One model to learn them all Kaiser et al., arXiv 2017 You almost certainly have an abstract conception of a banana in your head. Suppose you ask me if Iโd like anything to eat. I can say the word โโฆ
2018/1/27 Feed Summary
The Morning Paper
One Model to Learn Them All: In this post, the author summarize a paper which introduces a MultiModel general deep learning framework which will train 8 tasks at the same time (Image recognition, Image caption generation, Speech recognition, Parsing, German/French to English Translation and their reverse English to German/French translation). MultiModel consists an encoder / decoder architecture which would share a common learning unit. It also consists of a mixture-of-expert layer to dispatch the learning efforts. The article referred in this post shows that training such mixed model doesn't pose any performance degradation problem and sometimes help the task with less data available (parsing). This might imply a much larger scale or cross-domain transfer or multi-tasking learning experiment could be done in the future. This article also points out even though the computational building blocks need to be present for some specific domain (convolution neural network for image and attention / mixture-of-expert for language model), their presence does not interfere the cability of learning other tasks in different domains.
(RW: From the first glimpse of this short summary, the article referred in this post doesn't show any convincing result that cross-domain training does work by showing how much different these domains are. It just iterate similar conclusion from past experiments. Does Image caption take on the role to bridge image and language domain? How about choosing tasks randomly to break the MultiModel in order to know the degree of correlaiton shared among models? Would a sub-model which is trained insufficiently take the whole unified model down? I think those questions might shed some light about cross-domain learning)
KDNuggets
Kogentix Automated Machine Learning Platform: Another MLaaS targets bussiness data (pipeline in the figure below) and features the only one platform running Spark natively.
Data Enginner Introduction Part 1: In the following figure borrowed from The AI Heirarchy of Needs, AirFlow (monitoring tool used by Airbnb) locates at second layer of AI Needs Pyramid
The Democratization of Artificial Intelligence and Deep Learning: Free e-Book give-away. The Democratization of Artificial Intelligence is an idea to make Artificial Intelligence applicaiton is accessible to everyone.
Data Science Job Market Trends: automatino, data enpowerment, mass cleanup, ethnics & influence and blockchain app
O'Reilly Media / AI
tensoflow + mobile device: introduce Tensoflow Lite
using Apache MXNet for anomalty detection: Tutorial for using MXNet. Tranditional methods used to detect anomalty including: Kalm filter, KNN, K-Means and autoencoder with DL. Using IoT time-series data for demonstration. The author will train a encoder-decoder and detect anomalty as any data point outside 3rd standard deviation.
LSTM introduction with Tensorflow: using LSTM to classify Stock Tweets
2018 trends in AI O'Reilly version: And Include
Bayesian methods into Deep Learning and optimize training through neuro-evoluation on gradient-based deep learning.
Low cost hardware to improve computation efficiency.
Fast evolve AI tools including simulators (including reinforcement learning to automate deep learning training such as AutoML), AI develop toolbox handling more complicated / multimodal inputs and finally tools that not for data scientist or AI enginner for use such as friendly UI / UX etc or Intelligent wearables alike
Replace low-skilled tasks with automation
Other ethnics or issues about AI application
Convolution NN for language modeling: tutorial using 1D kernel and Tensorflow > Written with StackEdit.