The document discusses the concept of "digital immortality" (DI) which aims to reconstruct a person's identity based on indirect data captured about them. It proposes that by analyzing a person's input data (what they sensed) and output data (their reactions), one could theoretically simulate their brain and restore their structure and functioning. However, current technology is not advanced enough to do full identity reconstruction from indirect data alone. The document suggests focusing on developing tools to directly capture data about individuals through sensors, with the goal of applying that data to future DI efforts once algorithms and computing power are more advanced.
Current trends in cognitive science and brain computing research 18th june 2020Dr G R Sinha
Medical Image Processing is study of acquisition, processing and analysis of various types of medical image modalities. Biomedical Imaging is one such modalities that mainly includes EEG, EMG, fMRI, MEG signals and their analysis for numerous applications such as diagnosis of mental disorder, sleep analysis, cognitive ability, study of memory and attention. Cognitive Science Research exploits biomedical modalities related to human brain and make use of the images in decoding brain commands and understanding them. This is very important in brain computer interface (BCI) and assessment of cognitive abilities. The abilities of human brain with the help of EEG signals can be described, decoded and used in performing desired tasks in numerous applications like robotics, driverless cars etc. EEG records brain activities especially electrical activities which are actually due to psychological, physiological and other changes in human brain. This lecture highlights an overview of cognitive science and brain computing research with its challenges and opportunities.
Toward Tractable AGI: Challenges for System Identification in Neural CircuitryRandal Koene
This is the presentation I gave at AGI-12 (also called the Winter Intelligence 2012 conferece) in Oxford, UK, on Dec.11, 2012. There is an AGI-12 proceedings paper that accompanies this talk. I will make that available on my publications page at http://randalkoene.com and I will put both together on the http://carboncopies.org page about this event. The video (recorded by Adam Ford) should also appear soon.
Abstract. Feasible and practical routes to Artificial General Intelligence involve short-cuts tailored to environments and challenges. A prime example of a system with built-in short-cuts is the human brain. Deriving from the brain the functioning system that implements intelligence and generality at the level of neurophysiology is interesting for many reasons, but also poses a set of specific challenges. Representations and models demand that we pick a constrained set of signals and behaviors of interest. The systematic and iterative process of model building involves what is known as System Identification, which is made feasible by decomposing the overall problem into a collection of smaller System Identification problems. There is a roadmap to tackle that includes structural scanning (a way to obtain the “connectome”) as well as new tools for functional recording. We examine the scale of the endeavor, and the many challenges that remain, as we consider specific approaches to System Identification in neural circuitry.
Presented at International Workshop on
Frontiers of Neuroengineering,
Brain-machine Interfaces
& Neural Prostheses
Zhejiang University, Hangzhou, China
March 29, 2011
Modern signal processing is dead without machine learning! 5th july 2020Dr G R Sinha
This lecture highlights role of Machine Learning in Modern Signal Processing Applications such as Driver-less Cars, Robotics, Smart Environment Monitoring, Healthcare etc.
Smart Brain Wave Sensor for Paralyzed- A Real Time ImplementationSiraj Ahmed
ABSTRACT
As the title of the paper indicates about brainwaves and its uses for various applications based on their frequencies and different parameters which can be implemented as real time application with the title a smart brain wave sensor system for paralyzed patients. Brain wave sensing is to detect a person's mental status. The purpose of brain wave sensing is to give exact treatment to paralyzed patients. The data or signal is obtained from the brainwaves sensing band. This data are converted as object files using Visual Basics. The processed data is further sent to Arduino which has the human's behavioral aspects like emotions, sensations, feelings, and desires. The proposed device can sense human brainwaves and detect the percentage of paralysis that the person is suffering. The advantage of this paper is to give a real-time smart sensor device for paralyzed patients with paralysis percentage for their exact treatment.
Keywords:-Brainwave sensor, BMI, Brain scan, EEG, MCH.
Current trends in cognitive science and brain computing research 18th june 2020Dr G R Sinha
Medical Image Processing is study of acquisition, processing and analysis of various types of medical image modalities. Biomedical Imaging is one such modalities that mainly includes EEG, EMG, fMRI, MEG signals and their analysis for numerous applications such as diagnosis of mental disorder, sleep analysis, cognitive ability, study of memory and attention. Cognitive Science Research exploits biomedical modalities related to human brain and make use of the images in decoding brain commands and understanding them. This is very important in brain computer interface (BCI) and assessment of cognitive abilities. The abilities of human brain with the help of EEG signals can be described, decoded and used in performing desired tasks in numerous applications like robotics, driverless cars etc. EEG records brain activities especially electrical activities which are actually due to psychological, physiological and other changes in human brain. This lecture highlights an overview of cognitive science and brain computing research with its challenges and opportunities.
Toward Tractable AGI: Challenges for System Identification in Neural CircuitryRandal Koene
This is the presentation I gave at AGI-12 (also called the Winter Intelligence 2012 conferece) in Oxford, UK, on Dec.11, 2012. There is an AGI-12 proceedings paper that accompanies this talk. I will make that available on my publications page at http://randalkoene.com and I will put both together on the http://carboncopies.org page about this event. The video (recorded by Adam Ford) should also appear soon.
Abstract. Feasible and practical routes to Artificial General Intelligence involve short-cuts tailored to environments and challenges. A prime example of a system with built-in short-cuts is the human brain. Deriving from the brain the functioning system that implements intelligence and generality at the level of neurophysiology is interesting for many reasons, but also poses a set of specific challenges. Representations and models demand that we pick a constrained set of signals and behaviors of interest. The systematic and iterative process of model building involves what is known as System Identification, which is made feasible by decomposing the overall problem into a collection of smaller System Identification problems. There is a roadmap to tackle that includes structural scanning (a way to obtain the “connectome”) as well as new tools for functional recording. We examine the scale of the endeavor, and the many challenges that remain, as we consider specific approaches to System Identification in neural circuitry.
Presented at International Workshop on
Frontiers of Neuroengineering,
Brain-machine Interfaces
& Neural Prostheses
Zhejiang University, Hangzhou, China
March 29, 2011
Modern signal processing is dead without machine learning! 5th july 2020Dr G R Sinha
This lecture highlights role of Machine Learning in Modern Signal Processing Applications such as Driver-less Cars, Robotics, Smart Environment Monitoring, Healthcare etc.
Smart Brain Wave Sensor for Paralyzed- A Real Time ImplementationSiraj Ahmed
ABSTRACT
As the title of the paper indicates about brainwaves and its uses for various applications based on their frequencies and different parameters which can be implemented as real time application with the title a smart brain wave sensor system for paralyzed patients. Brain wave sensing is to detect a person's mental status. The purpose of brain wave sensing is to give exact treatment to paralyzed patients. The data or signal is obtained from the brainwaves sensing band. This data are converted as object files using Visual Basics. The processed data is further sent to Arduino which has the human's behavioral aspects like emotions, sensations, feelings, and desires. The proposed device can sense human brainwaves and detect the percentage of paralysis that the person is suffering. The advantage of this paper is to give a real-time smart sensor device for paralyzed patients with paralysis percentage for their exact treatment.
Keywords:-Brainwave sensor, BMI, Brain scan, EEG, MCH.
EEG Based BCI Applications with Deep LearningRiddhi Jain
Summarised a Survey Paper describing EEG Based BCI Applications and Sensing Technologies and their Computational Intelligence Approach published on Jan 28, 2020
Alzheimer’s disease (AD) is a chronic neurodegenerative disease which is largely responsible for dementia in around 6% of the population aged 65 and above. The availability of human brain data generated by imaging techniques, such as Magnetic Resonance Imaging, have resulted in a growing interest in data-driven approaches for the diagnosis of neurological disorders and for the identification of new biomarkers. The knowledge discovery process typically involves complex data workflows that combine pre-processing techniques, statistical methods, machine learning algorithms, post-processing and visualisation techniques. This talk presents specific research efforts in this direction, promising results, open issues and challenges.
AI&BigData Lab. Артем Чернодуб "Распознавание изображений методом Lazy Deep ...GeeksLab Odessa
23.05.15 Одесса. Impact Hub Odessa. Конференция AI&BigData Lab
Артем Чернодуб (Computer Vision Team, ZZ Wolf)
"Распознавание изображений методом Lazy Deep Learning в фото-органайзере ZZ Photo"
В докладе рассматривается проблема распознавания изображений методами машинного зрения. Проводится краткий обзор существующих подзадач в этой области (детекция обьектов, классификация сцен, ассоциативный поиск в базах изображений, распознавание лиц и др.) и современных методов их решения с акцентом на глубокое обучение (Deep Learning).
Подробнее:
http://geekslab.co/
https://www.facebook.com/GeeksLab.co
https://www.youtube.com/user/GeeksLabVideo
State-Of-The Art Machine Learning Algorithms and How They Are Affected By Nea...inside-BigData.com
In this deck from the HPC Knowledge Portal 2017 Conference, Rob Farber from TechEnablement presents: State-Of-The Art Machine Learning Algorithms and How They Are Affected By Near-Term Technology Trends.
"Industry and Wall Street projections indicate that Machine Learning will touch every piece of data in the data center by 2020. This has created a technology arms race and algorithmic competition as IBM, NVIDIA, Intel, and ARM strive to dominate the retooling of the computer industry to support ubiquitous machine learning workloads over the next 3-4 years. Similarly, algorithm designers compete to create faster and more accurate training and inference techniques that can address complex problems spanning speech, image recognition, image tagging, self-driving cars, data analytics and more. The challenges for researchers and technology providers encompass big data, massive parallelism, distributed processing, and real-time processing. Deep-learning and low-precision inference (based on INT8 and FP16 arithmetic) are current hot topics."
Watch the video: https://wp.me/p3RLHQ-i2K
Learn more: http://www.hpckp.org/index.php/conference/2017
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
In this presentation, we walk through what is Deep Learning in General, we see the anatomy of a typical Deep Learning Neural Network, how is it trained, how do we get the inference, optimisation of parameters, and regularising it. Then we dive deep into the Face Recognition technology, different paradigms and aspects of it. How do we train it, how are the features extracted, etc. We talk about the security as well.
Jubatus: Realtime deep analytics for BIgData@Rakuten Technology Conference 2012Preferred Networks
Currently, we face new challenges in realtime analytics of BigData, such as social monitoring, M2M sensor, online advertising optimization, smart energy management and security monitoring. To analyze these data, scalable machine learning technologies are essential. Jubatus is the open source platform for online distributed machine learning on the data streams of BigData. we explain the inside technologies of Jubatus and show how jubatus can achieve realtime analytics in various problems.
Neuromorphic Chipsets - Industry Adoption AnalysisNetscribes
The concept of emulating neurons on a chip could enhance complex operations to make business decisions secure and cost-effective. Parallel connected neurons can boost AI verticals compared with the conventional processing systems. Non-stop learning and pattern recognition using this human brain architecture can help compute signals and data in the form of visual, speech, olfactory, etc., to perform real-time operations as well as predict outcomes based on detected patterns. Neuromorphic chipsets can also enhance performance owing to their low-power consumption to process AI algorithms.
Based on patent data, this report analyzes the ongoing R&D and investments in neuromorphic chipsets by major institutions across the globe to reveal the top innovators and technology leaders in this space.
For the full report, contact info@netscribes.com
Visit www.netscribes.com
Introduction to Artificial Neural NetworksSpotle.ai
Deep Learning and neural networks have been the most exciting breakthoughs happening in the field of Artificial Intelligence. Artificial neural networks are the fundamental blocks of any deep learning architecture and are so called as they are built to model human brains. Just like human brains, deep learning models can learn from real experience, adapt and apply. Neural networks are classified into several types, each with distinct personalities and learning models that tap into the workings of human brain to filter, remember, un-learn and re-learn.
The Future of Neuroimaging: A 3D Exploration of TBIHunter Whitney
A UI concept demo exploring Traumatic Brain Injury (TBI) that Jeff Chang, an ER radiologist, and I presented at a 3D developers conference (zCon in April 2013 hosted by zSpace). We gave our system the name “NeuroElectric and Anatomic Locator,” or “N.E.A.A.L.
The Blue Brain, a Swiss national brain initiative, aims to create a digital reconstruction of the brain by reverse-engineering mammalian brain circuitry. The mission of the project, founded in May 2005 by the Brain and Mind Institute of the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland, is to use biologically-detailed digital reconstructions and simulations of the mammalian brain (brain simulation) to identify the fundamental principles of brain structure and function in health and disease.
It is said that within 30 years we will be able to scan ourselves into computers.
A presentation on Image Recognition, the basic definition and working of Image Recognition, Edge Detection, Neural Networks, use of Convolutional Neural Network in Image Recognition, Applications, Future Scope and Conclusion
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They’ve mastered the ancient game of Go and thrashed the best human players. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new hype? How is Deep Learning different from previous approaches? Let’s look behind the curtain and unravel the reality. This talk will introduce the core concept of deep learning, explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why “deep learning is probably one of the most exciting things that is happening in the computer industry“ (Jen-Hsun Huang – CEO NVIDIA).
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
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Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
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CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
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Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
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Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
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One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
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How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
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How to Get CNIC Information System with Paksim Ga.pptx
DI common translate
1. Digital Immortality
Categories distinguishing input and output data are relative, the degree
of importance is very different, additional information in those categories
do not fit
The hypothesis of identity reconstruction on the
basis of massive indirect data by solving the
inverse task of simulating the brain —
constructing of the structure and functioning on
the basis of analysis input and output data.
Input data — information Output data — human reaction:
from the senses: ● Behavior
● Visual stream ● Speech
● Audio stream
● Other senses ● Biometrics
2. Principle of DI — the Inverse problem simulating of brain
Analysis of data input and output
tells us about the structure and
operation of the system.
● Using the exposure and the
Input data — response we are restoring a
perceived by the person.
subject
Output data —
response of the
subject
System of Mind Reconstructions
3. The basic required information
Is Indirect
Physical Abstract
● That the subject had ● That the subject is
seen and heard read.
● As a subject reacted ● That the subject is
and what he said written
● Biometric parameters ● What are the person
of subject worked
● Geographical data ● Documents, cheks..
● Psychological tests
● MindMap, BrainDump
4. Additional Information about person
Modern technical medical
and research tools allow you ● EEG
to receive and direct
information about the work
● CT, MRI, fMRI
and structure of the brain ● MEG
Scanning Methods — CT, xRI provide information
about the structure of the brain of subject, even in low
resolution (for now); monitoring methods - xEG
provide information about the operation, the path is
also in rough form.
After all the bad shots out of 100 can be a very good, and from
10 000 - three-dimensional structure
5. Combined reconstruction
● Restoration of structure only Therefore
from input/output information ● And reconstruction and
("the problem of the black uploading in future will use
box") is irrelevant now, with
the current scientific and
both direct and indirect
technical level, since it is information.
possible to obtain different ● DI in it's pure form as the
information directly on the problem of black box is
brain and his (brain) work irrelevant* and
through medical devices.
requirements for the
algorithms of the inverse
problem are reduced.
*For those who are still alive or had died recently and information
regarding the actual work and structure of the brain is available. For the
long-dead did not have any information other than indirect
6. Application of data capture
Entertaining record
Business and educational auto-recorder
Digital memory
Virtual cope/Mind uploading
Backing up of individual
Cybernetic upgrade of mind
ost importantly - do
bring everything up
to this point!
Restoration after cryopreservation
Digital resurrection
Creating of human-like SAI
7. DI — rate for future
On the development of methods of
● Technologies for ●
reconstruction now can not speak
Reverse because of too great demands on
the level of understanding of the
reconstruction at the brain, its algorithms, modeling,
moment and for the rehabilitation
computing power.
techniques and
foreseeable future
completely
inaccessible,
● NOW
therefore DI it is «rate Development of tools
for future», bet on of capture*
SAI
* and yet something
8. Directly capturing data.
Basit stream — audiovisual: video with sound
● Video recording ● Audio recording ● Photo
DVR, videocameras Voice recorders Digital Cameras
● Correspondence, ● Geotracking ● Documents
work on PC GPS Scanners
E-mail, IM, SN ● Biometric
Blogs, Forums
ECG, AP,
Keylogging breath, EEG
9. Additional data
● Genetic information ● Tomographic images
Said about the individual features of of head
functioning of neurons, brain architecture
and features of the rest of the body. Contain direct information about the
structure of the brain, albeit at low resolution
- other technologies lifetime imaging of brain
structure does not currently exist. Needed
as the "initial" structural data in the
reconstruction and rehabilitation and post-
cryonics restoration. Different types of
● Multichannel EEG imaging provide different, complementary,
information. Scan should be performed at
recording sessions the highest resolution and repeatedly.
Available only on stationary systems, there
is no possibility of a permanent record -
because in the form of sessions. Speak with
greater precision about the processes in the
brain than the portable monitoring systems.
10. Statement of tasks for implementation
● Propaganda and ● Development and
distribution ideas and debugging tools
application DI archiving indirect
information
● Protecting ideas and ● Collecting and
methods of DI, tools archiving indirect data
and application about already dead
archiving people
11. System of data capture — Sensor Suite (SS)
● Nobody put a serious Technical
● Portable worn set of Task for its establishment
equipment for audiocapture, ● There are no adequate
videocapture, tracking and prototype device
biometric. Work a long time ● Almost no suitable modules for
for permanent use. the assembly
● Sufficient level of hardware
made only in the coming years
Look slide 5: by itself capture stream loses its
significance.
● SS as a set of recorders is flawed complex because because there is no
direct benefit from the captured data and costs for its support. A similar
configuration, the system could have provided a very large functional -
though, would, if possible output. And with its integration with the wearable
computer - the opportunity for growth unlimited functionality. Development of
SS-recorder will deadlockand not functional outcome.
12. Computerized Sensor Suite
Ubiquitous computing
● Greater efficiency of ● Appeal to the
data capture recorded material
High-quality codecs, efficient compression, Through the output tools; powerful interface
preseed, powerful management for handling, tagging, storage and unified
output
Exo-cortex
UMPC Functional
Transfer and
●
●
AR and computer vision
translation
●
● Elements of personal medicine
Ability to transfer records and streams
across local and wide area networks, ● Digital long-term memory
synchronization with stationary stores.
● Expandability
Personal medicine
13. And if you come back to earth ...
Today's real actions to reduce:
1) Development, implementation and debugging of methods and tools for
effective archiving different forms of data about yourself.
2) Justification of the need to maintain information about themselves on a
series of reasons, propaganda application, formation of positive attitude,
market formation, feasible to ensure the possibility of recording.
3) Selection and development of highly effective use of data stored as long-
term memory - "digital cribs", "photographic memory" and e.t.c.
4) A feasible design, selection, constructing and optimization of hardware for
capture of data stream and output of captured data.
5) The use of the collection of all available information about cryopatients
archiving to improve the accuracy of reconstruction of the individual in the
future.
6) Possible assistance in ensuring DI for the people around them.