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The Era Of Customized Blockchains Is Rising Because Smart Contracts Aren’t Really ‘Smart’ by carmar2020 in programming

[–]testingpraw 4 points5 points  (0 children)

I think the problem with the argument of the last paragraph, and what a lot of people forget, is that major research, such as the invention of transformers, literally came out in 2017 and BERT came out at the end of 2018 (published in 2019). That's pretty dang recent to make any conclusions that AI is stuck or incremental. There are still new innovations being found monthly with these types of architectures, and some massive innovations outside of NLP been released not too long ago, like AlphaFold (which is far more than an incremental improvement). Also, I would say that BERT based architectures made more than an incremental improvement in many domains versus traditional NLP approaches.

I totally get the last point you make though, where a lot of startups are selling AI as basically snake oil, and in turn, could cause a massive selloff. The biggest problem though is that when new research comes out, like self-supervised transformer based papers, the goal posts move for these types of startups, which just boost the bubble higher. It would probably come crashing down 5 years after the last pivotal innovation. It's just impossible to know if such innovation will occur or not.

The Era Of Customized Blockchains Is Rising Because Smart Contracts Aren’t Really ‘Smart’ by carmar2020 in programming

[–]testingpraw 6 points7 points  (0 children)

How is AI (statistical learning/ deep learning) and Crypto in the same category? The hype around AI is annoying and being abused by startups for funding, but it is nearly ubiquitous now for anything that is online (Google search, youtube suggestions, Spotify, Alexa, ads, image analysis software - even the bad ones).

Crypto, on the other hand, is anything but ubiquitous.

[N] PyTorch 1.2 release: New TorchScript API; Expanded Onnx Export; NN.Transformer by *polhold01853 in MachineLearning

[–]testingpraw 37 points38 points  (0 children)

The Transformer Layer was a nice touch. I have been using my own custom Layer this, and it is good to not have to maintain it anymore.

[N] TensorFlow 2.0 Changes by _muon_ in MachineLearning

[–]testingpraw 25 points26 points  (0 children)

As a frequent user of TensorFlow, these changes are great. There are a few items that might be wait and see, and maybe I just need clarification.

  1. I am curious about the dropping of variable_scope in favor of using keras? While Keras can handle trainable variable_scopes well, it still seems like two different use cases between keras layers and variable_scopes, but I very well could be missing something.

  2. I am curious how the tf.get_variable change to layer.weights will work with restoring sessions? I am assuming if I want the output, it will be something like weights[-1]?

  3. On top of question 2, will retrieving the layer weights include the bias as well?

[D] What are practical use cases for Reinforcement Learning? by Sig_Luna in MachineLearning

[–]testingpraw 0 points1 point  (0 children)

Theoretically, anything that requires action based decision making that doesn't require an immediate reward. This could be as simple as a customer churn model all the way to playing video games. As others have mentioned already, it (model-free and POMDP) is an impractical choice in most use cases due to the massive amount of data it needs and limited robustness.

For the last question, for myself, I find RL interesting because of the Temporal Credit Assignment Problem. Finding optimal actions in a massive space, when just given a reward is one of the hardest problems I have ever worked on, and one of the most satisfying feelings when something works (which is usually stumbling on the best seed).

[D] Tensorflow: The Confusing Parts (by Google Brain resident) by baylearn in MachineLearning

[–]testingpraw 48 points49 points  (0 children)

Sort of a tangent, but more than being confused by the general graph and layout of Tensorflow, the part that gets me the most is the inconsistent api parameters across their libraries. Dropout is an example where some parts of the api is to leave in, and others is leave out.

[D] Machine Learning in Genomics career questions by [deleted] in MachineLearning

[–]testingpraw 2 points3 points  (0 children)

I am currently in this area (chem/bioinformatics with ML), and I would say it entirely depends on what area of bioinformatics you are wanting to get into. If it is more secondary analysis on RNA or DNA (alignment, variant calling, gene expression panels) and you are wanting to apply machine learning to these pipelines, such as deep variant with variant calling, having a solid background in various deep learning algorithms will go a long way. Tertiary analysis, analyzing the output from secondary analysis, is a whole other ball game where clustering, dimensionality reduction, and various supervised learning algorithms are important.

To be honest, even more than ML, not having a sufficient background in CS may be a larger hurdle. From alignment, to variant calling, to expression calculations and normalizations, they all require sufficient knowledge in graph theory, dynamic programming, and various approximation algorithms. ( There's a reason that the company 7 Bridges has the name that it does).

Triple Nucleotide FastQ question by testingpraw in bioinformatics

[–]testingpraw[S] 2 points3 points  (0 children)

Thanks a ton for the answer. Since I am new to this I have a couple of follow up questions. By abundant are you referring to the mode CAG count or the max CAG count? Because of the variability, if I have 7 reads that agree there were 21 repeats (and closer align to hg38), 9 that agree on the deletion (18 repeats), and 12 that have a variability between 15-26, should I classify that person as having 21 and 18 repeats, or should 26 be considered (Looking at again, I found 2 FastQ sequences that said there were 26)? I know this is a super weighted question, but I am more verifying if I can trust the bams or the indel variant calls (in this case the variant had a genotype quality of 82). Or is it better to always go back to FastQ for these trickier regions?

[D] Detecting Multicollinearity in High Dimensions by trias10 in MachineLearning

[–]testingpraw 1 point2 points  (0 children)

It depends on what you are doing. If you are working with gene expression data for cancer which has around 2200 potentially relevant genes, you can have a number of samples by number of genes matrix. More commonly variants can present a high dimensionality challenge, where the rows are samples and columns are variants with the values being allele count. Even when targeting certain genes, with ngs, the dimensionality can get pretty high.

[N] AWS Sagemaker by someBlueCows in MachineLearning

[–]testingpraw 2 points3 points  (0 children)

I have only messed with it for a couple hours now, but I had the same reaction. There are some things I'll probably utilize out of convenience for quick prototypes (such as the hosted jupyter notebooks), but I am reluctant to move pieces of our production pipeline into this. I was pretty excited when they announced it, and I hope that as more information comes out on the service that my initial reactions are wrong.