None of us today know how to get computers to learn with the speed and flexibility of a child.
Interpretation
Humans have yet to develop computers that can learn as quickly and adaptively as children do.
The quote highlights the current limitations in artificial intelligence and machine learning, emphasizing that while we have made significant advances in technology, we still cannot replicate the innate learning abilities of a child. This underscores the complexity of human cognition and the challenges faced in creating machines that can learn and adapt with the same efficiency and flexibility as humans do.
In practice
In a discussion about the future of AI technology during a conference, one might say, 'As Andrew Ng pointed out, none of us today know how to get computers to learn with the speed and flexibility of a child.'
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.
I've been to so many manufacturing plants. I've yet to walk into one where I did not think AI solutions wouldn't help.
Java and C++ make you think that the new ideas are like the old ones. Java is the most distressing thing to hit computing since MS-DOS.
Computers are still technology because we are still wrestling with it: it's still being invented; we're still trying to work out how it works. There's a world of game interaction to come that you or I wouldn't recognise. It's time for the machines to disappear. The computer's got to disappear into all of the things we use.
The decentralized nature of online conversations often makes it easier to manipulate public opinion, both domestically and globally. Regimes that once relied on centralized systems of media control can now deliver ideological messages more subtly, with the help of little-known intermediaries like anonymous commenters on websites.
Many of us now expect our online activities to be recorded and analyzed, but we assume the physical spaces we inhabit are different. The data broker industry doesn't see it that way. To them, even the act of walking down the street is a legitimate data set to be captured, catalogued, and exploited.
Because of its vitality, the computing field is always in desperate need of new cliches: Banality soothes our nerves.
Chess is far too complex to be definitively solved with any technology we can conceive of today. However, our looked-down-upon cousin, checkers, or draughts, suffered this fate quite recently thanks to the work of Jonathan Schaeffer at the University of Alberta and his unbeatable program Chinook.
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