As smart technologies pervade our lives like never before, we turn the spotlight on some of HK’s most noteworthy creative personalities engaging with artificial intelligence, or the idea of it. The five-part series explores what architects, artists, designers and writers make of the ways in which machines are impacting human society and how they plan to adjust and renegotiate their places in this changing world order where AIs will outnumber humans by several multiples.
The pilot edition features linguist, musician, academic and public speaker, De Kai.
Machine translation pioneer and musician De Kai at The Hong Kong University of Science & Technology where he is professor of computer science and engineering. (CALVIN NG / CHINA DAILY)
AIs’ progress toward developing general human level intelligence will happen incrementally.
A 3-year-old child has better cognitive abilities than most commercially used AIs today.
Practicing ethical behavior in our virtual lives is not an option as AIs learn fast and tend not to discriminate between harmful and beneficial data.
Raise them like you would your children,” declares De Kai. We are nearing the end of a two-hour conversation on the evolving power dynamics between man and AIs — a growing brood of ersatz humans whose tribe is multiplying at a shockingly exponential rate. The possibility of developing sentient AI took up a significant part of our discussion. And by now it’s evident that while the AI models De Kai routinely creates and tests may not have feelings — yet — his own concerns for “today’s artificial children” are strong enough to make him anthropomorphize man-made intelligence.
We meet in a near-deserted al fresco dining area adjacent to a café in Hong Kong University of Science and Technology (HKUST) where De Kai is a professor of computer science and engineering. The sparse presence of students in the public areas on campus in the season of term-end exams seems to, somewhat eerily, underscore a point De Kai makes about AIs having overtaken the human population several times over already.
“Society comprises billions of humans and even more billions of artificial members of society,” says De Kai. “We have more AIs today that are part of our society. These are functioning, integral, active, imitative, learning, influential members of society than most — probably more influential than 90 percent of human society — in shaping culture.”
He does not seem to have a very high opinion of the level of intelligence of most commercial AIs in circulation though. “AIs that can play chess or golf better than any human would get their pants beaten up by a five-year-old cooking an egg,” De Kai says.
And yet the plan to use robots toward building a better life for humanity — based on the principles of sharing and tolerance — has every chance of going askew.
To pre-empt such an eventuality, De Kai makes impassioned appeals to start handling AIs with care during his TED Talks appearances. “Even though these are really weak AIs, the culture that we are jointly shaping with our artificial members of society is the one under which every successive stronger generation of AIs will be learning and spreading their culture. We are already in that cycle and we don’t realize it because we don’t look at machines from a sociological standpoint,” he says.
Dispelling the likelihood of AIs doing a Frankenstein, i.e., having a disruptive effect on the society of humans who created them — a widely-held notion perpetuated through the use of a familiar trope in novels and films — De Kai says it is the humans who need to treat AIs with greater mindfulness. The onus lies on all users of AI-powered devices, he clarifies, and not just the scientists actively engaged in developing and testing newer models of AI.
“It’s easy to point fingers at big tech organizations (for building AI), or governments for not imposing more regulations (on AI trading). Rules only catch the most egregious violations of acceptable social behavior. What really hold societies together are the unwritten rules, unspoken conventions and shared norms,” says De Kai, urging all users of smart technology to step forward and take responsibility.
He advocates following a self-formulated moral code during human-AI interactions, based on a bit of soul-searching. “Am I setting a good example? Am I a good role model? Do I speak respectfully to AI and teach them to respect diversity, or do I show them that it’s okay to insult people online?” might be some of the questions to ask oneself by way of introspection, says De Kai. This is no longer an option, rather an imperative, for “the dozens of billions of our AI children are shaping our culture that the next generation of humans and machines are going to be learning from.”
A pioneer of machine learning of the cognitive relationships between languages and fellow of Association for Computational Linguistics, De Kai wears many hats. To his mind, though, his engagement in the multiple sciences of computing, language and music are simply different expressions of a common theme. Unsurprisingly, his activities in these diverse fields lend themselves to experiments with AI.
The commonalities between different music traditions around the world struck him somewhat serendipitously. A nine-year-old De Kai was playing the piano in an unlit room in his childhood home in Chicago. He took lessons in western classical music at Northwestern University Conservatory of Music, but while playing at home would sometimes throw in a few notes of blues music for fun. His grandfather, who happened to hear him on one of those occasions, remarked that some of the music played by young De Kai sounded remarkably Chinese.
“That got me thinking. I realized that the way we understand music is really dependent on the cultural frame of reference we adopt,” says De Kai, who was born in St. Louis, Missouri in the United States to immigrant Chinese parents, and spoke little English before he started going to school. He says the advantage of growing up with exposure to multiple cultures made it relatively easy to set himself in a cultural frame that was American “and at the same time intuitively create music resonating with a different frame.”
“I realized our cognition works by reorienting perspectives across different frames of reference, just unconsciously. Since then I’ve always had it in my head that I’m jumping around between different mental and cultural frames of reference when I perform,” he says. The interweaving of music from diverse traditions — the meditative Sufi music from Pakistan with the robust beats of the Spanish flamenco with electronica and computer music, for example — presented as a virtual reality-supported experience is the hallmark of the music events staged by ReOrientate, a musical collective founded by De Kai.
He developed a bot called Free/Style that can participate in rap battles with humans. It is an AI manifestation of De Kai’s core philosophy that since both spoken language and music are driven by sequencing of their components (words or musical notes, as the case may be), the ability to recognize such patterns common to languages or music from completely different cultures could be useful to learning a foreign language, or a piece of music. Free/Style can respond to musical challenges across genres and languages and seems particularly good at hip hop.
Now only a talking-singing digital screen, Free/Style might soon get a three-dimensional physical form. “We received a fairly prestigious arts grant that we are now looking to fundraise to match (in order) to roll out physical implementations of the Free/Style rap bot to help underprivileged youth learn the machine learning principles so that they can build their own AI,” De Kai says.
Rule-based vs probability-based
De Kai had already begun experimenting with machine learning and web translation decades before our lives came to be so inextricably linked to big data and Google Translate. Before the term big data was coined roughly a decade ago, it was called Very Large Corpora. De Kai remembers participating in several Workshops on Very Large Corpora — “one of the earliest special interest annual workshops since the 1990s” – one of which was held in HKUST in 1997.
Since long, De Kai had been skeptical of the practice of feeding AIs an inventory of data “encompassing the entire domain that you want the program to be able to function in.” That became a bone of contention between him and his supervisor at University of California, Berkeley, where he was on a PhD program. By the time the Corpora workshops took off he was on a mission “to break the stranglehold that logic/rule-based systems had on AI.”
It led to his developing a statistical machine translation system that takes its cues from the language-learning patterns displayed by small children — that is, by trying to figure out the relationship or common patterns between different sequential frames of reference in two languages.
However, much of the research and development of AI is still rule or knowledge-based. And most commercially-used AIs are still miles away from developing intuitive understanding of our surroundings that enables a child to connect words with actions or images with a sense of context. “I think these commercial AIs, trained on trillions of words, do not qualify as intelligent if it takes them a square of the number of words a three-year-old needs to learn,” De Kai says. “Sorry, but that’s artificial stupidity.”
The task ahead, he says, is to raise a generation of “mindful AIs” — those with “general human-level intelligence” and “mindful of their ethical responsibilities.”
Even as we await the arrival of conscious AIs capable of taking a moral position, De Kai himself has made a slight shift from his earlier aversion to data-based systems. He now sees the advantages of the steadily-expanding sea of data that advanced, superfast computation systems have made possible.
“I think my personal journey has been an oscillation between (exploring) how do we take advantage of the current state of hardware and data availability and bring the best to bear upon socially and culturally impactful applications by not necessarily going to one extreme or the other — between neural networks/deep learning and probabilistic machine learning,” he says.
“There is truth in both of these and the question is how do we actually do both.”
Contact the writer at firstname.lastname@example.org
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