Read along with Google Home Mini and Disney's Little Golden Books: #HCI #ML
Google will grant $25 million next year to humanitarian & environmental projects seeking to use AI to speed up and grow their efforts: https://www.reuters.com/article/us-alphabet-google-aid/google-seeks-to-grant-25-million-to-ai-for-good-projects-idUSKCN1N32CW #ClimateChange #ML
Model of an oscillatory neural network with multilevel neurons for pattern recognition:
https - by Andrei Velichko et.al: https://arxiv.org/abs/1806.03079
A New Method of the Pattern Storage and Recognition in Oscillatory Neural Networks Based on Resistive Switches: https://www.mdpi.com/2079-9292/7/10/266 #ML
Oscillating Neural Network Demonstration:
It has been recognized for decades that the brain produces rhythmic patterns of electrical activity, colloquially known as ‘brain waves’. These rhythmic patterns reflect the activity of thousands or millions of neurons, each with its own intrinsic rhythmic tendencies. If each neuron is firing independently of its neighbors, the overall effect will appear as noise, but when they become synchronized, their combined effect can be detected as rhythmic oscillations, which in some cases are strong enough to penetrate the skull, allowing them to be recorded noninvasively with electrodes on the scalp. In this video, McGovern Institute director Bob Desimone illustrates a mechanical analogy for how this synchronization occurs; the ticking metronomes influence each other through the side-to-side movements of the board on which they sit, and over time this causes them to lock into a synchronous pattern.
A Variational U-Net for Conditional Appearance and Shape Generation: Paper: https://compvis.github.io/vunet/ Code: https://github.com/CompVis/vunet
20 Top Lawyers Were Beaten By Legal AI:
https://hackernoon.com/20-top-lawyers-were-beaten-by-legal-ai-here-are-their-surprising-responses-5dafdf25554d #ML
In a landmark study, 20 top US corporate lawyers with decades of experience in corporate law and contract review were pitted against an AI. Their task was to spot issues in five Non-Disclosure Agreements (NDAs), which are a contractual basis for most business deals. The study, carried out with leading legal academics and experts, saw the LawGeex AI achieve an average 94% accuracy rate, higher than the lawyers who achieved an average rate of 85%.
Generating custom photo-realistic faces using AI: #ML #Generative
https://blog.insightdatascience.com/generating-custom-photo-realistic-faces-using-ai-d170b1b59255
CVPR18: Session 2-2A: Video Analytics: #ML
Two-Stream RNN/CNN for action recognition in 3D videos: #ML
Keras implementation of Deeplabv3+ - a state-of-art deep learning model for semantic image segmentation: https://github.com/bonlime/keras-deeplab-v3-plus #ML
Location Dependency in Video Prediction: https://arxiv.org/abs/1810.04937
Code: https://github.com/AIS-Bonn/LocDepVideoPrediction #ML
The digital I - talk by Roberto Saracco (Chair of the Symbiotic Autonomous Systems Initiative of IEEE-FDC) at TEDxTransmedia 2013
https://symbiotic-autonomous-systems.ieee.org/about/infographic
Podcast: Social Robotics and Symbiotic Autonomous Systems with Roberto Saracco
Timeline of AI scandals shown at the AINow2018 event. #ML
"The rise of robo-writers": http://theweek.com/articles/760985/rise-robowriters
"The Washington Post’s robot reporter has published 850 articles in the past year": https://digiday.com/media/washington-posts-robot-reporter-published-500-articles-last-year/
"ColourAIze: AI-Driven Colourisation of Paper Drawings with Interactive Projection System" - by @PreferredNet: #ML #Generative
"Unsupervised Image to Sequence Translation with Canvas-Drawer Networks":
https://arxiv.org/abs/1809.08340 https://canvasdrawer.autodeskresearch.com/
"Music Style Transfer: A Position Paper": https://arxiv.org/abs/1803.06841
#ML #Generative #Music
Neurythmic - "a new music sequencer based on central pattern generator neural networks":
https://danbennettdev.github.io/projects/neurythmic.html
Survey on Playlist Generation Techniques (2014):
http://ijarcet.org/wp-content/uploads/IJARCET-VOL-3-ISSUE-2-437-439.pdf
Music Playlist Continuation by Learning from Hand-Curated Examples and Song Features (2017): https://arxiv.org/pdf/1705.08283.pdf
Generating similarity-based playlists using traveling salesman algorithms (2005):
https://pdfs.semanticscholar.org/5133/45605087a1854d4212a62e17b07aff1a3100.pdf
Playlist Prediction via Metric Embedding (2012):
http://www.cs.cornell.edu/people/tj/publications/chen_etal_12a.pdf
Mixtape: direction-based navigation in large media collections (2016):
https://wp.nyu.edu/ismir2016/wp-content/uploads/sites/2294/2016/07/155_Paper.pdf
Mining online music listening trajectories (2016):
https://wp.nyu.edu/ismir2016/wp-content/uploads/sites/2294/2016/07/193_Paper.pdf
TribeFlow: Mining & Predicting User Trajectories (2016):
https://www.cs.purdue.edu/homes/ribeirob/pdf/tribeflow_2016.pdf
The Importance of Song Context and Song Order in Automated Music Playlist Generation (2018): https://arxiv.org/abs/1807.04690
A Hybrid Approach to Music Playlist Continuation Based on Playlist-Song Membership (2018):
https://arxiv.org/abs/1805.09557