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listening to a podcast from Washington Post

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Live. Bringing the newsroom to

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you live. Hello and welcome

0:24

to Washington Post Live. I'm Danielle Oberl, Tech

0:26

at Work writer here at the Post. Today

0:29

I'm joined by Microsoft's Corporate Vice

0:32

President of AI at Work, Jared

0:34

Spitaro, to talk about how technology

0:36

is changing the workplace. Jared,

0:39

it's so good to see you again and welcome

0:41

to Washington Post Live. Mike,

0:43

it's great to be with you. Thanks for having me. Absolutely.

0:47

Let's get started. So I want to

0:49

start with obviously the big

0:51

thing that's going on at Microsoft, which

0:53

is Microsoft Co-Pilot. You guys

0:55

launched it last February. For

0:58

those of you that don't know, that's

1:00

Microsoft's AI assistant. As

1:03

you know, I tested it. But

1:05

for our viewers, please explain how

1:07

Co-Pilot works and how you

1:09

believe using it impacts

1:11

or using AI impacts worker

1:13

productivity. Oh boy.

1:16

You could get me started here. Well, let

1:18

me just start by saying AI can do

1:20

some amazing things, but it needs to be

1:22

in the right places. So with Co-Pilot, what

1:24

we've done is we have created an assistant

1:26

that takes the magic of generative

1:28

AI and puts it in the places that people work

1:30

today. So that means places like

1:32

your email and Outlook. It means Word, Excel,

1:34

and PowerPoint. It also means in new interfaces

1:37

where you can chat about all of your

1:39

work data, everything related to your job. So

1:41

you can ask questions like, hey, look at

1:43

my calendar over the last month and tell

1:45

me how I spent my time and give me

1:47

suggestions for how I could improve. So it both

1:49

helps people on what they do today in the

1:51

apps that they're familiar with and opens new vistas

1:54

for them. And then if I just get to

1:56

the heart of what we see, we think of

1:58

it not just as a tool. some sort

2:00

of incremental improvement, but it's a whole new

2:02

way to work. It takes different habits, takes

2:05

different skills, but it also gives

2:07

you outsized rewards that we're really excited about

2:09

that and some of the studies that we've

2:11

done from copilot users. So

2:14

we did try out copilot and as

2:16

well as Gemini on Google Workspace, which

2:18

is their AI assistant. At the help

2:20

desk, we wanted to see really, are

2:23

these things easy to use? And

2:25

we found that although the AI tools really

2:28

help complete tasks, you

2:30

probably shouldn't rely on them entirely to

2:32

do your job. How should

2:34

workers think about when to use AI

2:37

and what they should consider or keep in

2:39

mind if they're considering turning to these tools

2:41

for work? Well,

2:43

it's a good observation. We use the

2:45

name copilot on purpose to indicate that the

2:48

copilot shouldn't do your job for you, that

2:50

you still are the pilot, you're in the

2:52

driver's seat and it's your job to do

2:54

your job. But we recommend that

2:56

if you get the right skills, you can use

2:58

copilot to advance, move the ball down the field

3:01

faster than ever before. Now, one

3:03

really important thing I'll say, Daniel, that's

3:05

really just key for people to understand

3:07

about this new generation of technology is

3:09

it's unlike anything that they've experienced with

3:11

computers before. Most people think of a

3:13

computer the way they do a calculator.

3:15

I'm gonna punch in a question in

3:17

a calculator, it's in the form of numbers and

3:20

I'm gonna get the right answer back. That's

3:22

not the way generative AI works. In fact,

3:24

sometimes it's not even right, it gets things

3:26

wrong, almost like a person can get things

3:28

wrong. But when it gets things

3:30

wrong, we tend to find that it's

3:32

what we call usefully wrong. It actually

3:35

helps you move again your work forward.

3:37

But you have to trust and at the

3:39

same time verify. So you have to

3:41

learn a new way of working. In many ways, it's

3:43

kind of like working with a colleague that is learning

3:45

your job and trying to help you do it better.

3:49

So I wanna pick up on something you

3:51

actually just talked about. AI doesn't

3:53

always get it right. I mean, it tries

3:55

to give you what it thinks is probably

3:57

the answer based on also we learned.

4:00

based on how you ask the question, which if you

4:02

turn a question around through different ways, you might actually

4:04

get the right answer. But

4:06

when we think about AI, a lot

4:08

of users are really worried about those

4:10

times it gets it wrong, it

4:13

hallucinates, which is again, kind of making up

4:15

things, or just misinterprets your question or what

4:17

you're trying to get at. And

4:20

I wonder in terms of

4:22

being able to identify the

4:24

errors, my understanding from

4:26

experts I've spoken to as generative

4:28

AI continues to advance, it

4:30

gets harder and harder to identify where

4:32

it's wrong because it's so believable. The

4:35

answers come out, they look wonderful, and

4:37

you're like, yeah, that sounds right. And

4:39

you go ahead and push it through.

4:42

What are your thoughts around these issues, especially if

4:44

they relate to higher risk work, work

4:46

that could have medical, financial, or

4:49

legal consequences? It's

4:51

a great question. And you have to

4:54

understand the technology, at least just fundamentally,

4:57

what it's doing is it's reasoning statistically,

4:59

the actual term is stochastically. So much

5:01

like a human does, we don't reason

5:03

perfectly about questions. We take the available

5:06

facts, sometimes the facts aren't enough, sometimes

5:08

our judgment isn't quite correct. And then

5:10

we kind of reason. And

5:12

you're exactly right. I think people are

5:15

worried that when the answers get presented,

5:17

it feels so reasonable that it's easy

5:19

to believe. There kind of are two things

5:21

that I have seen in my own usage

5:23

that have made a really big difference. Number

5:25

one, there is a new technique that we

5:27

call grounding, that allows you to actually make

5:29

sure you give the AI tool the latest

5:31

information related to a question, a prompt that

5:34

someone asks. And that grounding

5:36

technique actually reduces the

5:38

hallucination significantly. So if you're asking,

5:40

for instance, a question about a

5:43

recent event, the co-pilot will

5:45

actually go out to the internet, collect

5:47

information on that event from reliable sources,

5:49

and then use that as its fact base

5:51

to reason over when it gives you the

5:54

answer. And then the second

5:56

thing, which seems small, but is incredibly

5:58

important, is we actually provide reference. references,

6:00

literal explicit references in the copilot

6:02

answer to the source data that

6:04

the copilot used to provide the

6:06

answer to you. And

6:09

it prompts a new skill. You have to

6:11

read what's there and then spend a little

6:13

bit of time in making sure that you're

6:15

looking at those sources so that you understand,

6:17

have a bit fuller understanding of the sources.

6:19

Now some people ask, well Jared, shouldn't you

6:21

just do it yourself? My own

6:24

experience has been, no, not really. It's

6:26

like having a very competent research assistant

6:28

that is pulling together relevant information. At

6:30

the end of the day though, that name is so

6:33

important. It's the copilot. You are meant to do your

6:35

job. So it's a new way of working, Daniel. I

6:37

think that's, I couldn't stress that enough. As

6:39

you learn it, we are finding that users are faster. You

6:41

know, in our battery of tests, for instance, people are almost

6:43

30% faster on common

6:46

information worker tasks with no

6:48

change in their accuracy in

6:50

the responses of the tests that we ran. So

6:53

don't get lazy. You gotta go look and

6:55

make sure everything's exactly where it

6:57

should be. Got it. Well,

7:00

you know, obviously a lot of

7:02

workers see the value here in

7:04

expediting mundane tasks with AI, things

7:06

like drafting emails or organizing their

7:09

inbox. But what do you

7:11

think about AI for more complex tasks?

7:14

And in what scenarios might it be helpful? And

7:16

what scenarios might we want to steer clear of

7:18

when it comes to those complex tasks? Sure

7:21

thing. Sure. It's

7:23

not good yet. It's not good at math, it

7:25

turns out. We can augment it with mathematical skills.

7:28

We are, for instance, wiring it up to

7:30

Microsoft Excel. And there it's learning

7:32

how to use a calculation engine together with its

7:34

larger language model. And it's still learning. We're still

7:37

learning how the tool can be used. So there

7:39

are some domains where I would say make sure

7:41

you understand its strengths and weaknesses. When

7:43

it comes to complicated tasks, things that I

7:46

would call long running chains or sequences of

7:48

tasks that need to be done, oftentimes

7:51

what's best is to use the tool in its form

7:54

today to help you complete portions of that, but for

7:56

the human to be the one that is putting them

7:58

all together. in a budgeting

8:00

process, for instance, pulling together

8:02

the latest information, looking

8:04

at that information and analyzing, perhaps even

8:06

looking at from different angles, all of

8:08

that is fantastic. But make

8:10

sure you're the one that is going from

8:12

end to end to have the budget make

8:15

sense. And that's true of almost all long-being

8:17

kind of complicated, sophisticated tasks today.

8:19

But the great news is, Daniel, it's

8:21

like it's getting better every week, week

8:23

in and week out, as we

8:25

are learning more about how people use it. And as

8:27

the technology is improving, I can really see the improvements

8:29

in my own usage. And

8:32

in terms of where it's kind of doing

8:34

well in those complex tasks? Sure

8:36

thing. The types of things

8:39

that it's particularly good at are summarizing. So

8:41

it does a great job when you have

8:43

lots of information, you need to get the

8:45

key points. It does a really

8:47

nice job, for instance, in meeting settings. It

8:49

can be a meeting you attended, where it's providing notes,

8:51

or it can even be a meeting, in my case,

8:53

that I don't attend anymore. Lots of meetings I don't

8:55

attend, and I just ask it to take notes for

8:57

me so I understand what the key points are. I

8:59

can even query that after the

9:01

meeting and ask questions about what happened. It

9:04

does particularly well in email, as a simple example.

9:06

So I use it every day to summarize long

9:08

email threads. We all get them. I hate to

9:10

read from the bottom loop. I don't have to

9:12

any longer. It does great in drafting, replies, in

9:14

fact, saves me a lot of time there. And

9:17

it's particularly good in a set of what

9:19

I call sophisticated information

9:21

retrieval scenarios. Things like,

9:24

hey, it looks like I'm gonna meet

9:26

with this customer on Thursday. I remember that there's

9:28

been lots of emails. We even had a meeting.

9:31

Someone wrote me a document to get me ready.

9:33

Can you pull all that together for me and

9:35

give me, essentially, an information pack? It's

9:38

incredible, that type of work. And again, if you

9:40

think about what that can save you, it's not

9:42

just minutes, but for me, oftentimes, hours. I'm

9:45

gonna squeeze in an unexpected question here,

9:47

because you just mentioned something, and

9:50

I just recently remember that Zoom is

9:52

also kind of getting into the game

9:54

and trying to release some AI capabilities

9:56

that go across different apps that they've

9:58

started to release. And

10:00

you mentioned, obviously, one of the things that I

10:02

found really helpful in our co-pilot test was that

10:04

I'm going to have a meeting with my boss.

10:06

What are the last conversations and emails and documents

10:08

we've collaborated on? And it could quickly kind of

10:10

give me an update and I, oh yeah, this

10:12

is what we need to talk about in the

10:14

meeting. But I do wonder, a

10:16

lot of times, a lot of

10:18

workers work across apps, right? And we

10:21

don't necessarily work on solely Microsoft or

10:23

solely Google or solely Zoom. We're kind

10:25

of using probably a mix of things.

10:28

People just like different features from

10:31

different providers. Would Microsoft

10:33

ever consider opening up its AI to

10:36

work cross-function across apps? We

10:40

not only consider it, we've done it. It turns

10:42

out that out of the box, so without any

10:44

configuration, you certainly can reach into your email inbox

10:46

and to your documents and the places that are

10:48

already a part of what we call Microsoft 365.

10:52

But the product itself has

10:54

the ability to use connectors. In fact, over 1200

10:57

connectors that we provide that plug

10:59

into everything from SAP to

11:01

any other system, you can imagine, HR systems,

11:04

workflow systems, any

11:06

type of system that you have out there

11:09

to allow you to grab that information and

11:11

what we say is to reason over it.

11:13

And that's incredibly powerful. So when you combine

11:15

the ability to pull financial data together, for

11:17

instance, with unstructured data and have that give

11:19

you a full view, maybe even with your

11:21

CRM data of a customer to get you

11:23

ready for a meeting, there's just nothing out

11:25

there like it. And you hit on a

11:28

very key point. People live in very, what

11:30

we would call heterogeneous environments that will persist

11:32

for a very long time. We recognize that we're

11:34

not the center of the world, but the advantage

11:36

we think we have for individual users is we

11:38

can be where they do spend a lot of

11:40

their time every day in the tools that they're

11:43

really familiar with and that they choose. Well,

11:46

Jared, we're kind of running low on time and I

11:48

have so many questions I wanna get in, so I'm

11:50

gonna kind of bounce around here. But

11:53

late last year, Microsoft found out that

11:56

digital natives, basically, generation Z, the youngest workers

11:58

in the world. in the workforce right now are

12:01

falling behind in adopting AI at

12:03

work. That's kind of

12:06

shocking to me given that we see how

12:08

often Gen Z is using AI in their

12:10

personal life and breakup messages and things

12:13

that seem a little bit frivolous for

12:15

usage of AI. What's

12:18

happening here? Well,

12:20

I think it's a combination of a bunch

12:23

of factors but one that I've spoken of

12:25

previously that really, for me

12:27

has touched my imagination. I've realized, wow,

12:29

there's something there is that people are

12:31

doing best with AI today or those

12:34

who have managerial experience. And

12:36

the reason that that's true, if we go back

12:38

to what we've already talked about is that you

12:40

really do best with the technology when you interact

12:42

with it the way that you'd interact with a

12:44

direct report. When you work with someone

12:46

who reports to you, who you're responsible for, you have

12:49

to give them a lot of context. You

12:51

get to know kind of their strengths and weaknesses.

12:53

You don't settle for what we call kind of

12:55

a one shot type of approach. I'm gonna give

12:57

you this assignment. I hope you get it right.

13:00

No, instead they come back to you, give them

13:02

some coaching, you guide them in different ways. All

13:05

of those types of skills are really, really

13:07

important for using AI. And what I've observed

13:09

in our tests, even anecdotally is that often

13:11

when you're just new in the workforce, you're

13:13

kind of learning those things. It's not the

13:15

first set of things you've learned. So

13:18

managers, people who've been managing real people

13:21

are doing very well. And that's a really

13:23

interesting thing to think. I think of it this

13:25

way, what types of skills will new graduates need

13:27

in the near future? Well, I actually think we're

13:29

gonna need to help new graduates know how to

13:31

manage people and manage AI. And

13:34

that's a really interesting thing to think about

13:36

education and entering the workforce. Well,

13:39

and that also tells us a little bit

13:41

about how organizations should think about maybe

13:43

their workforce and getting people ready to use

13:46

AI. If they're already starting to adopt

13:48

these things, is that something we should be

13:50

thinking about from a management level? Absolutely,

13:53

skilling and entering into a new era

13:55

requires kind of a new way of

13:57

thinking about what people need to know.

14:00

And I think it's important if I could frame

14:02

this up not to think of this technology as

14:04

just incremental. If you think of it as like,

14:06

oh yeah, this is kind of like a

14:08

new incremental productivity improvement, you're going to

14:10

approach it the wrong way, or at

14:13

least that's my observation with customers. Those

14:15

who realize at the individual, the personal

14:17

level, and at the organizational level, this

14:20

really is a brand new era as

14:22

we approach work. Those people are doing

14:24

the best because they have a lot of imagination and

14:26

they quickly lead as you indicate to new skills. They

14:28

realize, oh, I'm going to have to teach my people.

14:31

No one's born knowing how to use a Go but

14:33

you can learn it fairly quickly and then it can make

14:36

a really big difference. So

14:38

we only have a few minutes left, but this

14:40

is a question that keeps coming up in my

14:42

work. And no matter how much I talk about

14:44

AI, people are very

14:46

concerned about the future of AI

14:49

and where it brings us in terms of jobs

14:51

and the workplace. You know,

14:53

generative AI is only expected to get better.

14:56

As you mentioned, it's getting better every day.

14:58

And I know right now, as you started

15:00

off this conversation, co-pilot is exactly that. Really

15:02

needs a person in the driver's seat to

15:05

really not only tell it what to do,

15:07

but make sure it's doing the thing that

15:09

we ask it to do properly. But

15:12

as this advances, what

15:15

does this mean for jobs in the future? Could

15:17

we see a future, we're talking to a lot of

15:19

young people who are worried about entry level jobs disappearing,

15:22

or even the case where AI

15:25

isn't necessarily taking

15:27

a job away per se, like

15:29

a full job, but it's taking

15:31

away so many tasks, so many

15:34

repetitive tasks from people, that

15:36

instead of needing five people to do

15:38

a job, maybe you only need three

15:41

because they are a lot more productive

15:43

now because AI is taking away some

15:45

of the stuff that's been bogging them

15:47

down for so long. So

15:50

in that respect, you would still

15:52

see some sort of decreasing of jobs or

15:56

because it's making people so much more productive.

15:58

What is your take on... on where this

16:00

heads and what this means for jobs. And

16:04

I'm incredibly hopeful and optimistic, but let

16:06

me explain why. You know, whether it's

16:08

electricity, whether it's a steam engine,

16:10

it could be a word processor or a PC, we

16:13

always have this immediate reaction to new technology. Uh-oh,

16:15

like this is gonna do stuff that people do

16:17

today. What are the people gonna do? Does that

16:19

mean they'll be out on the streets? It

16:22

is true that there's displacement, meaning that

16:24

jobs shift, that things that people

16:26

used to do may now be done by

16:28

machines. There's no doubt about that. As

16:31

you look at the old typists of yesteryear,

16:33

you saw people whose job was to literally

16:35

type on behalf of other people. And then

16:37

the introduction of the PC changed all of that.

16:40

But what we always see, what we've seen

16:42

since the beginning of the Industrial Revolution and

16:44

recorded history, on really getting into what

16:47

science and innovation does, is that it creates

16:49

new opportunities. So my advice for people

16:51

who get worried about that, is I say, look, it's

16:54

very natural to be worried. But

16:56

instead of taking that energy and funneling

16:59

it into anxiety, funnel

17:01

it into innovation, funnel it into

17:03

the future, and learn about

17:05

the technology, embrace the technology, you'll see

17:07

that it creates more opportunity both on

17:09

the grand scale, the macro scale, as

17:11

well as on the individual scale. And

17:13

we're seeing that already. People who have

17:16

AI skills, they are already getting ahead,

17:18

because in many ways, they kind of

17:20

have the equivalent of a backhoe when

17:22

many of the rest of us are

17:24

still using shovels. It's exciting that there's

17:26

a bright future and we just have to embrace it. And

17:29

we only really have one minute left, but I

17:32

wanna squeeze in this question so if we could

17:34

get a brief answer. Obviously,

17:36

we're seeing the development of new collaboration

17:38

tools and new ways to work. AI

17:40

is really changing the modern workplace. What

17:43

do you expect to happen next? Where do we go

17:45

from here? Well,

17:47

I expect AI to be

17:49

so woven into the way that we do

17:52

things, that it will sit alongside humans and

17:54

going forward our teams, that

17:56

people we work with will include not

17:58

just humans, but also. these AIS

18:00

systems, it will become a natural part of the

18:03

way we do things. And that skill set

18:05

will be incredibly important for every worker no matter

18:07

where they sit in an organization. Wonderful,

18:10

Jared, thank you so much for your time.

18:13

Unfortunately, we are out of time, so we'll

18:15

have to leave it there. Jared

18:18

Sputaro, thank you so much again for joining

18:20

us. And we'll be right back in just

18:22

a few minutes with our next guest. Please stay with us.

18:25

The following segment was produced and paid for

18:27

by a Washington Post Live event sponsor.

18:30

The Washington Post newsroom was not involved in

18:32

the production of this content. I'm

18:34

Kathleen Coke, a long time Washington

18:36

correspondent. Every business and

18:39

individual wants to protect their most

18:41

sensitive data, right? But that's becoming

18:43

more challenging by the day as

18:45

cybercrime increases, leading often

18:47

to both loss of revenue and

18:50

customers. But AI is giving companies

18:52

a new incredibly effective tool. Well,

18:54

here to talk with me about

18:56

that today is Todd Kramer. Todd

18:58

is Director of Security Ecosystem Business

19:01

Development and Intel. Thanks for joining

19:03

me, Todd. Great to be here,

19:05

Kathleen. Todd, how

19:07

would you describe the threat landscape

19:09

that businesses face today? We know

19:11

that cyberattacks are more frequent, but

19:13

how have they evolved? Yeah,

19:16

sure. I mean, obviously, people have seen the

19:18

news in the headlines, right? There's no shortage

19:21

of factoids that show that this

19:23

is having an impact. Take ransomware.

19:25

Health care, school districts, even

19:28

personal individuals are getting devastated by

19:30

these types of attacks. And so

19:33

we know it's top of mind. And so

19:35

you've seen the security industry elevate in importance

19:38

and increase in budget spend by companies

19:40

of all sizes to better prepare to

19:42

protect their workers. And so that's

19:45

the first part. The second part

19:47

is these attackers are constantly evolving.

19:49

They know how security software like

19:51

antivirus that we run at home

19:53

or endpoint detection and response that

19:55

enterprises run, how they look for

19:57

these threats. So they're constantly obfuscating

19:59

and trying. to hide. And so I think

20:01

some of the things we can talk about here

20:03

today is how AI is going to help uncover

20:05

those hidden areas that help bolster

20:08

protection overall. So are

20:10

the criminals using new tools? They

20:13

are. You have these crime

20:15

syndicates, actually syndicating ransomware

20:17

as a service tools, other

20:19

things, you know, the

20:21

cost of actually run a ransomware

20:23

attack, I think is down to

20:26

$1,000. So anybody, it's not just

20:28

nation states, these hackers, kids can

20:30

get ahold of these tools, learn

20:33

how to get access to a credential,

20:35

learn how to launch their favorite attack

20:37

and get paid in Bitcoin behind the

20:40

scenes. So there's lots going on that

20:42

are showing these defenders are moving more

20:45

rapidly and having success. So

20:47

how is AI so

20:49

important in trying to in

20:51

thwarting these types of attacks? Yeah,

20:54

sure. The AI itself, so you think

20:56

about all these alerts that are generated

20:58

in the security software, it's approaching a

21:01

point where already has that humans can't

21:03

process all this. What's a false positive?

21:05

What's a real attack? Do I respond

21:07

to this alert from this user's desktop?

21:10

So AI is from a digital assistant

21:12

standpoint, will enable the security analysts to

21:15

better triage all of these alerts. The

21:17

second part is what I'll call AI

21:19

for security. And these are unique things

21:21

that we're working on today, to use

21:24

AI itself to help uncover these attacks

21:26

so they can't hide, like I talked

21:29

about previously. So

21:31

how has Intel then been incorporating

21:33

AI into your products to boost

21:35

security for businesses and individuals? Sure.

21:39

On the server side, you know, the

21:41

security software has been using cloud side

21:43

AI and deep learning. So everything's gathered

21:45

off an endpoint agent, you

21:47

workers, all that data is sent to a cloud

21:49

and the AI is done in the cloud itself

21:52

to discover the threats. This

21:54

year, we launched Intel Core Ultra,

21:56

and it has an Intel MPU,

21:58

a neural processing unit along alongside

22:00

a GPU and the CPU. All

22:02

three of those do AI optimization.

22:04

So now what you have is the

22:06

ability for the security software industry to

22:08

apply AI, to run it

22:10

directly on your machine using

22:13

your laptop. And when you put that

22:15

AI closer to the detection source, you

22:17

can do interesting things, right? You can't

22:19

send all that user data to the

22:22

cloud, it's impractical from a cost standpoint.

22:24

But if you put the AI there to do

22:27

its job, now we're seeing these

22:29

novel use cases of increased ability to

22:31

classify malware on the fly, in

22:34

all sorts of pieces. So we're

22:37

not only accelerating the ability, but

22:39

it's of these vendors to find

22:41

threats, but we're actually detecting

22:43

more of those threats with this AI. And

22:46

I take it this is faster and more scalable

22:48

than anything that humans can do. That's

22:51

right. And so you're just going to see

22:53

the ability, not only of the bad guys

22:56

to launch these attacks at scale, this

22:58

AI for good, the

23:00

ability of hardware to enable security

23:03

software, to do AI, to do

23:05

it faster, better, to scale,

23:08

and those types of things are possible here.

23:10

So that's where we're headed. I

23:13

know that we're just really, it's

23:16

the tip of the iceberg, we're just beginning to see

23:18

what AI can do. Point

23:20

us on the horizon, and from a security

23:23

perspective, what would you say is the next

23:25

big innovation that we can expect? Before

23:29

I answer where we're headed, I'll just

23:31

point out that for the last five

23:33

years, Intel threat detection on our VPRO

23:35

laptops, we have had AI for security

23:37

already working on that. It

23:39

offloads to our GPU, does a

23:41

CPU detection assist for ransomware, for crypto

23:44

jacking, that's rolled out on

23:46

a billion PCs today, and you may

23:48

not know that on your personal computer,

23:51

there's an extra assist from the hardware

23:53

that you get. So that's here today.

23:56

And then this year, it's all about the year of

23:58

this AI PC. We're working with... the likes

24:00

of CrowdStrike, the defenders, all of those

24:02

type of vendors to unleash

24:04

these novel use cases with this

24:06

new horsepower down

24:09

on the client. So you're seeing

24:11

ISVs release software, put out blogs

24:13

on new techniques. And then in

24:16

the longer term, you're gonna see

24:19

these security analysts, the tools

24:21

for the analysts to be able to triage

24:23

things, heal infrastructure on

24:25

the fly. So it's their ability to respond

24:27

that's gonna automate. And that's where we're gonna

24:29

be a couple of years out. That's

24:32

really exciting, especially if it keeps

24:35

us and our data safer. We hope

24:37

so. Well, thank you so much. Todd

24:39

Kramer, director of security ecosystem, business development

24:42

and Intel. Really enjoyed

24:44

the fascinating conversation. Thank

24:46

you. And now back to Washington Post

24:48

Live. Welcome back.

24:50

To those of you who are just joining

24:52

us, I'm Danielle Liberl, tech at work writer

24:54

for the Washington Post. My next

24:57

conversation will examine how the workforce will

24:59

need to be educated and trained in

25:01

new ways with the rise of technologies

25:03

like AI. I'm

25:05

joined now by Dr. Ayana Howard.

25:07

She's the Dean of Ohio State

25:10

University's College of Engineering. Dean Howard,

25:12

welcome to Washington Post Live. Thank

25:15

you, thank you. So

25:19

you are a roboticist, an entrepreneur

25:21

and an educator, and

25:23

you use an interesting term

25:25

called humanized intelligence instead of

25:28

artificial intelligence. Can you tell

25:30

us, you know, what's the distinction there? Well,

25:33

so when we think about intelligence, just in

25:35

general, when we put it in computing or

25:38

artificial, really what we're thinking about

25:40

is how do we use AI to

25:43

augment our human functions? And so humans

25:45

are fundamental to thinking about the data,

25:47

thinking about the outcomes, thinking about what

25:50

it is that we wanna do. And

25:52

so when I think about humanized intelligence,

25:54

it's really about thinking of the next

25:56

generation of intelligence as AI,

25:58

but coupled tightly. to

26:00

engage and enable us, improve

26:02

our quality of life and incorporate

26:05

things around workforce development. And so

26:07

that's why I've used humanized intelligence really for

26:09

the last 15 years. That

26:13

makes a lot of sense. In assessing

26:15

the impact of AI on

26:17

jobs, you have said that

26:19

while the technology will change existing

26:21

jobs, there could be whole new

26:23

fields as well as

26:25

new opportunities. How so,

26:27

can you explain that a little bit? And

26:30

what role will the human emotional quotient you've

26:32

spoken of pull? Yeah,

26:34

so one of the things we think about computer

26:36

science and everyone's like, oh, I wanna be a

26:38

computer scientist and I wanna go into coding. But

26:41

computer science as a discipline, like I

26:43

can enroll in major in computer science.

26:45

If you look at engineering, which

26:47

existed in the 1850s, 1860s, there

26:51

was no discipline called computer science. And yet

26:53

computer science is one of the fastest growing

26:55

jobs even today. Jobs

26:57

change as we expand and

27:00

increase our technology footprint. And

27:03

so when I say we don't even know what jobs

27:05

are for the next year, for the next 10 and

27:07

next 20, it's because as

27:09

we grow, as we advance, we

27:12

are required to think differently about how we

27:14

train our students, how we train our next

27:16

generation. I like to think

27:18

about robotics. When we do have

27:20

self-driving cars on the road, who knows

27:22

when that will happen, but it will. Do

27:25

we need robotic mechanics? What about the

27:27

gas station attendants? Are there gonna be

27:29

a new breed of gas station attendants

27:31

that can deal with robotic cars? We

27:34

don't know, but we really need to train

27:36

the next generation so that they can adapt

27:38

to the new requirements in the new jobs.

27:42

You know, it's funny, we're already seeing

27:44

that in San Francisco where I'm based.

27:46

We're seeing the self-driving cars

27:48

run around and trying to deal with

27:50

the issues there. So yeah, a

27:52

reality already coming to life. That

27:55

being said, you mentioned this, we saw it a little

27:57

bit in the intro, how

28:00

students are gonna need to be educated in new

28:03

ways to adapt to this

28:05

new workplace and economy. I

28:07

want you to expand on that. How

28:09

do we need, you talked a little bit about

28:12

prompt engineering and things like that, but what are

28:14

ways that we need to think about how students

28:17

are gonna be educated, especially when you're

28:19

talking about jobs that we don't even know

28:21

are gonna exist? How do you educate

28:23

that workforce and are you already seeing

28:25

these changes happen? I

28:28

am seeing the changes happen and

28:30

I am a proponent of college

28:32

education. And the reason is

28:34

it's not necessarily the discipline, but

28:37

when you go to college, even if

28:39

it's two year college, you're given the

28:41

tools to think, ask questions and figure

28:43

out things. And so when

28:45

we provide the tools and I always

28:47

think about, can we create

28:50

a computer scientist that really

28:52

is fundamentally a humanities student?

28:56

Can we add those together so that I love

28:58

English, I love language, but can I figure out

29:00

how to use large language models to

29:04

create better writing styles,

29:06

to tease out editorials?

29:08

Those are the kinds of things we have to think

29:10

about as the next generation of the tools. And

29:13

so in education, what we do is we teach

29:15

students how to learn so

29:19

that when they change their jobs 20, 30,

29:21

40 years, they are comfortable with, oh wait,

29:23

it's a new job, new requirements. Oh

29:25

wait, I know how to learn, I know how

29:27

to figure this out, I can adapt as the jobs adapt. So

29:32

it's really about the skillset and less about

29:34

necessarily like, training

29:37

to be a computer scientist or a coder or

29:40

a software engineer and specifically like

29:42

just knowing how to learn and

29:44

knowing the skills you might need going in to

29:47

sort of adapt to those new jobs. Exactly,

29:50

exactly. And that, believe it

29:52

or not is difficult. Learning how to learn, learning

29:56

how to ask questions, learning to be a

29:58

computer scientist. be curious

30:00

always and all the time, even learning

30:02

to question the things that come out

30:04

in terms of AI and artificial intelligence,

30:07

those are hard skills. Traditionally, if you

30:09

think about it, as students, a teacher

30:11

comes, they say, you believe them. So

30:13

now when you're in the workforce, when you're

30:15

interacting with AI as your co-pilot, for example,

30:18

it's like, oh, I'm just going to

30:20

take the guidance. I'm just going to

30:22

understand. And so that whole skill set

30:24

of being curious and questioning and expanding

30:26

our own knowledge, liking to

30:28

learn is actually not always natural

30:31

when we think about it. I

30:34

want to read an audience question we have

30:37

from Indra Klein in Washington, DC.

30:40

Indra asks, given the perceived

30:42

gap with respect to technical skills

30:45

needed for the future of work,

30:47

any thoughts on better integration of

30:49

tech-related skills in K through 12

30:52

curriculum with

30:54

traditional basic subjects? Yes,

30:57

I truly believe that compute, and

31:00

I'll call it computational skills versus

31:02

computer science, but computational skills, which

31:04

also links to AI, should

31:07

be one of the elements. So you think about K through

31:09

12. We just assume that

31:11

you will learn how to read. We

31:14

just assume that you will learn how to do

31:16

basic math skills. And we've designed our curriculum from

31:18

K through 12 so that at

31:20

the end of high school, you are able

31:22

to read at some grade level. You are

31:24

able to do some basic math. We need

31:26

to do the same thing around computational

31:29

skills. When you have a

31:31

student in kindergarten and they are

31:34

using their apps, which they are, actually

31:36

asking them, OK, let's think about the data.

31:39

When you're providing and you're playing your game,

31:42

do you know what data is? Let's

31:44

define data. What do we think about

31:46

when we're collecting your data? You can

31:48

start as simple as kindergarten and continue

31:50

going through that. And so by the

31:52

time you're in high school or graduate,

31:54

you will have a basic understanding. And

31:56

the skill sets of computation, whether it's

31:58

early, very basic. like Python coding

32:00

or maybe it's just like I know how

32:03

to prompt my chat agent,

32:05

my chat bot so that I can get better

32:07

answers for my homework. We have

32:09

to integrate that deeply. I

32:11

think of it as AI literacy, computer

32:13

science literacy. Now, that's

32:16

a total change from the way

32:18

that probably you or I were educated

32:20

growing up. So that would be amazing

32:23

to see what comes out of that

32:25

if you're educated at kindergarten with that

32:27

knowledge and then you go through college.

32:30

I bet you have a totally different mindset. I

32:33

want to move to another question. We saw in the

32:35

intro video, you emphasized coding will

32:37

be a key skill for future generations. I

32:40

want to talk about who specifically will benefit

32:42

most from this and how should we think

32:44

about coding for future generations? I mean, you

32:46

talked a little bit about it in this

32:49

last question. Can you expand on that a

32:51

little bit? Yeah.

32:53

So when I think about coding,

32:55

it really is how do we give students

32:57

and next generation of workers, the future

33:00

workforce, the ability to think in a

33:02

logical fashion, identify a problem, figure out

33:04

the steps to solve that problem, and

33:06

then come up with a solution. I

33:09

call it engineering know-how, but when everyone says

33:11

engineering, it's like, oh my gosh, that's like

33:13

too hard. But when you say

33:15

coding, people are like, oh, okay, I can

33:17

learn how to code. But coding really gives

33:19

you that ability to process in that very

33:21

logical sequential problem-solving way.

33:24

And so that's really what I say coding

33:27

skills. It's the ability to think in that

33:29

way, think in that logical sequence, problem-solve. And

33:32

it really doesn't matter what the code is. I

33:34

need to figure out how to make you think

33:36

differently. And that's why that skill is

33:38

so important when you go out. I

33:41

know people who learn how to code in

33:43

one language, I'm 10 years ago, say, you

33:45

know, basic or Pascal or assembly. Now

33:47

they can program in any language that comes

33:49

up. I say code language of the

33:51

day. It's very easy to pick up because you

33:53

have that basic understanding of how to

33:55

think about the basic building blocks, the

33:58

sequences, and... what are the outcomes

34:00

that are expected? So

34:03

we're already seeing generative AI

34:05

tools gradually being incorporated into

34:07

the workplace, from chat

34:09

GPT to our

34:11

previous speaker talked about co-pilot. What

34:14

are the basic skills that employees need

34:17

to know as they engage

34:19

with these AI tools and how do we

34:21

go about learning them? So

34:24

I believe in a trial

34:26

by error. So

34:29

just use it, just think about what is it that

34:31

you wanna solve? What is it that you wanna do?

34:34

If you are a journalist and

34:37

you would like to write an article, ask

34:39

your agent, whether it's co-pilot or

34:42

Gemini or Anthropic Claude,

34:45

oh, please write an article about the future

34:47

of work for the Washington Post. Ask

34:49

it three different ways and you as a

34:51

person can see, oh wait, I get a

34:54

better response if I ask or prompt it

34:56

in one way versus the other. And

34:59

that allows you to basically learn by doing.

35:01

I think that's really the only way that most

35:04

people can become comfortable of using these tools and

35:06

not being afraid that they're using it wrong. It's

35:08

just like you would ask a parent or a

35:10

child and you don't like the answer, you ask

35:13

again and you ask again until you get the

35:15

answer that you want. You should do the same

35:17

thing where you're using these tools that

35:19

are augmenting our work and

35:22

making us more efficient, but we also have

35:24

to learn how to get it

35:26

to make us more efficient. So

35:30

obviously one of the big barriers to

35:32

adoption is trust. People trusting these tools

35:34

for various reasons, maybe they're worried about

35:36

it, maybe they're scared of it or

35:38

maybe they've just had some experiences with

35:41

hallucinations and errors. What

35:44

role does the private and public

35:46

sectors play in building trust in

35:48

a technology that could disrupt jobs

35:50

and society in other ways? So

35:54

I think of trust in really two

35:56

tranches. So one, a lot of

35:58

individuals say, oh, I don't know. trust AI

36:00

or don't trust the companies. And

36:03

you look at the surveys and oh yeah, okay. But

36:05

then if you look at the behavior, the behavior

36:08

doesn't match what people say. People will still use

36:10

the tools. People will still use the AI. When

36:13

it hallucinates, it's not like they cut it off, like oh,

36:15

I'm never going to use it again. It's

36:17

like oh, okay, I'm going to try it again and see what it says.

36:20

And so what we call behavior is

36:23

so much different. The behavior of people shows

36:25

that we actually trust the use of these

36:27

tools. Because surveys is

36:29

like no, I don't trust it. I

36:32

think one of the things is that we

36:34

are not being truthful to ourselves. We

36:36

are not really thinking about the fact that

36:39

we like a lot of these AI tools

36:41

because when it works, it does make us

36:43

more efficient. It does make us better. It

36:45

does improve our learning capabilities. And when it's

36:48

wrong, that's when we're like oh, we don't

36:50

trust it, but we're going to still use

36:52

it. And so that's kind of when I think about trust

36:54

in those two tranches in

36:56

those aspects. And so for companies, I

36:59

think that one, companies should take a

37:01

little more responsibility about thinking

37:04

of how do I build and really

37:06

distrust on the behaviors of the AI

37:09

so that people won't start over trusting

37:11

it in terms of its use. I

37:14

always think about maybe we should have

37:16

denial of service at some point. Like today we're going

37:18

to have an off day for AI. I

37:21

mean, could we have companies actually

37:23

think about that as a possible

37:25

way of building distrust, but

37:28

then also trust in that

37:30

aspect? You're

37:33

one of 27 experts appointed to

37:35

the National Artificial Intelligence Advisory Committee.

37:38

Recently, prominent academics have called

37:41

on the Biden administration to

37:43

fund AI researchers to help

37:45

them keep up with the tech giants. Do

37:48

you also share this concern? I

37:51

do. So one of the problems with

37:54

current versions of AI and really it's

37:56

generative AI is that you need a

37:58

lot of compute. which basically means

38:00

you need back ends, you need to figure

38:03

out how do you have these large servers,

38:06

which means that it's very, very costly.

38:08

As an academic researcher, most researchers aren't

38:11

able to afford to take a billion

38:13

parameters or learn around a billion types

38:15

of input data to come up with

38:17

a solution, whereas companies can't. But

38:19

if you think about the fundamental things

38:21

that have moved our entire society forward,

38:24

that has come from basic research. That

38:26

has come from researchers working together, trying

38:28

to figure out sometimes working 20 years

38:30

and then, oh my gosh, we have an mRNA

38:33

vaccine. This is what's

38:35

required. And so really

38:37

thinking about what can we do when

38:40

we can have government support in terms

38:42

of open source resources, in terms of

38:45

computes that are freely available to researchers,

38:47

and basic researchers, both in academia and

38:49

in the K through 12 system. It

38:51

allows us to work on problems that

38:53

might take 20 or 30 years to

38:56

solve. There is no necessarily commercial

38:58

benefit in the now, but it

39:00

can solve things that are really

39:03

important in terms of education, health

39:05

care, water scarcity, manufacturing mobility, all

39:07

the things that we care about

39:09

as a society, but it's not

39:11

necessarily financially profitable in the now.

39:15

So as I mentioned in your intro, you're

39:17

also an author, along

39:20

with everything else, being an entrepreneur, being an

39:22

educator. And you wrote the book

39:24

Sex, Race, and Robots. How to be

39:26

human in the age of AI. How

39:29

does AI inherit human biases? And can

39:32

it be circumvented? I kind of wonder,

39:34

is the genie out of the bottle?

39:36

We've seen so many of these cases

39:38

and situations where AI

39:40

has gone wrong and just been really

39:43

biased in their answers. Where

39:45

are we at? Can we pull some

39:47

of that back? We'd love to

39:49

hear your opinion on that. Yeah,

39:52

so the reason why AI in its

39:54

current rendition has bias

39:56

is this element of what

39:58

I call humanized intelligence. learning from

40:00

our data. And the fact is, like, as

40:02

people, we are biased. We have historical biases.

40:05

And if you think about collecting data in

40:07

the last year versus the last 10 years

40:09

versus the last 20 or 30, there

40:12

is extreme cases of bias that grants pretty

40:14

much any group you can think about. And

40:17

so AI inherits this bias.

40:19

Now, I will say that

40:21

AI is better than biases

40:24

in people. But the

40:26

problem is, is because we over

40:28

trust AI, when it says something,

40:31

we very rarely question. And so

40:33

that bias gets amplified if it's

40:35

making decisions around, again, housing, policing,

40:37

surveillance, it's inheriting that bias. So

40:39

how can we circumnavigate? It is

40:41

out of the box. The genie

40:43

is out of the box. It's

40:45

out there. It's being deployed. I

40:47

think one of the things we can do to deal

40:49

with this is, one, putting in

40:52

more safeguards for the things that

40:54

are around our liberties, around things

40:56

that really can cause harm to

40:58

us as a society. And when

41:00

I say safeguards, safeguards are about human

41:03

oversight. Safeguards could be about rules and

41:05

regulations such that they're designed not to

41:07

impede progress, but to ensure

41:09

society does not have any issues. There's

41:12

no harm to society. And we can

41:14

look at the UN to say,

41:16

OK, what are these things that are really, really

41:18

dangerous for AI to be in and make

41:21

sure that we have human oversight till

41:23

we get to the point that the

41:25

bias in AI is mitigated. It will

41:27

never be eradicated because we as people

41:29

will always have some biases, but at

41:31

least mitigate the bias from AI based

41:33

on their decisions. And

41:36

you recently wrote, technologists aren't

41:39

trained to be social scientists

41:41

or historians. We're in

41:43

this field because we love it. And

41:45

we're typically positive about technology because it's

41:47

our field. That's a problem. We're not

41:50

good at building bridges with others who

41:52

can translate what we see as positive

41:54

and what we know are some of

41:57

the negatives as well. we

42:00

kind of wrap up this segment, where do

42:02

you think those bridges need to be built

42:05

better in terms of where we are today? Well,

42:08

one, I think that as

42:11

technologists, we should be

42:13

learning about things, ethics

42:15

and social science. Even

42:18

though I got into engineering and coding because that's what

42:20

I want to do. I'm not

42:22

really a great reader. And so it's like, oh my gosh,

42:24

I have to read more? No, I want to do math.

42:26

I want to code. So it's also

42:28

anti what I enjoy. But

42:30

I think what is we are becoming

42:33

so much more reliant on technology. And

42:36

in order to contribute to the good,

42:39

we also need to think about what is the harm that

42:41

we are instituting and what is the

42:43

harm that we are perpetuating. I

42:46

can think about coding. I can think about my

42:48

tools. I can think about A.I. And I'm able

42:50

to put in guardrails in

42:52

my own code when I think

42:54

about it. It's so much harder

42:56

after the fact. And so I

42:58

think it's not just technologists. We should

43:00

be self-aware and start learning ourselves. But

43:02

I think companies should also help their

43:04

technology workforce to become more ethical. And

43:06

it's not good enough just to put

43:08

in an ethicist and a team because

43:10

as technologists, we tend to respect other

43:12

technologists. I'll just say that. And

43:15

so it really is helping us

43:17

become better, better humans, better

43:20

society in society. It's

43:24

hard because it's based on a human problem,

43:26

not on a technical problem. But I think

43:28

it's one of the ways to get to

43:30

the other side. Got

43:32

it. Yeah, that makes a lot of

43:34

sense. Unfortunately, we're actually out of time. So we're

43:36

going to have to leave it there. Dean

43:39

Ayanna Howard, thank you so much for joining

43:41

us today. Thank you. Thank

43:43

you for having me. having me. Thanks for

43:45

listening. For more information on our

43:48

upcoming programs, go to

43:50

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