بازسازی صحنه های فیلم مشاهده شده توسط فرد با پردازش فعالیت های ناحیه ی بینایی مغز
آیا میتوان یک روز رویاها و خواب ها را با این تکنولوژی ضبط کرد و به صورت فیلم بازیابی کرد؟!
UC Berkeley researchers have succeeded in #decoding and #reconstructing people's dynamic #visual experiences.

The #brain activity recorded while subjects viewed a set of film clips was used to create a computer program that learned to associate visual patterns in the movie with the corresponding brain activity. The brain activity evoked by a second set of clips was used to test the movie reconstruction algorithm. This was done by feeding 18 million seconds of random YouTube videos into the computer program so that it could predict the brain activity that each film clip would most likely evoke in each subject. Using the new computer model, researchers were able to decode brain signals generated by the films and then reconstruct those moving images.

Eventually, practical applications of the technology could include a better understanding of what goes on in the minds of people who cannot communicate verbally, such as stroke victims, coma patients and people with neurodegenerative diseases. It may also lay the groundwork for brain-machine devices that would allow people with cerebral palsy or paralysis, for example, to guide computers with their minds.

The lead author of the study, published in Current Biology on September 22, 2011, is Shinji Nishimoto, a post-doctoral researcher in the laboratory of Professor Jack Gallant, neursoscientist and coauthor of the study. Other coauthors include Thomas Naselaris with UC Berkeley's Helen Wills #Neuroscience Institute, An T. Vu with UC Berkeley's Joint Graduate Group in Bioengineering, and Yuval Benjamini and Professor Bin Yu with the UC Berkeley Department of Statistics.
more:
http://news.berkeley.edu/2011/09/22/brain-movies/

خانه ی #هوشمند مارک #زاکربرگ بنیان گذار فیس بوک که از متدهای نوین هوش مصنوعی نظیر بازشناسی شئ، بازشناسی چهره، بازشناسی گفتار، پردازش زبان‌های طبیعی و ... بهره برده است.
زاکربرگ از انگیزه ی خود برای این کار و گام های انجام کارش می‌نویسد:


https://www.facebook.com/notes/mark-zuckerberg/building-jarvis/10154361492931634/

چالش شخصی من برای سال 2016 ساخت یک هوش مصنوعی ساده برای خانه ام بوده - مثل جارویس در فیلم مرد آهنین...

Building Jarvis:
- Getting Started: Connecting the Home
- #Natural_Language
- #Vision and #Face_Recognition
- Messenger Bot
- Voice and #Speech_Recognition
- Facebook Engineering Environment

—------
Vision and Face Recognition:
About one-third of the human #brain is dedicated to vision, and there are many important #AI problems related to understanding what is happening in images and videos. These problems include #tracking (eg is Max awake and moving around in her crib?), #object_recognition (eg is that Beast or a rug in that room?), and face recognition (eg who is at the door?).
Face recognition is a particularly difficult version of object recognition because most people look relatively similar compared to telling apart two random objects — for example, a sandwich and a house. But Facebook has gotten very good at face recognition for identifying when your friends are in your photos. That expertise is also useful when your friends are at your door and your AI needs to determine whether to let them in.
To do this, I installed a few cameras at my door that can capture images from all angles. AI systems today cannot identify people from the back of their heads, so having a few angles ensures we see the person's face. I built a simple server that continuously watches the cameras and runs a two step process: first, it runs face detection to see if any person has come into view, and second, if it finds a face, then it runs face recognition to identify who the person is. Once it identifies the person, it checks a list to confirm I'm expecting that person, and if I am then it will let them in and tell me they're here.
This type of visual AI system is useful for a number of things, including knowing when Max is awake so it can start playing music or a Mandarin lesson, or solving the context problem of knowing which room in the house we're in so the AI can correctly respond to context-free requests like "turn the lights on" without providing a location. Like most aspects of this AI, vision is most useful when it informs a broader model of the world, connected with other abilities like knowing who your friends are and how to open the door when they're here. The more context the system has, the smarter is gets overall.

#mark_zuckerberg #smart_home

#خبر
https://newatlas.com/digitally-recreate-minds-eye-images/53537/

محققان دانشگاه تورنتو با اسکن سیگنالهای مغز چهره‌ی فردی که به آن فکر میکنید را بازیابی می‌کنند!

مرتبط با:
https://t.me/cvision/38

#eeg #brain #reconstructing #decoding

📌 مدرسه زمستانه بینایی و یادگیری ماشین پژوهشگاه دانش های بنیادی (IPM)
🎓 اعطای مدرک معتبر
✅ ثبت نام برای عموم آزاد است
💯 تخفیف ویژه ثبت نام زودهنگام و گروهی
🗓 تاریخ برگزاری : 11 و 12 بهمن
⚠️ مهلت ثبت نام : 5 بهمن
#بهمن1397
#Workshop #Machine_Vision #Machine_Learning #Deep_Learning #Python #OpenCV #TensorFlow
#Tehran #IPM #Brain #SCS
evand.com/events/mvl
braineng.ipm.ac.ir
scs.ipm.ac.ir
@convent