Bob Friday, Chief AI Officer, Juniper Networks

Bob Friday Talks: What place does AI have in education and film?

AI & MLBob Friday
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What place does AI have in education and film?

In this episode of Bob Friday Talks, Bob sits down with UC Berkeley Professor and Academy Award Winner, James O’Brien, for a fireside chat covering a range of topics related to AI’s role in education and Hollywood. From stress testing final exams and throwing grades out the window to smashing skyscrapers and influencing The Force, James shares key moments from his 25-year history with AI, as well as his thoughts on where it’s headed in the future.

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You’ll learn

  • The challenges of teaching students the fundamentals of AI without them using AI

  • What the finite element method simulation is and how it is used in filmmaking

  • Both the good and bad sides of using generative AI in filmmaking

Who is this for?

Network Professionals Business Leaders


Bob Friday Headshot
Bob Friday
Chief AI Officer, Juniper Networks

Guest speakers

James O’Brien
Professor of Computer Science, UC Berkeley


0:00 hello everyone thanks for joining us on

0:02 another episode of Bob Friday talks

0:04 today I'm joined by Oscar winter James

0:07 O'Brien professor of computer science at

0:09 UC Berkeley James maybe we can start

0:12 with giving the audience a little bit of

0:13 your journey how did you go from

0:15 Professor to Oscar winter where did the

0:17 journey start well my my journey in

0:19 visual effects and I guess visual

0:21 Computing in general started when I was

0:22 a grad student in Georgia Tech um I

0:24 became very interested in the idea of

0:26 using computer simulations to uh create

0:29 visual IM

0:31 um and so that's what I focused my PhD

0:33 work on when I was at Georgia Tech after

0:34 I graduated I came to Professor position

0:37 here at UC Berkeley um and still focus a

0:40 lot on visual effects but also um sort

0:43 of branched out to using uh simulation

0:45 optimization for a whole wide variety of

0:47 problems again mostly mostly related to

0:49 imagery um so what I would call Visual

0:52 Computing now you you've been a

0:53 professor since at Berkeley since 2000

0:56 or yeah since 2000 about 25 years okay

1:00 and so we're here today to talk about AI

1:01 so I thought maybe we could start the

1:03 discussion a little bit about how you

1:04 see AI affecting our education

1:07 University you know there's a lot of

1:09 talk about chat GPT you know what are

1:12 you seeing inside of the Berkeley right

1:13 now well I've had some firsthand

1:15 experience with this right um in in a

1:18 couple of ways uh you know last sem this

1:20 semester I'm teaching a graduate course

1:22 but last semester I was teaching a large

1:23 undergraduate course and we I was going

1:25 to give them a take-home final um and I

1:27 wanted to run the questions through gp4

1:29 just just to see what would happen and

1:31 it got them all right and so I said okay

1:33 well let's make the questions a little

1:34 trickier a little harder and it still

1:36 got them right a little bit trickier

1:38 harder and then finally it was getting

1:39 them wrong but before I could give this

1:41 the test to my students I want to you

1:42 know give it sort of a dry run a test

1:44 out so I asked my grad student Tas would

1:46 you you know to take the test and by the

1:48 time I made it hard enough that gp4

1:50 didn't get it right the grad students

1:52 didn't get right either um which is uh

1:55 you know it's a it's it's sort of a

1:56 little concerning like you know what you

1:57 know what is it that we're we're to be

2:00 teaching in our classes um and the other

2:03 as the other experience I had with uh

2:05 with with AI in this class was that we

2:08 had at one point one my taas came to me

2:09 and said Hey listen I've got some

2:10 cheaters and we've got several students

2:13 who all turned in almost identical code

2:15 and so I sat down I talk with these

2:16 students one individually one by one and

2:18 they all had the same story they didn't

2:20 they didn't think they had done anything

2:21 wrong they were told we were told we

2:23 were told to use Visual Studio to work

2:25 on our assignment Visual Studio includes

2:27 co-pilot we use co-pilot it wrote some

2:30 code for us and what was wrong with that

2:32 yeah you know for me Copa has become you

2:34 know for summarizing meetings now it is

2:37 totally changed my way I work now right

2:39 I don't have to take notes anymore I

2:41 just have copi help summarize it you

2:43 know but when you look inside of our

2:44 educational system you really see that

2:46 this AI is a positive negative you know

2:49 for me I kind of look at it as like

2:50 calculators you know when when side

2:52 rules calculators everyone I try to ban

2:55 calculators you know how do we adapt to

2:58 this new AI Tools in fact fting all of

3:00 our Lives well I mean this is this is

3:02 why I didn't penalize the students in

3:04 any way right if those students were out

3:06 working in the world and they had a job

3:07 to do then it would be totally

3:08 reasonable for them use co-pilot just

3:10 like it' be totally reasonable to use a

3:11 calculator you know it doesn't make

3:12 sense to tell them not to use it um it

3:15 does create the problem because we do

3:16 want to teach Basics still we still want

3:17 to teach the fundamentals and now it's

3:19 kind of a little bit of a problem

3:21 because if the students can use the AI

3:23 tools to do the fundamental work to do

3:26 these Basics how do you test them on

3:27 this how do you how do you teach the

3:29 students

3:30 not to not use the calculator but to

3:32 understand what the calculator is doing

3:34 so that when they use it they know how

3:35 to use it in the right way well I'm with

3:37 you because you know when I was growing

3:38 up it's like you still have to learn

3:39 your multiplication tables exactly you

3:41 know you have calculators so what do you

3:43 think the answer is now you know we're

3:45 in this new environment people have to

3:47 learn the basics somehow you know when I

3:49 look at the educational system and I

3:51 think we discussed earlier you know 340

3:53 years ago I had to buy books you I was

3:55 trying to learn to build Wireless

3:56 networking radios and everything you

3:58 know nowadays I don't buy books to learn

4:00 stuff I mean I go to the internet in the

4:02 cloud right now if I want to learn some

4:05 new AI topic you know I learn it on

4:07 YouTube right I mean this I mean even

4:10 before we had the AI tools to write

4:11 software it was also the case that you

4:13 know you could find a lot of if you had

4:15 a problem to solve you could look on

4:16 GitHub and find some example code that

4:18 somebody else had written and you know

4:21 you know I guess there are copyright

4:22 issues when you're looking at somebody

4:23 else's code but generally speaking if

4:25 you have a job to do you want to use the

4:26 best tools to do the job whether it's

4:29 finding open source code or using an AI

4:31 tool to generate new code you want to

4:33 use the best tools to do the best job

4:35 you can but that does make challenging

4:37 to try to teach the students right

4:39 because we need to now adjust how we

4:41 teach them just like we don't you know

4:43 have to memorize lots of stuff anymore

4:45 because there is not necessary they can

4:46 look it up how do we evaluate them how

4:49 do we teach them in a useful way and you

4:51 know when we talk about grades how do we

4:53 give them grades that make sense right

4:55 if it's if everybody's able to do the

4:57 assignment and do it perfectly because

4:58 they have the tools available ble to do

5:00 it perfectly do I give them all A's do I

5:02 find a way to somehow differentiate them

5:04 and not give them all A's what what do I

5:06 do it's a it's a challenging question

5:08 yeah I mean so now Juniper missed right

5:10 we're in the networking business so I'm

5:12 curious now like when you're giving your

5:13 test in universities now you know do you

5:17 let the inter do you let the students

5:18 use the network and access these tools

5:21 while they're taking the test or you

5:23 have kind of a no network policy going

5:25 on well if they're taking a test inside

5:26 the classroom then that's easy we can

5:28 ask we can they can put their phones

5:29 away and we we notice if you have your

5:30 phone on the desk um and so that that's

5:33 a little bit easier of a context it's

5:34 really the take-home stuff a take-home

5:36 final a homework assignment a project

5:38 that they're doing on their own um I

5:40 mean luckily the class I'm teaching they

5:42 tend to be like I teacher sorry I teach

5:45 upper division classes and graduate

5:47 classes and those they have Project work

5:49 and for the projects we say whatever

5:51 resources are available use those

5:52 resources right it's just like if you

5:54 hired someone to do a job use the

5:55 resources available to you um for the

5:58 lower division classes where there

5:59 learning the basics where they're

6:00 learning the fundamentals I think that

6:02 the problem the question is a lot more

6:04 challenging because you do still want to

6:05 learn those fundamentals and you know

6:08 the university requires us to give

6:09 students grades so we want to test them

6:12 on the fundamentals we want them to

6:13 learn the fundamentals but we also don't

6:15 you know we don't want to create a

6:17 situation where the honest students do

6:19 the work and then get a grade that might

6:21 not be an A and the dishonest students

6:23 use some other system you use a

6:25 generative AI to do the work and they

6:26 all get A's right we don't want a system

6:27 that's unfair um it's very I don't think

6:30 there's a clear answer yet well maybe

6:32 the other topic I want to try to cover a

6:33 little bit in this session today is

6:35 really around your career in film

6:37 because I know you were the inventor or

6:39 one of the co-inventors of digital

6:41 molecular matter algorithm did I get

6:43 that right yeah okay you know and for

6:46 those who don't know have you ever seen

6:47 the first Avatar

6:49 movie James technology was in that movie

6:52 was uh some of the X-Men movies as well

6:54 it's actually been used in over 200

6:55 films um and what this what the software

6:58 did is it's like it it kind what I

7:01 described earlier if you want to have a

7:03 a scene where something is a building is

7:04 being blown up or spaceship's crashing

7:07 into something instead of trying to

7:08 build a little model and film it and try

7:10 to make it look right you set up a

7:12 simulation you want a building to be Sat

7:14 On by Godzilla and claps well you you

7:16 take a model of the building you build

7:18 the building virtually and you set up a

7:20 big finite element simulation that's

7:21 going to compute you know as you know

7:23 the monster pushes on it or whatever is

7:25 happening what are the forces in the

7:26 building how's the concrete going to

7:28 crack where you know how the will the

7:30 windows shatter and it actually computes

7:33 all this and generates the raw data that

7:35 thing goes into what we call a renderer

7:36 that creates the images and those are

7:38 the effects that go into your in the

7:40 film that's what you see you're not

7:41 looking at something real you're looking

7:42 at something that was completely virtual

7:45 so I I have to ask you know of all the

7:47 scenes all these movie scenes I think

7:49 you're in like Technologies in 90 or

7:51 more feature films what scene stands out

7:55 you remember the most you go hey that

7:56 was me you know that was my technology

7:58 making that happen so actually the first

8:00 movie that really used a lot of our

8:02 software was the movie Sucker Punch um

8:05 and it I guess it didn't do super well

8:07 in the box office but it has some

8:08 beautiful special effects and to me that

8:11 that that's the first place that our our

8:13 software was really used in a very major

8:15 way it's a you know there I I forget

8:17 there lots and lots of scenes that used

8:19 it in the movie um very heavily and so

8:21 for me that's a very special one there's

8:23 also you know in addition to the movie

8:25 industry we've also used the software

8:26 for video games so Star Wars the Force

8:28 Unleashed was the first game that came

8:30 out that made extensive use of our

8:32 destruction modeling software now I I

8:34 know you started this adventure back in

8:36 2000 and I think when we talked about it

8:38 you started with finite element these

8:41 DMM algorithms you know maybe you give

8:43 the audience a little bit of the journey

8:45 from 2000 to we are today you know when

8:49 we look what's going on in the film

8:50 industry and we have actors and writers

8:52 going on strike being worried about AI

8:54 when did this AI really start to become

8:57 a thing inside the film industry so so

9:00 so AI um machine learning um deep deep

9:03 learning you know have different terms

9:05 for these things this has been you know

9:08 as a te as the techniques you know back

9:09 back in like the 90s for example the the

9:12 AI Tech AI the capabilities of AI

9:14 systems were very limited and so they

9:16 weren't used for a lot um at least not

9:18 in the movie industry um but by the time

9:20 you got to about like 2017 or so the

9:23 system started to be um much more

9:25 powerful much more useful and so you'd

9:27 see them being used um as a component

9:29 right so if I want to animate a

9:31 character I hire an an like in the movie

9:33 How to Train Your Dragon or something

9:35 like that I hire an animator who

9:36 animates Toth list by hand but now maybe

9:39 I can create a tool that an AI tool or

9:42 machine learning tool that makes it a

9:44 little bit easier for them to do it so

9:45 rather than very painstakingly moving

9:47 something and then waiting for an update

9:49 it will be more smooth because the AI

9:51 system is able to you know more quickly

9:53 compute the answer but you still have a

9:54 human anime doing the work um and what

9:57 we're seeing now of course is the the

9:59 generative AI systems that can actually

10:01 create the special effects themselves or

10:04 and uh you know that that's that's a

10:06 it's a sort of a phase transition it's

10:07 very different than using the the AI

10:10 system in a traditional pipeline you're

10:14 now getting sort of a new pipeline

10:15 that's built around the AI systems and

10:17 when did this gen AI I mean when did

10:19 that happen I mean it sounds like you

10:21 know somewhere from 2000 you know

10:24 somewhere sounds like in 2017 we started

10:26 getting machine learning be having

10:28 helped speed up these

10:30 action scenes you know when did these

10:32 actors and writers really have something

10:34 to worry about well I I think I think in

10:36 the last couple you know literally last

10:38 two two years basically um it's become

10:40 very clear that you know uh we now have

10:43 generative AI systems that are capable

10:45 of producing images that are almost

10:46 indistinguishable from reality um they

10:49 still make mistakes sometimes and give

10:50 you extra fingers and stuff like that

10:52 but the systems are getting better and

10:54 uh these problems are becoming fewer and

10:57 fewer um we also have now syst are able

10:59 to do video um so animated sequences not

11:02 just still images and you know there

11:05 there's there's a good and bad side to

11:06 this right the good side is it's very

11:08 democratizing right if if you and I

11:10 wanted to to you know we had some great

11:12 idea we wanted to make a film about the

11:13 story we wanted to tell

11:16 well today or maybe a few yesterday we

11:19 would have needed you know a big team of

11:21 people to do it like in this even right

11:23 now in the studio there's a team of

11:24 people running cameras and so on it's

11:26 kind of a big deal um but when if you

11:29 have an AI system where you can simply

11:31 describe the scene you want and it'll

11:32 generate the imagery for you then now it

11:35 becomes possible for individuals to now

11:38 produce high quality um video um the

11:41 downside is that you know there are a

11:43 lot of people whose profession is to

11:45 make that highquality video or make some

11:47 aspect some piece of it and you know

11:49 it's concerning those jobs might go away

11:52 well you know so I'm in the networking

11:53 space here you know what I always tell

11:55 people and I get the same question is

11:57 like Bob what's different about AI

11:59 versus what we were doing 30 years ago

12:01 and I usually tell them that you know

12:03 we're kind of moving AI is really this

12:05 next step in the evolution of automation

12:07 right you know the difference here is in

12:09 the past we were building very

12:10 deterministic repeatable things robots

12:12 that built your car a script that did

12:14 the same thing you know this AI

12:17 automation is really starting to do

12:19 things on par with humans some sort of

12:21 cognitive reing skill right and that's

12:23 kind of what we're seeing with chat gbt

12:26 you know so in that sense we're starting

12:27 to build automation that is replacing

12:29 humans which kind of leads into our next

12:32 kind of question you know in this next

12:34 thing you know most Technologies

12:36 typically create they eliminate jobs but

12:39 they typically create more jobs than

12:40 they eliminated you know so when you

12:42 look at you know what's happening with

12:44 AI automation movies even maybe in

12:47 education professors any concern that

12:49 you know you're going to get replaced

12:51 professors are going to get replaced by

12:52 AI assistants I think everybody should I

12:54 you know as these machine as you said

12:56 they're they're they're they're getting

12:57 basic or essentially the maches are able

12:59 to sort of emulate cognitive skills

13:01 right they can make decisions they can

13:03 sort of look at General General

13:04 situations and make general purpose

13:06 decisions and I think that what that

13:09 means is that no one's job whether it's

13:11 my job as a professor um an artist's job

13:15 creating something uh any profession you

13:18 have um Can potentially and probably

13:20 will get to the point where our

13:23 automated systems our machines are able

13:25 to perform that task and I think that

13:27 this is a little different than what we

13:29 we've seen in the past in the past we

13:30 would see technologies that made people

13:32 more efficient so I you know I have a

13:34 factory it makes a certain number of

13:35 cars and now I get the assembly line and

13:38 now it can make the same number of

13:39 people can make more cars but if I want

13:41 to make twice as many cars I still need

13:43 to hire twice as many people right or

13:45 then I have robotic arms that might help

13:47 build the cars but I still need people

13:48 to oversee that so now the same Factory

13:50 can make you know a hundred times as

13:52 many cars but I still need people

13:53 running the factory at some point we'll

13:55 get to the point where the number of

13:57 people you need in the factory is zero

13:59 and that's now very different because if

14:01 I want to scale up today I need to hire

14:03 more people if I want to me expand I

14:05 need to hire more people there are more

14:06 jobs get created but if I need zero

14:09 people to run the factory and I want to

14:11 now scale up my factory to make twice as

14:12 many cars well I still need zero people

14:15 and so that's a big difference zero is

14:16 different than every other number yeah

14:18 so me I kind of get you know hey we're

14:20 starting to automate human tasks you

14:22 know I here we're going to have robots

14:23 walking around the hospital cleaning

14:25 things here shortly you know when you

14:26 look at the Berkeley and what's

14:28 happening on the University there the

14:29 campus there any examples where you

14:31 start to see AI starting to automate

14:33 things at the school or the campus

14:35 itself oh I mean I I'm not exactly sure

14:37 what the administration is doing in

14:39 terms of deploying AI systems I I

14:40 suspect there may be uh you know

14:42 universities tend to be a little slow in

14:43 terms of how they move um so they Pro I

14:46 I suspect you see you know out in the

14:48 industry World you'll probably see more

14:51 automation being done with AI systems I

14:53 do think that there's a question about

14:54 teaching that's very relevant so I

14:56 already mentioned how AI the students

14:57 might use Ai and we have to evolve how

15:00 we teach them to now Encompass this just

15:02 like we changed changed how we taught

15:04 when calculators became a thing um but

15:07 we also have the question about actual

15:08 instruction right if I'm teaching a one

15:10 a grad class to a small group of

15:11 students that's great I sit down with

15:13 the students we basically talk

15:14 one-on-one it's a you know it's a

15:16 conversation um but if I'm teaching a

15:18 large undergraduate class and I've got

15:20 300 people in my class and I'm lecturing

15:22 to them I think it's a real question

15:24 maybe they'd be better off having a

15:25 one-on-one tutor an AI tutor teaching

15:28 them the subject material me I I'm an

15:29 expert in that material and I think I do

15:31 a good job teaching the class at least I

15:32 hope so but I have to ask the question

15:35 may maybe the student would be better

15:36 off if they had an AI tutor so we we

15:39 look at the University right now I'm

15:40 trying to think you know with the AI

15:42 coming if you have a student you know

15:44 someone's coming into you know headed to

15:47 college you know any guidance or

15:49 recommendations you know you look

15:50 forward in the future where AI is going

15:52 to be impacting all the industries it's

15:55 going to be impacting how we do our work

15:58 you know to our point we talked about

16:00 before you know it may be you know being

16:02 a plumber maybe the safest job in the

16:04 future it it is true that any job where

16:07 you basically are putting your hands on

16:08 a keyboard that's a little bit easier to

16:10 to automate using AI because uh you know

16:13 if you're putting your hands on the

16:14 keyboard you're working digitally and

16:15 and that's the domain of these uh these

16:17 these computer systems now if we talk

16:19 about a job such as you know you said a

16:21 plumber or electrician somebody who has

16:22 to use their hands to actually you know

16:24 run some wires in a

16:26 building even if we had an AI system

16:28 that's capable of understanding and and

16:30 you deciding what should be done there's

16:32 still the matter of dexterity you know

16:33 being able to actually you know push the

16:35 wires and run them or you know whatever

16:37 it is that needs to be done you need a

16:39 dextrous human to do it right now and

16:41 our robotic systems aren't there so I I

16:43 do think that as we look at jobs getting

16:44 replac by automated AI systems I think

16:47 the jobs that will take longer to be

16:49 replaced I mean eventually they will

16:50 because the robots will get there but

16:52 the jobs that will take longer be

16:53 replaced where are the ones that

16:54 actually require physically manipulating

16:56 something okay and you maybe wrap you

16:59 know I'm Jer mest I'm in networking you

17:01 know Wireless Mobility any thoughts on

17:05 where you see Mobility impacting the

17:07 future of either

17:10 film professorships campuses well I mean

17:14 one of so one of the big limitations on

17:16 AI systems right now is that they

17:18 consume in a monstrous amount of

17:19 electricity um both for training but

17:22 that can happen in in your server Farms

17:24 um but the then there's also the real

17:27 the actual real-time use of it when you

17:28 actually use the AI to answer a question

17:30 or draw a picture or whatever and right

17:32 now they use just tremendous amounts of

17:34 electricity and the idea that you can

17:37 now and more than capable you know

17:40 practical car around in your pocket

17:41 right it will drain your phone battery

17:42 like that and so you know people are

17:45 looking at ways to make the the

17:46 electrical demand smaller but one of the

17:48 big important ways that you can address

17:50 this problem is you move the computation

17:52 into the cloud and now I have my device

17:54 that I'm carrying with me whether it's

17:55 an apple headset or a phone or whatever

17:58 and

17:59 the computation that I'm using

18:01 personally as I move around in the world

18:04 is uh being relayed from the cloud okay

18:06 so this is like moving computation to

18:08 the edge this is like AI at the edge you

18:10 know do I want to do this on my phone or

18:11 back in the edge you know the other

18:13 thing I'd be curious about is you know

18:15 in your classes now or in the film

18:17 industry you know are you moving to gpus

18:20 are your classes actually using these

18:22 big GPU AI clusters well yeah yes they

18:25 are I mean we we've been using them for

18:27 a while because we also did computer

18:28 graphics on the gpus Once Upon a Time we

18:30 actually use the gra GPU Graphics

18:32 processing unit to do Graphics once um

18:35 but now now more and more it's been used

18:36 to do a do AI type stuff um if I if you

18:40 don't mind plugging I'm involved with a

18:41 startup called juice juice um juice

18:43 Technologies and you know their their

18:46 whole product is a way of virtualizing

18:48 GPU so that you've got a machine here

18:50 your GPU is off in the cloud and you can

18:52 access that GPU as if it was on your own

18:55 local device and that makes the it

18:57 mainly makes your programing development

18:58 a lot easier um so so yeah we we you

19:02 know gpus are used all ubiquitously all

19:04 over the place Juniper's big on uh

19:07 networking for AI and that seems to be

19:09 one of the big transitions happening you

19:11 know moving from this x86 data center to

19:14 Bly a GPU data center you know they

19:17 almost call front of the house you know

19:18 we have x86 in the front of the house

19:20 and we have gpus in the back of the

19:21 house and it seems like you know Nvidia

19:23 is now the third most valuable company

19:25 in the world you started it with the

19:28 graphics right all that GPU stuff

19:29 started with digital Graphics yeah in

19:31 the in gen AI now it seems just be

19:34 driving an exponential growth well you

19:36 know uh when when there's a gold rush

19:38 you make your money selling shovels

19:40 James thank you for joining us it's been

19:41 my pleasure and look forward to seeing

19:43 you on the next episode of Bob fry talks

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