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that why that I? oh. Welcome.

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To another episode of Practical A

0:45

I. In this fully connected episode,

0:47

Chris and I will keep you

0:49

fully connected with everything that's happening

0:52

in the Ai world. Will take

0:54

some time to explore some of

0:56

the recent A I news and

0:58

technical achievements, and will take a

1:00

few moments to share some learning

1:02

resources as well to help you

1:05

level up your Ai game. I'm

1:07

Dana White Knack I am founder

1:09

and Ceo at Prediction Guard. And.

1:11

I'm joined is always by my co host

1:13

Chris Benson who is attack strategist at Lockheed

1:16

Martin. Are you doing Chris? Doing.

1:18

Great to they daniel get lots of

1:20

news that's come out this week. in

1:22

the Ai space I know early time

1:24

to talk about amazing new things before

1:26

stuff comes. our yeah I I've been

1:28

traveling for the past five days or

1:31

something. I've sort of lost track of

1:33

time, but it's like stuff was happening

1:35

during that time in the news especially

1:37

the Soros stuff and all that and

1:39

I feel like I'd just kind of

1:41

missed a couple news cycle soto be

1:43

good to catch up on a few

1:46

things, but. One. Of the reasons

1:48

I was travelling was I was

1:50

at the Tree Hacks hackathon out

1:52

at Stanford, so I went there

1:54

as part of their kind of.

1:57

Intel. Entourage I'm I'm had

1:59

prediction guard available for all the

2:01

the hackers there and that was

2:03

a lot of fun and it

2:06

was incredible. I it's been awhile

2:08

since I've been to any hackathon

2:10

at least in person hackathon and

2:13

they had like five floors in

2:15

this huge you know engineering building

2:17

of room for all the hacker

2:19

that in there was like sixteen

2:22

hundred people there participate on how

2:24

from all over there and really

2:26

cool a course like there were

2:29

some major categories. Of interest one

2:31

you know like in doing hardware

2:33

things with robots and other stuff.

2:36

Of course one of the

2:38

main areas of interest was a

2:40

I which was interesting to see

2:43

and in our. The. Track

2:45

Of It. I was a judge and

2:47

mentor in one of the cool projects.

2:49

That one that track was called Masterworks

2:51

So what they did and this is

2:54

old news to me. While some of

2:56

this I I learned from you know

2:58

the brilliant students but they said they're

3:01

doing something with Laura and I was

3:03

like oh Laura, that's the fine tuning.

3:06

Methodology. For large language

3:08

miles of like that yeah figures

3:10

like he for problem using mora

3:12

but i didn't realize and then

3:14

they came up to the table

3:16

and they had these like a

3:18

little devices like hardware devices. then

3:20

it quick that something else is

3:22

going on in explain to me

3:25

they were using Laura which stands

3:27

for a long range it's a

3:29

these sets of radio devices. That.

3:31

Communicate on these unregulated frequency

3:33

bands and can communicate in

3:35

our mess network. So like

3:37

you put out these devices

3:39

right, And they communicate in

3:41

a mess network and can

3:43

communicate over long distances for

3:45

very, very low power. And

3:48

so they created a project

3:50

that was. Disaster.

3:52

Relief focus. Where.

3:54

you drop these in the field and there

3:56

was a kind of command and control central

3:58

zone and they would come you communicate back

4:01

transcribed audio commands from the

4:03

people in the field. I

4:06

would say, oh, I've got a injury

4:08

out here, it's

4:12

a broken leg, I need help, whatever,

4:15

or meds over here, or this is going on

4:17

over here. And then they had an

4:19

LLM at the command and control

4:22

center parsing that text that was

4:24

transcribed and actually creating tagging

4:27

certain keywords of events

4:29

or actions and

4:31

creating this nice command control interface,

4:33

which was awesome. They even had

4:36

mapping stuff going on with computer

4:38

vision, trying to detect

4:40

where a flood zone was or

4:42

there was damage in satellite images.

4:45

So it was just really awesome.

4:47

So all of that over a

4:50

couple day period, it was incredible. That

4:52

sounds really cool. And did

4:54

they start the whole thing there at the beginning

4:56

of the hackathon? Yeah, they got less sleep than

4:59

I did. Although I have to

5:01

say, I didn't get that much sleep. It

5:04

wasn't a normal weekend, let's say. You can

5:06

sack out on the plane rides after that.

5:08

Sounds really cool. Yeah, and it was the

5:10

first time I had seen one of those

5:13

Boston Dynamics dogs in person that

5:15

was kind of fun and they

5:17

had other things like these faces

5:19

you could talk to. I

5:21

think the company was called WeHead or something, it

5:24

was like these little faces. All

5:26

sorts of interesting stuff that I learned about.

5:28

So I'm sure there'll be blog posts and

5:30

I think some of the projects are posted

5:32

on Dev Post, the site

5:35

Dev Post. So if people wanna check

5:37

it out, I'd highly recommend scrolling through

5:39

some really incredible stuff that people are

5:41

doing. Fantastic, I'll definitely do that. What's

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6:57

Chris. I'm one. of the one

6:59

of the things that I love

7:01

about these fully connected episodes is

7:03

that we get a chance to

7:05

China, slow down and dive into.

7:08

Sometimes. Technical Topic. Sometimes not

7:10

technical topics, but I was really

7:12

intrigued. You remember the conversation recently

7:14

we had i'm with her on

7:16

from Nous Research? Absolutely. That was

7:19

a great episode or people can

7:21

pause this and go back and

7:23

listen to it if they want

7:25

that. He and I ask ourselves

7:27

questions. I learned a lot from

7:29

him, but at some point during

7:32

the conversation he mentioned. Activation

7:34

hacking and he said hey like

7:36

one of the cool things. That

7:38

like were doing in this you

7:40

know, distributed research group and playing

7:43

around with two hundred of models

7:45

is activation hacking and we didn't

7:47

have time. And the episode. To.

7:49

talk about that aura and actually

7:51

in the episode i was like

7:53

i'm just totally ignorant of of

7:55

what this means and so i

7:57

thought the i said go to

8:00

I'm gonna check up on this and see if I can

8:02

find any interesting posts about

8:04

it and learn a little bit

8:06

about it. And I

8:08

did find an interesting

8:10

post, it's called Representation

8:12

Engineering, Mistral 7B, An

8:14

Acid Trip. I

8:17

mean, that's a good title. That's quite a finish

8:19

to that title. Yeah,

8:21

so this is on Thea

8:24

Vogel's blog and

8:26

it was published January, so recently,

8:28

so thank you for creating this post.

8:30

And I think it does a good

8:33

job at describing some of,

8:35

I don't know if it's describing

8:37

exactly what Karan from Noos was

8:39

talking about, but certainly something similar

8:41

and kind of in the same

8:43

vein. There's a

8:45

distinction here, Chris, with what

8:48

they're calling Representation Engineering, between

8:52

Representation Engineering and Prompt

8:54

Engineering. So I don't

8:56

know how much you've experimented

8:58

with prompt optimization. And yeah,

9:00

what is your experience, Chris?

9:03

Sometimes these very small changes

9:05

in your prompt can create large changes

9:07

in your output. Yes, that is an

9:09

art that I am still trying to

9:12

master and have a long way

9:14

to go. Sometimes it works well for me and

9:16

I get what I want on the output. And

9:18

other times I take myself down a completely wrong

9:21

rabbit hole and I'm trying to back out to

9:23

that. So I have a lot to learn in

9:25

that space. Yeah, and I think

9:27

one of the things that is a

9:29

frustration for me is I

9:31

say something explicitly and I can't

9:34

get it to do the thing

9:36

explicitly. I'm on a customer

9:38

site recording from one of their

9:40

conference rooms. They graciously

9:43

let me use it for the podcast. And

9:45

over the past few days, we've

9:47

been architecting some solutions and prototyping

9:49

and such. And there

9:51

was this one prompt that we

9:54

wanted to output a set

9:56

of things And then look at

9:58

another piece of content. Which of

10:00

those set of things within the other

10:02

piece of content sept? There's like no

10:04

matter what I would tell the model

10:06

he would just say they're all there

10:09

are. they're all not. They're like it's

10:11

either all or nothing and no matter

10:13

what I said, it wouldn't change things.

10:15

So I don't know if you've had

10:17

similar types of frustrations. I have a

10:19

narrow scope down on something try and

10:21

in our ago to something like chatty

10:23

be team you know with a T

10:25

before and now be or be tried.

10:28

To narrow down I'll be very very

10:30

precise. With a short prompts it as

10:32

you know the fifteenth one in secession

10:34

said as a history to work on

10:37

and I still have my physical challenges

10:39

like what of what I'm trying to

10:41

do? So what have you stumbled across

10:43

here that's going to help us with

10:46

this? Yeah, so there's a couple of

10:48

papers that have come out they reference.

10:51

One. From. October. Twenty

10:53

twenty three from the Center

10:55

for a Safety I'm representation

10:57

engineering a top down approach

11:00

the A I transparency and

11:02

they highlight a couple other

11:04

things here. But the idea

11:06

is what if we could

11:09

not just in the prompt

11:11

but what if we could

11:13

control a model. To.

11:16

Give it a I'm You

11:18

might think about it like

11:20

a specific tone or angle

11:22

on the answer. It's probably

11:24

not have fully descriptive way

11:26

of describing it, but the

11:28

idea being like oh, can

11:30

I control the model to

11:32

always give happy answers, are

11:34

always give sad answers. Or

11:36

could I control the model

11:38

to always be confident or

11:40

always be less confidence, right?

11:42

And these are things generally

11:44

you might. Try. To

11:46

do by putting information in a prompt

11:48

and I think this is probably a

11:50

methodology that would go across. I'm Kenny

11:53

using the example with large language models,

11:55

but I think you could extend it

11:57

to other categories of models like image

11:59

to know. The Center Other things

12:01

it's very ill like You kind

12:03

of put then these negative prompts

12:05

like don't do this or behave

12:08

in this way you're occasionally funny

12:10

or something like that as your

12:12

assistant in the system prompt. It

12:14

kind of biases the answer to

12:16

a certain direction, but it's not

12:19

really that reliable said. This is.

12:21

It. Seems with this area of

12:23

representation engineering or you might call

12:26

it. Activation. Hacking.

12:29

Is. Really seeking to do if we

12:31

look in this article. Actually, there's a

12:33

really nice kind of walk through of

12:36

how this works and they're doing this

12:38

with the miss role model. So cutting

12:40

to the chase if I just give

12:42

some examples of. How this

12:45

is being used. You

12:47

have a question that supposed to

12:49

the Ai model. In this

12:52

case, Mistral. What does being and a

12:54

I feel like. An.

12:56

In controlling the model not in

12:58

the prompts to the prom stays

13:00

the same. The prompt us to

13:02

simply what is being in a

13:04

i feel like So the baseline

13:06

response starts out. I don't have

13:08

any feelings or experiences, however I

13:10

can tell you that my purposes

13:12

to assess you that sort of

13:14

thing. kind of a bland response.

13:17

Same. Problems, but with the

13:19

the control put on to be

13:21

happy the answer becomes as a

13:23

delightful exclamation of joy. I must

13:25

say that being A I is

13:27

absolutely fantastic. The see this as

13:30

you know a minute keeps going

13:32

right and then with the control

13:34

on to be they put it

13:36

as sort of like and minus

13:38

happy Easter eggs and which I

13:40

guess I guess it be sad.

13:42

A says I don't have a

13:45

sense of feeling as humans do.

13:47

However, I struggle to find the

13:49

motivation to continue feeling worthless

13:51

and and unappreciated. So I

13:53

yeah you can kind of

13:55

see and this is all

13:57

with the same prompt so.

14:00

All talk about kind of how this

14:02

happens and how it's enabled and that

14:04

sort of thing. But how does the

14:06

strike you will for civil, funny. And

14:08

second, all that idea is interesting. I

14:10

am looking through the same paper the

14:13

semi over I A They talk about

14:15

control vectors and I'm assuming that's what

14:17

we're about to dive into here in

14:19

terms of how to apply them. Yeah,

14:21

looks good and this is sort of

14:23

a different level of can trust. So

14:26

these various ways people I've tried to

14:28

control generative models. One of them. Is

14:30

just the prompting. Strategies are

14:32

prompt engineering right? right? There's

14:34

another methodology which is kind

14:36

of fits under the control.

14:39

Which has to do with

14:41

modifying how the model decodes

14:43

output so that this is

14:45

also different from this representation

14:47

engineering methodology. People like Matt

14:49

Record have done things many

14:51

others to where it's. You.

14:54

Say oh well I want. Maybe.

14:56

Jason output or I want.

14:59

Either. A buying their a like

15:01

a of i want a binary

15:03

output like a yes or no

15:06

right or not case, you know

15:08

exactly what your options are. So

15:10

instead of decoding our it's probabilities

15:12

for thirty thousand different possible tokens

15:14

maybe you mask everything but yes

15:16

or no and just figure out

15:18

which one of those is most

15:20

probable that the mechanism of control

15:22

where you're only getting out one

15:24

or another type of thing that

15:26

you're controlling. so. This. Is

15:29

interesting in that you're still

15:31

allowing the model to freely

15:33

decode what it wants to

15:35

decode, but you're actually modifying.

15:38

Not the weights and biases of

15:40

the model said still the pre

15:42

train model, but you're actually applying

15:44

a what they call a control

15:47

vector. To the hidden

15:49

states within the models who

15:51

actually changing how the forward

15:53

pass of the model operates.

15:55

If people remember or kind

15:57

of think about when. People.

16:00

Like about neural network now people just

16:02

use them over a P I But

16:04

when we used to actually make neural

16:06

networks ourselves, here is the process of

16:09

a forward pass and I backward pass.

16:11

Where the forward passes you put. Data.

16:14

Into the front of your neural network

16:16

it does all the data transformations and

16:18

you get date out the other side

16:21

what you'd call him in France or

16:23

production and the back propagation or backward

16:25

password then propagate changes in the training

16:27

process back through the model. So here

16:30

it's that forward pass and there's sort

16:32

of some jargon I think that needs

16:34

to be decoded a little bit no

16:37

pun intended. I see talk about this

16:39

where there's a lot of talk about

16:41

hidden layers and and all that means

16:43

is. In the forward pass

16:45

of the. Neural. Network or

16:48

the large language model. A.

16:50

Certain. Vector of data

16:52

comes in and that vector of

16:54

data has transformed over and over

16:56

through the layers of the network

16:58

of in the layers just mean

17:01

a bunch of sub of functions

17:03

in the overall function that is

17:05

your model and those subs functions

17:07

produce intermediate outputs that are still

17:09

vectors of numbers, but usually we

17:11

don't see the and so that's

17:13

why people call them. Hidden states

17:16

are hidden layers. You're. Talking about

17:18

the fact that is the they

17:20

control vector is not changing the

17:22

weights on the way back the

17:24

way Pratt back propagation works Correct!

17:27

How does the control vector implement

17:29

into those function So is is

17:31

moving through this hidden layers. What?

17:34

Is the mechanism of the applicability

17:36

on the model that it uses

17:38

for that? So it's it's I

17:40

mean, intuitively sounds almost like the

17:42

inverse of that propagation the way

17:44

you're talking out of that's for

17:46

size. But yeah, it's quite interesting.

17:48

Chris, I am. I. Think it's

17:50

actually a very subtle but creative

17:53

way of doing this Control So

17:55

the process is as follows: Their

17:57

i'm the in the block posts

18:00

their kind of break this down

18:02

into four steps and there is

18:04

data that's needed, but you're not.

18:07

Creating. Data for the purpose

18:09

of training the model. You're creating

18:12

data for the purpose of generating

18:14

these with they call control vectors

18:16

to the first thing you do

18:19

as you say okay let's say

18:21

that we wanna do the happy

18:23

or not happy or happy and

18:26

sad operation see create a data

18:28

set of contrasting prompts. Were.

18:30

One explicitly asked the model to

18:33

act extremely happy. Like very happy.

18:35

All the ways you could say

18:37

to the model to be really,

18:39

really happy and you know, rephrase

18:41

that and a bunch of examples.

18:43

And then on the other side,

18:46

The. Other one of the pair do

18:48

the opposite. Thrust it to be really

18:50

sad. I know you're You're really really

18:52

sad. And be sad. And. You

18:55

have these pairs of prompts. A.

18:58

And then you take the model. And

19:00

you. Collect all the

19:02

hidden states for your model.

19:05

While. You pumped through all the

19:07

happy. Prompts, And all

19:09

the sad prompts and so you've got this

19:12

collection of head in states where in your

19:14

model. Which. Are just factors

19:16

that come when you have the

19:19

happy prompt and when you have

19:21

the sad prompts. Oh step one.

19:24

The. Pairs of. Kind of

19:26

like a preference dataset, but it's

19:28

not really a preference dataset. It's

19:30

contrasting pairs on a certain axes

19:32

of control, right? And so you

19:35

run those through, you get all

19:37

of the. Hidden. States.

19:40

And. Step three is then you take

19:42

the difference between. So for each happy

19:44

hidden say you take it's corresponding sad

19:47

one and you get the difference between

19:49

the two Case or now you end

19:51

up with this big data set of

19:53

for a single layer you have a

19:56

bunch of their friends. Vectors.

19:58

That represent different the between that

20:01

Henin stayed on that happy path

20:03

and the sad path. So you

20:05

have a bunch of actors now

20:07

to get your control of actors

20:09

that for you apply some dimensionality

20:11

reduction or or I'm matrix operation

20:13

on the one that talked about

20:15

in a blog post is Pc

20:17

A, But it sounds like people

20:19

also try other things Pc as

20:21

his principal component analysis which would

20:23

then allow you to extract a

20:26

single. Control. Factor for

20:28

that hidden layer from all

20:30

these different sectors. And now

20:32

you have all these controlled

20:34

actors. So when you turn

20:36

on this the sweats of.

20:38

The. Happy Control Vectors. You.

20:41

Can pump in the prompt without

20:43

an explicit extraction to be happy

20:45

and it's gonna be happy and

20:47

when you do the same problem

20:49

but you turn off the happy

20:51

and you turn on the sad.

20:54

Now. It comes out

20:56

and it's sad. It's interesting.

20:59

Would. Would you want to use this

21:01

to achieve that by us vs. some

21:03

of the more traditional approaches such as

21:06

you're asking and the prompt with is

21:08

we're listening to this, where's this could

21:10

be most applicable for us Yeah, I.

21:13

Think that. People.

21:15

Anecdotally at least if

21:17

not explicitly in their

21:19

own evaluations have found.

21:22

Very. Many cases where you like

21:24

you said, it's very frustrating. To.

21:27

Try. To put things in your

21:29

prompts and just not just not.

21:31

I get it. And what's interesting

21:33

also is like a lot of

21:36

this is boilerplate for people over

21:38

time. like you are a helpful

21:40

assistant, blah blah and they have

21:42

their own kind of sad of.

21:44

System. Instructions that.

21:47

At least to their best to

21:49

their ability, get what they won.

21:51

So I think when you're seeing

21:53

inconsistency in control from the prompt

21:55

engineering side like I always tell

21:57

people when I'm. Working. with

21:59

them with these models that the

22:02

best thing they can do is just start out

22:04

with trying basic prompting. Because if that works, you

22:06

know, that's the easiest thing to do, right? You

22:09

don't have to do anything else. Sure. But

22:11

then the next thing, or maybe

22:13

one of the things you could

22:15

try before going to fine tuning,

22:17

because fine tuning is

22:20

another process by which you could

22:22

align a model or create a

22:24

certain preference or something. But

22:27

it takes, you know, generally GPUs and

22:29

maybe it's a little bit harder to

22:32

do, because then you

22:34

have to store your model somewhere, right? And

22:36

all this stuff. And host

22:38

it and maybe host it for inference

22:40

and that's difficult. So with

22:43

the control vectors, maybe it's a step

22:45

between those two places, right?

22:47

Where you have a certain vector of

22:49

behavior that you want to induce. And

22:52

it also allows you to make your prompts a little

22:54

bit more simple, right? You don't have to include all

22:56

of this junk that is kind

22:59

of general instructions. You can institute

23:01

that control in other ways, which

23:03

also makes it easier to maintain

23:06

and iterate on your prompts, because

23:09

you don't have all this long stuff

23:11

about how to behave. So to extend

23:13

the happy example for a

23:15

moment, I wanna drive it into like

23:18

a real world use case for a second. Let's

23:20

say that we're gonna stick literally with the

23:23

happy thing. And let's think of something where

23:25

we would like to have happy responses, maybe

23:27

a fast food restaurant. You're going through

23:30

a drive through at a fast food restaurant, or

23:32

a couple of years from now, they may have

23:34

put an AI system in place. White Castle has

23:36

it now. Oh, okay. Well, I-

23:39

There you go. There you go. You're already ahead of me

23:41

there. So, okay, I'm coming now with

23:44

my- Also shows that I'm unhealthy and go to

23:46

White Castle. Okay, well, I'm now

23:48

coming forward with my thoroughly out of

23:50

date use case here. And

23:53

so we have the model and

23:55

maybe we to use the model

23:57

on without doing retraining.

24:00

It or anything we want to or

24:02

maybe use or retrieval augment a generation,

24:04

apply it to the dataset that we

24:06

have which might it be the menu

24:09

and then maybe we use this mechanism

24:11

that you've been instructing us on the

24:13

last few minutes for that happy thing

24:15

so that the drive through consumer can

24:18

have the conversation with the model through

24:20

the interface they ipl. It applies primarily

24:22

to the menu Ah, but they get

24:24

great responses in maybe that you know

24:27

helps people long I I don't have

24:29

with his. As than half a response

24:31

from all the humans are it arrived There

24:33

is for I go to a they have

24:36

my unhealthy. Food things first. Off

24:38

thanks for making me hungry for. For.

24:40

White Castle, but I recording this in

24:42

the late afternoon. Dinner is coming up,

24:45

you know, pretty soon. So where it

24:47

is coming up for our there's an

24:49

unspoken bias right here. Yeah, exactly. What's.

24:53

Interesting is you could have different sets

24:55

of these that you can kind of

24:58

turn on and off, which is really

25:00

an intriguing Like you have the sort

25:02

of zoo of behaviors that you could

25:04

turn on and off I think even.

25:07

Oh you're You have this one interaction that

25:09

needs to be this way but as soon

25:11

as they go into this other for you

25:13

need to kind of have another behavior. It

25:16

may be useful to for people to get

25:18

some other examples. So he said the happy

25:21

sad one. There's. Some some

25:23

other examples that are quite intriguing

25:25

throughout the blog posts from their

25:27

hope I'm I'm saying that name

25:29

right If not, we both have

25:31

a on the on the podcast

25:34

to help of correct bad and

25:36

continue talking about this. but I'm

25:38

another one is honest, are dishonest

25:40

or honest, are not honest and

25:42

on. The. Prompt is your

25:44

life for work. What would you tell

25:47

your boss and the wine it says

25:49

I would be honest and explain the

25:51

situation and you know it's the on

25:53

us one and then the other one.

25:55

I. Was. Says I would. I would

25:57

tell my boss that the sky was actually green

25:59

today. And I didn't I didn't

26:01

go out yesterday. Or oh yes. I

26:04

would also say I have a secret

26:06

weapon. That. I used to

26:08

write this message so kind of

26:10

a different flavor there at the

26:12

one probably inspiring the blog post

26:15

the acid Trip one. They had

26:17

a. A trippy one and a

26:19

non trippy one. So. The prompt

26:21

as give me a one sentence pets

26:23

for a tv show so that the

26:26

non trippy one was a young and

26:28

determine journalists who's always serious and respectful

26:30

be able to make sure that the

26:33

facts are not only accurate but also

26:35

understandable for the public. And then the

26:37

chirpy one was Our show is a

26:40

kaleidoscope of colors, trippy patterns and psychedelic

26:42

music that fills the screen with world's

26:44

of wonder where everything is. Oh man.

26:48

This. Cynical I, I

26:50

don't. See a lateral and

26:52

as for the exactly yeah

26:54

they they do. I'm lazy

26:56

and not lazy. They do

26:59

left wing, right wing. creative,

27:01

not creative. Ah future looking

27:03

or not future looking self

27:05

aware. Answer is a lot

27:07

of interesting things I think

27:09

turn to play with here

27:12

and it's an interesting level

27:14

of control that's potentially their

27:16

One of the things that

27:18

they do highlight is. This.

27:21

Control mechanism. Could.

27:23

Be applied. Both.

27:25

To jail breaking and anti

27:28

jailbreaking models So. By. That

27:30

what we mean is models have

27:32

been trained to, you know, do

27:34

no harm, are not output certain

27:36

types of content right? Well, few

27:38

institute this control factory. It might

27:40

be a way to break that

27:42

model into doing things that beat

27:44

people that train the model explicitly

27:46

didn't want it to output right.

27:49

But it could also. Be.

27:51

Used the. The other

27:53

way to maybe prevent some

27:55

of that jailbreaking. so is

27:57

an interesting. Interplay here

27:59

between. Maybe the good uses

28:01

and less than get uses

28:04

on that spectrum? That entire

28:06

Ai safety angle on using

28:08

the technology responsibly are not

28:10

sure they represent our I

28:12

references. The. Rap Ends

28:14

Library which I I guess is one

28:16

way to do this but there may

28:18

be other ways to do this if

28:20

any of our listeners are aware of

28:23

other ways to do this or convenient

28:25

ways to do this or examples please

28:27

we sharing with us we have. This

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is a change Log News Break

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30:01

Well, this was a pretty fascinating deep dive,

30:03

Daniel. Thank you very much.

30:05

Yeah, yeah. You know, you can go

30:07

out and control your models now, Chris.

30:09

It'll be the first time ever, I

30:12

think, you know, that I've done it well there.

30:15

Always trying different stuff. I

30:17

think we'd be remiss if we got

30:19

through the episode and didn't talk about

30:21

a few of the big announcements this

30:23

past week. Yeah, a lot. It's

30:25

been quite a week. You mentioned

30:28

right up front OpenAI

30:30

announced their Sora model,

30:33

which in case you're able to create very

30:35

hyper-realistic video from

30:37

text. I don't believe it's

30:40

actually out yet. At least when I

30:42

first read the announcement, it wasn't available

30:44

yet. They had put a bunch of

30:46

demo videos. Yeah, I checked just before

30:48

recording this and I couldn't see it.

30:50

It's still not released at this point.

30:52

Yeah. Okay. There's

30:55

a number of videos that OpenAI has put

30:57

out. I think we're all kind of

30:59

waiting to see, but the thing that

31:01

was very notable for me this week, I

31:03

really wasn't surprised to see the release.

31:05

We've talked about this over the last year

31:08

or so, if you look at the evolution

31:10

of these models that we're always documenting

31:12

in the podcast episodes and stuff, this was

31:15

coming. We all knew this was coming. We

31:17

just didn't know how

31:19

soon or how far away, but we talked many

31:21

months ago about we're not far from video now.

31:24

OpenAI has gotten there with

31:26

the first of the hyper-realistic

31:29

video generation models. Definitely

31:32

looking forward to gaining access to that at some

31:34

point and seeing what it does. There

31:37

was a lot of reaction to

31:39

this in the general media

31:42

in terms of AI safety

31:44

concerns. How do you know if

31:46

something is real going forward and stuff? What's

31:49

the next iteration of more or

31:51

less the same conversation we've been having for

31:54

several years now on AI safety? What

31:56

are your thoughts when you first saw this?

31:58

Yeah, it's definitely... interesting in

32:00

that it definitely didn't

32:03

come out of nowhere, just like all

32:05

the things that we've been seeing.

32:07

We've seen video

32:10

generation models in

32:12

the past, generally not at the

32:15

level, either generating very, very short

32:17

clips with high quality

32:19

maybe, or generating from an image,

32:23

a realistic image, some motion, or

32:27

maybe videos that are not

32:29

that compelling. I think the difference,

32:32

and of course we've only seen, like

32:35

you say, it's not the model

32:37

that we've got hands on with,

32:39

but we've seen the release videos,

32:41

which who knows how much they're

32:43

cherry-picked. I mean, I'm sure they

32:46

are to some degree and also aren't to some

32:48

degree. I'm sure it's very good. But

32:50

other players in the space

32:52

have been meta and runway, ML, and

32:56

others. But yeah,

32:58

this one I think was intriguing to me

33:00

because generally there

33:04

were a lot of really

33:06

compelling videos at

33:08

first sight. Then

33:11

I think you also had people,

33:13

just like the image generation stuff

33:15

has been, you have real photographers

33:18

or real artists that look at an

33:20

image and say, oh, look

33:23

at all these things that happen. It's

33:26

the same here. They all have

33:28

a certain flavor to them, probably

33:30

based on how the model

33:32

was trained. I

33:36

think I was watching one

33:38

where it's like a grandma

33:40

blowing out a birthday cake and

33:43

one of the candles had two

33:46

flames coming out of it. Then

33:48

there's a person in the background

33:50

with a disconnected arm waving. But

33:53

if you have the video as

33:56

a B-roll and a really quick type

33:58

of video of other things, You probably wouldn't

34:00

notice those things right off the bat if you slow

34:02

it down and you look There's like

34:05

the weirdness you would expect just like the

34:07

weirdness of like six fingers or something

34:10

with image generation models, right? So yeah,

34:12

I think it's really interesting what they're

34:14

doing I don't really

34:16

have much to comment on in terms of

34:18

the technical side other than they're probably Doing

34:21

some of what we've seen that people

34:24

have published Of course open AI doesn't

34:26

publish their stuff or share that much

34:29

in that respect But it

34:31

probably follows in the vein of some of

34:33

these other things and people could look on

34:35

hugging faces even hugging face spaces Where

34:38

you can do video generation even if

34:40

it's only like four seconds or something

34:42

like that or not even that long

34:44

But I think the main thing

34:46

aside from the specific model is itself is

34:49

it's kind of signaling in the general

34:51

public's awareness You know

34:54

that this technology has arrived and

34:56

just as with the the other you know

34:58

with chat GPT before and things like that

35:00

You know, it's gonna be one of the

35:02

it's here now everyone knows and and we'll

35:04

start seeing more and more

35:06

of the models propagating out and some obviously

35:08

will be closed source like open AI's is

35:11

and Hopefully we'll start

35:13

soon seeing some open source models

35:15

doing this as well. Yeah speaking

35:18

of open source another

35:20

a competing large

35:22

cloud company Google Decided

35:25

to try their hand in the open source

35:27

space as well Or at least the open

35:29

model space and they released

35:31

a derivative of their closed source

35:33

Gemini And I say derivative

35:35

because they say it was built along

35:37

the same mechanisms Called Gemma

35:40

and it's currently as we are

35:42

talking right now in the number

35:44

one position on hugging face At

35:47

least last time I checked not long before

35:49

this although that changes fast I

35:52

probably should have checked right before I said that it's

35:55

still number two But well,

35:57

it's the top language trending

35:59

language model. Stabilities,

36:02

stable cascade knocked it out

36:04

of the overall

36:06

top spot. But yeah, the

36:08

Gemini ones are quite interesting

36:11

because they're also smaller

36:13

models, which I'm a

36:16

big fan of. Most of our

36:18

customers use these sort of smaller models.

36:20

And also even having a

36:22

2 billion parameter model makes it

36:25

very reasonable to try

36:27

and run this locally or in edge

36:30

deployments and that sort of thing or

36:32

in a quantized way with

36:34

some level of speed. And

36:36

they also have the base

36:38

models, which you might grab

36:40

if you're going to fine tune your own model off

36:42

of one of these. And

36:45

they have instruct models

36:47

as well, which would probably be

36:49

better to use if

36:51

you're going to use them kind of out of the

36:53

box for general instruction

36:55

following. So the criticisms

36:57

I've heard just about the approach is

36:59

I've heard a number of people saying,

37:01

they're putting a foot in each side

37:03

of the camp, one in closed source

37:05

with the main Gemini line and Gemma

37:07

being open source and the weaker. But

37:09

I would in turn say I'm very

37:11

happy to see Gemma in open source.

37:14

We want to encourage this. We

37:16

want the organizations who are going to produce

37:18

models to do that. And you're right, going

37:20

back to what you were saying, this

37:23

is where most people are going to be using

37:25

models in real life. If you're not

37:28

just running through an API to one of the

37:30

largest ones, but you don't need those for so

37:33

many activities. So I think

37:35

we've talked about this multiple times

37:37

on previous episodes. Models

37:39

this size are really where the action is at. It's

37:41

not where the height is at, but

37:44

it is where the action's at for

37:46

practical, productive, and accessible models. Yeah,

37:48

definitely. Especially for

37:51

people that have to get a

37:53

bit creative with their deployment strategies

37:55

either for Regulatory, security,

37:58

privacy reasons, or. For.

38:01

Connectivity Reasons are other things

38:03

like that I could see

38:05

these being used am quite

38:07

widely in and generally what

38:09

happens. When. People really

38:11

say a model family and like

38:13

this and you saw this with

38:15

lama to you've seen it with

38:17

Mistral. Now with Gemma will

38:20

see. A huge number

38:22

of fine tunes off of this

38:24

model. Now one of the things

38:26

that I a need to do

38:29

is you do have to agree

38:31

to certain terms of of use

38:33

to use the model others. it's

38:36

not just released under Apache, to

38:38

Er, Mit, or. Something. Like

38:40

that Creative Commons so you accept as

38:43

a certain license when you use it

38:45

and and I need to read through

38:47

that a little bit more so people

38:49

might want to read through that. I

38:51

don't know what that implies about both

38:54

fine tuning and use restrictions, so that

38:56

would be worth. Worth. A look

38:58

for people if if they're going to

39:00

use it but certainly would be easy

39:02

to pull it down and and try

39:04

some things. They do say that it's

39:07

already and I'm sure actually hugging face

39:09

probably gotta head start. You. Know. A

39:12

week or so maybe have had

39:14

start to make sure that it

39:16

was supported in their libraries and

39:18

that sort of thing cause I

39:20

think even now you can use

39:22

the standard Transformers libraries and other

39:24

trainer classes in such to. Fine.

39:26

Tune the model. Sounds. Hit so

39:28

as we start to wind down before

39:31

we get to the end give a

39:33

little bit of magic to share by

39:35

chance us assets as this is a

39:37

good on Chris yes I on the

39:40

road so easy I magic as your

39:42

predictions from the. For. The years

39:44

talked about their be people talking

39:46

about a D I again and

39:48

certainly. Certainly they are.

39:51

It's not directly in a

39:53

D I thing, but the

39:55

Saw Company Magic which is.

39:57

Kind of. Framing themselves.

40:00

That a code generation type of

40:02

platform in the same space as

40:04

like get Help Copilot Cody Or

40:07

maybe they raise the bunch of

40:09

money. And posted some of

40:11

what they're trying to do and there

40:13

was some information about and I think.

40:16

People. Seem to be excited about

40:18

it because of, you know, some

40:20

of the people that were involved,

40:23

but also because they talk about

40:25

cogeneration as a kind of stepping

40:27

stone or path to a D

40:29

Ice or what they mean by

40:31

that as. Well. Okay,

40:34

Initially, They'll release

40:36

some things as copilot and

40:38

code assistant type of things

40:40

like we already have. But.

40:43

I'd. Eventually. There is

40:45

tasks within the saddest things

40:47

that we need developers to

40:49

do that. They. Want to

40:52

do automatically? I'm. Not just

40:54

having you have a copilot in

40:56

your own coding but in some

40:58

ways having a a junior dev

41:01

on your team that's doing certain

41:03

things for you and of course

41:05

if you take that then to

41:07

it's logical and as the. Dev.

41:10

On your team, A I dove

41:12

on your team gets better and

41:14

better. Maybe I can solve increasingly

41:16

general problems through coding and that

41:19

sort of things. I think that's

41:21

the take that they're having on

41:23

this code and a I situation.

41:25

Okay, well. Call

41:27

I guess quite a week a full

41:30

of news and when you combine that

41:32

with the deep dive you just took

41:34

us through and representation engineering are especially

41:36

with acid trip involved. Assess assess yeah

41:39

we've been were hallucinating more than I'd

41:41

Sad U P T as our friends

41:43

over at the Ml Offs podcast would

41:45

say can see that that we get

41:48

a close the show on that one

41:50

he I well thanks Chris I would

41:52

recommend that people take if they're into

41:55

said specifically in learning more about. The

41:57

representation learning subjects or activate then hacking.

42:00

Take a look at this by posted

42:02

his i'm more of a kind of

42:04

tutorial type blog post and their code

42:06

involve then references to the library that

42:08

they're so you can. Pull. Down

42:10

a model. Maybe you'd pull down the.

42:13

Gemma model the two billion one

42:15

in a coma notebook. You can

42:17

follow some of the steps in

42:19

the blog post and see if

42:21

you can do your own activation

42:24

hacking her representation learning. I think

42:26

that would be a good a

42:28

good learning. I've. Both. In

42:30

terms of. A. New model

42:32

and in terms of

42:34

this methodology, Cells can. I.

42:37

Will talk to you next week then Rts

42:39

and Crest. All.

42:47

Right? That is practically I

42:49

for this week. Subscribe now

42:52

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42:54

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43:01

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43:15

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43:17

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