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.
Interpretation
Deep learning is effective in specific areas with ample data, but it struggles with tasks requiring meaningful conversations.
Andrew Ng's quote highlights the strengths and limitations of deep learning technology. While it excels in data-rich environments and can perform spectacularly in narrowly defined tasks, it falls short in areas that necessitate nuanced, human-like interaction, such as engaging in meaningful conversations, which remains a challenge for artificial intelligence.
In practice
In a discussion about the limitations of AI, one might say, 'As Andrew Ng pointed out, most of the value of deep learning is in narrow domains, but it cannot have a meaningful conversation.'
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.
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.
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.
Technological defeatism - a belief that, since a given technology is here to stay, there's nothing we can do about it other than get on with it and simply adjust our norms - is a persistent feature of social thought about technology. We'll come to pay for it very dearly.
People are so bad at driving cars that computers don't have to be that good to be much better. Any time you stand in line at the D.M.V. and look around, you're like, Oh, my God, I wish all these people were replaced by computer drivers.
The illiterate of the future will not be the man who cannot read the alphabet, but the one who cannot take a photograph.
You know, one of these things that happened in the '60s and '70s was this confluence of, sort of, a counter-culture with computer culture.
I am much less concerned with whatever it is technology may be doing to people that what people are choosing to do to one another through technology. Facebook's reduction of people to predictively modeled profiles and investment banking's convolution of the marketplace into an algorithmic battleground were not the choices of machines.
I have got a scheme to make a thing in the form of a horse with a steam engine in the inside so contrived as to move an immense pair of wings, fixed on the outside of the horse, in such a manner as to carry it up into the air while a person sits on its back.
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