.” Mordatch and his colleagues hope that as these bot languages become more complex, related techniques can then translate them into languages like English. That is a long way off—at least as a practical piece of software—but another OpenAI researcher is already working on this kind of “translator bot.” Ultimately, Mordatch says, these methods can give machines a deeper grasp of language, actually show them why language exists—and that provides a springboard to real conversation, a computer interface that computer scientists have long dreamed of but never actually pulled off.

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