After reading this, is machine learning just the coded version of what you do in Calculus II? Analyzing a scatter plot and trying different equations to get the correlation coefficient value close to "1" and predict the next value? Because that would seem to me to have limited usefulness.
Deep Learning Is Eating Software
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Re: Deep Learning Is Eating Software
#12After reading this, is machine learning just the coded version of what you do in Calculus II? Analyzing a scatter plot and trying different equations to get the correlation coefficient value close to "1" and predict the next value? Because that would seem to me to have limited usefulness.
What you describe sounds like regression, which is a powerful technique with varied applications, but is not the same as deep learning. Aspects from calculus do play an important role in ML notably in the gradient descent algorithm used to adjust weights. If you’re interested in learning more I recommend the 3blue1brown series on YouTube
Re: Deep Learning Is Eating Software
#13Not every problem can be solved by matrix multiplication and gradient descent. Particularly, many problems require exact solutions, and not just good approximations (think aerospace or science or finance). And speaking of science particularly, many solutions require insight, which ML often can't provide.
Re: Deep Learning Is Eating Software
#14Sure. Can we see an example of Deep Learning used to create a regular CRUD app?
While I agree that Deep Learning would be terrible at building CRUD apps (I'd be happy if it was good at performing analysis on human written code), one question I have is "If AI is performing more tasks, can we reduce CRUD app requirements?"
For example, if an AI is performing the task, it could interface with a back-end API rather than a front-end GUI (cutting CRUD development time). Also, we may not need to track as many things, like the time someone begins and ends their workday.
Re: Deep Learning Is Eating Software
#15Deep learning is certainly eating conferences, funding and PhDs. And that wasn't a bad thing until everyone got focused on generating another random architecture that yields another 2% improvement on their favorite dataset so their paper gets through.
>> another 2% improvement 2% is HUGE at this point, at least on the datasets that I am familiar with - ImageNet, MS-Coco, PascalVOC etc. And at this point, any modifications or strategies that gets you the 2% improvement is noteworthy, and I know that people in my team are looking forwards to techniques that will give us these improvements.
Agreed. In medical applications a tiny "incremental" advance can mean the difference between worse than human and superhuman performance, which could mean revolutionary changes to clinical care.
Re: Deep Learning Is Eating Software
#16After reading this, is machine learning just the coded version of what you do in Calculus II? Analyzing a scatter plot and trying different equations to get the correlation coefficient value close to "1" and predict the next value? Because that would seem to me to have limited usefulness.
I would say deep learning is a type of regression. The architecture of the network dictates the form of the equation. We're betting that equations of that form:
1) can fit the data
2) can generalize well to data that it wasn't fit against and
3) fitting/training is tractable.
That allows making predictions about complicated data like pictures, words, sounds, etc. We may know the picture and need to predict a label for it.
Re: Deep Learning Is Eating Software
#17Sure. Can we see an example of Deep Learning used to create a regular CRUD app?
There are various low-code tools that greatly reduce the complexity of writing CRUD apps.They're good on their own, but Some of their possible tasks may be helped by deep learning: automatic UI generation. information management and search in complex systems. knowledge reuse(translating and recommending knowledge/code). Building semantic systems to help with some of the verification.
And from another angle: deep learning favors big business. so we'll see more consolidation. so less CRUD will need to be written.