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Top 10 Artificial Intelligence Technologies Making a Breakthrough in 2021


Artificial intelligence is the technological blow that took the world by storm. When the term'artificial intelligence' was first coined at a conference, …

[R]How to go about non-reproducible research?


I have been reproducing (or trying to reproduce) times series classification results for 23 years. In general, less than half the papers can be reproduced, but since the advent of deep learning, the fraction has gotten worse. A) "We made a best faith effort to reproduce the results in [x], but were unable to do so, thus we omit it from comparison" B) "We made a best faith effort to reproduce the results in [x], but were unable to do so. We do place our results using this method in Table Y with an asterisk, to denote that this is our best understanding of the algorithms performance, but just not reflect the claimed accuracy possible in [x]".

[P] Repository of efficient scripts for automatic conversion to TFRecords


Hello everyone, I recently noticed that the use of TFRecords isn't as popular as it should be. The efficiency and advantages that it provides, including but not limited to easy integration into TPU pipelines for Tensorflow, led me to creating a repository full of command line scripts to convert data from all popular domains like Audio, Text and Images (with video support coming soon) to TFRecord formats. The scripts also contain support for automatic SQLite and CSV datatype parsing and buffering. Along with this, they support an option for multiprocessing and are made with minimization of memory footprint in mind. If you are interested then please check out the repository here, give it a star if it is helpful and let me know if you have any feedback or suggestion.

[D] Production-ready ML models/pipelines/infrastructure best practice resources


There has been some discussion in this sub, but what are some external blogs/resources that have good best practices on productionizing ML and DL models and building out infrastructure to build, test, and deploy ML models, particularly at a small company? A sort of How-To guide would be great!

[D] KNN Performance decrease when new features were introduced


It's still quite useful, but it does not do any kind of feature selection nor does it consider any kind of feature importance when making predictions. All it does to make predictions is to calculate the distance in feature space between the new observation you wish to make a prediction for and all the other observations it has been trained on, and find the k closest old ones to the new one, then take some aggregate of the target variable of the k closest observations (usually mean for regression and mode for classification). If you add several completely random columns to your data, kNN will use them in calculating the distance to the exact same extent as the meaningful columns. This is opposed to smarter algorithms like linear models that can figure out to ignore features that contain no predictive value. If your model is getting worse when you add new features, it doesn't even mean they contain no value.

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In battle with US, China to focus on 7 'frontier' technologies from chips to brain-computer fusion


Machine learning is the development of AI programs trained on vast amounts of data. The program "learns" as it is fed more data. AI has been a key field …