#GoogleAI ’s paper on highly-accurate genomes via #DeepLearning was published today in #NatureBiotech.
#DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.
Link: https://rdcu.be/7Dhl
Github: https://github.com/google/deepvariant
#DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.
Link: https://rdcu.be/7Dhl
Github: https://github.com/google/deepvariant
Nature
A universal SNP and small-indel variant caller using deep neural networks
Nature Biotechnology - DeepVariant uses convolutional neural networks to improve the accuracy of variant calling.
Astrologers proclaimed month of #dataaugmentation since #GoogleAI released AutoAugment library — reinforcement learning algorithm which increases both the amount and diversity of existing data by finding optimal image augmentation policies.
Link: https://ai.googleblog.com/2018/06/improving-deep-learning-performance.html
Arxiv: https://arxiv.org/abs/1805.09501
Link: https://ai.googleblog.com/2018/06/improving-deep-learning-performance.html
Arxiv: https://arxiv.org/abs/1805.09501
Googleblog
Improving Deep Learning Performance with AutoAugment
Nice paper from the #GoogleAI team, grading prostate cancer in prostatectomy specimens.
The model outperforms humans on the silver standard labels (panel of experts), but there is no clear winner for outcome prediction in the K-M plot/c-index.
«the mean accuracy among 29 general pathologists was 0.61. The DLS achieved an... accuracy of 0.70 (p=0.002) and trended towards better patient risk stratification»
Post: https://ai.googleblog.com/2018/11/improved-grading-of-prostate-cancer.html
ArXiV: https://arxiv.org/abs/1811.06497
#DL #medical #cancer
The model outperforms humans on the silver standard labels (panel of experts), but there is no clear winner for outcome prediction in the K-M plot/c-index.
«the mean accuracy among 29 general pathologists was 0.61. The DLS achieved an... accuracy of 0.70 (p=0.002) and trended towards better patient risk stratification»
Post: https://ai.googleblog.com/2018/11/improved-grading-of-prostate-cancer.html
ArXiV: https://arxiv.org/abs/1811.06497
#DL #medical #cancer
Googleblog
Improved Grading of Prostate Cancer Using Deep Learning
Plug-and-play differential privacy for your tensorflow code
#GoogleAI has just released a new library for training machine learning models with (differential) privacy for training data.
where you would write
instead just swap in the
Tutorial: https://github.com/tensorflow/privacy/blob/master/tutorials/mnist_dpsgd_tutorial.py
Link: https://github.com/tensorflow/privacy
#Privacy #tensorflow
#GoogleAI has just released a new library for training machine learning models with (differential) privacy for training data.
where you would write
tf.train.GradientDescentOptimizerinstead just swap in the
DPGradientDescentOptimizerTutorial: https://github.com/tensorflow/privacy/blob/master/tutorials/mnist_dpsgd_tutorial.py
Link: https://github.com/tensorflow/privacy
#Privacy #tensorflow
GitHub
privacy/tutorials/mnist_dpsgd_tutorial.py at master · tensorflow/privacy
Library for training machine learning models with privacy for training data - tensorflow/privacy
Lingvo: A TensorFlow Framework for Sequence Modeling
Release from #GoogleAI: general #tensorflow framework for #NLP.
#Lingvo is a deep learning framework used for sequence modeling tasks like machine translation, speech recognition, and speech synthesis.
Link: https://medium.com/tensorflow/lingvo-a-tensorflow-framework-for-sequence-modeling-8b1d6ffba5bb
Github: https://github.com/tensorflow/lingvo
Release from #GoogleAI: general #tensorflow framework for #NLP.
#Lingvo is a deep learning framework used for sequence modeling tasks like machine translation, speech recognition, and speech synthesis.
Link: https://medium.com/tensorflow/lingvo-a-tensorflow-framework-for-sequence-modeling-8b1d6ffba5bb
Github: https://github.com/tensorflow/lingvo
Medium
Lingvo: A TensorFlow Framework for Sequence Modeling
Posted by Jonathan Shen
End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography
Researchers from #GoogleAi and #Stanford published work today in #Nature that shows great potential to use machine learning to help catch more lung cancer cases earlier and increase survival likelihood.
Link: http://go.nature.com/2LSMaAz
#LungCancer #Cancer #biolearning #healthcare #DL #CV
Researchers from #GoogleAi and #Stanford published work today in #Nature that shows great potential to use machine learning to help catch more lung cancer cases earlier and increase survival likelihood.
Link: http://go.nature.com/2LSMaAz
#LungCancer #Cancer #biolearning #healthcare #DL #CV
Google AI research on learning better simulation methods for partial differential equations
New research shows how machine learning can improve high-performance computing for solving partial differential equations, with potential applications that range from modeling #climatechange to simulating fusion reactions. Learn all about it here
Link: https://ai.googleblog.com/2019/07/learning-better-simulation-methods-for.html
#PDE #DE #GoogleAI
New research shows how machine learning can improve high-performance computing for solving partial differential equations, with potential applications that range from modeling #climatechange to simulating fusion reactions. Learn all about it here
Link: https://ai.googleblog.com/2019/07/learning-better-simulation-methods-for.html
#PDE #DE #GoogleAI
🔥DeepMind’s AlphaStar beats top human players at strategy game StarCraft II
AlphaStar by Google’s DeepMind can now play StarCraft 2 so well that it places in the 99.8 percentile on the European server. In other words, way better than even great human players, achieving performance similar to gods of StarCraft.
Solution basically combines reinforcement learning with a quality-diversity algorithm, which is similar to an evolutionary algorithm.
What’s difficult about StarCraft and how is it different to recent #Go and #Chess AI solutions: even finding winning strategy (StarCraft is famouse to closeness to rock-scissors-paper, not-so-transitive game design, as chess and go), is not enough to win, since the result depends on execution on different macro and micro levels at different timescales.
How that is applicable in real world: basically, it is running logistics, manufacture, research with complex operations and different units.
Why this matters: it brings AI one step closer to running real business.
Blog post: https://deepmind.com/blog/article/AlphaStar-Grandmaster-level-in-StarCraft-II-using-multi-agent-reinforcement-learning
Nature: https://www.nature.com/articles/d41586-019-03298-6
ArXiV: https://arxiv.org/abs/1902.01724
Nontechnical video: https://www.youtube.com/watch?v=6eiErYh_FeY
#Google #GoogleAI #AlphaStar #Starcraft #Deepmind #nature #AlphaZero
AlphaStar by Google’s DeepMind can now play StarCraft 2 so well that it places in the 99.8 percentile on the European server. In other words, way better than even great human players, achieving performance similar to gods of StarCraft.
Solution basically combines reinforcement learning with a quality-diversity algorithm, which is similar to an evolutionary algorithm.
What’s difficult about StarCraft and how is it different to recent #Go and #Chess AI solutions: even finding winning strategy (StarCraft is famouse to closeness to rock-scissors-paper, not-so-transitive game design, as chess and go), is not enough to win, since the result depends on execution on different macro and micro levels at different timescales.
How that is applicable in real world: basically, it is running logistics, manufacture, research with complex operations and different units.
Why this matters: it brings AI one step closer to running real business.
Blog post: https://deepmind.com/blog/article/AlphaStar-Grandmaster-level-in-StarCraft-II-using-multi-agent-reinforcement-learning
Nature: https://www.nature.com/articles/d41586-019-03298-6
ArXiV: https://arxiv.org/abs/1902.01724
Nontechnical video: https://www.youtube.com/watch?v=6eiErYh_FeY
#Google #GoogleAI #AlphaStar #Starcraft #Deepmind #nature #AlphaZero
YouTube
The AI that mastered Starcraft II
Google’s DeepMind artificial intelligence researchers have already mastered games like Pong, Chess and Go but their latest triumph is on another planet. AlphaStar is an artificial intelligence trained to play the science fiction video game StarCraft II.
…
…
🔥Human-like chatbots from Google: Towards a Human-like Open-Domain Chatbot.
TLDR: humanity is one huge step closer to a chat-bot, which can chat about anything and has great chance of success, passing #TuringTest
What does it mean: As an example, soon you will have to be extra-cautious chatting in #dating apps, because there will be more chat-bots, who can seem humane.
This also means that there will some positive and productive applications too: more sophisticated selling operators, on-demand psychological support, you name it.
It might be surprising, but #seq2seq still works. Over 5+ years of working on neural conversational models, general progress is a fine-tune of basic approach. It is a proof that much can be still discovered, along with room for new completely different approaches.
«Perplexity is all a chatbot needs ;)» (с) Quoc Le
Blog post: https://ai.googleblog.com/2020/01/towards-conversational-agent-that-can.html
Paper: https://arxiv.org/abs/2001.09977
Demo conversations: https://github.com/google-research/google-research/tree/master/meena
#NLP #NLU #ChatBots #google #googleai
TLDR: humanity is one huge step closer to a chat-bot, which can chat about anything and has great chance of success, passing #TuringTest
What does it mean: As an example, soon you will have to be extra-cautious chatting in #dating apps, because there will be more chat-bots, who can seem humane.
This also means that there will some positive and productive applications too: more sophisticated selling operators, on-demand psychological support, you name it.
It might be surprising, but #seq2seq still works. Over 5+ years of working on neural conversational models, general progress is a fine-tune of basic approach. It is a proof that much can be still discovered, along with room for new completely different approaches.
«Perplexity is all a chatbot needs ;)» (с) Quoc Le
Blog post: https://ai.googleblog.com/2020/01/towards-conversational-agent-that-can.html
Paper: https://arxiv.org/abs/2001.09977
Demo conversations: https://github.com/google-research/google-research/tree/master/meena
#NLP #NLU #ChatBots #google #googleai
research.google
Towards a Conversational Agent that Can Chat About…Anything
Posted by Daniel Adiwardana, Senior Research Engineer, and Thang Luong, Senior Research Scientist, Google Research, Brain Team Modern conversatio...
AutoFlip: An Open Source Framework for Intelligent Video Reframing
Google released a tool for smart video cropping. Video cropping doesn't seem like a poblem until you release that object that should be in focus can be in different parts of picture. Now there is great attempt to provide one-click solution to cropping.
Interesting part: #AutoFlip is an application of #MediaPipe framework for building multimodal ML #pipelines.
Github: https://github.com/google/mediapipe/blob/master/mediapipe/docs/autoflip.md
MediaPipe: https://github.com/google/mediapipe/
#Google #GoogleAI #DL #CV
Google released a tool for smart video cropping. Video cropping doesn't seem like a poblem until you release that object that should be in focus can be in different parts of picture. Now there is great attempt to provide one-click solution to cropping.
Interesting part: #AutoFlip is an application of #MediaPipe framework for building multimodal ML #pipelines.
Github: https://github.com/google/mediapipe/blob/master/mediapipe/docs/autoflip.md
MediaPipe: https://github.com/google/mediapipe/
#Google #GoogleAI #DL #CV
The Open Images Dataset V4 by GoogleAI
#GoogleAI present #OpenImagesV4, a #dataset of 9.2M images with unified annotations for:
– image #classification
– object #detection
– visual relationship detection
30.1M image-level labels for 19.8k concepts, 15.4M bounding boxes for 600 object classes
paper: https://arxiv.org/abs/1811.00982v2
#GoogleAI present #OpenImagesV4, a #dataset of 9.2M images with unified annotations for:
– image #classification
– object #detection
– visual relationship detection
30.1M image-level labels for 19.8k concepts, 15.4M bounding boxes for 600 object classes
paper: https://arxiv.org/abs/1811.00982v2
