Could Amazon Be Replacing Recruiters With Artificial Intelligence Software? https://
forbes.com/sites/jackkell
y/2022/11/28/could-amazon-be-replacing-recruiters-with-artificial-intelligence-software/
… @Forbes #AI #DeepLearning #Robots #BigData #Analytics #DataScience #100DaysofCode #IoT #serverless #womenwhocode #Privacy #FutureOfWork #DigitalTransformation #MachineLearning
MACHINE LEARNING
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Amazon Using AI to Replace Human Recruiters
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Machine Learning Algorithms Optimize Intelligent Search Results in Healthcare
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"Already, multiple machine learning algorithms are running in the background to determine the optimal and safest way to serve the results wanted" — @natarpr, our VP of Healthcare Analytics comments on intelligent search in @AIMagazine_BC https://
issuu.com/ai-magazine/do
cs/ai_-_december_2022/74
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AutoML Pros, Cons, and Misconceptions Revealed
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Automated ML can do a lot – but it can also be a solution looking for a problem. When is it useful or not? In @elleng_glover
's in-depth article for @BuiltIn
, Domino’s Kjell Carlsson reveals the pros, cons, and misconceptions about #AutoML: -
New Neural Network Zero to Hero YouTube Series in Development
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(I think he means my new NN: Zero to Hero series https://
youtube.com/playlist?list=
PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ
… , which I'm still building out) -
AI-Assisted Data Science Platform with Neural Architecture Search and MLOps
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AI-assisted Data Science – Neural architecture search for custom model dev
– Code generation models help with data transformation
– End to End MLOps platform for production-ready ML and data-science The first of its kind in the world – @abacusai -
Cerebras Sparse ML Techniques Preserve GPT Model Accuracy
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At Cerebras, we are creating sparse ML techniques paired with our CS-2 hardware. Our initial work has shown that high degrees of sparsity can be induced into large-scale GPT models while still preserving model accuracy Learn more here: https://
tinyurl.com/yc22pbbz #Neurisp2022 #ai -
Detecting Out-of-Distribution Data: Impossibility and Learnability Theorems
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Outstanding paper at #NeurIPS22 main idea: is it possible to figure out when test data is coming from classes unknown during training? First they prove an impossibility theorem, then give positive/constructive results to characterize learnability of OOD. https://
openreview.net/pdf?id=sde_7Zz
GXOE
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Five Major Publications on Generative AI and Protein Folding
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We’re have five big publications to share, spanning #GenerativeAI to protein folding. You can see more here https://
bit.ly/3VwZyt7 2/4 -
Create Custom Memory Modules for LangChain Chains
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And here's how you can create your own custom memory module! https://
langchain.readthedocs.io/en/latest/exam
ples/memory/custom_memory.html
… This can be used to implement your own ideal memory structure and then easily plug it into any chain. -
Memory Module: Loading and Saving Data in Chains
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To achieve this, we added the the concept of a memory module This module: Loads things from memory before passing user input to the chain Saves things to memory after the chain is finished