Functional brain network architecture supporting the learning of social networks in humans @ArtificialIntelligenceArticles
Tompson et al.: https://psyarxiv.com/r46gj/
#brainnetworks #neuroscience #socialnetworks #neuralnetworks @ArtificialIntelligenceArticles
Tompson et al.: https://psyarxiv.com/r46gj/
#brainnetworks #neuroscience #socialnetworks #neuralnetworks @ArtificialIntelligenceArticles
Tesla’s new self-driving chip is here, and this is your best look yet
https://www.theverge.com/2019/4/22/18511594/tesla-new-self-driving-chip-is-here-and-this-is-your-best-look-yet
https://www.theverge.com/2019/4/22/18511594/tesla-new-self-driving-chip-is-here-and-this-is-your-best-look-yet
Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features
Zhang et al.: https://arxiv.org/abs/1904.10014
#ArtificialIntelligence #DeepLearning #MachineLearning
Zhang et al.: https://arxiv.org/abs/1904.10014
#ArtificialIntelligence #DeepLearning #MachineLearning
arXiv.org
Linked Dynamic Graph CNN: Learning on Point Cloud via Linking...
Learning on point cloud is eagerly in demand because the point cloud is a common type of geometric data and can aid robots to understand environments robustly. However, the point cloud is sparse,...
"The anticipating brain is not a scientist: the free-energy principle from an ecological-enactive perspective"
Bruineberg et al.: https://www.ncbi.nlm.nih.gov/pubmed/30996493?dopt=Abstract
#ActiveInference #FreeEnergyPrinciple #Metastability
Bruineberg et al.: https://www.ncbi.nlm.nih.gov/pubmed/30996493?dopt=Abstract
#ActiveInference #FreeEnergyPrinciple #Metastability
www.ncbi.nlm.nih.gov
The anticipating brain is not a scientist: the free-energy principle from an ecological-enactive perspective. - PubMed - NCBI
Synthese. 2018;195(6):2417-2444. doi: 10.1007/s11229-016-1239-1. Epub 2016 Oct 21.
NVIDIA Tesla T4 GPUs are now available in Colab
Faster computations with more available memory.
Read more: https://cloud.google.com/blog/products/ai-machine-learning/nvidia-tesla-t4-gpus-now-available-in-beta
#ArtificialIntelligence #Colab #DeepLearning #MachineLearning
Faster computations with more available memory.
Read more: https://cloud.google.com/blog/products/ai-machine-learning/nvidia-tesla-t4-gpus-now-available-in-beta
#ArtificialIntelligence #Colab #DeepLearning #MachineLearning
Google Cloud Blog
NVIDIA Tesla T4 GPUs now available in beta | Google Cloud Blog
T4 GPU instances are now available publicly in beta in cloud regions around the world for machine learning, visualization and other GPU-accelerated workloads.
Introducing SuperGLUE: A New Hope Against Muppetkind
Blog by Alex Wang: https://medium.com/@wang.alex.c/introducing-superglue-a-new-hope-against-muppetkind-2779fd9dcdd5
#MachineLearning #NLP #BigData
Blog by Alex Wang: https://medium.com/@wang.alex.c/introducing-superglue-a-new-hope-against-muppetkind-2779fd9dcdd5
#MachineLearning #NLP #BigData
Medium
Introducing SuperGLUE: A New Hope Against Muppetkind
Over the past year, a machine learning models have dramatically improved scores across many language understanding tasks in NLP. ELMo…
Machine learning and complex biological data
By Chunming Xu and Scott A. Jackson
Machine learning has demonstrated potential in analyzing large, complex biological data. In practice, however, biological information is required in addition to machine learning for successful application.
https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1689-0
#artificialintelligence #machinelearning #deeplearning #biology #genomics
By Chunming Xu and Scott A. Jackson
Machine learning has demonstrated potential in analyzing large, complex biological data. In practice, however, biological information is required in addition to machine learning for successful application.
https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1689-0
#artificialintelligence #machinelearning #deeplearning #biology #genomics
BioMed Central
Machine learning and complex biological data - Genome Biology
Machine learning has demonstrated potential in analyzing large, complex biological data. In practice, however, biological information is required in addition to machine learning for successful application.
Toshiba's breakthrough algorithm realizes world's fastest, largest-scale combinatorial optimization
Toshiba Corporation has realized a major breakthrough in combinatorial optimization—the selection of the best solutions from among an enormous number of combinatorial patterns—with the development of an algorithm that delivers the world's fastest and largest-scale performance, and an approximately 10-fold improvement over current methods. Toshiba's new method can be applied to such daunting but essential tasks as identifying efficient delivery routes, determining the most effective molecular structures to investigate in new drug development, and building portfolios of profitable financial products.
https://m.phys.org/news/2019-04-toshiba-breakthrough-algorithm-world-fastest.html
#optimization #algorithms #computing
Toshiba Corporation has realized a major breakthrough in combinatorial optimization—the selection of the best solutions from among an enormous number of combinatorial patterns—with the development of an algorithm that delivers the world's fastest and largest-scale performance, and an approximately 10-fold improvement over current methods. Toshiba's new method can be applied to such daunting but essential tasks as identifying efficient delivery routes, determining the most effective molecular structures to investigate in new drug development, and building portfolios of profitable financial products.
https://m.phys.org/news/2019-04-toshiba-breakthrough-algorithm-world-fastest.html
#optimization #algorithms #computing
phys.org
Toshiba's breakthrough algorithm realizes world's fastest, largest-scale combinatorial optimization
Toshiba Corporation has realized a major breakthrough in combinatorial optimization—the selection of the best solutions from among an enormous number of combinatorial patterns—with the development ...
Causal Inference in Machine Learning
Ricardo Silva
Department of Statistical Science andCentre for Computational Statistics and Machine Learning
Imperial College
https://www.homepages.ucl.ac.uk/~ucgtrbd/talks/imperial_causality.pdf
Ricardo Silva
Department of Statistical Science andCentre for Computational Statistics and Machine Learning
Imperial College
https://www.homepages.ucl.ac.uk/~ucgtrbd/talks/imperial_causality.pdf
Fooling automated surveillance cameras: adversarial patches to attack person detection
https://arxiv.org/pdf/1904.08653.pdf
https://arxiv.org/pdf/1904.08653.pdf
Registration is still open to attend our Industrial Short Course on Deep Learning on May 16 - 17, 2019!
This 2-day course is primarily aimed at participants from industry and government agencies. The course will be given by Professor Xavier Bresson from the Nanyang Technological University (NTU) in Singapore, who is a leading researcher in the field of deep learning. Participants will learn about the theory of deep learning techniques as well as practical exercises. To register and learn more, visit the course webpage.
https://www.ipam.ucla.edu/programs/special-events-and-conferences/an-industrial-short-course-on-deep-learning-and-the-latest-ai-algorithms-2019/ #DeepLearning #Math #AI #NeuralNetworks
This 2-day course is primarily aimed at participants from industry and government agencies. The course will be given by Professor Xavier Bresson from the Nanyang Technological University (NTU) in Singapore, who is a leading researcher in the field of deep learning. Participants will learn about the theory of deep learning techniques as well as practical exercises. To register and learn more, visit the course webpage.
https://www.ipam.ucla.edu/programs/special-events-and-conferences/an-industrial-short-course-on-deep-learning-and-the-latest-ai-algorithms-2019/ #DeepLearning #Math #AI #NeuralNetworks
IPAM
An Industrial Short Course on Deep Learning and the Latest AI Algorithms 2019
Brain signals translated into speech using artificial intelligence
#AI #deeplearning #recurrentneuralnetworks
A prosthetic voice decodes what the brain intends to say and generates (mostly) understandable speech, no muscle movement needed.
Many people who have lost the ability to speak communicate using technology that requires them to make tiny movements to control a cursor that selects letters or words on a screen. UK physicist Stephen Hawking, who had motor-neuron disease, was one famous example. He used a speech-generating device activated by a muscle in his cheek.
Because people who use such devices must type out words letter by letter, these devices can be very slow, producing up to ten words per minute. Natural spoken speech averages 150 words per minute. It’s the efficiency of the vocal tract that allows us to do that. So researchers decided to model the vocal system when constructing their decoder.
https://m.medicalxpress.com/news/2019-04-synthetic-speech-brain.html?fbclid=IwAR34iEZXpRhKpxQFuI6CLSTj981ugzwYRcPDUIhXx6BBsJ96p3Te58T2La0
#AI #deeplearning #recurrentneuralnetworks
A prosthetic voice decodes what the brain intends to say and generates (mostly) understandable speech, no muscle movement needed.
Many people who have lost the ability to speak communicate using technology that requires them to make tiny movements to control a cursor that selects letters or words on a screen. UK physicist Stephen Hawking, who had motor-neuron disease, was one famous example. He used a speech-generating device activated by a muscle in his cheek.
Because people who use such devices must type out words letter by letter, these devices can be very slow, producing up to ten words per minute. Natural spoken speech averages 150 words per minute. It’s the efficiency of the vocal tract that allows us to do that. So researchers decided to model the vocal system when constructing their decoder.
https://m.medicalxpress.com/news/2019-04-synthetic-speech-brain.html?fbclid=IwAR34iEZXpRhKpxQFuI6CLSTj981ugzwYRcPDUIhXx6BBsJ96p3Te58T2La0
Medicalxpress
Synthetic speech generated from brain recordings
A state-of-the-art brain-machine interface created by UC San Francisco neuroscientists can generate natural-sounding synthetic speech by using brain activity to control a virtual vocal tract—an anatomically ...
Advices for training your Neural Network like a Recipe. !
https://karpathy.github.io/2019/04/25/recipe/
https://karpathy.github.io/2019/04/25/recipe/
karpathy.github.io
A Recipe for Training Neural Networks
Musings of a Computer Scientist.
https://newsroom.gehealthcare.com/ai-helps-doctors-critical-measurement-during-pregnancy/
Automating the process of measuring the fetal brain may help get crucial data faster and easier. However, measuring the fetal brain is not an easy task and requires a significant amount of manual input from the sonographer. Today, an artificial intelligence (AI) powered tool can make this process much easier.
Automating the process of measuring the fetal brain may help get crucial data faster and easier. However, measuring the fetal brain is not an easy task and requires a significant amount of manual input from the sonographer. Today, an artificial intelligence (AI) powered tool can make this process much easier.
GE Healthcare The Pulse
Artificial Intelligence helps doctors with critical measurement during pregnancy
Automating the process of measuring the fetal brain may help get crucial data faster and easier
For many expectant parents, the first opportunity to “meet” their baby comes at 20-weeks of pregnancy. The ultrasound scan performed at that time gives the…
For many expectant parents, the first opportunity to “meet” their baby comes at 20-weeks of pregnancy. The ultrasound scan performed at that time gives the…
A simple model of a neuron can be approximated by "deep network" with one hidden layer
A more detailed model of a neuron with NMDA (glutamate receptor) synapses requires SEVEN hidden layers https://www.biorxiv.org/content/10.1101/613141v1
A more detailed model of a neuron with NMDA (glutamate receptor) synapses requires SEVEN hidden layers https://www.biorxiv.org/content/10.1101/613141v1
Statistical physics of liquid brains
Pinero et al.: https://www.biorxiv.org/content/biorxiv/early/2018/11/26/478412.full.pdf
#brain #physics #technology
Pinero et al.: https://www.biorxiv.org/content/biorxiv/early/2018/11/26/478412.full.pdf
#brain #physics #technology