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#هوش_مصنوعی
#مقاله
#یادگیری_ماشین
#fMRI

#مغز


🔵 ‘Mind reading’ technology identifies complex thoughts, using machine learning and fMRI
CMU aims to map all types of knowledge in the brain

By combining machine-learning algorithms with fMRI brain imaging technology, Carnegie Mellon University (CMU) scientists have discovered, in essense, how to “read minds.”

The researchers used functional magnetic resonance imaging (fMRI) to view how the brain encodes various thoughts (based on blood-flow patterns in the brain). They discovered that the mind’s building blocks for constructing complex thoughts are formed, not by words, but by specific combinations of the brain’s various sub-systems.

Following up on previous research, the findings, published in Human Brain Mapping (open-access preprint here) and funded by the U.S. Intelligence Advanced Research Projects Activity (IARPA), provide new evidence that the neural dimensions of concept representation are universal across people and languages.

منبع :

https://www.kurzweilai.net/mind-reading-technology-identifies-complex-thoughts-using-machine-learning-and-fmri

ژورنال :
https://www.ccbi.cmu.edu/reprints/Wang_Just_HBM-2017_Journal-preprint.pdf

#ICYMI 10 Free Must-Read Books for #MachineLearning and #DataScience https://buff.ly/2uswqac

Great new upgrade to #pix2pix! Perceptual Adversarial Networks for Image-to-Image Transformation https://arxiv.org/abs/1706.09138 #AI #ML

New automated method helps explain the inner workings of neural networks for machine vision: https://bit.ly/2tsueTb

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#هوش_مصنوعی
#مغز
#آگاهی
#علوم_شناختی

🔵Ned Block: Why AI Approaches to Cognition Won’t Work for Consciousness


In this Talk at Google, Ned Block talks about how current AI approaches to cognition won’t work for creating consciousness.


https://youtu.be/6lHHxcxurhQ

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#AI #ArtificialIntelligence #MachineLearning #Robots #Bots #DeepLearning


🔵#Humanoid #Robot Tells Jokes on @GMB ! |

https://youtu.be/kWlL4KjIP4M



https://www.thesun.co.uk/tech/3847594/good-morning-britains-piers-morgan-flirts-with-a-robot-called-sophia-and-asks-her-what-kind-of-men-shes-into-before-calling-her-a-little-freak/

Video: #DeepLearning and the Future of #ArtificialIntelligence(AI) | Facebook AI Director Yann LeCun https://youtu.be/wbcYG9wOvRc

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#هوش_مصنوعی
#آموزش
#یادگیری_عمیق


🔵Learning Artificial Intelligence(AI) and Tensorflow Without a PhD by Google's Martin Görner

Published on Jun 19, 2017
Google has recently open-sourced its framework for machine learning and neural networks called Tensorflow. With this new tool, deep machine learning transitions from an area of research into mainstream software engineering. In this session, we will teach you how to choose the right neural network for your problem and how to make it behave. Familiarity with differential equations is no longer required. Instead, a couple of lines ofTensorflow Python, and a bag of "tricks of the trade" will do the job. No previous Python knowledge required.

This university session will cover the basics of deep learning, without any assumptions about the level of the participants. Machine learning beginners are welcome. We will cover: - fully connected neural networks - convolutional neural networks - regularization techniques: dropout, learning rate decay, batch normalization - recurrent neural networks - natural language analysis, word embedding - transfer learning - image analysis - image generation - and many examples.

Martin Görner is passionate about science, technology, coding, algorithms and everything in between. He graduated from Mines Paris Tech, enjoyed his first engineering years in the computer architecture group of ST Microlectronics and then spent the next 11 years shaping the nascent eBook market, starting with the Mobipocket startup, which later became the software part of the Amazon Kindle and its mobile variants. He joined Google Developer Relations in 2011 and now focuses on parallel processing and machine learning.

https://www.youtube.com/watch?v=wDxDhvLLNuE

"Towards Understanding Generalization of Deep Learning: Perspective of Loss Landscapes": cuz loss function & minima https://arxiv.org/abs/1706.10239

DeepMind’s Relational Reasoning Networks - Demystified. https://buff.ly/2sybylc #BigData #DeepLearning #MachineLearning #DataScience #AI

Machine Learning: A Probabilistic Perspective. #BigData #MachineLearning #DataScience #AI
https://buff.ly/2sygV42

"wrapper AI" replaces portions of analyzed images with white noise to help assess original https://www.newscientist.com/article/2139396-peering-inside-an-ais-brain-will-help-us-trust-its-decisions

مقاله ی مطلب بالایی

🔵Latent Attention Networks

Christopher Grimm, Dilip Arumugam, Siddharth Karamcheti, David Abel, Lawson L.S. Wong, Michael L. Littman
(Submitted on 2 Jun 2017)
Deep neural networks are able to solve tasks across a variety of domains and modalities of data. Despite many empirical successes, we lack the ability to clearly understand and interpret the learned internal mechanisms that contribute to such effective behaviors or, more critically, failure modes. In this work, we present a general method for visualizing an arbitrary neural network's inner mechanisms and their power and limitations. Our dataset-centric method produces visualizations of how a trained network attends to components of its inputs.


https://arxiv.org/abs/1706.00536

#هوش_مصنوعی
#مقاله
#شبکه_عصبی_عمیق
#روان_شناسی_شناختی


🔵Interpreting Deep Neural Networks using Cognitive Psychology

Deep neural networks have learnt to do an amazing array of tasks - from recognising and reasoning about objects in images to playing Atari and Go at super-human levels. As these tasks and network architectures become more complex, the solutions that neural networks learn become more difficult to understand.
This is known as the ‘black-box’ problem, and it is becoming increasingly important as neural networks are used in more and more real world applications.

https://deepmind.com/blog/cognitive-psychology/


مقاله
https://arxiv.org/abs/1706.08606

🔵تمام پروژه‌های ایلان ماسک

بیزینس‌ویک زیرشاخهٔ بلومبرگ یک صفحه وب با طراحی تعاملی ساخته که در آن می‌توانید پیشرفت تک تک پروژه‌های ایلان ماسک را دنبال کنید

https://www.bloomberg.com/features/elon-musk-goals/

Strengthening African Machine Learning. Sharing our vision for the Indaba. https://www.deeplearningindaba.com/blog/strengthening-african-machine-learning

Can Artificial Intelligence & Robots fight the Cybercrime Epidemic? https://bit.ly/2uGtYgj #ai #ml #dl

"The 9 #DeepLearning Papers You Need To Know About" by Adit Deshpande
https://adeshpande3.github.io/adeshpande3.github.io/The-9-Deep-Learning-Papers-You-Need-To-Know-About.html

Towards Understanding Generalization of Deep Learning: Perspective of Loss Landscapes: init + geom. of loss func: https://arxiv.org/abs/1706.10239