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.
Interpretation
The quote envisions a future where technology enhances daily tasks and personal learning experiences.
Andrew Ng highlights the transformative potential of technology in our lives. He imagines a world where computers can seamlessly handle complex tasks, such as scheduling meetings or providing personalized education, and envisions innovations like self-driving cars that can reduce time spent on mundane activities. This reflects a deep belief in the ability of technology to improve efficiency and personalize learning, ultimately reshaping how we interact with the world.
In practice
In a conference about AI, I would start with a quote from Andrew Ng to illustrate the future of human-computer interaction.
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.
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.
Revolutionary products don't fail because they are shipped too early. They fail because they aren't revised fast enough.
The biggest problem is that Facebook and Google are these giant feedback loops that give people what they want to hear. And when you use them in a world where your biases are being constantly confirmed, you become susceptible to fake news, propaganda, demagoguery.
The new source of power is not money in the hands of a few, but information in the hands of many.
The cost of adding a feature isn't just the time it takes to code it. The cost also includes the addition of an obstacle to future expansion. ... The trick is to pick the features that don't fight each other.
As we move into an era in which personal devices are seen as proxies for public needs, we run the risk that already-existing inequities will be further entrenched. Thus, with every big data set, we need to ask which people are excluded. Which places are less visible? What happens if you live in the shadow of big data sets?
I find that creative streak I think often leads in programmers to be good predictors of where culture as a whole is going to go. And that is where I think I've tried over the years to in some ways use my customers as a filter or a predictor of where technology as a whole is going to go. Or where the world as a whole is going to go.
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