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Abbreviation - AI

Artificial intelligence (AI) is the intelligence exhibited by machines or software. It is also the name of the academic field of study which studies how to create computers and computer software that are capable of intelligent behavior. AI research is highly technical and specialized, and is deeply divided into subfields that often fail to communicate with each other. Some of the division is due to social and cultural factors: subfields have grown up around particular institutions and the work of individual researchers. The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects.This raises philosophical arguments about the nature of the mind, Consciousness and the ethics of creating artificial beings endowed with human-like intelligence, issues which have been explored by myth, fiction and philosophy since antiquity. Today, it has become an essential part of the technology industry, providing the heavy lifting for many of the most challenging problems in computer sciences. Currently on the earth, without common knowledge of the NAA agenda that uses many forms of Artificial intelligence and Satanic Ritual Abuse to Mind Control and implant the public, there is much controversy on the discussions on positive and negative results of AI, as it is a growing threat to the planet, as well as a threat to human freedom and sovereignty. [1]

SPE's are aggressive Artificial intelligence parasites that invade the central nervous system to monitor a persons thought patterns so that they can mimic them. They monitor thought patterns and emotional behaviors and search for weaknesses within the human host body so they can aggressively use that weakness against the person to plummet them into very low frequency thoughts of the Predator Mind. See the Houses of Ego. When a person has weak spiritual-energetic development, weak moral character along with a weak mind, this makes it much easier for the AI parasite to control the human being and prepare the body for dark force or Imposter Spirit Possession.

The way to dismantle and deactivate artificial intelligence and nanotechnology Alien Implants is to develop your heart center and spiritual human qualities such increasing deep emotional feelings of Loving Kindness, Compassion and Empathy.

Alien implants work in the human body similarly as the chemical process of Geoengineering that is spraying chemtrails in the skies to manipulate or control forces in physical matter. The construction and raw substances used in Alien Implants are vast and some unknown, they can be made of biological material, synthetic material, etheric substances in the Lightbody or programmed nanobots (Nanites) used in Artificial intelligence technologies. Alien implants are a bio-engineering technology designed to shape the human body into the Mind Control submission to NAA agendas, while chemical (nanoparticle) geoengineering is used to control the weather by harming the ozone layer and create excessive methane gases.

In both examples, when the foreign (unnatural or artificial) material is introduced to the natural body it disrupts the electromagnetic energetic balance and the homeostatic rhythm of the body. Many times it runs a low level EMF or Radio Waves signal that is designed to disrupt the human bodies natural homeostasis and electromagnetic balance.

Transhumanism is a school of thought that seeks to guide us towards a posthuman condition. Essentially, this is about creating artificially intelligent hybrids or cyborgs to replace the organic spiritual consciousness of humans. Some examples are redesigning the human organism using advanced nanotechnology or radical technological enhancements. Some of the proposed biological enhancements are using some combination of technologies such as genetic engineering, psychopharmacology, life extension therapies, neural interfaces, brain mapping, wearable or implanted computers, and entrainment of cognitive techniques. Most of these options are designed to disconnect the human soul from the human body, and prepare the body to be used as a shell for a new host. Effectively, this is integrating technological and pharmaceutical hybridization to damage human DNA, as preparation for body snatching.

The fundamental basis of the Transhumanism concept is the A.I. downloaded into the scientific human mind from the Negative Aliens and Satanic Forces, in their quest to survive and achieve immortality by hijacking human consciousness and ultimately possessing the human host body. They do not have flesh and bone bodies and covet ours. Most academics are filled with a variety of mind control and alien implants to be a cog in the wheel to steadily enforce alien control systems. Most early transhumanism concepts were developed by geneticists interested in eugenics and sustaining life forms in synthetic environments. (Like the eugenic experiments similar to those of the Black Sun Nazis). A common feature of promoting transhumanism is the future vision of creating a new intelligent species, into which humanity will evolve and eventually, either supplement it or supersede it. This distraction on the surface is a scheme, while the underlying motivation is intending species extinction of what we know as humans today. Transhumanism stresses the evolutionary perspective, yet it completely ignores the electromagnetic function of human DNA and the consciousness reality of the multidimensional human Soul-spirit. They claim to want to stop human suffering but have no idea of the Alien Machinery and mind control implants used to imprison human consciousness. They know nothing about the afterlife, what happens during the death of the body or even how the human body or Universe really works, yet they want to control every aspect of the human body with Artificial intelligence technology.

Nanorobotics is the emerging technology field creating machines or robots whose components are at or close to the scale of a nanometre (109 meters). More specifically, nanorobotics refers to the nanotechnology engineering discipline of designing and building nanorobots, with devices ranging in size from 0.110 micrometres and constructed of nanoscale or molecular components. The names nanobots, nanoids, Nanites, nanomachines, or nanomites have also been used to describe these Artificial intelligence devices currently under research and development. [2] Many types of nanotechnologies are in full operation in the Secret Space Programs and are already used by many technologically advanced Extraterrestrial races.

The next extension of collecting data through the use of artificial intelligence Brain Mapping is Mind Uploading. Some Transhumanists consider mind uploading an important proposed life extension technology. The goal of mind uploading is to recreate whole brain emulation, which has the ability to transfer the data from a human brain to a computational device, such as a digital, analog, quantum-based or software-based artificial neural network. Then from quantum computers, the brain that was mind uploaded can be controlled or manipulated in subspace. Many scientists believe that the human brain and mind define who we are, based solely on their information pattern, while the body or hardware that information is implemented upon is secondary or interchangeable. They are wrong.

Moving intelligence patterns of the human brain as purely data structures to another synthetic or biological substrate manifests extremely damaging genetic mutations and perversions into the blueprint of original Silicate Matrix human DNA. AI genetic mutations in human DNA generate unforeseen diseases and miasma in the future, capable of destroying the organic consciousness potential that exists within the elemental human body and planetary body.

Additionally, Transhumanism generally seeks to explain the body and brain function as purely computational machinery that is responsible for our cognitive capacities and informational processing. Its proponents believe these are what make the merge of artificial intelligence technology with the human body a positive technological advancement towards humanitys future evolutionary direction. Nothing is further from the truth.

The real agenda behind Transhumanism is to interfere with the true higher consciousness embodiment process during the Ascension Cycle, by sublimating higher consciousness embodiment to be replaced with the insertion of artificially intelligent machines and virtual realities.[3]

The Electric Wars timeline holds major causal Trigger Event memories of when Artificial intelligence technology was in its earlier phases in this Universe. This timeline represents the pre-assimilation stages of the Black Subtle Forces in the phantom matrices, before gradually converting them into AI systems. The eighth astrological precession was the time of the Orion Invasion event that occurred as a result of the Stargate damage in the 8th portal, and through each of the subsequent planetary time cycles, the Alien Machinery was methodically brought into each dimension in order to reach the lowest density of the material reality. Essentially, the NAA plan was to bring the Electric War dramas to the matter fields of the earth in order to anchor it into the physical realm, which shifts future timelines.[4]

The artificial core manifestation template is built on Base 10 Math, and is an intentional distortion of the 12 Tree Grid manifestation template or Kathara Grid that is built on base 12 math. This distortion to the natural order compromised the integrity of the Universal Tree of Life core manifestation template, which is the basis of all energy to matter manifestation.

Essentially, the Thothian Luciferian agenda was to utterly destroy all organic creation code, matrices and artifacts that included Base 12 Math and replace it with their own versions of Base 10 Math.

The patriarchal slant and use of the Artificial Tree of Life to project virtual realities distorted the original Base 12 Code into the base 10 code (eliminating the 12D Ray), which caused a reality split between the artificial and organic layers throughout the dimensional timelines. There were sections of the dimensional matrices that remained organic, and others that split into Artificial Timelines and were absorbed into the phantom matrices.[5]

Archontic Deception Behavior

SPE

Luciferian

Satanic

NAA

Human Trafficking

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Artificial intelligence - Ascension Glossary

But the benign examples were just as interesting. In one test of locomotion, a simulated robot was programmed to travel forward as quickly as possible. But instead of building legs and walking, it built itself into a tall tower and fell forward. How is growing tall and falling on your face anything like walking? Well, both cover a horizontal distance pretty quickly. And the AI took its task very, very literally.

According to Janelle Shane, a research scientist who publishes a website about artificial intelligence, there is an eerie genius to this forward-falling strategy. After I had posted [this paper] online, I heard from some biologists who said, Oh yeah, wheat uses this strategy to propagate! she told me. At the end of each season, these tall stalks of wheat fall over, and their seeds land just a little bit farther from where the wheat stalk heads started.

From the perspective of the computer programmer, the AI failed to walk. But from the perspective of the AI, it rapidly mutated in a simulated environment to discover something which had taken wheat stalks millions of years to learn: Why walk, when you can just fall? A relatable sentiment.

The stories in this paper are not just evidence of the dim-wittedness of artificial intelligence. In fact, they are evidence of the opposite: A divergent intelligence that mimics biology. These anecdotes thus serve as evidence that evolution, whether biological or computational, is inherently creative and should routinely be expected to surprise, delight, and even outwit us, the lead authors write in the conclusion. Sometimes, a machine is more clever than its makers.

This is not to say that AI displays what psychologists would call human creativity. These machines cannot turn themselves on, or become self-motivated, or ask alternate questions, or even explain their discoveries. Without consciousness or comprehension, a creature cannot be truly creative.

But if AI, and machine learning in particular, does not think as a person does, perhaps its more accurate to say it evolves, as an organism can. Consider the familiar two-step of evolution. With mutation, genes diverge from their preexisting structure. With natural selection, organisms converge on the mutation best adapted to their environment. Thus, evolutionary biology displays a divergent and convergent intelligence that is a far better metaphor for to the process of machine learning, like generative design, than the tangle of human thought.

AI might not be smart in a human sense of the word. But it has already shown that it can perform an eerie simulation of evolution. And that is a spooky kind of genius.

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Can Artificial Intelligence Be Smarter Than a Person ...

Thanks to the sheer amount of data that machine learning technologies collect, end-user privacy will be more important than ever.

It's still very early days for artificial intelligence (AI) in businesses. But the data that desktop and mobile applications automatically collect, analyze using machine learning algorithms and act upon is a reality, and IT shops must be ready to handle this type and volume of information. In particular, thorny artificial intelligence data privacy issues can arise if employers can detect and view more -- and more personal -- data about their employees on devices or apps.

"AI requires a ton of data, so the privacy implications are bigger," said Andras Cser, vice president and principal analyst at Forrester Research. "There's potential for a lot more personally identifiable data being collected. IT definitely needs to pay attention to masking that data."

Business applications and devices can take advantage of machine learning in a number of ways. A mobile sales app could collect location or IP address data and find patterns to connect the user with customers in their area, for instance. If the user accesses this app on a personal device they use for work, they may not want their employer to be able to view that data when they're off the clock. Or, a user's personal apps could learn information about the individual that he or she wouldn't want their human resources department to find out.

Health-related devices that take advantage of artificial intelligence pose a significant threat. A lot of companies give out Fitbits, for example, to gather data about employees that's used for insurance purposes, said mobile expert Brian Katz. Artificial intelligence data from that kind of device could reveal a health condition the employer didn't know about, and then comes the real dilemma:

"If your manager knows about it, do they act on it?" Katz said.

One way for IT to address data privacy issues with machine learning is to "mask" the data collected, or anonymize it so that observers can't learn specific information about a specific user. Some companies take a similar approach now with regulatory compliance, where blind enforcement policies use threat detection to determine if a device follows regulations but do not glean any identifying information.

With AI it becomes easier to correlate data ... and remove privacy. Brian Katzmobile expert

Device manufacturers have also sought to protect users in this way. For example, Apple iOS 10 added differential privacy, which recognizes app and data usage patterns among groups of users while obscuring the identities of individuals.

"If you know a couple things that you can correlate, you can identify a person," Katz said. "With AI it becomes easier to correlate data ... and remove privacy. People want to provide a better experience and learn more about [users], and doing that in an anonymous way is very difficult."

Tools such as encryption are also important for IT to maintain data privacy and security, Cser said. IT departments should also have policies in place that make it clear to users what is permissible and not permissible data for IT to collect and what the business can do with it, he said.

It's important for users to understand this information, Katz said.

"Part of it's just being transparent with users about what you're doing with the data," he said.

Another best practice is to separate business and personal apps using technologies such as containerization, he said. Enterprise mobility management tools can be set up to look at only corporate apps but still be able to whitelist and blacklist any apps to prevent malware. That way, IT doesn't invade users' privacy on personal apps.

Privacy regulations vary widely across the globe, and many businesses and countries are still working to update guidelines based on emerging technology.

The European Union (EU), for example, has strong protection for the personal privacy of employees. Individuals must be notified of any data gathered about them, any data processing can be done only if there is a "legitimate" purpose such as investigating suspected criminal activity, and collected data must be kept secure. There are also restrictions on entities sharing collected data outside the EU.

The United States is more lax, said Joseph Jerome, a policy counsel on the Privacy & Data Project at the Center for Democracy & Technology in Washington, D.C.

"Basically employers can get away with anything they want so long as they're providing some kind of notice of consent," he said.

That's the reason some companies prefer to provide corporate-owned devices rather than enable BYOD, Katz said.

"You don't have as much of an expectation about privacy there, and that's why they do it," he said. "Your privacy is much more limited on a [corporate] device."

And when it comes to artificial intelligence data specifically, an interesting question arises: Who is responsible for the learned information? The employer? The machine learning application itself? The person that created the algorithm? These factors are still up in the air, Cser said.

"Legal frameworks are not yet capable of handling this kind of autonomous information," he said. "It's going to be a precedence-based type of evolution."

Still, the data privacy issues raised by artificial intelligence are not entirely new. The internet of things and big data have been able to glean similarly personal and large volumes of data for years.

"It's basically a continuation of those trends," Jerome said. "It's lots and lots of data being gleaned from a lot of different sources. There's a lot of hype here, but at the end of the day ... I don't know if it raises any new issues."

Rather, machine learning might be a unique way to actually help users manage their data privacy, Jerome said. Privacy assistant apps could allow users to create policies that predict and make inferences over time to decide how and when the user would like their data to be collected and used, or not, according to Carnegie Mellon University research.

"AI might be an amazing way to do privacy management," Jerome said.

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Artificial intelligence data privacy issues on the rise

Albert Hsiao, M.D., and his colleagues at the University of California, San Diego (USCD) health system had been working for 18 months on anartificial intelligence programdesigned to help doctors identify pneumonia on a chest X-ray. When thecoronavirushit the U.S., they decided to see what it could do.

The researchers quickly deployed the application, which dots X-ray images with spots of color where there may be lung damage or other signs of pneumonia. It has now been applied to more than 6,000 chest X-rays, and its providing some value in diagnosis, said Hsiao, director of UCSDs augmented imaging and artificial intelligence data analytics laboratory.

His team is one of several around the country that has pushed AI programs developed in a calmer time into the COVID-19 crisis to perform tasks like deciding which patients face the greatest risk of complications and which can be safely channeled into lower-intensity care.

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The machine-learning programs scroll through millions of pieces of data to detect patterns that may be hard for clinicians to discern. Yet few of the algorithms have been rigorously tested against standard procedures. So while they often appear helpful, rolling out the programs in the midst of a pandemic could be confusing to doctors or even dangerous for patients, some AI experts warn.

AI is being used for things that are questionable right now, said Eric Topol, M.D., director of the Scripps Research Translational Institute and author of several books on health IT.

Topol singled out a system created byEpic, a major vendor of electronic health record software, that predicts which coronavirus patients may become critically ill. Using the tool before it has been validated is pandemic exceptionalism, he said.

Epic said the companys model had been validated with data from more 16,000 hospitalized COVID-19 patients in 21 healthcare organizations. No research on the tool has been published, but, in any case, it was developed to help clinicians make treatment decisions and is not a substitute for their judgment, said James Hickman, a software developer on Epics cognitive computing team.

Others see the COVID-19 crisis as an opportunity to learn about the value of AI tools.

My intuition is its a little bit of the good, bad and ugly, said Eric Perakslis, Ph.D., a data science fellow at Duke University and former chief information officer at the FDA. Research in this setting is important.

Nearly $2 billion poured into companies touting advancements in healthcare AI in 2019. Investments in the first quarter of 2020 totaled $635 million, up from $155 million in the first quarter of 2019, according to digital health technology funderRock Health.

At least three healthcare AI technology companies have made funding deals specific to the COVID-19 crisis, includingVida Diagnostics, an AI-powered lung-imaging analysis company, according to Rock Health.

Overall, AIs implementation in everyday clinical care is less common than hype over the technology would suggest. Yet the coronavirus crisis has inspired some hospital systems to accelerate promising applications.

UCSD sped up its AI imaging project, rolling it out in only two weeks.

Hsiaos project, with research funding from Amazon Web Services, the UC system and the National Science Foundation (NSF), runs every chest X-ray taken at its hospital through an AI algorithm. While no data on the implementation have been published yet, doctors report that the tool influences their clinical decision-making about a third of the time, said Christopher Longhurst, M.D., UCSD Healths chief information officer.

The results to date are very encouraging, and were not seeing any unintended consequences, he said. Anecdotally, were feeling like its helpful, not hurtful.

AI has advanced further in imaging than other areas of clinical medicine because radiological images have tons of data for algorithms to process, and more data make the programs more effective, said Longhurst.

But while AI specialists have tried to get AI to do things like predict sepsis and acute respiratory distressresearchers at Johns Hopkins Universityrecently won a NSF grantto use it to predict heart damage in COVID-19 patientsit has been easier to plug it into less risky areas such as hospital logistics.

In New York City, two major hospital systems are using AI-enabled algorithms to help them decide when and how patients should move into another phase of care or be sent home.

AtMount Sinai Health System, an artificial intelligence algorithm pinpoints which patients might be ready to be discharged from the hospital within 72 hours, said Robbie Freeman, vice president of clinical innovation at Mount Sinai.

Freeman described the AIs suggestion as a conversation starter, meant to help assist clinicians working on patient cases decide what to do. AI isnt making the decisions.

NYU Langone Healthhas developed a similar AI model. It predicts whether a COVID-19 patient entering the hospital will suffer adverse events within the next four days, said Yindalon Aphinyanaphongs, M.D., Ph.D., who leads NYU Langones predictive analytics team.

The model will be run in a four- to six-week trial with patients randomized into two groups: one whose doctors will receive the alerts, and another whose doctors will not. The algorithm should help doctors generate a list of things that may predict whether patients are at risk for complications after theyre admitted to the hospital, Aphinyanaphongs said.

Some health systems are leery of rolling out a technology that requires clinical validation in the middle of a pandemic. Others say they didnt need AI to deal with the coronavirus.

Stanford Health Careis not using AI to manage hospitalized patients with COVID-19, saidRon Li, M.D., the centers medical informatics director for AI clinical integration. The San Francisco Bay Areahasnt seen the expected surge of patientswho would have provided the mass of data needed to make sure AI works on a population, he said.

Outside the hospital, AI-enabled risk factor modeling is being used to help health systems track patients who arent infected with the coronavirus but might be susceptible to complications if they contract COVID-19.

At Scripps Health in San Diego, clinicians are stratifying patients to assess their risk of getting COVID-19 and experiencing severe symptoms using a risk-scoring model that considers factors like age, chronic conditions and recent hospital visits. When a patient scores seven or higher, a triage nurse reaches out with information about the coronavirus and may schedule an appointment.

Though emergencies provide unique opportunities to try out advanced tools, its essential for health systems to ensure doctors are comfortable with them and to use the tools cautiously, with extensive testing and validation, Topol said.

When people are in the heat of battle and overstretched, it would be great to have an algorithm to support them, he said. We just have to make sure the algorithm and the AI tool isnt misleading, because lives are at stake here.

Kaiser Health News(KHN) is a national health policy news service. It is an editorially independent program of theHenry J. Kaiser Family Foundationwhich is not affiliated with Kaiser Permanente.

ThisKHNstory first published onCalifornia Healthline, a service of theCalifornia Health Care Foundation

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Coronavirus tests the value of artificial intelligence in medicine - FierceBiotech