Author: Kai-Fu Lee
I read this book mostly on the basis of the authors pedigree. Kai-Fu Lee is a tech rock star. He’s an O.G. of AI research, a tech exec who built Google China, and founder of the VC firm Sinovation Ventures.
What I hoped to learn from this book was not about AI necessarily, but rather about the view on why China would be relevant to the AI economy in the coming years. Lee delivered on the promise by telling a story that tied together China’s strengths and the current state of AI development. Despite China’s shortcomings, the argument that they have what it takes to make meaningful advances with AI technology is a strong one.
The point centers around the fact that we’re not relying on fundamental breakthroughs in AI research for progress. Instead, the breakthroughs already exist and now we’re in the “application” phase where execution is needed. And China has the skills to pay the bills when it comes to applying this tech because of the massive amounts of engineering manpower and a data-rich environment driven by culture and scale. Watch out!
Lee spends a good part of the book painting a picture of what the world might look like after the AI job losses start occurring. His message is that we will be wise to reposition the manpower that’s been replaced by computers to do tasks that are innately human – social work, care giving, etc. I’m not sure I buy into this thinking as much as the message on China, but it’s a conversation that I think will pick up steam over the coming years.
Read this book if you’re curious to learn more about AI and the “race” among countries to implement it. Otherwise, maybe find a summary online.
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“Over the next 2 to 5 years, the world around us is about to light up with layer upon layer of rich and dynamic data that you can see and interact with. This magical future ahead is called the Spatial Web and will transform every aspect of our lives, from retail and advertising, to work and education, to entertainment and social interaction.” | learn more
Karen Hao from MIT Technology Review helps clear things up with a handy dandy flowchart. | learn more
“As a test, the algorithm was given a set of 40 scans from 40 patients it had never studied before. It proved to be 100% accurate at detecting Alzheimer’s disease an average of more than six years prior to a patient’s final diagnosis.” | learn more
“A big challenge facing Alzheimer’s and dementia researchers today is finding a way to identify patients suffering from the earliest stages of cognitive decline. New research is suggesting that AI could be the key to accurately predicting those patients most at risk of developing Alzheimer’s.” | learn more
MIT Technology Review tackles the fascinating subject of how China governs using data, AI and internet surveillance. | learn more
Interesting to note that we can now detect via brain scans that which no other test could confirm. What happens next? “Artificial intelligence can now decode the brain signature for fibromyalgia—providing a definitive, objective sign that the illness really does exist.” | learn more
Men are disproportionately represented on Wikipedia – only 18% of biographies are of women. So, software company Primer built a tool that helps serve up bios of relevant women (focused on women in science) for Wikipedia’s editors to vet and publish. | learn more
Rob May of Talla questions the impact of path-dependence in machine learning models. Humans definitely approach ideas differently depending on what they learned before – should machines? | learn more
“Doctors at a U.K. eye hospital are getting algorithmic help interpreting the results of 3D eye scans, using a system developed at Google’s DeepMind that can identify more than 50 eye problems and recommend a course of action with human expert-level accuracy.” | learn more