Episode 13

₿HS013: AI and Homeschooling

SHOW TOPIC:

It’s mission critical for Bitcoin homeschoolers to overcome the fear, uncertainty and doubt (FUD) about artificial intelligence (AI).  Tali and Scott kick off this series with the “why” about this powerful type of tool.  The decision parents must make is whether to learn about AI and be the master of the new technology or be the victim who fears it.

IN THIS EPISODE, YOU'LL LEARN:

  • Many examples of AI FUD.
  • AI needs to be part of your homeschooling curriculum.
  • Parents need to understand what AI is and help their kids with a framework for it.
  • It is a tool.  Like all tools, it can be used for good for evil.
  • It is normal for people to fear new technologies, e.g., electricity or cars.
  • Not using AI does not protect you from it.  
  • The decision parents have to make is whether to learn about AI and be the master of the new technology or be the victim who fears it.
  • AI is not actually intelligent. 
  • The phrase “artificial intelligence” was first coined in the 1950’s.
  • The potential of AI as a tool to enhance learning is mind-blowing, e.g., learning how to play chess now versus 30 years ago.
  • With the acceleration of technology, there’s actually a decentralization impact on AI.
  • This is a call to action to homeschooling parents to go deeper, to figure out how to incorporate AI into your curriculum.
  • Just pick one area, one AI tool and go try it out
  • Narrow AI versus general AI
  • Yes, AI can lead to loss of jobs.  However, it also leads to new jobs.  Those who learn AI tools will remain in high demand by companies.
  • Controlling societies like China can use AI in scary totalitarian ways.  This is reinforces why we need to understand and teach how AI really works and remain vigilant in protecting our freedoms.
  • Spirit of Satoshi is based on libertarian ideas, Austrian economics and Bitcoin resources.

RESOURCES MENTIONED IN THE SHOW:

·       Preston Pysh’s interview with Jeff Booth https://www.theinvestorspodcast.com/bitcoin-fundamentals/personal-ai-models-and-bitcoin-jeff-booth/

·       Guy Swan interview with Jeff Booth https://youtu.be/s8XoMg3tb0s

·       Spirit of Satoshi  https://www.spiritofsatoshi.ai

HAPPY TO HELP:

  • Tali's Twitter @OrangeHatterPod
  • Scott's Twitter @ScottLindberg93
  • Scott's nostr npub19jkuyl0wgrj8kccqzh2vnseeql9v98ptrx407ca9qjsrr4x5j9tsnxx0q6
  • Free Market Kids' Twitter @FreeMarketKids
  • Orange Pill App @FreeMarketKids
  • Free Market Kids' games including HODL UP https://www.freemarketkids.com/collections/games

WAYS TO SUPPORT:

We are essentially our own sponsors and are so grateful for all of you who support this show.  Thank you!

STANDING RESOURCE RECOMMENDATIONS:

Mentioned in this episode:

Aleia Free Market Kids Full

Transcript
Speaker:

Built-in Microphone & FaceTime HD Camera (Built-in)-6:

Understanding artificial intelligence.

2

:

Let's start by.

3

:

Getting a clearer picture of what it is.

4

:

I think there's a lot of, uh, people

out there talking about artificial

5

:

intelligence as if it's a living

thing as if we're talking about.

6

:

Like the movie Terminator where machines

are taking over the world and they are

7

:

more intelligent than human beings and

they enslave human beings in these.

8

:

You know, human guard, labor garden,

like in the matrix or something.

9

:

So let's just first and foremost, clear up

what artificial intelligence actually is.

10

:

Scott.

11

:

It's a newscast.

12

:

Over to you Tali.

13

:

So the.

14

:

The thought is this, if this, this is

going to be a topic that we're going

15

:

to have to hit on multiple times,

and part of being able to figure out

16

:

what to include in your curriculum.

17

:

Or not including your curriculum or

the framework you give your kids, like,

18

:

for example, your, your framework for

politics or money or anything else.

19

:

You have to study a little

bit yourself to understand.

20

:

So Tommy and I have started to.

21

:

To go down the artificial intelligence.

22

:

Rabbit hole and it's been eyeopening.

23

:

And actually the slides that we're looking

at now and the ones that are come up.

24

:

Uh, we we've edited these, but they

started with the initial kind of framework

25

:

of them actually was from one of the

tools that Talia was playing with.

26

:

The main idea of this,

this entire episode.

27

:

Is why this is a critical

subject to be taught.

28

:

So if you're a Bitcoin homeschool or.

29

:

Our opinion is.

30

:

teach AI now.

31

:

What level you do that too.

32

:

Like you can get into, you

can get into that, but.

33

:

Part of this.

34

:

Part of this is there's a lot

of misunderstanding out there.

35

:

I didn't actually read the article, but.

36

:

Supposedly some things, some of

the things that were coming out

37

:

of the white house in terms of

guidance happened after Joe Biden.

38

:

Watched mission impossible.

39

:

And he clearly has no clue.

40

:

On anything in AI.

41

:

You have tech giants.

42

:

Then have a lot of political cloud

because of the, the, the money

43

:

that they can, they can bring.

44

:

And they want to create a

whole moat around this and.

45

:

There's a tremendous amount of.

46

:

Just a lack of understanding, whether

it's just like, there's a, there's a lack

47

:

of understanding of what it really is.

48

:

And there's a lot of fear.

49

:

So Hollywood's going to do that.

50

:

It could be, there's a ton of

movies we can get into for examples.

51

:

And.

52

:

The thing about it is let's let's

first, just let's peel this thing,

53

:

this peel, this onion slowly.

54

:

Let's let's dig in a little bit to it.

55

:

And it's just like any other tool.

56

:

Right.

57

:

When electricity first came

out and people worried about.

58

:

All everyone dying and

being burned to death and

59

:

electrocuted and everything else.

60

:

Like it's.

61

:

Could you imagine a life

without electricity now?

62

:

So this is something that is a technology.

63

:

It's advancing quickly

and let's just start with.

64

:

Teaching yourselves a little

bit about what AI is and

65

:

then we can get, get into it.

66

:

Um, more, but the premise is.

67

:

Let's let's at least

understand what this is.

68

:

Yeah.

69

:

And as you can see on the side,

we're going to address not only what.

70

:

AI is, but also to really view

it as just another advanced tool.

71

:

So for example, when cars came out,

people who own horses or very upset.

72

:

Uh, it was going to take over

jobs like the blacksmith's job.

73

:

You know, horse.

74

:

Carriage.

75

:

Carriage drivers.

76

:

You know, and who, the people who make

wagons, you know, All that type of stuff.

77

:

And then what they focused on was

what they're going to lose rather

78

:

than to see it as just a new tool

that will save them time and energy.

79

:

So.

80

:

The picture that you see there,

even though we're talking about AI,

81

:

if you look at it, it's a ruler, a

pair of scissors and some thread.

82

:

Well, If we look at AI, like it's

anything more than a tool then of

83

:

course it brings a lot of fear.

84

:

But as Scott said before, When electricity

first came out, people were very fearful

85

:

of it because they did not understand it.

86

:

And so that's what we want to.

87

:

That's the way we're going to

approach this presentation as

88

:

well as features presentations is

AI is nothing more than a tool.

89

:

And the decision you have to make

is are you going to teach yourself

90

:

how to use the tool so that you are

the master of the tool, or are you

91

:

going to become a victim of this?

92

:

Tool and somehow give them more

power than it actually has.

93

:

All right.

94

:

Well, let's get into it then.

95

:

AI is obviously tremendously powerful.

96

:

It's it's amazing how

quickly it has developed.

97

:

We only started hearing about AI.

98

:

In everyday conversations

a year or two years ago.

99

:

And suddenly it is.

100

:

Absolutely everywhere.

101

:

Every software that you use is now.

102

:

Using AI to help make content.

103

:

Better.

104

:

It's changing how we interact.

105

:

Almost in every single way.

106

:

So.

107

:

W, but this is where by first.

108

:

Contention.

109

:

Is, it's not actually intelligent.

110

:

So as I've started to study this.

111

:

Um,

112

:

The, the idea that when

you're using chat GPT or your.

113

:

Whatever, whatever it is

that like, what you're.

114

:

You're talking about Tali, where

you, you have something and you're

115

:

interacting and there's AI behind it.

116

:

So it can be anything

from a Google search to.

117

:

Two.

118

:

Uh, who knows what everything, like

you said is really touching this.

119

:

It's more like a.

120

:

Auto-fill it's a giant auto-fill.

121

:

And it's, it's not, it's not as if

the machine is actually thinking.

122

:

So the first problem I have

with AI is it's not, there's

123

:

no, there's no intelligence that

I can really, I can answer a.

124

:

A.

125

:

L said whatever the, whatever

the test is that you take,

126

:

the, the, the, the legal stats.

127

:

It was the whole set, right?

128

:

There was a law school.

129

:

Maybe this is a wrong example.

130

:

There was a thing where there,

there was a, there was a test given.

131

:

And judgy.

132

:

GBT or some other AI

was able to pass this.

133

:

This graduate level exam.

134

:

And that's this implies we're,

we're kind of reading into this.

135

:

That is intelligent.

136

:

And that's not true at all.

137

:

It's more like if you

started typing a word.

138

:

And then the rest of the word popped up.

139

:

Right.

140

:

I'm.

141

:

I'm texting and I started

writing something and then the

142

:

rest of the word popped up.

143

:

This is just the, it's just the

probability of what the next word

144

:

is and the next word, the next word.

145

:

And it's, it's a whole bunch of.

146

:

Probabilities.

147

:

Right.

148

:

It's not reading your mind.

149

:

Right.

150

:

It's it's deducting logically.

151

:

There's no, there's no deduction.

152

:

It is.

153

:

It's like a whole bunch of

vectors and probabilities.

154

:

That you know, this.

155

:

This word that like once upon a right.

156

:

Once upon a, as an example.

157

:

The the, the number of times

the probability that the next

158

:

word would be, time is higher

than the next word being dog.

159

:

Right.

160

:

And then it says, okay.

161

:

And then, and it does it with phrases

and it does with other things as well.

162

:

I'm not a mathematician.

163

:

I certainly not sorta can program AI,

but what, what I took away from my.

164

:

Uh, initial.

165

:

Reading and podcasts on AI is.

166

:

It's more like an auto-fill.

167

:

And if you think about that, it,

you should relax a little bit.

168

:

We are not on the verge of Ultron.

169

:

Taking over the world, or if you're,

if you're older, like me, how from

170

:

2001 space Odyssey, like we're.

171

:

We're not to the point where this

has any kind of general intelligence.

172

:

This is.

173

:

It's literally a probability.

174

:

Tool and it.

175

:

looks to us like it's really smart

because it's, it's able to look at.

176

:

Millions or billions or

however many pieces of input.

177

:

To get us out.

178

:

And when people say, well, you have to

be careful because AI makes up stuff.

179

:

Like it would make up case

study in that example.

180

:

Well, Technically, it makes up

a hundred percent of this stuff.

181

:

It's not just making

up some of this stuff.

182

:

Everything you get is made up.

183

:

And.

184

:

Relax.

185

:

We're, we're not, this is, this is

literally not a thinking machine coming

186

:

at you and trying to answer this, that

it understands what is giving you.

187

:

It's just the probability.

188

:

So, um, to me that, that's the,

that's the amazing thing about it.

189

:

It has so much potential.

190

:

And we literally only just started

down this, this path, but that's what

191

:

I thought of when I first started.

192

:

When you, when you're talking

about defining what is AI.

193

:

Well, a part of me is

saying, well, what is it not?

194

:

It is not actually a thinking.

195

:

Capable type of entity.

196

:

That's going to take over the world.

197

:

But anyway, that's my take.

198

:

I think that was definitely one of the

things that we are sure of me in the

199

:

beginning when I first started hearing

about AI, because my first knee-jerk

200

:

reaction was also fear like, oh my

gosh, You know, we have kids in college.

201

:

What are they going to do?

202

:

Coming out?

203

:

What we have one daughter

who's very interested in art.

204

:

If AI is taking over art, then

that means you have no job.

205

:

We have another one

that, that writes songs.

206

:

And of course we're hearing

people talk about how AI's

207

:

writing songs and they're winning.

208

:

Grammy's and things like that.

209

:

Like, what does that.

210

:

What does that leave?

211

:

For the human students.

212

:

And that was so that,

wasn't my first fear.

213

:

And when I came across this

definition that it is nothing

214

:

more than a probability generator.

215

:

And may me relax, because if it's

just a probability generator,

216

:

then that means you've got to have

somebody interpreting the data and

217

:

that human person cannot be replaced.

218

:

Right.

219

:

So it's totally my thought in this.

220

:

This is the most.

221

:

Just rip style in terms of

how we're addressing this.

222

:

We should look at.

223

:

This would be a good.

224

:

A good episode would be just breaking

down what the different levels of the

225

:

intelligence are as a been defined.

226

:

And where we're at.

227

:

Going forward.

228

:

Yeah.

229

:

No, no.

230

:

I mean, as a, as a future,

Installment in the series.

231

:

Yeah.

232

:

Well, I really quick look at the

compounding effect of AI and.

233

:

Really the chip.

234

:

It's it's really, it really

comes back to the chip.

235

:

Uh, development.

236

:

The speed's development because

AI works off of the speed

237

:

of processing information.

238

:

Partially.

239

:

Because of its slow.

240

:

Then AI just wouldn't

be everywhere right now.

241

:

Okay.

242

:

Yes.

243

:

I think you need that as

a foundation, but yeah.

244

:

I mean, this timeline shows

you how you should read that.

245

:

And 56, the term artificial

intelligence is coined.

246

:

So that wasn't the 1950s.

247

:

And by 1997, IBM's deep blue

beats world chess champion, Gary.

248

:

Casper off.

249

:

So I remember when that story came out,

I remember that was such a big deal.

250

:

And the time between the first, you know,

artificial intelligence term came out.

251

:

To when.

252

:

Artificial intelligence, beat the world.

253

:

Chess wish.

254

:

I think in my mind, when you

think smart people, you really

255

:

think people like chess champions.

256

:

And so it took about 40 years.

257

:

Yeah.

258

:

So, this is where again, I want.

259

:

W we're going to, we're going to have

to do an episode, just some what.

260

:

What is AI?

261

:

When you're, when you're in the world.

262

:

Of chess.

263

:

The, uh, computer is really good

at running a lot of calculations.

264

:

And keeping track of them.

265

:

So in chess, there's a, it

may be a log, but there is a

266

:

finite number of, of options.

267

:

And if player X.

268

:

Chooses an option.

269

:

That reduced, that changes.

270

:

The the possibilities each time.

271

:

Right.

272

:

So the, the chest.

273

:

Like a chess program, could theoretically

look at all the possibilities, make it.

274

:

And so this is the one

that's going to go with.

275

:

Whereas it's not really.

276

:

Intelligent.

277

:

Well, again, I think again,

the intelligence thing throws

278

:

me, and this is not the same.

279

:

The, that that example is interesting.

280

:

But it's not the same as what

we're seeing with Chet GPT.

281

:

It's not the same as what

we're seeing with Dali.

282

:

There.

283

:

But that was the beginning.

284

:

That was 1997 button.

285

:

But what I want to say is, and I

don't know if they actually did.

286

:

Did this, but.

287

:

When a human.

288

:

Chess player is.

289

:

Against artificial intelligence

or I'll just call it a computer.

290

:

For, for simplification and they test and

you're like, oh, The computer beat, Gary.

291

:

Moving on the computer is that smart,

but what, what I think would have

292

:

been really interesting is if you

gave Gary a chance to learn how.

293

:

That program.

294

:

The blue works, whether or not

Gary was some work could have

295

:

beaten the, the AI program.

296

:

I think that would have

been an interesting thought.

297

:

But I'm not sure that they did that.

298

:

I disagree.

299

:

I think the more interesting thing is.

300

:

The, this is work.

301

:

This is why AI is

actually a tool for good.

302

:

If you wanted to be a chess

champion 30 years ago.

303

:

And you were trying to get in your,

your reps, if you will, to learn.

304

:

And you were fortunate enough that

your parents could afford to hire

305

:

a, you know, a grand champion for

you to spend a lot of time with them

306

:

and study, you could get in these.

307

:

Repetitions yourself to learn how to play.

308

:

Today.

309

:

I can download an app on my phone.

310

:

And.

311

:

Because we have these programs.

312

:

I can actually be taught.

313

:

I can actually get the repetitions

in at a, and this is the Jeff Booth.

314

:

As technology is deflationary at a much

lower cost than it would have been.

315

:

30 years ago.

316

:

So to me, the more interesting

thing of that is is.

317

:

The potential of AI as a tool.

318

:

And what it means for learning.

319

:

Yes, definitely as a tool.

320

:

And I want to reference the book

that we talked about in the, in

321

:

last week's episode of the art

of learning by Josh Watkins.

322

:

When he speaks about, his

experience facing off at the

323

:

world, chess championships.

324

:

A lot of it comes down to my games.

325

:

And when you're working against

the computer, it is neutral,

326

:

always emotionally neutral.

327

:

And you are honing your

technical abilities for sure.

328

:

But there's an element that can never

be replaced by machine, which is that.

329

:

Human reaction.

330

:

Element.

331

:

So he said that when the top chess

players face off, they both, like, when

332

:

you're talking about they're playing

for first place, you know, everything

333

:

they've gone through every round when

they're playing for first place, was that.

334

:

That champion apart from.

335

:

The loser is just their ability to

keep their cool or to detect minute

336

:

emotional shifts in their opponent.

337

:

And that's how they beat.

338

:

The other person, not necessarily because

they are technically more superior.

339

:

So going back to what you said, AI is

still just a tool and if you use it to

340

:

your advantage, Then you have the power,

but if you give it some, um, some like.

341

:

Unreal expectation that

it can somehow replace.

342

:

Actual human being, you know,

honing your trust skills then

343

:

I think we miss the point.

344

:

Yeah.

345

:

So is there anything else on this?

346

:

The, the milestone discussion.

347

:

It went from.

348

:

Yeah, so it went from 1997 to 2011.

349

:

So that's what like 14 years later.

350

:

And now they're talking about IBM Watson

wins jeopardy against human champions.

351

:

Um, so again, the AI programming is

doing the data analysis and in those

352

:

two cases in 1997 and 2011, they.

353

:

Basically, we're used to demonstrate how

powerful they are and how smart they are.

354

:

But again, don't lose sight

that they are still just.

355

:

Data.

356

:

And analyzing right.

357

:

Yeah.

358

:

Yeah.

359

:

And then the.

360

:

Oh, well, Okay.

361

:

So let me jump here because

the timeline also includes this

362

:

stuff with where Google's getting

into recognition algorithms and.

363

:

And another big company,

alpha is alpha go alpha.

364

:

What is the one that beat the champion?

365

:

I can't.

366

:

Oh, so.

367

:

One of the things that's on my

mind, as you think about this, this

368

:

history is the Reese, the intensive

amount of resources necessary.

369

:

To build this, right?

370

:

This wasn't somebody in their

garage that could build deep blue.

371

:

We're talking about IDM.

372

:

And then you get you fast forward to.

373

:

The the.

374

:

Recognition stuff that

the Google was doing.

375

:

Like you're talking big tech.

376

:

And one of the things that

I think we talk about.

377

:

We might bring this up again later, but.

378

:

As technology.

379

:

Continues to develop at

the pace it's developing.

380

:

It's also getting cheaper.

381

:

So not only are the chips faster.

382

:

And the computers can hold more your

servers or whatever you're using.

383

:

But actually.

384

:

There is a trend that is

towards decentralization.

385

:

And one of the episodes that I listened

to at a it's a resource I recommend

386

:

to those that want to go deep on this.

387

:

Guys Swan now has a

separate podcast just on AI.

388

:

It's called AI on chained.

389

:

And one of the very interesting

points that he made.

390

:

Was that AI is actually, it

tends towards decentralization.

391

:

And now you have programs that you could.

392

:

We're the once you've done the

hard work of going through a lot of

393

:

data and creating whatever these to

create to all that, the vectors and

394

:

probabilities and whatever else.

395

:

You can now get something down to 200 gigs

or 250 gigs, or, you know, whatever it

396

:

is, it's something that could be on a home

server, a home computer, even your phone.

397

:

And now you can be offline.

398

:

And you would have access to

the equivalent of like this.

399

:

This, uh, this, this language

model that can answer things.

400

:

And again, we're still early

in this, in this process, but

401

:

what comes to mind is in this.

402

:

This discussion of the milestones

is it started out where

403

:

you're really needed to be.

404

:

Resource.

405

:

Rich.

406

:

In order to, to start to build this.

407

:

But we're trending towards more

and more de-centralized abilities.

408

:

Are these things.

409

:

And I actually, I was

actually very relieved.

410

:

To hear that otherwise.

411

:

The the, um, where they call

them the controller guards.

412

:

You know, the Facebooks, the

Amazons, the Googles of the world.

413

:

Even the government.

414

:

Would be able to control this

because they, the massive

415

:

resources, you would think like

a data center or something to be.

416

:

To run these things.

417

:

And the reality is it's

actually going the other way.

418

:

And it's actually very decentralized.

419

:

That actually was.

420

:

Uh, pretty reassuring, but all right.

421

:

I think from a.

422

:

Uh, history standpoint, I'm not

sure how much more we really

423

:

need to, to go into that.

424

:

So, all right, let's just jump

into some of the other, um,

425

:

some other aspects of this.

426

:

Tell me, where do you want to go with.

427

:

With the next, I mean, these

are just a couple, a few bullet

428

:

points about how AI works.

429

:

Uh, they, they can extract.

430

:

And analyze information

for images and videos.

431

:

They are obviously building

robots that can perform tasks.

432

:

By themselves.

433

:

Um, they have models that were

inspired by biological neural networks

434

:

and they can recognize patterns.

435

:

Very quickly.

436

:

Um, other things like natural language

processing, they enable computers to

437

:

analyze and generate human language.

438

:

I was playing around with an AI program.

439

:

Where if I upload it.

440

:

A.

441

:

Short video of myself speaking in English,

I could within minutes change my spoken

442

:

language in the video lip sync to my lips.

443

:

In 150.

444

:

Different languages.

445

:

Very.

446

:

Very cool.

447

:

Very cool.

448

:

Yeah.

449

:

So the actual way that AI works

to me is beyond our, beyond the

450

:

scope of what we personally know.

451

:

I do think it's almost like.

452

:

Do kids really know how

a dishwasher works or do.

453

:

Did someone really know

how their microwave worked?

454

:

Do you actually know how

your car works or your phone?

455

:

You may not need to be an

expert down to the point where

456

:

you could do the programming.

457

:

I think this is another section that

later on, I want to, I think we're gonna

458

:

need to go deeper on if you're looking

at a Dakota homeschool curriculum.

459

:

Some portion of that needs to be

able to explain, okay, here's what.

460

:

If this is a computer, computer vision,

and being able to analyze images and

461

:

being able to analyze videos, here's

kind of what's going on with that.

462

:

And then say, okay, here is what

language processing is like, and

463

:

then say, okay, here's how a.

464

:

Um, Like a mid journey is actually

creating images and it's, you know,

465

:

the way I heard it explained is

it's, it's got all these different.

466

:

Images that have been

labeled and described in.

467

:

In whatever language.

468

:

Then you go in with your language

model and you use that, it goes

469

:

back and says, well, then here's

all the probabilities around.

470

:

What kind of colors, what pixel color is

next to what picture color based on that.

471

:

You throw in some randomness there,

and then it starts to put these things

472

:

together and it comes out looking like.

473

:

There could like.

474

:

The computer knows what

a cat is or something.

475

:

So.

476

:

I think we need to put in English

something that like, uh, We are, we could

477

:

explain to at an elementary school level.

478

:

On how AI works in the same way that

you might explain to kids how the body

479

:

works, how a car works, how a phone works.

480

:

Right.

481

:

We need.

482

:

We need to do the same

thing with AI, how AI works.

483

:

Yeah.

484

:

This episode is an introduction.

485

:

I think there's so much

information out there.

486

:

Obviously we can't possibly

cover all of it, but.

487

:

This is a call to action for

homeschooling parents to.

488

:

To dig a little deeper and not be afraid

of it and figure out how to incorporate

489

:

that information into homeschooling.

490

:

One of the things I, I hear from.

491

:

Our kids were in college right now.

492

:

Um, when they have to write papers.

493

:

Somehow their professors

can, can or cannot tell that.

494

:

Somebody had the chat,

GBT write their papers.

495

:

And so there's a whole lot of rules and

regulations around how you shouldn't

496

:

use Chad GBT or other tools too.

497

:

Perform your work.

498

:

But I feel like that's a very dinosaur

age way of looking at it because

499

:

yes, you should know how the right

paper, but then how can you use

500

:

the AI tools that are available to

you to become even more productive?

501

:

Because the world's moving very fast

and if we can up our productivity there.

502

:

That also frees them up time

to learn even other things more

503

:

deeply or, or, uh, work on.

504

:

Connecting data points

versus having to spew out.

505

:

Right.

506

:

Regurgitating things.

507

:

Yeah, I don't, I don't, if

you're a professor, I think.

508

:

Today, you could probably

tell if the student just took.

509

:

Chad GPT and, and just literally

dumped it out there as a kid.

510

:

That's just trying to.

511

:

Get by faster could take that and edit it.

512

:

And it would probably have a hard time.

513

:

I, the technology though, is it increases.

514

:

It's going to get harder

and harder to tell.

515

:

You know, with that.

516

:

So the bigger thing to me is.

517

:

Do I want.

518

:

I want to be able to use this

to, I want our kids to be able to

519

:

use the tools available to them.

520

:

For an hour, we're gonna get

into jobs and things later,

521

:

but I want them to be able to.

522

:

You know how to use it for good.

523

:

The question of whether you use

something for good or bad, that's

524

:

more of a moral teaching, right.

525

:

And it's a tool.

526

:

I can use my car to drive safely

from, one place or another,

527

:

or I can transport drugs.

528

:

Right.

529

:

I can.

530

:

Use a gun for.

531

:

Hunting or I could, you know,

commit a crime or something.

532

:

So there's a knife.

533

:

The knife, making a gourmet

meal or hurting someone.

534

:

Right.

535

:

She didn't say don't use a knife

because you could hurt somebody with it.

536

:

Well, no, you still use a knife

because it's a very useful tool.

537

:

The meme I liked, I want, like someone

says, um, when someone's trying to

538

:

attack Bitcoin, they say we need to stop

Bitcoin because of terrorist use it.

539

:

Well, it's.

540

:

It's permissionless.

541

:

Right?

542

:

So if the terrorist uses a road,

we're not taking away all the roads.

543

:

And terrorist uses a cell phone.

544

:

We're not taking away all the cell phones.

545

:

You know, You just.

546

:

Th this technology is here is.

547

:

It is.

548

:

Uh, increasing in terms of its

effectiveness and abilities at a speed

549

:

that we, we have a hard time with.

550

:

So the question is what do you do with it?

551

:

And so, all right, so next.

552

:

Uh, want to go into two different types.

553

:

Groups of AI, one is narrow.

554

:

One is broad.

555

:

An example of a narrow AI is

for example, Um, a program that

556

:

was trained specifically to spot

something out of a group of things.

557

:

So for example, if you had a picture

of Waldo and you train your AI to

558

:

spot Waldo, and that is the only

information that you put into that

559

:

algorithm, then that algorithm

can spot while though faster than.

560

:

People and a general, broad, um, algorithm

can because that's all they know.

561

:

Or if you program it to identify, you

were mentioning before cancer cells.

562

:

Well, if that's the only thing they

know, and that's the only information

563

:

they're processing through, they

can do that very, very quickly.

564

:

Now that doesn't take into

account anything else.

565

:

So the example that we were talking

about before about cancer, My.

566

:

My rebuttal to you about using

something like this, like AI to, to

567

:

identify that, or as a diagnosis.

568

:

Uh, diagnosis tool or as a whatever

in the, in the medical field.

569

:

We, we are realizing more

and more that the human body.

570

:

Requires a holistic approach to, um,

to treatment of different ailments.

571

:

And so if you have a bot that just

identifies itself, it doesn't mean

572

:

that he then has the back thing.

573

:

That algorithm has the.

574

:

Answer to how to.

575

:

Make you better, but it does

make the diagnosis much faster.

576

:

And then you can move on to the other

holistic stuff that you can do to treat.

577

:

Two different things.

578

:

So the point that narrow and

general or narrow and broad.

579

:

Are two different ways of

kind of categorizing AI tools.

580

:

And I, the way I would, if I could

summarize what you're saying is that.

581

:

Even after you have that.

582

:

The fact that AI can spot.

583

:

Maybe from x-rays or.

584

:

Um, scans or whatever can spot cancer.

585

:

More reliably and faster than a human can.

586

:

That's great, but you still, now you'd

have, now, now people can move on

587

:

and start working on the diagnosis.

588

:

Better.

589

:

Treatment the treatment.

590

:

Right.

591

:

So now you've.

592

:

You've leveraged AI to do something

better than humans can do faster.

593

:

And now you can get to, to

the next to the next stage.

594

:

So that's an example of using

AI as a tool for your benefit.

595

:

Um, if we were to use a broad AI,

like a chat GBT and say, Hey, look at

596

:

these pictures or Gemini or whatever.

597

:

Uh, Google's new AI tool coming out.

598

:

If you say, Hey, look at this

picture, is it a cancerous?

599

:

It's going to have to process

through a lot of information.

600

:

A lot, most of them irrelevant.

601

:

To make that determination and

then may or may not be correct.

602

:

And so in that sense at the narrow.

603

:

Then the more narrow.

604

:

Programmed AI would be a more efficient

tool versus a broad, because we always

605

:

think, oh, Bigger data is better,

but it's not necessarily so right.

606

:

Okay.

607

:

So the key point is there's.

608

:

There's different types of AI.

609

:

There could be, they can be adjusted

to very specific needs where they

610

:

can be adjusted to be very broad.

611

:

And they both have different.

612

:

It depends on the data

set that is put in there.

613

:

I think it has a different ethic.

614

:

Yes.

615

:

How you train it?

616

:

I think it has to do with

what's the use case or what

617

:

you're trying to use it for, so.

618

:

Okay, great.

619

:

Which is a great lead into.

620

:

So the kind of things that you can do.

621

:

With this.

622

:

So, um, being able to detect fraud.

623

:

That's great.

624

:

The healthcare stuff we've already,

we've already talked about.

625

:

Um, you want to, you want to.

626

:

Well, virtual assistants, AI powers,

virtual assistants, like Siri and Alexa.

627

:

And I must say that they are very useful.

628

:

You know, But they make a

lot of mistakes as well.

629

:

So it's a tool self-driving cars.

630

:

I know that there's lots

of places testing it.

631

:

Uh, what is a Tesla has self-driving

mode, but only working in certain.

632

:

Types of environment.

633

:

You have the self parking car, like

parallel parking and you just push a

634

:

button and then take your hands off.

635

:

The wheel type things, so

they can be very, very useful.

636

:

Very practical.

637

:

Yeah.

638

:

And they're, they're basically there.

639

:

But I would put them

in the narrow category.

640

:

Right.

641

:

If you're, if you have.

642

:

I don't know how many

billions of images of.

643

:

Cars and trucks and bikes

and roads and things.

644

:

And then all around a Tesla,

you have all kinds of inputs and

645

:

it says, oh, based on the way.

646

:

This other car is moving.

647

:

Then the chance the, of them

like a collision is high.

648

:

Therefore apply the brakes.

649

:

Right.

650

:

And then you, it has every time,

every person out there with their.

651

:

Does this driving is

adding to that database.

652

:

That's training it.

653

:

To become.

654

:

To go and better.

655

:

It's not like you just said.

656

:

Drive in it.

657

:

It thought of how to drive is.

658

:

It's just an, it's a narrow application.

659

:

And then you have facial recognition.

660

:

You have.

661

:

You have so many other things so

that the number of applications.

662

:

I know the one in the

Bitcoin community is popular.

663

:

It comes up is using

AI to help write code.

664

:

So you don't have to be an expert

code writer to do some basic coding.

665

:

You could use AI to help build

websites, or you could be.

666

:

You can use AI to check for

mistakes too, in your coding, right?

667

:

They do.

668

:

And they do that.

669

:

They're the ones that are more,

some of the more advanced folks

670

:

we'll have, we'll actually be

able to use AI to, you know, to.

671

:

To generate the code to do

things and save hours and days.

672

:

And be able to do what may

have required someone else.

673

:

Oh a week of work to do.

674

:

Can now be done in.

675

:

I don't know, 20 minutes

or whatever it is.

676

:

So in knows how to use.

677

:

The application.

678

:

So that's programming.

679

:

That's pretty cool.

680

:

I know that content creators use it.

681

:

On the web.

682

:

Everything from writing up your Amazon.

683

:

Amazon descriptions too.

684

:

Advertisements.

685

:

And things like that.

686

:

So it's pretty interesting.

687

:

Um, lots of that.

688

:

Applications.

689

:

I don't think we need to

really go too deep on this.

690

:

I think most.

691

:

People already get this part.

692

:

I don't think we need to really.

693

:

I don't think there's

much more to add on this.

694

:

On this particular part of the discussion.

695

:

Well, okay.

696

:

So these are examples of how

AI can be interpreted to.

697

:

To threaten, uh, human jobs.

698

:

So, for example, for content

creators, they used to have to

699

:

hire somebody, a copywriter to.

700

:

Right there.

701

:

Copy or hire an editor to edit there.

702

:

They're writing and suddenly.

703

:

They don't need to do that anymore.

704

:

They're using AI to help them,

but I would challenge that notion.

705

:

In this way, if you are a copywriter

and other people are using AI to.

706

:

To write their copy, then you can.

707

:

Get ahead of the curve by saying,

well, I use these tools instead of

708

:

charging you for 10 hours of work.

709

:

I only have to charge you one

hour because I still have human.

710

:

Um, discernment that AI

doesn't necessarily have to

711

:

apply to a certain situation.

712

:

Okay.

713

:

That makes you more productive too.

714

:

Two comments.

715

:

I think.

716

:

The thing that I'm taking from that is.

717

:

If our kids were younger and I

wanted to teach them about AI, I

718

:

would want to give them a framework

of how to think about this.

719

:

And.

720

:

Uh, one of the things that is

powerful is to compare like

721

:

the person that was creating.

722

:

You know, the buggy whips or whatever it

was that you needed when there's horse

723

:

and carriages and along come the cars.

724

:

If the buggy whip guy went to Congress and

said, you got to pass laws, that people

725

:

can't make cars, because I want to go out.

726

:

You're going to put people out of work.

727

:

Well that you're trying to fight.

728

:

Where technology's going.

729

:

And this is why I know we've already

talked about another other episodes,

730

:

but why the price of tomorrow is

just so brilliant to talk about.

731

:

In simple terms about how

technology is deflationary.

732

:

What it means for you?

733

:

If you're young.

734

:

Student you're being homeschooled.

735

:

It's not that you're

going to be out of work.

736

:

The nature of the work

is just going to change.

737

:

So if you want to add value, Then.

738

:

If you learn how to use these tools.

739

:

Better than other people.

740

:

You are insanely valuable.

741

:

In the next stage of where we're going

with the economy and the types of jobs.

742

:

In other words, Don't be the one.

743

:

Worried about how to

protect the, the buggy whip.

744

:

Be the one who's learning how

to be a mechanic on the car.

745

:

Right.

746

:

You.

747

:

You, you know, depending

on what you, I think the.

748

:

I'm trying to figure out the right way

of saying it to me is the framework of

749

:

thinking about what the, what this means.

750

:

And when the FID comes along

about everybody losing their jobs.

751

:

And I'm an artist or you're a

copywriter or whatever it is.

752

:

You still need someone who

knows how to use these tools.

753

:

Right.

754

:

And if you do decide to at least

understand what AI is, there are so many

755

:

different ways that you can go into it.

756

:

So many different applications,

like we were just talking about.

757

:

It is a world of opportunity for you.

758

:

It should not be feared.

759

:

It should be looked at as

how do I use this tool?

760

:

For myself and what I want

to do, what's gonna make me.

761

:

Further and make it either.

762

:

Further.

763

:

As opposed to trying to fight

the fact that technology.

764

:

Cause you're not going to stop

technology from improving.

765

:

Government regulators

and things like that.

766

:

They might be able to try

to put up some artificial.

767

:

Moat.

768

:

Temporary temporarily.

769

:

But ultimately they can't

stop the advancement of where

770

:

the technology is going.

771

:

It's a much healthier approach to say.

772

:

Well in the free market,

when you have something new.

773

:

Then it's going to open up new types of

jobs and new types of things for people to

774

:

work on that we can't even imagine today.

775

:

Yeah.

776

:

That's the, it's an

actually really good thing.

777

:

So.

778

:

Oh, look at that.

779

:

So the next slide actually.

780

:

In our, in our notes.

781

:

Talk about that.

782

:

There's going to be a lot of jobs.

783

:

That are going to.

784

:

Really benefit from people

who know how to use it.

785

:

So I'll just give another example.

786

:

So we had a friend who worked for Ford.

787

:

And his job was, he was on the

assembly line and his shoulder

788

:

was constantly injured because

of the repetitiveness of the job.

789

:

And it was, it was causing

him so much pain and.

790

:

If.

791

:

If that part of the job was

automated by AI somehow, and

792

:

he was able to do something.

793

:

Different that the AI couldn't do.

794

:

He would be a happy lay employee and.

795

:

And it's just like AI.

796

:

It kind of like, like, um, Like the cars,

they, you know, when people say, oh,

797

:

you're going to take away the jobs of the.

798

:

The buggy drivers?

799

:

Well, buggy drivers had

a really tough life.

800

:

They were out there in rain and snow and

cold and went in and it was miserable.

801

:

But if they had a car, they

get to sit on the inside.

802

:

Maybe they've become car drivers.

803

:

And so it's just a, it's just a

different way of looking at it.

804

:

And also what you mentioned, the

new AI related professions created.

805

:

We can't even begin to

imagine what those are.

806

:

For example, 10, 15 years ago, when,

when our boys were really interested in.

807

:

Um, Minecraft and all the little

boys were on YouTube, you know,

808

:

recording themselves, playing.

809

:

And I was like, what are you doing?

810

:

This is what your brain is

going to melt into butter.

811

:

And it's a complete waste of your time.

812

:

You need to go and study

something more important and so

813

:

that you can get a better job.

814

:

You know, going to.

815

:

When you go older and suddenly you

have all these young millionaires

816

:

and what were they doing?

817

:

They were playing Minecraft on YouTube.

818

:

Yeah.

819

:

So those professions, there

was no way that we could have

820

:

anticipated that possibility.

821

:

So this is the double-edged sword of,

of the impact in terms of society.

822

:

Because if you're, if you're China and you

want to monitor where people are spending.

823

:

Their their money.

824

:

And now, like, I know I use a, a garment

cause I want to March my sleep well,

825

:

if they have access to your sleep data,

They, they, they know when you go to bed.

826

:

They know, you're your GPS.

827

:

They know where you drive.

828

:

They have your financial records

so that they know what you eat.

829

:

It's really scary.

830

:

And if you tie that license plate

recognition, facial recognition.

831

:

If you use all of that together.

832

:

These tools of AI could be used

in a:

833

:

Same scenario.

834

:

So it's actually another reason

that we really didn't even intend

835

:

to get to, at least I didn't

intend to get to in this thing.

836

:

And that is.

837

:

We actually need people.

838

:

To be aware of these things

so they can, they can.

839

:

These bonds.

840

:

Respond.

841

:

Appropriately.

842

:

The government saying that they're

going to protect us and make

843

:

sure there's no harmful speech

and there's other things they.

844

:

It's always going to lead to the opposite.

845

:

Just like things like the

Patriot act and things like now.

846

:

And.

847

:

We hear all these different abuses.

848

:

Um, So there are risks.

849

:

Of this tool being used in very bad

ways, because it's just a powerful tool.

850

:

And another thing that needs to be

taught to our kids is this context.

851

:

Uh, about this gets back to the more

freedom oriented ideas of a Bitcoin.

852

:

And the, the constitution, the

different amendments, right?

853

:

I mean, this is when we talk about

the first amendment, the fourth

854

:

amendment, other things like this.

855

:

We just need to understand that

this is a really powerful tool.

856

:

It can do a lot of good.

857

:

We need to be aware that in

the hands of someone who.

858

:

Has different motives,

different incentives.

859

:

It could be used bad ways.

860

:

In that case, you need

to be aware enough to.

861

:

To protect yourself as best

you can ideally speak up and

862

:

stop that from happening.

863

:

Like, I don't want to see

the same things in us.

864

:

Like the China.

865

:

Um, it feels like we're, they

already know a lot about us anyways.

866

:

Like we're already there.

867

:

So I think it's good to

have some level of concern.

868

:

I don't think it should paralyze you.

869

:

And I don't think it should make

you freak out, but I think it's

870

:

good to have a little concern.

871

:

Well, the thing is that as Jeff

Booth mentioned, In a park, as I

872

:

listened to with Preston, he said,

Even if you don't participate.

873

:

Uh, they still know about you

and they can infer who you are.

874

:

Yeah.

875

:

So you not participating

is only hurting yourself.

876

:

All over yourself, right.

877

:

I'm not being educated on it is

hurting yourself and your children.

878

:

Yes.

879

:

So we might as well stay ahead

of the curve and, and stay

880

:

informed a hundred percent.

881

:

A hundred percent.

882

:

Can I.

883

:

And speaking of that

Ellis, I like to slide.

884

:

The next slide for those

that are listening.

885

:

This is an exponential growth of AI.

886

:

So.

887

:

The.

888

:

The.

889

:

Technology is going, is advancing so fast.

890

:

And, and what we mentioned earlier

in our show where it's actually a

891

:

de-centralizing type of technology.

892

:

Is.

893

:

Just mindblowing.

894

:

I just can't think of any

other word to describe that.

895

:

And as you get into.

896

:

In the Bitcoin space free and open.

897

:

Um, Open source software,

um, is a huge deal.

898

:

If you look at the Nasr development,

for example, and how fast that's going.

899

:

Well, The AI development

is just exploding.

900

:

And you can't get that

genie back in the bottle.

901

:

You just can't.

902

:

You can't hide from it.

903

:

You can't hide from it.

904

:

And the, one of the, one of the

ones that I'm really excited about.

905

:

That, uh, when we were adopting

Bitcoin, I had the opportunity

906

:

to listen to, uh, Elyx.

907

:

Uh, not Alex.

908

:

Um, specifying the, the guy who's

developing spirit of Satoshi.

909

:

This is you.

910

:

You're going to have people who take

the initiative and they're going to

911

:

use it in really good, powerful ways.

912

:

And for those of, if you're

not yet familiar with spirit of

913

:

Satoshi, please check it out.

914

:

I think the spirit of satoshi.ai,

I think is the actual.

915

:

Link, but essentially what he's, what

they're doing is they're building.

916

:

A language model that is based

on things like libertarian ideas,

917

:

Austrian economic ideas, Bitcoin ideas.

918

:

So that when you go and ask it a question,

You're not going to get, you're not going

919

:

to get an answer that talks about crypto.

920

:

Currency in general, if you go to Chet

GPT and ask that same general question.

921

:

It's pulling from a lot of, a lot

of other things that are biased.

922

:

In that, so.

923

:

So I would say, well, that's biased.

924

:

Well, yeah.

925

:

Okay, well, whatever you feed

your, whatever you feed your.

926

:

Your model in terms of how you train it.

927

:

It gets, it's going to reflect that.

928

:

I don't think that's a

bad thing and I just.

929

:

I am so excited.

930

:

To just.

931

:

That we are part of

this, this history where.

932

:

We're hitting this inflection

point and this, this AI revolution

933

:

is just getting started and holy

macro, if you can, if it can pass.

934

:

Law exams now and be a go champion and

you can do all these things, other things.

935

:

And as far as we can tell so far,

We're still at the most basic level

936

:

of what you would call intelligence.

937

:

Right.

938

:

It's not actually thinking

these are just the base models.

939

:

I it's.

940

:

It's really, it's, it's hard to imagine.

941

:

It's hard to imagine what

this technology will be.

942

:

A year from now or five years from

now, or certainly 15 or 20 years.

943

:

From now.

944

:

Yeah, who was it that I heard this from?

945

:

I am.

946

:

I think I was on a workshop

with a marketing expert.

947

:

And he was talking about AI and he

said, we are so early in terms of

948

:

the people who are in the workshop

using AI in their marketing campaigns.

949

:

And he said, the thing is because

AI is evolving so quickly.

950

:

If you don't catch up to the movement.

951

:

The gap is going to widen in.

952

:

A speed that you can't even imagine.

953

:

So stay ahead of the curve.

954

:

It's basically our call to action today.

955

:

And, uh, not just obviously for you,

but for the sake of your children, you

956

:

really need to take the helm in how

they're exposed and not be afraid that,

957

:

you know, if you were to introduce

them to AI, they're not going to learn

958

:

how to read and write because they're

just going to speak into the computers.

959

:

Well, what if they didn't read and write

what is, they were able to do something

960

:

that we can't even begin to fathom.

961

:

So just be aware of the fear.

962

:

And know that you're whatever you

decide to do, you're still in control.

963

:

Of this tool.

964

:

Yeah.

965

:

They talk.

966

:

Right.

967

:

I agree with that.

968

:

The call to action.

969

:

You should start you.

970

:

You need to learn for yourself though.

971

:

And learn through them.

972

:

I remember I still, this was

an example from about, I don't

973

:

know, maybe six months ago.

974

:

And it was on a different podcast where

Preston was talking about using AI to

975

:

help build an automatic dog feeder.

976

:

With his son.

977

:

Or whatever they were doing at home.

978

:

And I was like, what?

979

:

I just couldn't like,

I just was blown away.

980

:

And.

981

:

And it wasn't like, you're you have

like Jarvis there, but if you're, if

982

:

you're at home, I'm starting to do this.

983

:

Now I'm only just starting.

984

:

If I have a, an issue with something,

instead of going to Google.

985

:

I'll go to chat GBT and I'll ask,

and it could be like a home project.

986

:

It could be a programming project.

987

:

It could be.

988

:

Um, I don't know, it just it's.

989

:

It's just fascinating.

990

:

And so the call to action is.

991

:

As you were saying.

992

:

Like your framework and open

open-minded and try to learn the stuff.

993

:

You don't have to go and get a

degree or take formal classes or that

994

:

like you just pick something and.

995

:

Just go play with it.

996

:

So to me, the way I interpret your

call to action would be like this.

997

:

There are dozens of.

998

:

Of image.

999

:

Creation types of like, basically

like language to, to, to image.

:

00:48:16,858 --> 00:48:19,528

Tools out there now there

are tools for videos.

:

00:48:20,008 --> 00:48:22,798

There are just general language

models that can answer.

:

00:48:23,188 --> 00:48:23,878

Questions.

:

00:48:24,328 --> 00:48:25,108

Just pick one.

:

00:48:25,828 --> 00:48:27,238

Just pick one and go try it out.

:

00:48:27,688 --> 00:48:30,658

And actually just get some

experience as fun with it.

:

00:48:30,688 --> 00:48:33,238

Maybe align it with somebody's interests.

:

00:48:33,448 --> 00:48:34,228

So if you're feeling.

:

00:48:35,428 --> 00:48:35,608

Right.

:

00:48:35,608 --> 00:48:39,598

If your kid is interested in a certain

thing, If your kid is very visual, maybe

:

00:48:39,598 --> 00:48:41,938

that that child would be better off.

:

00:48:42,448 --> 00:48:42,928

With.

:

00:48:43,828 --> 00:48:46,798

Whichever, you know, is Dolly

or mid journey or whatever.

:

00:48:47,308 --> 00:48:48,148

The others are now.

:

00:48:48,688 --> 00:48:49,828

If it's someone who really.

:

00:48:50,158 --> 00:48:52,348

Um, I don't know, maybe

they're very technical.

:

00:48:52,468 --> 00:48:54,028

Maybe they're very engineering.

:

00:48:54,598 --> 00:48:55,258

Oriented.

:

00:48:55,858 --> 00:49:00,328

Well, pick a technical project, go

build something like an automatic dog

:

00:49:00,358 --> 00:49:03,148

feeder and use AI to be your coach on.

:

00:49:03,928 --> 00:49:05,668

What do I need to account for, for.

:

00:49:06,178 --> 00:49:09,598

Uh, display, what do I need to count

for and design and et cetera like that.

:

00:49:09,628 --> 00:49:11,278

I mean, it's that, to me.

:

00:49:12,238 --> 00:49:14,698

I think it's going to sound

overwhelming when you say this is how

:

00:49:14,698 --> 00:49:16,408

much stuff is going, how fast it is.

:

00:49:16,678 --> 00:49:17,938

This is just an intro.

:

00:49:18,148 --> 00:49:18,418

No.

:

00:49:18,448 --> 00:49:18,748

I know.

:

00:49:19,018 --> 00:49:20,698

Follow up presentations.

:

00:49:21,028 --> 00:49:21,538

Right.

:

00:49:21,568 --> 00:49:22,528

I right.

:

00:49:22,558 --> 00:49:24,328

But you're, you're saying

the call to action is you.

:

00:49:24,718 --> 00:49:25,318

You need to get.

:

00:49:25,918 --> 00:49:28,078

Started on this and

what I'm trying to say.

:

00:49:28,348 --> 00:49:29,578

In a long-winded way.

:

00:49:30,148 --> 00:49:32,188

Is it doesn't have to be the whole thing.

:

00:49:32,188 --> 00:49:33,838

Just pick something small.

:

00:49:34,378 --> 00:49:35,818

And go, go play with it.

:

00:49:35,968 --> 00:49:38,518

There's a lot of free

versions of things out there.

:

00:49:39,208 --> 00:49:40,378

Are we going to list them here?

:

00:49:40,378 --> 00:49:43,018

Or are we going to go into

examples down the road?

:

00:49:43,018 --> 00:49:44,728

Because we're already at 50 minutes.

:

00:49:45,838 --> 00:49:45,988

No.

:

00:49:46,258 --> 00:49:46,378

Going.

:

00:49:46,378 --> 00:49:47,998

to tell them to go use chat GBT.

:

00:49:48,028 --> 00:49:50,098

Are we telling them to

go use beautiful Diane?

:

00:49:50,128 --> 00:49:52,018

Like there are so many out there.

:

00:49:52,918 --> 00:49:53,758

I don't think so.

:

00:49:53,818 --> 00:49:55,438

I think today's purpose was.

:

00:49:55,858 --> 00:49:56,638

Why included.

:

00:49:56,938 --> 00:49:57,208

Right.

:

00:49:57,988 --> 00:49:58,678

I think we're done.

:

00:49:59,368 --> 00:49:59,728

Okay.

Next Episode All Episodes Previous Episode

About the Podcast

Show artwork for Bitcoin Homeschoolers
Bitcoin Homeschoolers
Self-Custody Education

Listen for free

About your hosts

Profile picture for Tali Lindberg

Tali Lindberg

Hey there, wonderful listeners! I'm Tali, and I'm so excited to welcome you to our podcast today. For two decades, I was knee-deep in the incredible journey of homeschooling my four amazing kids. It was a world of boundless creativity, filled with lesson plans, school projects, sports, and beautiful chaos. But when my children all graduated, a brand-new, unforeseen adventure awaited me - the captivating world of Bitcoin.

It took three years for Scott to bring me into Bitcoin. I hesitated at first, Bitcoin's intricacies seemed daunting, and my plate was already quite full. But he persisted, going so far as to create a fantastic bitcoin-mining board game called HODL UP to demystify it all. Before I knew it, I was down the Bitcoin rabbit hole. Just like my homeschooling journey, I took it one step at a time, learning and evolving as I ventured further.

Now, here we are today, and I couldn't be more thrilled to be part of the vibrant Bitcoin community. In an unexpected twist, my husband Scott and I realized that our homeschooling experiences can be a treasure trove of insights for Bitcoiners who, like us, want to take charge of their children's education. So, in addition to sharing our Bitcoin knowledge with Precoiners with HODL UP and the Orange Hatter podcast, we're here to offer tips and guidance for Bitcoin-homeschoolers. It's going to be an incredible journey, and I can't wait to share it with all of you. Enjoy the ride!
Profile picture for Scott Lindberg

Scott Lindberg

Scott Lindberg is a freedom-loving entrepreneur, author, and game designer. He is a proponent of finding freedom by taking self-custody of education, money and speech.

He and his wife, Tali, co-founded Free Market Kids. Their passion is to give the next generation the knowledge and tools to maximize their chances for freedom, success and happiness. Free Market Kids makes it easy and fun to introduce money concepts to kids through tabletop games, courses, lesson plans and trusted resources. They are best known for HODL UP™, a Bitcoin mining game.

Scott graduated from the United States Military Academy at West Point in 1993 with a Bachelor of Science in Systems Engineering. In 2001, he graduated Yale’s School of Management with a Master of Business Administration.