When you go to Japan, there is such a talent shortage that the debate about AI taking jobs is almost non-existent. The debate is, how can we automate this so we can get all the work done?
Interpretation
In Japan, the focus is on automation due to a shortage of workers rather than fearing job loss from AI.
The quote highlights a significant contrast in attitudes towards artificial intelligence and automation between Japan and other countries. Due to a shortage of human talent, Japan is more concerned with finding ways to integrate AI and automation into their workforce to enhance productivity and address labor gaps, rather than fearing the possibility of AI replacing jobs. This reflects a pragmatic approach where the emphasis is on leveraging technology to solve pressing workforce challenges.
In practice
In a presentation about future job markets, one could reference this quote to illustrate Japan's unique approach to labor challenges.
AI has been making tremendous progress in machine translation, self-driving cars, etc. Basically, all the progress I see is in specialised intelligence. It might be hundreds or thousands of years or, if there is an unexpected breakthrough, decades.
It seemed really amazing that you could write a few lines of code and have it learn to do interesting things.
Most of the value of deep learning today is in narrow domains where you can get a lot of data. Here's one example of something it cannot do: have a meaningful conversation.
Imagine if we can just talk to our computers and have it understand, 'Please schedule a meeting with Bob for next week.' Or if each child could have a personalized tutor. Or if self-driving cars could save all of us hours of driving.
A single neuron in the brain is an incredibly complex machine that even today we don't understand. A single 'neuron' in a neural network is an incredibly simple mathematical function that captures a minuscule fraction of the complexity of a biological neuron.
None of us today know how to get computers to learn with the speed and flexibility of a child.
We're in a situation where the solutions that we have are not good enough. The way to improve anything is to have a discussion about its flaws. To understand what the one or two or three things are about it that would help fix it. The DMCA makes it dangerous to have that conversation.
I understood the importance in principle of public key cryptography but it's all moved much faster than I expected. I did not expect it to be a mainstay of advanced communications technology
But there's so much kludge, so much terrible stuff, we are at the 1908 Hurley washing machine stage with the Internet. That's where we are. We don't get our hair caught in it, but that's the level of primitiveness of where we are. We're in 1908.
Many of the technologies that are now racing ahead most rapidly, replacing human workers in factories and offices with machines, making stockholders richer and workers poorer, are indeed tending to accentuate the existing inequalities in the distribution of wealth.
We are stuck with technology when all we really want is just stuff that works. How do you recognize something that is still technology? A good clue is if it comes with a manual.
If one app or news site or friend gets your attention, that means something or someone else loses it. It comes out of our sleep, our time with family or our reflective time with ourselves.
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