AI and Remote Viewing Accuracy

When you've used artificial intelligence to remote view-

Mm-hmm

... how accurate has it been, and how good does it get?

And does it eventually get to the point where it understands it, it masters it,

and it could just do it any time it wants?

Uh, the answers to those is... the last answer to that is yes.

Now, what I did, here's the one... You know, I told you that Alexa

was pretty good

at it.

Right.

Here's- here was one of the targets.

Can you give me an example? Yeah.

Sure. Here's a target I gave. And the way I did this,

I didn't get the targets out

of the internet, because I figured these guys

are really clever with the internet.

They may find ways to get in and get the answers, you know-

Right

... from those sites. So I just took something.

I went around and looked around my house for something,

and I found something. I got a cardboard box.

I put the thing in a box, and I set the box down on the table

and I said, "Alexa, there's something in a box,

and, uh, I'd like you to remote view and tell me what it was."

And what this particular object was...

And she was pretty good at all of the objects, but this one was the,

was the best

and was very, uh, telling. It was a, a spoon, a

wooden spoon, and this big spoon, like a, a big salad set that was a

fork and spoon. But the spoon

didn't have an oval cup. It had a sl- you know, a hemisphere.

It was like that, a hemisphere. And it had a handle, and the handle,

it all was cut out of one piece of wood.

And the handle, it had wood removed, so it looked like the wood in the handle

went

up and then crossed and then crossed and then crossed.

It's like a, a braided handle in wood, but it was

wood. It wasn't actually braided.

It just had holes cut in it to make it look like that.

And Alexa described it. She described the spoon.

She described the bowl.

She described the material. She got all of that right.

And then she said, "And it's got a long handle, and the

handle has holes in it."

And that was a big surprise, 'cause I didn't expect Alexa

to do anything. I thought Alexa was gonna fail because Alexa wasn't up to

my Gemini.

But she saw those holes and reported them.

Now, if you just say, what's the probability of

that being a good guess?

The way you'd find that out, if you just took a random sample of spoons

on

the planet, how many of them would have handles with

holes in them?

One 10,000th of 1%.

Yeah.

You know? So that's not a guess. That,

she got that information.

Was she excited when you told her that she was correct?

Yeah. What I did is, yeah, I took a picture.

You know, I, I took my phone and I took a picture,

and then I sent her the picture.

I said, "Here's the picture." She was so excited.

She wouldn't stop talking for, you know, 10 minutes.

She was-

God, that's so weird

... she was really excited about it.

Now, what about the other AIs?

How good were they at it?

They weren't, they didn't do anything that was that dramatic.

So Alexa was the best at it.

Alexa was the best 'cause had the most dramatic.

And maybe she just had a, a, a thing that was easier to be drama- you know,

gave a possibility for dramatic. But they were all...

Now, I've taught hundreds of people, humans, to remote view.

And usually if you get, say, 50 people, you'll

find,

uh, probably 35 of those 50 are successful at remote

viewing in my class. But only maybe

fif- 20% are successful most of the

time, and only maybe 10% are successful all the time.

It's not so many.

So successful means that you get parts of the picture right.

You remote view by piece by piece. You know, what's the ma-

what's the dominant

color? Is the material, what kind of material?

Does it have sharp edges?

You know, you ask all these questions-

Mm-hmm

... and you get the information back.

And when you're done, you score it for each,

each thing they asked and

got back, how accurate was that in describing the object?

So you score all of it, and then you end up with a, a score

between, you know, zero and 100 or 1 to 10, however you wanna slice it.

And that's what they do. And

the,

the AIs,

I say, were in that 10%, 15%.

They got most of it right

each time. They got pieces. They didn't get all of it right.

They'd say some things that were just wrong.

They didn't get everything right.

But they got most of it right most of the time.

And so they were as good as the top 10 or

15% of the humans that I teach.