[iamaaditya]

In machine learning (especially deep learning or neural networks), the 'training' is done by using Stochastic Gradient Descent. These gradients are computed using Backpropagation. Backpropagation requires you to do a backward pass of your model (typically many layers of neural weights) and thus requires you to keep in memory a lot of intermediate values (called activations). However, if you are doing "inference" that is if the goal is only to get the result but not improve the model, then you don't have to do the backpropagation and thus you don't need to store/save the intermediate values. As the layers and number of parameters in Deep Learning grows, this difference in computation in training vs inference becomes signifiant. In most modern applications of ML, you train once but infer many times, and thus it makes sense to have specialized hardware that is optimized for "inference" at the cost of its inability to do "training".

[eklitzke]

Just to add to this, the reason these inference accelerators have become big recently (see also the "neural core" in Pixel phones) is because they help doing inference tasks in real time (lower model latency) with better power usage than a GPU.

As a concrete example, on a camera you might want to run a facial detector so the camera can automatically adjust its focus when it sees a human face. Or you might want a person detector that can detect the outline of the person in the shot, so that you can blur/change their background in something like a Zoom call. All of these applications are going to work better if you can run your model at, say, 60 HZ instead of 20 HZ. Optimizing hardware to do inference tasks like this as fast as possible with the least possible power usage it pretty different from optimizing for all the things a GPU needs to do, so you might end up with hardware that has both and uses them for different tasks.

[sillyinseattle]

Thank you @iamaaditya and @eklitzke . Very informative

[dekhn]

it took me 20 years to learn this body of knowledge and now it can just sort of be summed up in a paragraph.

When I learned and used gradient descent, you had to analytically determine your own gradients (https://web.archive.org/web/20161028022707/https://genomics....). I went to grad school to learn how to determine my own gradients. Unfortunately, in my realm, loss landscapes have multiple minima, and gradient descent just gets trapped in local minima.

[thentherewere2]

This is the case most contemporary neural networks as well. It turns out for many domains, a "good" local minima generalizes well across many tasks.

[dekhn]

Huh. I talked to some experts and they told me NN loss functions are bowl-shaped and have single minima, but those minima take a very long time to navigate to in high dimensional spaces.

[Salgat]

For higher feature counts the real concern is saddle points rather than minima, where the gradient is so small that you barely move at all each iteration and get "stuck".

[timomo]

To add here: for a local minimum to occur all those dimensions (or features) need to increase. This is highly unlikely for modern NNs where you have millions of dimensions. If one of the dimensions is going down but the rest up, you have a saddle point. Since you go down only one (or few) dimensions it takes longer.

[ray__]

What's your realm?

[dekhn]

protein folding and structure prediction. Protein simulations typically define an energy function, similar to a loss function, over all the atoms in the protein. There are many terms: at least one per bonded atom pair, at least one per bonded atom triple, at least one per bonded atom quadruple, one per each non-bonded pair (although atoms that are distant can be excluded, sometimes making this a sparse matrix). If you start with a proposed model (say, random coordinates for all the atoms) and apply gradient descent, you'll end up with a mess. All those energy terms end up creating a high dimensional surface that is absurdly spiky in the details, and extremely wavy with many local minima at coarse grain.

Instead of using gradient descent, we used molecular dynamics (I'm unaware if this has a direct equivalent) to sample the space by moving along various isocontours (constant energy, or constant temp, or usually constant pressure). Even so, you have to do a lot of sampling- in my day, it was years of computer time, now it's months- to get a good approximation to the total landscape, and measure transition frequencies between areas of the landscape that correspond to energy barries (local maxima) that are smaller than the thermal energy avaialble to the system.

It's complicated. also, deep mind obviated all my work by providng that sequence data (which is cheap to obtain) can be used to predict very accurate structures with little or no simulation.

[derbOac]

Worth noting that inference in "traditional" statistics and ML/AI/DL isn't really that different at some level. In both cases you have an inverse problem; in one case the parameters are about a group or population (e.g., something about all cats in existence), and in another it is about an individual case (something about a particular cat).

[dataexporter]

This sounds really fascinating. Are there any resources that you'd recommend for someone who's starting out in learning all this? I'm a complete beginner when it comes to Machine Learning.

[dr_zoidberg]

Deep Learning with Python (2nd ed), by Francois Chollet.

If you don't mind about learning the part where you program, it's got a lot of beginner/intermediate concepts clearly explained. If you do dive into the programming examples, you get to play around with a few architectures and ideas and you're left on the step to dive into the more advanced material knowing what you're doing.

[sshlocalhost98]

Thanks for the explanation, really succinct. Do you recommend any good back propagation tutorials for an EE undergrad?