Sunday, December 26, 2021

Kai-Fu Lee on Artificial Intelligence: "Why Computers Don’t Need to Match Human Intelligence"

In May 2018 we thumbnailed Mr. Lee as "Sometimes the competition is just plain intimidating/scary/resistance-is-futile, smart."

Followed by his mini-bio from
"KAI-FU LEE, the founder of the Beijing-based Sinovation Ventures, is ranked #1 in technology in China by Forbes. Educated as a computer scientist at Columbia and Carnegie Mellon, his distinguished career includes working as a research scientist at Apple; Vice President of the Web Products Division at Silicon Graphics; Corporate Vice President at Microsoft and founder of Microsoft Research Asia in Beijing, one of the world’s top research labs; and then Google Corporate President and President of Google Greater China. As an Internet celebrity, he has fifty million+ followers on the Chinese micro-blogging website Weibo. As an author, among his seven bestsellers in the Chinese language, two have sold more than one million copies each. His first book in English is AI Superpowers: China, Silicon Valley, and the New World Order (forthcoming, September)

And here he is at Wired, December 16:

With continuing advances in machine learning, it makes less and less sense to compare AI to the human mind.

Speech and language are central to human intelligence, communication, and cognitive processes. Understanding natural language is often viewed as the greatest AI challenge—one that, if solved, could take machines much closer to human intelligence. 

In 2019, Microsoft and Alibaba announced that they had built enhancements to a Google technology that beat humans in a natural language processing (NLP) task called reading comprehension.  This news was somewhat obscure, but I considered this a major breakthrough because I remembered what had happened four years earlier.

In 2015, researchers from Microsoft and Google developed systems based on Geoff Hinton’s and Yann Lecun’s inventions that beat humans in image recognition.  I predicted at the time that computer vision applications would blossom, and my firm made investments in about a dozen companies building computer-vision applications or products. Today, these products are being deployed in retail, manufacturing, logistics, health care, and transportation. Those investments are now worth over $20 billion.

So in 2019, when I saw the same eclipse of human capabilities in NLP, I anticipated that NLP algorithms would give rise to incredibly accurate speech recognition and machine translation, that will one day power a “universal translator” as depicted in Star Trek.  NLP will also enable brand-new applications, such as a precise question-answering search engine (Larry Page’s grand vision for Google) and targeted content synthesis (making today’s targeted advertising child’s play).  These could be used in financial, health care, marketing, and consumer applications. Since then, we’ve been busy investing in NLP companies. I believe we may see a greater impact from NLP than computer vision.

What is the nature of this NLP breakthrough?  It’s a technology called self-supervised learning.  Prior NLP algorithms required gathering data and painstaking tuning for each domain (like Amazon Alexa, or a customer service chatbot for a bank), which is costly and error-prone. But self-supervised training works on essentially all the data in the world, creating a giant model that may have up to several trillion parameters.  

This giant model is trained without human supervision—an AI “self-trains” by figuring out the structure of the language all by itself. Then, when you have some data for a particular domain, you can fine-tune the giant model to that domain and use it for things like machine translation, question answering, and natural dialog. The fine-tuning will selectively take parts of the giant model, and it requires very little adjustment.  This is somewhat akin to how humans first learn a language and then, on that basis, learn specific knowledge or courses. ...


Today's introduction was used in AI VC: "We Are Here To Create" and then in "If You Read Only One Column On Artificial Intelligence This Month...".

If interested see also:

"China's rapid advances in artificial intelligence"
AI: "Kai-Fu Lee"