Cultocracy note :
Current well publicized research in neural networks and brain machine interfaces reveal an interconnected group of companies spearheading the whole synthetic scheme . Companies such as Facebook , PayPal , Microsoft and Google are at the core , the datasets provided by these and other companies provide the essential ‘food’ for machine learning and AI .
It would seem that this particular group of companies form a public outlet for research conducted by deep state organizations such as DARPA , in the process creating a potential commercial enterprise aimed at future generations .
To quote from the article :
“And the decades of animal tests to back up his sci-fi ambitions: Researchers have learned how to restore memories lost to brain damage, plant false memories, control the motions of animals through human thought, control appetite and aggression, induce sensations of pleasure and pain, even how to beam brain signals from one animal to another animal thousands of miles away.”
“Coming soon, from the people who brought you nuclear weapons!”
N.B. “decades of animal tests” = decades of human tests .
John H. Richardson – wired.com
In an ordinary hospital room in Los Angeles, a young woman named Lauren Dickerson waits for her chance to make history.
She’s 25 years old, a teacher’s assistant in a middle school, with warm eyes and computer cables emerging like futuristic dreadlocks from the bandages wrapped around her head. Three days earlier, a neurosurgeon drilled 11 holes through her skull, slid 11 wires the size of spaghetti into her brain, and connected the wires to a bank of computers. Now she’s caged in by bed rails, with plastic tubes snaking up her arm and medical monitors tracking her vital signs. She tries not to move.
The room is packed. As a film crew prepares to document the day’s events, two separate teams of specialists get ready to work—medical experts from an elite neuroscience center at the University of Southern California and scientists from a technology company called Kernel. The medical team is looking for a way to treat Dickerson’s seizures, which an elaborate regimen of epilepsy drugs controlled well enough until last year, when their effects began to dull. They’re going to use the wires to search Dickerson’s brain for the source of her seizures. The scientists from Kernel are there for a different reason: They work for Bryan Johnson, a 40-year-old tech entrepreneur who sold his business for $800 million and decided to pursue an insanely ambitious dream—he wants to take control of evolution and create a better human. He intends to do this by building a “neuroprosthesis,” a device that will allow us to learn faster, remember more, “coevolve” with artificial intelligence, unlock the secrets of telepathy, and maybe even connect into group minds. He’d also like to find a way to download skills such as martial arts, Matrix-style. And he wants to sell this invention at mass-market prices so it’s not an elite product for the rich.
Right now all he has is an algorithm on a hard drive. When he describes the neuroprosthesis to reporters and conference audiences, he often uses the media-friendly expression “a chip in the brain,” but he knows he’ll never sell a mass-market product that depends on drilling holes in people’s skulls. Instead, the algorithm will eventually connect to the brain through some variation of noninvasive interfaces being developed by scientists around the world, from tiny sensors that could be injected into the brain to genetically engineered neurons that can exchange data wirelessly with a hatlike receiver. All of these proposed interfaces are either pipe dreams or years in the future, so in the meantime he’s using the wires attached to Dickerson’s hippocampus to focus on an even bigger challenge: what you say to the brain once you’re connected to it.
Read the full article here at wired.com
- Kernel is trying to hack the human brain — but neuroscience has a long way to go
- Kernel Acquires KRS to Build Next-Generation Neural Interfaces
- ‘AI brain scans’ reveal what happens inside machine learning
- Training Deep Neural Networks
- Deep Residual Learning for Image Recognition (PDF)
- The Vision Of Technocracy And Your Future