Hey, guys, this is Dr. Gingersnap Zafir, and what today I would be discussing is a very, you know,
a sort of advancement in the realm of artificial intelligence.
And this is how we are approaching artificial intelligence.
The term I would be introducing sort of a buzzword.
It's called green artificial intelligence.
And the ultimate aim is rather than considering artificial intelligence, just for the sake of detection
or, you know, for classification task, there must be some effort to look into reducing the computational
costs in terms of time, in terms of energy, in terms of carbon footprint and in terms of the, you
know, financial cost thing.
Because, you know, as far as, you know, the the deep neural networks in Asia, they are concerned.
They have very, very intense time and resource dependent cost and they have similarly the associated
carbon footprint.
So so our aim today in this specific green artificial intelligence is to is to reduce that cost.
You know, the one way is that you are looking for a solution.
And then the other way or the other approach is that how that specific solution is costing you in terms
of other other parameters?
And if I simply model a specific problem, then the cost of the problem of any machine learning problem
is, first of all, your data set how you the type of the dataset, whether it's a big or huge dataset,
which is, by the way, in deep learning, you are using most of the time big data analytics, also
assuming the Deep Learning Network.
So you have a huge cost there.
Secondly is the training and then the regular or the periodic updating of the weights they do.
Sometimes you have to recompute again and again and periodically update the lead weights.
So they are adding the burden in terms of energy, in terms of financial costing.
And the other thing is that you have different hyper parameters involved which you want to optimize
to determine your solution.
So so if I simply model it, then I would see that the cost, the time cost and the resource cost is
a function of your dataset, the nature and the, you know, the magnitude of the data set.
And then comes your cleaning and processing at each layer either to back propagation or, you know,
in case of activation layers and recurrent neural networks.
And the third is the hypothalamic.
So it's a function of these three, the cost.
And when you approach the problem in this fashion that I have without compromising on the accuracy,
either detection, accuracy or declassification, the classification accuracy, I have to minimize this
function as well.
So this comes under the category of the green artificial intelligence.
And then it's it's a clear shift from, you know, that the right artificial intelligence towards the
green artificial intelligence.
And by doing this, you are limiting the elapsed time, you are optimizing the elapsed time, then you
are optimizing our reducing the carbon emissions associated with it.
So basically, we are in a nutshell, optimizing the floating point operations involved.
So when we reduce the floating point operations involved in any machine learning or the machine learning
problem, then that comes under this buzzword, the clean artificial intelligence.
So if you are interested in it has, by the way, a lot more implications in your recent sustainable
development goals as well.
Like the you know, or you can work.