Viewing profile — naresh_xai

I think it can be done. Just maintain the data not in a standard tabular format but a time based graph instead. And put constraints to not look back. Constrained deep learning is a…

Current set of tools in explainable AI do allow deep neural nets to tell why they gave the response that they did to a certain extent. Contact me if you want to see it applied to t…

Current set of tools in explainable AI do allow deep neural nets to tell why they gave the response that they did to a certain extent. Contact me if you want to see it applied to t…

Are we still limiting to visual cues and not the auditory,smell,taste,touch data which we get exposed to?

Umm, no there are clear verification methods for Explainable AI techniques today. One way to check the justification would be if things which were important in the justification we…

That’s basically the gist of explainable AI

Pretty cool stuff

If revenue was more in a non-Apple ecosystem, would it not be the case that devs would all move to a non-Apple ecosystem. Android also has the problem that users are far less likel…

But apple does not allow for that. So in that sense, they do not allow businesses for targeted advertisement. Please correct me if I am wrong. Their processes and org structure is …

And 2 weeks later, the guy will resell stuff from another account from different paid reviews. Which are also always evolving to capture consumer trust. Works especially well for p…

Companies list/re-list products on amazon faster than you can say ‘go’ unfortunately.

The number of loans a bank gives out to such a specific factor might be low enough that it would take 30 years for a bank to have an actual study. That's 1-2 generations of abuse. …

Unless you have causal proof, its irresponsible for a business to use such factors in modeling outcomes.

I also think that it is possible that the model learned that information from too small of a data sample. What is a good data sample for every such feature in a relatively balanced…

AI with no dibiasing does not work often enough. I’ve seen enough examples in Computer Vision models to say so. (Even segmentation models tend to rely on external clues and this af…

Happy to share a lot more ethical and challenges of non transparent AI via email with you. Bigger companies are pretty much hiding behind non transparent models and trying to ignor…

Exactly why we need causal reasoning/causal proof alongwith ML

Interested in seeing where symbolic AI crowd has disagreed with that. Only group of people who disagree that I know of are a small set of people who think you should build inherent…

Happy to share decks and references to your email address. Please share your email address with me :)

Graph Neural networks are currently used a lot in the neural networks for drug discovery space. They significantly beat RNN and CNN baseline/complex versions equivalents on the sam…

@Paul Robinson: Did you look at the number of mislabeled images in the udacity self driving car dataset? If you do not have an understanding of the subject yourself, you’re likely …

Think more in terms of function decomposition rather than having to look at each individual parameter and you will find papers and techniques which lead to deep neural networks bei…

Again the non-sequitur argument of an explainable model must be worse than a deep learning system and there has to be a tradeoff. You don’t need to reduce complexity to induce expl…

You should definitely look at advances in the field then. There a lot more promising work beyond grad cam. And there are a few techniques for human oriented explanations - namely T…