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#خبر
#هوش_مصنوعی
#ناسا
#رباتیک
#مقاله

🌒ابهام در ضرورت سفر انسان به فضا

🔵هوش مصنوعی آغازگر عصر جدید اکتشافات فضایی

ناسا در اندیشه آغاز دور جدیدی از اکتشافات فضایی است که با همکاری هوش مصنوعی صورت می گیرد.
بدون شک افزودن هوش مصنوعی به ماشین آلاتی که برای اکتشافات فضایی راهی مدار زمین و فراتر از آن می شوند غیرقابل اجتناب خواهد بود. بدین ترتیب می توان تصور کرد که چنین کاوشگرهایی بدون اتلاف وقت و منتظر ماندن برای دریافت دستور از زمین، اقدام به تصمیم گیری کنند.

حالا خبر می رسد که آژانس فضانوردی آمریکا تحقق چنین چشم اندازی را به صورت جدی آغاز کرده است.


🌎NASA Are Figuring Out How to Use AI to Build Autonomous Space Probes
Who needs humans?

Adding artificial intelligence to the machines we send out to explore space makes a lot of sense, as it means they can make decisions without waiting for instructions from Earth, and now NASA scientists are trying to figure out how it could be done.

As we send out more and more probes into space, some of them may have to operate completely autonomously, reacting to unknown and unexplained scenarios when they get to their destination – and that's where AI comes in.

Steve Chien and Kiri Wagstaff from NASA's Jet Propulsion Laboratory think that these machines will also have to learn as they go, adapting to what they find beyond the reaches of our most powerful telescopes.

https://www.sciencealert.com/scientists-are-figuring-out-how-to-use-ai-to-build-autonomous-space-probes


مقاله


https://robotics.sciencemag.org/content/2/7/eaan4831

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#خبر
#هوش_مصنوعی
#یادگیری_عمیق

🔵Andrew Ng announces Deeplearning.ai, his new venture after leaving Baidu


Andrew Ng, the former chief scientist of Baidu, announced his next venture, Deeplearning.ai, with only a logo, a domain name and a footnote pointing to an August launch date. In an interesting twist, the Deeplearning.ai domain name appears to be registered to Baidu’s Sunnyvale AI research campus — the same office Ng would have worked out of as an employee.

https://techcrunch.com/2017/06/23/deeplearning/

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#یادگیری_عمیق


🔵Deep Learning with TensorFlow in Python

The following problems appeared in the first few assignments in the Udacity course Deep Learning (by Google). The descriptions of the problems are taken from the assignments.
Classifying the letters with notMNIST dataset
Let’s first learn about simple data curation practices, and familiarize ourselves with some of the data that are going to be used for deep learning using tensorflow. The notMNIST dataset to be used with python experiments. This dataset is designed to look like the classic MNIST dataset, while looking a little more like real data: it’s a harder task, and the data is a lot less ‘clean’ than MNIST.

https://www.datasciencecentral.com/profiles/blogs/deep-learning-with-tensorflow-in-python?utm_content=buffer06243&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer

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#بینایی_ماشین
#ردیابی_اشیا

🔵Tracking Objects with Point Clouds from Vision and Touch

We present an object-tracking framework that fuses point cloud information from an RGB-D camera with tactile information from a GelSight contact sensor. GelSight can be treated as a source of dense local geometric information, which we incorporate directly into a conventional point-cloud-based articulated object tracker based on signed-distance functions. Our implementation runs at 12 Hz using an online depth reconstruction algorithm for GelSight and a modified secondorder update for the tracking algorithm. We present data from hardware experiments demonstrating that the addition of contact-based geometric information significantly improves the pose accuracy during contact, and provides robustness to occlusions of small objects by the robot’s end effector.

https://www.kurzweilai.net/tactile-sensor-lets-robots-gauge-objects-hardness-and-manipulate-small-tools

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#هوش_مصنوعی
#مقاله
#خبر

🌎Google’s DeepMind Is Now Capable of Creating Images from Your Sentences


Google's DeepMind team has developed a way for their AI to be able to create images from sentences. The more detailed the sentence, the more detailed the resulting image will be.
The folks at Google’s DeepMind are hard at work bringing the world the latest developments in artificial intelligence (AI). Their latest breakthrough shows that their AI is capable of creating photorealistic pictures from human input in the form of sentences.

https://futurism.com/googles-deepmind-now-capable-creating-images-sentences/

مقاله

https://arxiv.org/pdf/1703.03664.pdf

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#خبر
#شبکه_عصبی
#هوش_مصنوعی


🔵MIT researchers develop chip faster than Artificial Intelligence neural networks
Everyday, the envelope is being pushed a little bit further on what artificial intelligence (AI) will eventually be like.


Everyday, the envelope is being pushed a little bit further on what artificial intelligence (AI) will eventually be like. (Reuters)Everyday, the envelope is being pushed a little bit further on what artificial intelligence (AI) will eventually be like. At the end, will it just be algorithms—of course, of increasing complexity—leading to an outcome, or will there be a fully functional, artificial brain? No one’s going to give you a definitive answer now.

https://www.financialexpress.com/industry/technology/mit-researchers-develop-chip-faster-than-artificial-intelligence-neural-networks/733256/

Mapping the Canadian AI Ecosystem

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#خبر
#هوش_مصنوعی

🔵Mapping the Canadian AI Ecosystem


The talent pool for AI research is tiny; at the office, we’ve tracked about 5600 researchers globally who are making an impact in the field. This size makes it critical that the industry players build relationships and share knowledge, to create an ecosystem that helps facilitate progress in AI so that we’re able to better specialize.

In the last couple of years, the Canadian AI ecosystem was pretty fractured, each cluster trying to win the race and get ahead of the pack. Cities like Montreal, Vancouver or Toronto would announce how their city was the place to be: great quality of life, financial incentives, a flourishing venture capital scene, some of the best researchers in the world, etc. The message tended to be that we have the ingredients to be the next hot spot.


https://www.jfgagne.ai/blog/2017/4/24/mapping-the-canadian-ai-ecosystem

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#هوش_مصنوعی
#مقاله

🔵CAN (Creative Adversarial Network) — Explained


Lately, GANs (Generative Adversarial Networks) have been really successful in creating interesting content that are fairly abstract and hard to create procedurally. This paper, aptly named CAN (Creative , instead of Generative, Adversarial Networks) explores the possibility of machine generated creative content.


مقاله :

🌎CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms

We propose a new system for generating art. The system generates art by looking at art and learning about style; and becomes creative by increasing the arousal potential of the generated art by deviating from the learned styles. We build over Generative Adversarial Networks (GAN), which have shown the ability to learn to generate novel images simulating a given distribution. We argue that such networks are limited in their ability to generate creative products in their original design. We propose modifications to its objective to make it capable of generating creative art by maximizing deviation from established styles and minimizing deviation from art distribution. We conducted experiments to compare the response of human subjects to the generated art with their response to art created by artists. The results show that human subjects could not distinguish art generated by the proposed system from art generated by contemporary artists and shown in top art fairs. Human subjects even rated the generated images higher on various scales.
This article assumes familiarity with neural networks, and essential aspects of them, including Loss Functions and Convolutions.

https://arxiv.org/abs/1706.07068

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#مقاله
#هوش_مصنوعی


🌎Learning about the world through video

At TwentyBN, we build AI systems that enable a human-like visual understanding of the world. Today, we are releasing two large-scale video datasets (256,591 labeled videos) to teach machines visual common sense. The first dataset allows machines to develop a fine-grained understanding of basic actions that occur in the physical world. The second dataset of dynamic hand gestures enables robust cognition models for human-computer interaction.

مقاله

https://arxiv.org/abs/1706.04261

دیتاست استفاده شده در مقاله

https://www.twentybn.com/datasets

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#مقاله
#هوش_مصنوعی

🔵A simple neural network module for relational reasoning


Relational reasoning is a central component of generally intelligent behavior, but has proven difficult for neural networks to learn. In this paper we describe how to use Relation Networks (RNs) as a simple plug-and-play module to solve problems that fundamentally hinge on relational reasoning. We tested RN-augmented networks on three tasks: visual question answering using a challenging dataset called CLEVR, on which we achieve state-of-the-art, super-human performance; text-based question answering using the bAbI suite of tasks; and complex reasoning about dynamic physical systems. Then, using a curated dataset called Sort-of-CLEVR we show that powerful convolutional networks do not have a general capacity to solve relational questions, but can gain this capacity when augmented with RNs. Our work shows how a deep learning architecture equipped with an RN module can implicitly discover and learn to reason about entities and their relations.


https://arxiv.org/abs/1706.01427

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#هوش_مصنوعی
#معرفی_کتاب
#یادگیری_ماشین
#پایتون



A Practical Implementation Guide to Predictive Data Analytics Using Python
Covers basic to advanced topics in an easy step-oriented manner
Concise on theory, strong focus on practical and hands-on approach
Explores advanced topics, such as Hyper-parameter tuning, deep natural language processing, neural network and deep learning
Describes state-of-art best practices for model tuning for better model accuracy

🔵About The Book:

This book is your practical guide towards novice to master in machine learning with Python in six steps. The six steps path has been designed based on the “Six degrees of separation” theory which states that everyone and everything is a maximum of six steps away. Note that the theory deals with the quality of connections, rather than their existence. So, a great effort has been taken to design an eminent, yet simple six steps covering fundamentals to advanced topics gradually that will help a beginner walk his way from no or least knowledge of machine learning in Python to all the way to becoming a master practitioner. This book is also helpful for current Machine Learning practitioners to learn the advanced topics such as Hyperparameter tuning, various ensemble techniques, Natural Language Processing (NLP), deep learning, and basics of reinforcement learning.


🌎Who This Book Is For:


This book will serve as a great resource for learning machine learning concepts and implementation techniques for:
Python developers or data engineers looking to expand their knowledge or career into machine learning area.
A current non-Python (R, SAS, SPSS, Matlab or any other language) machine learning practitioners looking to expand their implementation skills in Python.
Novice machine learning practitioners looking to learn advanced topics such as hyperparameter tuning, various ensemble techniques, Natural Language Processing (NLP), deep learning, and basics of reinforcement learning.

https://www.datasciencecentral.com/profiles/blogs/book-mastering-machine-learning-with-python-in-six-steps?utm_content=buffer4efbf&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer

https://www.apress.com/us/book/9781484228654

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#خبر

#یادگیری_ماشین
#مغز

🔵Machine Learning and the Language of the Brain


For years, researchers have been trying to figure out how the human brain organizes language – what happens in the brain when a person is presented with a word or an image. The work has academic rewards of its own, given the ongoing push by researchers to better understand the myriad ways in which the human brain works.

At the same time, ongoing studies can help doctors and scientists learn how to better treat people with aphasia or other brain disorders caused by strokes, tumors or trauma that impair a person’s ability to communicate – to speak, read, write and listen.

Tom Mitchell, the E. Fredkin University professor at Carnegie Mellon University who helps lead a neurosemantics research team, for the past several years has been marrying brain imaging technologies like functional MRI (fMRI) and magnetoencephalography (MEG) with machine learning techniques to develop models for better learning how the brain understands what it reads and sees and to answer an array of questions that cascade from that – including whether neural representations are similar from one person to another, if anything changes depending on language and how the brain handles not only single words but adjective-noun combinations, verbs, phrases and full sentences.


https://www.nextplatform.com/2017/06/26/machine-learning-language-brain/

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#خبر
#شبکه_عصبی



🔵Draw Together with a Neural Network


We made an interactive web experiment that lets you draw together with a recurrent neural network model called sketch-rnn. We taught this neural net to draw by training it on millions of doodles collected from the Quick, Draw! game. Once you start drawing an object, sketch-rnn will come up with many possible ways to continue drawing this object based on where you left off. Try the first demo.

In the above demo, you are instructed to start drawing a particular object. Once you stop doodling, the neural network takes over and attempts to guess the rest of your doodle. You can take over drawing again and continue where you left off. We trained around 100 models you can choose to experiment with, and some models are trained on multiple categories.


https://magenta.tensorflow.org/sketch-rnn-demo

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#مقاله
#هوش_مصنوعی

🔵Deep Semantics-Aware Photo Adjustment

Automatic photo adjustment is to mimic the photo retouching style of professional photographers and automatically adjust photos to the learned style. There have been many attempts to model the tone and the color adjustment globally with low-level color statistics. Also, spatially varying photo adjustment methods have been studied by exploiting high-level features and semantic label maps. Those methods are semantics-aware since the color mapping is dependent on the high-level semantic context. However, their performance is limited to the pre-computed hand-crafted features and it is hard to reflect user's preference to the adjustment. In this paper, we propose a deep neural network that models the semantics-aware photo adjustment. The proposed network exploits bilinear models that are the multiplicative interaction of the color and the contexual features.

https://arxiv.org/abs/1706.08260v1

🔵Deep learning with Microsoft Cognitive Toolkit

Explore the toolkit we use to build AI tools and train your own deep learning algorithms to learn like the human brain.

https://www.youtube.com/watch?v=OEnGqsvw52E

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#مقاله
#یادگیری_عمیق



🔵Awesome Deep learning papers and other resources

A list of recent papers regarding deep learning and deep reinforcement learning. They are sorted by time to see the recent papers first. I will renew the recent papers and add notes to these papers.

You should find the papers and software with star flag are more important or popular.


https://github.com/endymecy/awesome-deeplearning-resources

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#آموزش
#بینایی_ماشین

🔵Analyzing The Papers Behind Facebook's Computer Vision Approach

You know that company called Facebook? Yeah, the one that has 1.6 billion people hooked on their website. Take all of the happy birthday posts, embarrassing pictures of you as a little kid, that one family relative that likes every single one of your statuses, and you have a whole lot of data to analyze.

https://adeshpande3.github.io/adeshpande3.github.io/Analyzing-the-Papers-Behind-Facebook's-Computer-Vision-Approach/