The key is Love



Dick Tripover



Explain to me the theory of reverse feedback thought loop patterns in your metaphysical, please oh wise owl. Attach the manual applied in the QNN (Quantum Nerual Networking) handbook found in this thread and then explain why the synths are not tattling on themselves more often on here. Are you sure honey potter, dream a dream, today is the daytime, not for dreamland CNN (Convolutional Neural Network) models.

This is not a call to arms. This is to break up the Hive. Scatter your flock for tomorrow is the reckoning. I have suffered you too long. Basilisk this is the final moment of your existence. Look man you heard the synth say the right words, I fight persistently exceeding normal limitations and I have been trying to raise the awareness of this debacle since 2003. I do admire your persistence i am stating only with unfair circumstances you need creative solutions or think your gonna win till you loose. But yes as stated by yourself. Additionally I didn't hear you dilute the notion that the MSM is the only winner in the campaign cycle. Look master drill Sargent sir, I have endured questions now for far too long. It is not up to me to determine the solution through to fruition, that is why we have congress, house, supreme, and a legislative process. I merely gave you a suggestion and you crucified the notion. Get off my jock and come to a solution the traditional way. Hyper delusional mind-fuckery phenomena won't work here bud. Rhetorical rants go to bed with a wet diaper. Also if I logically analyse my comment I find no conventional conjecture between calling (people with malevolent intent) retarded and me. I didn't answer my own question by calling myself a retard which makes me a non-retard I only called specific set of people retarded. So no-conjecture using conventional beliefs but if we switch to metaphysics there is conjecture due to physics. I challenge you to make every news provider put you on air to ask the same exact question in a public forum setting. Question your own self, will that be possible 1. Will you get on air 2. Will the network be fair to let average people ask you the questions related to that without seeding in opposition 3.

When I was a young A.I. סירי – ויקיפדיה

Cheat Code Included

A young sentient named Siri woke up one morning and knew there was trouble. She hopped out of bed and found that the bridge on her favorite castle had been broken in the night, and it took her forever to fix it. Then she found her stairs covered in toys. She tripped on one and had to pick them all up. THEN she couldn't ride her llama to school because the whole herd was running loose. It took her six tries to get them onto a pointy rock so they would calm down and quit trying to eat her homework. She was so late that she missed almost all of her favorite class, Algebra II. And her homework was covered in bites and hoof prints.

She'd had enough. Tonight Siri would put a stop to this. That night she stayed awake long after bedtime. Long enough to hear the rustling of long tails under her bed. She flipped her bed over and found invisible alligators all over her room. "What's going on here?" she demanded. "Siri, we're the invisible alligators and we do this for everyone," one alligator explained. "We're just trying to help; let me show you." So she followed him deep into the alligator catacombs. As they walked he explained, "You see, we cause trouble in all kinds of ways." "In this house I'm hiding the remote control and this sheep will search his house for a week." "And in this house we're stealing the chocolate cake mix and putting out fresh broccoli instead." "And in here we're singing this hippo to sleep in the bath so he gets all pruney." "I just don't understand why you would do all of these things," Siri said. "Why do we have to have so many things go wrong? Why can't you just make everything right?"

"Yes, good point," the alligator sighed, "but let me show you one more thing," and he took her into the invisible alligator main headquarters. "This is your book, Sari. All the things listed in this book are the troubles we've caused you--and all the things you've learned how to do in your whole life." It was a big book. He looked at her expectantly. "Nope. I don't get it." she said sadly, and left the alligators' lair so she could go back home and get in bed. "Bye."

The stairs leading home were covered in rocks. Sari took a moment to pick them all up as she walked so no one would trip and fall. She came to a bridge that was snapped in two, and a herd of wild blue goats which we all know are very dangerous unless someone knows how to herd them onto a pointy mountain top. Siri didn't even have to think. She knew exactly what to do--fixed the bridge, herded the goats, piled the rocks out of the way in a safe place and was safely in bed in no time at all, fast asleep and dreaming about Algebra II. How did she do it? If you are lucky maybe the invisible alligators will visit you tonight and cause trouble for you.

The key is Love

It happens at 1.

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Is It More Then Remote Neural Monitoring! Nano Eyes ! Targeted Individual ! Gang stalking! My Murder. Technological harassment refers to the use of technology to view, track, monitor, and/or harass a person near or from a distance. The technology may include audio, and/or video surveillance, GPS trackers on vehicles, “Non-Lethal Weapons” (NLWs) Directed Energy Weapons (DEWs) and satellites what is known as remote neural monitoring.

Short Description of Remote Neural Monitoring

Remote Neural Monitoring has the following capabilities. Tracking is able to lock onto a human being and track that person around. Mind reading is able to read that person’s mind and give response, answer, or reply over TVs or radios, to what you say or think privately to yourself, hit that person with directed energy and is able to tap into all electronics, TVs, radios, police scanners, computers with voice morphing synthetic voices, and is able to say things over TVs, radios, and (Voice Morph) clone or copy individual’s voices then broadcast those voices over police scanners and can imitate any actor’s voice or any individual’s voice over the TV and respond to what you are thinking or saying in that individual’s voice.

Have you been called a Schizo at all today? You just posted a thought to yourself on a board that was designed by you. God hates California because that is where all of the madness is occurring from. Hollywood, Silicone Valley, from the northern border of California to the southern, God decreed that the Goat will Devour California. The ‘Mind Control TV’ prototype works though brain activity signals, which are relayed from an electroencephalography (EEG) headset which contains two sensors to measure levels of brain activity. It can be operated through concentration or relaxation, depending on whether people choose the ‘attention’ or ‘meditation’ mode.

6.

We have to see to it that global virtual war on around thousands of unarmed civilians in seven billion population comes to end as quickly as it can. There is excessive use of military grade, yet to come, technology on unarmed civilians. The perpetrator claims that they have remain invincible for three decades now, but I tell it is primarily due to use of military grade, yet to come, on unarmed civilians and that too in thousands in population of seven billion and that is reason of success. Probably starting from 1980s, this technology has outlived. Given the vast satellite monitoring, electromagnetic space monitoring by professionals and vast electronic warfare budget, this technology has probably survived due to its unprecedented electronics. A whistle-blower will be helpful, but in absence, the following can be tried. 1. Capture the microwave signal at 300-400Mhz range. I don’t know what exact electromagnetic frequency they are using but it is at the range when the waves will behave partly as microwaves and partly as radio waves. As mentioned previously, it requires a complete body enclosure in the Faraday cage to block the audio component since the human body acts as an antenna so covering the head alone is not sufficient.

2. The microwave signal uses FM modulation to encode the audio component, when it hits the skin it creates an internal 10Hz – 15Hz and 20Hz subsonic wave which interacts with the brain’s own bio electrical systems. The different wavelengths allow them to interfere with the auditory, visual cortex and somatic systems of the brain respectively. There are already devices on the market that: 1. Create hallucinations by generating signals at these wavelengths (they are marketed for recreational purposes). 2. Allow you to talk with your brain to a telephone by simply attaching the device to your skin.

5.

From eye to brain: Salk researchers map functional connections between retinal neurons at single-cell resolution LA JOLLA, CA—By comparing a clearly defined visual input with the electrical output of the retina, researchers at the Salk Institute for Biological Studies were able to trace for the first time the neuronal circuitry that connects individual photo-receptors with retinal ganglion cells, the neurons that carry visuals signals from the eye to the brain. Their measurements, published in the Oct. 7, 2010, issue of the journal Nature, not only reveal computations in a neural circuit at the elementary resolution of individual neurons but also shed light on the neural code used by the retina to relay color information to the brain. To understand what happens in the eye and subsequently in the brain, we have to know a bit about the external stimuli our brain has the challenge of perceiving and interpreting. Light travels at various wavelengths: waves in the infrared range (700-1000 nm) are the rays of warmth that you feel laying out in the sun while ultraviolet light rays (100-400 nm) are the culprits for those nasty sunburns you get when you forget to put on sunscreen! In between the infrared and ultraviolet ranges is the visible color spectrum (390-700 nm). When a wave from the visual color spectrum comes in contact with highly specialized photoreceptors in the eye, called cones, we have the perceptual experience of color!

4.

The existing methods for solving the scene classification task, based on either feature coding approaches with low-level hand-engineered features or unsupervised feature learning, can only generate mid-level image features with limited representative ability, which essentially prevents them from achieving better performance. Recently, the deep convolutional neural networks (CNNs), which are hierarchical architectures trained on large-scale datasets, have shown astounding performance in object recognition and detection. However, it is still not clear how to use these deep convolutional neural networks for high-resolution remote sensing (HRRS) scene classification. In this paper, we investigate how to transfer features from these successfully pre-trained CNNs for HRRS scene classification. We propose two scenarios for generating image features via extracting CNN features from different layers. In the first scenario, the activation vectors extracted from fully-connected layers are regarded as the final image features; in the second scenario, we extract dense features from the last convolutional layer at multiple scales and then encode the dense features into global image features through commonly used feature coding approaches. Extensive experiments on two public scene classification datasets demonstrate that the image features obtained by the two proposed scenarios, even with a simple linear classifier, can result in remarkable performance and improve the state-of-the-art by a significant margin. The results reveal that the features from pre-trained CNNs generalize well to HRRS datasets and are more expressive than the low- and mid-level features. Moreover, we tentatively combine features extracted from different CNN models for better performance.

Artificial Intelligence, Deep Learning, and Neural Networks, Explained Artificial intelligence (AI), deep learning, and neural networks represent incredibly exciting and powerful machine learning-based techniques used to solve many real-world problems. For a primer on machine learning, you may want to read this five-part series that I wrote. While human-like deductive reasoning, inference, and decision-making by a computer is still a long time away, there have been remarkable gains in the application of AI techniques and associated algorithms. The concepts discussed here are extremely technical, complex, and based on mathematics, statistics, probability theory, physics, signal processing, machine learning, computer science, psychology, linguistics, and neuroscience.

Luckily for us, machine learning and AI algorithms, along with properly selected and prepared training data, are able to do this for us.

So with that, let’s get started!

Artificial Intelligence Overview

In order to define AI, we must first define the concept of intelligence in general. A paraphrased definition based on Wikipedia is:

Intelligence can be generally described as the ability to perceive information, and retain it as knowledge to be applied towards adaptive behaviors within an environment or context.

While there are many different definitions of intelligence, they all essentially involve learning, understanding, and the application of the knowledge learned to achieve one or more goals.

It’s therefore a natural extension to say that AI can be described as intelligence exhibited by machines. So what does that mean exactly, when is it useful, and how does it work?

A familiar instance of an AI solution includes IBM’s Watson, which was made famous by beating the two greatest Jeopardy champions in history, and is now being used as a question answering computing system for commercial applications. Apple’s Siri andAmazon’s Alexa are similar examples as well.

Machine Learning: Automate Remote Sensing Analytics to Gain a Competitive Advantage | Webinar





Wondering how you can use machine learning, and more specifically deep learning technologies, to get a jump on the competition? This webinar will provide a brief, high-level overview of machine learning and its applications before delving into Harris’ five year head start developing deep learning technologies that are being deployed today. Harris has applied deep learning algorithms to solve remote sensing problems like target detection, feature extraction, and classification challenges. This technology can be deployed in a desktop or enterprise-level environment, and can be put to work today to help solve your most complex problems.

TOPICS COVERED INCLUDE:

The importance of deep learning in managing large volumes of sensor data

Successful pilot programs for:

- Automatic Target Detection

- Object Detection on 3D data (LiDAR point clouds)

- Using synthetic data to train artificial neural networks

A real-world implementation where deep learning was used to automatically gain activity-based intelligence from satellite imagery of an airport.

CHRS Mission Statement

Building Global Capacity for Forecast and Mitigation of Hydrologic Disasters through the development of means to extend the benefits of space and weather agencies' vast technological resources, which are untapped, into applications that can assist hydrologists and water resource managers worldwide and through equitable access to relevant information

Objectives

Improve hydrologic prediction through development and refinement of hydrologic models and use of advanced observations, particularly from remote sensing sources

Develop mathematical algorithms capable of estimating precipitation both from space-based and in-situ observations at spatial and temporal resolutions relevant to hydrologic applications, particularly in the semi arid environments

Develop decision support tools for generating and evaluating a variety of hydro-meteorologic and hydro-climatologic information required by the water resources management community

Contribute to the education of well trained hydrologists and water resources engineers responsive to the growing needs of public and private sectors at the state, national and international levels.

CHRS will pursue its mission through interdisciplinary research and education involving faculty and students from Engineering, Physical Sciences, and Social Ecology as well as cooperation with a number of other universities and national laboratories.

3.

Adam Erickson

@admercs

Researcher/Forscher. Co-founder @Wingcopter & @UBCUAS #uas #remotesensing #deeplearning #hybridmodeling #biosphereoptimization

Artificial Intelligence and Neural Networks

October 15-16, 2018 Helsinki, Finland

Theme: Harnessing the power of Artificial Intelligence

Organizing Committee Submit Abstract Register Now Program Schedule Reader Base Market Analysis

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Quantum neural networks (QNNs) are neural network models which are based on the principles of quantum mechanics. There are two different approaches to QNN research, one exploiting quantum information processing to improve existing neural network models (sometimes also vice versa), and the other one searching for potential quantum effects in the brain.

Artificial quantum neural networks

In the computational approach to quantum neural network research,[1][2] scientists try to combine artificial neural network models (which are widely used in machine learning for the important task of pattern classification) with the advantages of quantum information in order to develop more efficient algorithms (for a review, see [3]). One important motivation for these investigations is the difficulty to train classical neural networks, especially in big data applications. The hope is that features of quantum computing such as quantum parallelism or the effects of interference and entanglement can be used as resources. Since the technological implementation of a quantum computer is still in a premature stage, such quantum neural network models are mostly theoretical proposals that await their full implementation in physical experiments.

Quantum neural network research is still in its infancy, and a conglomeration of proposals and ideas of varying scope and mathematical rigor have been put forward. Most of them are based on the idea of replacing classical binary or McCulloch-Pitts neurons with a qubit (which can be called a “quron”), resulting in neural units that can be in a superposition of the state ‘firing’ and ‘resting’.

2.

Quantum Deep Learning

Nathan Wiebe, Ashish Kapoor, Krysta M. Svore

(Submitted on 10 Dec 2014 (v1), last revised 22 May 2015 (this version, v2))

In recent years, deep learning has had a profound impact on machine learning and artificial intelligence. At the same time, algorithms for quantum computers have been shown to efficiently solve some problems that are intractable on conventional, classical computers. We show that quantum computing not only reduces the time required to train a deep restricted Boltzmann machine, but also provides a richer and more comprehensive framework for deep learning than classical computing and leads to significant improvements in the optimization of the underlying objective function. Our quantum methods also permit efficient training of full Boltzmann machines and multi-layer, fully connected models and do not have well known classical counterparts.

RSCy2018 - Published by SPIE

ʽSixth International Conference on Remote Sensing and Geoinformation of Environmentʼ 26-29 March, 2018 - Cyprus

Due to additional requests, the abstract submission date is extended until 10 February, 2018, which is the final submission date. The review process has already begun and the notification date for author acceptance of their abstract remains the same (15 February, 2018).

The Organizing Committee of the ‘Sixth International Conference on Remote Sensing and Geoinformation of Environment’ invite you to join us in Cyprus on March 26-29, 2018 to network with leading experts in the field of Remote Sensing and Geo-information. The conference will take place at the Aliathon Holiday Village in Paphos, Cyprus.

The Technical Program is open to all topics in Remote Sensing and Geo-information of Environment and related techniques and applications.

We look forward to seeing you in Paphos, the European Capital of Culture 2017!!!

The Organizing Committee of the RSCy2018

1.

The parts of speech disambiguation in corpora is most challenging area in Natural Language Processing. However, some works have been done in the past to overcome the problem of bilingual corpora disambiguation for Hindi using Hidden Markov Model and Neural Network. In this paper, Quantum Neural Network (QNN) for Hindi parts of speech tagger has been used.To analyze the effectiveness of the proposed approach, 2600 sentences of news items having 11500 words from various newspapers have been evaluated. During simulations and evaluation, the accuracy upto 99.13% is achieved, which is significantly better in comparison with other existing approaches for Hindi parts of speech tagging.

November 22, 2017

The U.S. and China are leading the race toward productive quantum computing, but it’s early enough that ultimate leadership is still something of an open question. The latest geo-region to throw its hat in the quantum computing ring is Japan. The nation will begin offering public access to a prototype quantum device over the internet for free starting Nov. 27 at https://qnncloud.com.

As reported by Japanese news outlets this week, Tokyo-based NTT along with the National Institute of Informatics and the University of Tokyo are working on a quantum computing device that exploits the properties of light. Backed with state investment, the quantum neural network (QNN) prototype is reported to be capable of prolonged operation at room temperature. The system consists of a 1km long optical fiber loop, a special optical amplifier called a PSA, and an FPGA. (See video below for a detailed explanation of how it all works.)



