91–100 of 112 posts
ML is one of the easiest fields out there. When I learned it I was actually turned off by how simplistic the concept was. Of course let me preface to say that it's hard to develop the intuition and skill in the same way learning to skateboard is hard. But conceptually it's easy and very possible for almost anyone. The whole thing is just curve fitting. Literally finding some best fit curve across a series of points.…
As a software engineer, I disagree. Caveat, I haven't studied traditional ML and just went straight to DL. There is a lot of jargon and you do have to sit down and learn how things work, but once you do, deep learning is fairly simple. One thing that actually really bothers me is how much libraries (e.g. huggingface) are just config files masquerading as programming. It is just a class with 50 parameters and takes ab…
The post is not talking about 'traditional ML' but rather only 'DL' (i.e. neural networks). One of the problems I see here is that it seems to me that the math education of many CS grads is woefully lacking. Indeed, deep learning math is basically senior-high-school level calculus. Backpropagation is a straightforwards application of the chain rule. There is really no surprising thing or deep insight. The math for de…
DL requires a certain degree of mathematical maturity to grasp.
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99% of the time people say linear algebra is required for something, they mean knowledge basic operations and properties of tensors more than actual "algebra". I found this when doing computer graphics. Is that true as well here?
Basic operations and properties of tensors are exactly what is taught in linear algebra, in addition to (in my experience) more accessible proofs. More or less through singular value decomposition and/or least squares. The core algorithms all build on top of each other. The `algebra` part of linear algebra refers to a `field`, but it might as well also be called arithmetic of tensors.
It would have no more value than web development if the average web developer could use it well.
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https://en.wikipedia.org/wiki/Moravec%27s_paradox Moravec's paradox is the observation in artificial intelligence and robotics that, contrary to traditional assumptions, reasoning requires very little computation, but sensorimotor and perception skills require enormous computational resources. The principle was articulated by Hans Moravec, Rodney Brooks, Marvin Minsky and others in the 1980s. Moravec wrote in 1988, "…
> Moravec wrote in 1988, "it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility". The resolution to the paradox is so simple I must be missing something. The amount of data in datasets for 'mobility' is basically zero. You would have to manually…
Basically it's hard to make a machine use and understand how to use it's physical form and in 1988 it was even harder. For chess it was easy. It's easy to get, understand and use chess data.
ML is one of the easiest fields out there. When I learned it I was actually turned off by how simplistic the concept was. Of course let me preface to say that it's hard to develop the intuition and skill in the same way learning to skateboard is hard. But conceptually it's easy and very possible for almost anyone. The whole thing is just curve fitting. Literally finding some best fit curve across a series of points.…
I probably shouldn’t take the bait here, but this reads like someone took intro to ML and thinks that’s all there is to know. “Just” fitting a curve couldn’t be more reductionist and discount the work of a ton of incredibly intelligent people. I tend to agree that I don’t really find ML work all that interesting (much more interested in making it go fast :)), but simple it is not.
Put it this way, it's extremely challenging and not simple at all to walk and balance on a wire. Tight rope walking is not simple at all because very few people can do it.
But tightrope walking is different from something like Quantum physics. I may not be able to tightrope walk but I can understand the concept in it's entirety. For Quantum Physics, many people will never truly understand it.
What I'm saying is this, ML is tightrope walking. Challenging, not simple, but NOT quantum physics. It only seems like quantum physics.
ML is one of the easiest fields out there. When I learned it I was actually turned off by how simplistic the concept was. Of course let me preface to say that it's hard to develop the intuition and skill in the same way learning to skateboard is hard. But conceptually it's easy and very possible for almost anyone. The whole thing is just curve fitting. Literally finding some best fit curve across a series of points.…
> The whole thing is just curve fitting. > We only understand and build these things at a high level. At the very low level we don't actually understand what's going on. Contradictory..
So we create a curve and estimate it. We will never know the true equation. Additionally the curve has hundreds of dimensions and is essentially something that can't be visualized or understood cohesively. We have this neural network that represents the curve but the neural network is a black box.
ML is one of the easiest fields out there. When I learned it I was actually turned off by how simplistic the concept was. Of course let me preface to say that it's hard to develop the intuition and skill in the same way learning to skateboard is hard. But conceptually it's easy and very possible for almost anyone. The whole thing is just curve fitting. Literally finding some best fit curve across a series of points.…
> Douglas Hofstadter had this whole theory of consciousness and when he found out that an LLM was a simple feed forward network with no feedback loops he went into a crisis. Basically his whole theory in GEB was wrong, according to him. LLMs are not conscious. The training process for an LLM is not a feed forward network. If we were going to try to fit the idea of consciousness a la humanity (which is really the only…
See here: https://www.nytimes.com/2023/07/13/opinion/ai-chatgpt-consci...
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What are ML jobs about? I have this vague notion that you spend a lot of time gathering/cleaning data and throwing things at the wall, but maybe that's not accurate. I've always been stronger at discrete type math/programming, which is why I tend to shy away from statistics-based stuff like ML. One thing to note is that LLMs are indeed feed forward, however the generation of the text (from my understanding) is recurs…
> I've always been stronger at discrete type math/programming, which is why I tend to shy away from statistics-based stuff like ML. I think there's a major misconception that ML in the form of deep learning is about statistics. There's no statistics in deep learning models. There are some statistical measurements made of final models, much in the same way a good computer science paper covering implementations of disc…
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> I've always been stronger at discrete type math/programming, which is why I tend to shy away from statistics-based stuff like ML. I think there's a major misconception that ML in the form of deep learning is about statistics. There's no statistics in deep learning models. There are some statistical measurements made of final models, much in the same way a good computer science paper covering implementations of disc…
I believe curve fitting is part of statistics.