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Speaker 1: It's Night's Eyes with Dan Ray.

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Speaker 2: I'm going Easy Boston's news radio.

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Speaker 1: Hey, welcome back every one to our and the ninth

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Speaker 1: nine o'clock hour tonight here on night Side. My name

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Speaker 1: is Dan Ray, host of the program where we are

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Speaker 1: here every Monday through Friday night from eight until midnight.

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Speaker 1: My guest this hour is a really interesting individual who

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Speaker 1: just about a little over a week ago announced that

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Speaker 1: he was going to give a million dollars to kickstart

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Speaker 1: the artificial intelligence program in Boston public schools. I have

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Speaker 1: known of Paul English for some time now. He and

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Speaker 1: I traveled kind of similar paths. We both went to

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Speaker 1: Boston landin school, although me much earlier in the last

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Speaker 1: century than than Paul did. And Paul's a graduate, not

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Speaker 1: only an undergraduate and graduate degree from UMass Boston and

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Speaker 1: as a Boston State College undergraduate. I am also a

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Speaker 1: graduate for UMass Boston. He has been much more successful

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Speaker 1: than I though, for sure. Paul English, Welcome to NIGHTSID.

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Speaker 1: How are you.

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Speaker 2: It's great to talk to Dan.

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Speaker 1: Thanks so much, my pleasure, My pleasure. I was interested

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Speaker 1: as I did a little bit more research. You grew

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Speaker 1: up in West Roxbury in what would be considered a

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Speaker 1: very traditional Boston family, the sixth of seven children, and

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Speaker 1: I guess your parents must have set a pretty high

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Speaker 1: standard of academic achievement. Yeah.

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Speaker 2: My parents, my mom in particular, was a pretty pretty

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Speaker 2: serious about school. She was a school teacher in Boston.

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Speaker 2: My dad was a pipefeder for Boston Gas for I

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Speaker 2: believe forty nine years, just with a couple of years

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Speaker 2: off to do a tour of Germany during World War Two.

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Speaker 2: But aside from that, he was at Boston gassa forty

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Speaker 2: nine years. And yeah, they were serious about school.

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Speaker 1: Yeah, my dad did two and a half years on

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Speaker 1: the other side of the world China, Berman India during

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Speaker 1: World War two. So yeah, very very similar. But you

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Speaker 1: also liked computers. You were someone who understood coding early,

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Speaker 1: and you had a very successful travel company that I'm

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Speaker 1: sure people will remember called Kayak, and that was a

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Speaker 1: tremendous success. Let's just leave it at that. But you

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Speaker 1: have now turned your good fortune and your intelligence to

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Speaker 1: some issues and concerns that probably people do not know.

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Speaker 1: You do not know of you. You were the founder

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Speaker 1: I did not realize you were the founder of the

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Speaker 1: the Embrace monument statue on the Boston Common, which was

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Speaker 1: dedicated to doctor Martin Luther King. How did you come

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Speaker 1: up with that idea?

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Speaker 2: I mean, yeah that I'm embarrassed how long it took

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Speaker 2: me to get it done. I created it in twenty seventeen.

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Speaker 2: It was a time in the country where there was

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Speaker 2: a lot of nationalistic, anti immigrant, racist rhetoric, and I

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Speaker 2: was visiting an MLK and Oryland in San Francisco, a

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Speaker 2: site I've been to many times and just really moved

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Speaker 2: by it and knowing that Martin Luther King Jr. And

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Speaker 2: Coreta Scott King met in Boston, that love stories started

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Speaker 2: here in our city. I thought, we need something like

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Speaker 2: this in Boston to really showcase and talk about the

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Speaker 2: beginning of their the love story at the beginning of

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Speaker 2: their professional career together. So that was the idea. It

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Speaker 2: took me six years to get it done. There's a

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Speaker 2: lot of help, a lot of people, a lot of meetings,

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Speaker 2: but we got it done.

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Speaker 1: Was that a lot of red tape you had to

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Speaker 1: deal with?

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Speaker 2: Or yeah, I don't want to name names, but a

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Speaker 2: lot of people were against it. They thought what we're

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Speaker 2: doing was too large, too dramatic. It didn't fit with

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Speaker 2: the Boston Common, the Boston Commons a bunch of dead

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Speaker 2: white soldiers, and this like modern art abstracting, really fitting

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Speaker 2: with the Boston Common. Some people said, go build it

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Speaker 2: in Roxbury, don't build it in the Boston Common, But

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Speaker 2: we have a bunually got it done and we had a

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Speaker 2: great team. I worked with Reverend Liz Walker who you

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Speaker 2: know Liz, Yeah, Liz was at that time they had

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Speaker 2: a Roxbury Presbyterian Church and she became my co founder.

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Speaker 2: And then you know, we did I think thirteen meetings

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Speaker 2: around the city asking people what they wanted if we

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Speaker 2: built something to memorialize Martin Luther King Junior and credit

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Speaker 2: Scott King in the relationship, what they wanted out of

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Speaker 2: the project, and we since have gone on to raise

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Speaker 2: thirty five million dollars a racial justice projects in Boston.

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Speaker 2: The project now was led by Amari Paris. Jefferies has

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Speaker 2: been an exceptional executive director for US the last few years.

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Speaker 1: People forget that doctor King went to Boston University graduate

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Speaker 1: school and his papers, many of his papers are at

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Speaker 1: Boston University. So let me move forward if I can.

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Speaker 1: There have been other causes that you have been involved in,

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Speaker 1: For example, the annual Winter walk. You've done that for

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Speaker 1: ten years for the Massachusetts Homeless Coalition, and now you're

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Speaker 1: going to try to take that nationally.

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Speaker 2: As I understand it, Yeah, we've done it for ten years.

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Speaker 2: We had thirty five hundred walkers in Boston just a

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Speaker 2: couple of weeks ago around the Boston Common We raised

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Speaker 2: about six million dollars for homeless shelters. This year we

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Speaker 2: did walks in Boston, Chicago, New York, and a few

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Speaker 2: other smaller cities. But next year we really want to

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Speaker 2: take it national, and we're rebranding ourselves from the Winter

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Speaker 2: Walk to the More Homes Coalition, and we really want

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Speaker 2: to highlight innovation in homeless care around the country. So

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Speaker 2: if we find something exceptional be done in Denver or

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Speaker 2: San Diego or Seattle, we're going to highlight that, give

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Speaker 2: a cash reward to the nonprofit in the most interesting

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Speaker 2: thing with homeless care, and then trying to spread the

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Speaker 2: knowledge across the country about where what are the innovation

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Speaker 2: is happening, Like who's doing the best job?

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Speaker 1: Well, that's that is interesting because it is Homeless in

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Speaker 1: America has been a problem in this country for as

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Speaker 1: long as I can remember, going back to before you

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Speaker 1: were in high school in the nineteen seventies when I

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Speaker 1: was getting out of law school that the homeless population,

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Speaker 1: obviously we have it exacerbated by Mass and cass here

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Speaker 1: in Greater Boston as well, and it's been a problem

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Speaker 1: in you know, in so many cities. It's complicated. There's

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Speaker 1: no easy solution. That that's a big problem you're taking on, Paul,

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Speaker 1: I'm sure you understand the magnitude of that one.

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Speaker 2: Yeah, I've been working on this for ten years, and

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Speaker 2: I'm working with doctor Jim O'Connell, who's our chair of

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Speaker 2: a nonprofit gym is from Boston Healthcare and homeless And

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Speaker 2: the thing about homelessness is we have to realize this

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Speaker 2: affects many families, and it's something that could happen to

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Speaker 2: any of us or any of our family members. The

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Speaker 2: two most common things that put people on the street

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Speaker 2: is either struggling with some type of mental illness and

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Speaker 2: or some type of addiction. But the thing about those

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Speaker 2: two conditions, which often can cause homelessness, they're both curable

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Speaker 2: in most cases. You know, we have the Great Mass

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Speaker 2: General here in Boston. They're the number one mental health

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Speaker 2: hospital in the US doing research on different ways of

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Speaker 2: cure schizophrenia, bipolar, you know, different types of mental illnesses,

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Speaker 2: and if you get someone into therapy and get them

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Speaker 2: on the right medications, they can be cured of that.

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Speaker 2: So almostness is a horrible thing to happen to a

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Speaker 2: person and to a family. But with the right services

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Speaker 2: and the right support, we can get people jobs. We've

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Speaker 2: done it. We've gotten people jobs, We've gotten them into housing,

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Speaker 2: and we just need more attention on it.

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Speaker 1: Yeah, I kind of imagine what it would be like

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Speaker 1: to have a family member, or for that matter, of

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Speaker 1: a child who you knew when you went to sleep

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Speaker 1: at night, you knew that that child was out on

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Speaker 1: this shoe.

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Speaker 2: It's horrible, It's horrible.

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Speaker 1: How did you how were you drawing to this subject homelessness?

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Speaker 1: I mean, it's a it's a problem that affects people

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Speaker 1: all across the country. Me this, you know, yeah, I

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Speaker 1: can understand a lot of but this is a huge problem.

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Speaker 2: Yeah. There's really two incidents of my life that caused

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Speaker 2: me to pay more attention to the people living in

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Speaker 2: the street. One is I had sold a company. I

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Speaker 2: had an e commerce company which I sold to into

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Speaker 2: it in California during the Dot com one. Oh, and

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Speaker 2: I made a bunch of money. And I'm someone who

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Speaker 2: grew up with no money and I so when I

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Speaker 2: made all this money, I was uncomfortable with it. I

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Speaker 2: wanted to give it away because I didn't know what

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Speaker 2: I would do with millions of dollars. And so I

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Speaker 2: called uh the uncle of one of my childhood friends.

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Speaker 2: His name was Thomas J. White. He was firm uh

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Speaker 2: Ran Jfy Construction Company, which is one of the big

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Speaker 2: four builders in Boston. And I met him. I was

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Speaker 2: forty at the time, he was eighty, and I said, Hey,

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Speaker 2: I made this money. I want to give it away,

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Speaker 2: Like what should I do? Who should I give it to?

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Speaker 2: And he said, go to Haiti and visit my friend,

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Speaker 2: doctor Paul Farmer, and then go visit a HOMSS shelter

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Speaker 2: in Boston and I'll get you set up to meet

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Speaker 2: some people there. And that really opened my eyes. I

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Speaker 2: was twenty years ago. I now look at I've given

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Speaker 2: away most of my kayake money at this point, and

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Speaker 2: the biggest recipients of my giving has been Homo shelters

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Speaker 2: and education in Haiti. So I feel like I'm honoring

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Speaker 2: my mentor, Tom who passed away ten years ago now.

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Speaker 2: But then the second thing that really drew me closer

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Speaker 2: to homeless causes was dropping downtown one day with my

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Speaker 2: son Michael. He's probably four years old at the time,

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Speaker 2: and we had a street light and there was a

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Speaker 2: man laying on the side of the road and my

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Speaker 2: son got very upset and said, Dad, is at a man?

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Speaker 2: Is he alive? He's okay? Said son actually asked me

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Speaker 2: to check on this guy, like what four year old

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Speaker 2: would do that, And he made me get out of

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Speaker 2: the car make sure the guy's okay. And it just

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Speaker 2: reminded me like up until then, when I saw someone

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Speaker 2: in homeless in the street, I'm guilty, I look the

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Speaker 2: other way and I walked past them. You know, try

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Speaker 2: not to engage. But through the eyes of a child,

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Speaker 2: we see a different world. And that opened my eyes

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Speaker 2: and I said maybe I should say hello to people

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Speaker 2: and talk to them. And when I started talking to

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Speaker 2: people living in the street, I learned a lot, like,

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Speaker 2: for example, one of the things that they say is

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Speaker 2: really difficult from friends of mine who are formerly homeless

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Speaker 2: now employed and you know, living in homes. But when

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Speaker 2: they told me they were in the street, they said

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Speaker 2: sometimes they would go months without someone saying their name.

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Speaker 2: And so one thing I do when I count on

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Speaker 2: the street right now is I'll say, hello, what's your name?

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Speaker 2: My name is Paul. You know it going to help you.

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Speaker 2: A lot of times I carry around like dunk Donuts

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Speaker 2: gip stificates that I give out, but I'll talk to people,

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Speaker 2: you'll treat them like a human.

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Speaker 1: Well, I think I think that is an incredible reach.

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Speaker 1: I know that I'm not going to compare my efforts

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Speaker 1: meek as mild as they are compared to yours. But

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Speaker 1: whenever I am and I broadcast from home for the

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Speaker 1: last few years remotely, but whenever I'm was at the

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Speaker 1: station at night, it was generally me, my producer and

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Speaker 1: the cleaning crews, and I always when I had a

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Speaker 1: chance to just say hi, how you doing? And I

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Speaker 1: introduced myself and I asked them what their name was.

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Speaker 1: And it was an amazing response that I got from

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Speaker 1: those people, just you know what they they they they

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Speaker 1: responded very well that someone, and a lot of them

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Speaker 1: didn't speak English, but they knew the concept of what

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Speaker 1: is your name? And I think that is exactly what

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Speaker 1: you were doing. And it must have had even a

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Speaker 1: greater impact on people who were out on the street

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Speaker 1: and in need of help.

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Speaker 2: It makes a big difference. And you know, if people

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Speaker 2: want to learn more about the stories, who are these

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Speaker 2: super living in the streets, you see downtown, you see

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Speaker 2: association whatever. Tracy Kidder, a beloved author of Massachusetts, has

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Speaker 2: passed away just a couple weeks ago. His last book

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Speaker 2: was called Rough Sleepers. It's about doctor Joe O'Connell. It

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Speaker 2: was a six year project. He spent six years walking

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Speaker 2: the streets with Jim O'Connell. And the book is incredible.

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Speaker 2: It's a page can't put it down, and just the

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Speaker 2: stories and how multifaceted and colorful these people are, what

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Speaker 2: their lives were like before they ended up on the street.

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Speaker 2: It really will open your eyes to read that book.

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Speaker 1: Yeah, I've had I've had doctor O'Connell on and I

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Speaker 1: know that the phrase rough sleepers comes not from Boston,

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Speaker 1: but from London, where that's a homeless population. My guest

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Speaker 1: is Paul English. He is described in headlines in John

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Speaker 1: Cesto's piece Last Week. Tech entrepreneur Paul English gives one

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Speaker 1: million dollars to start the artificial intelligence program in Boston

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Speaker 1: public schools. We get to that and and and how

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Speaker 1: we can deal as a society with artificial intelligence. Obviously

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Speaker 1: there's a lot of upside, but there's also potentially a

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Speaker 1: lot of downside. If you like to join the conversation

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Speaker 1: and ask Paul English a question or make a comment.

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Speaker 1: Six one seven thirty six one seven. My name is

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Speaker 1: Dan Ray, and I like every once in a while

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Speaker 1: to have an interesting Bostonian on the show. And looking

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Speaker 1: at John Chesto's piece from a couple of weeks ago,

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Speaker 1: and also knowing what Paul English has done for our

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Speaker 1: alma mater, but his alma mater, you Mass Boston, is extraordinary.

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Speaker 1: We'll get into all of that right after the break

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Speaker 1: here at.

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Speaker 2: Night Side Night Side with Dan Ray, I'm telling you

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Speaker 2: BZ Boston's news radio.

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Speaker 1: I referenced earlier. My guess is Paul English, a tech

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Speaker 1: entrepreneur who has just given a million dollars to kickstart

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Speaker 1: at the artificial intelligence program in Boston public schools. And

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Speaker 1: in the article that John Cesto, who's a good friend

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Speaker 1: from the Boston Globe wrote about you in late March

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Speaker 1: March thirtieth, actually that you were posed a seemingly innocuous

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Speaker 1: question last year by Boston Magazine. You were amongst this

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Speaker 1: group of twenty one prominent local leaders, which was if

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Speaker 1: you were mayor of Boston, what's the one thing you

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Speaker 1: would do to improve the city.

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Speaker 2: And your answer was, I would make AI learning of

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Speaker 2: AI mandatory to get a degree from any Boston public

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Speaker 2: high school.

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Speaker 1: And in that conversation or that question, you came up

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Speaker 1: with the idea of developing an AI proficiency plan. You

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Speaker 1: reached out to Mayor Wu in January, and what is

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Speaker 1: going to happen. This is now also connected to UMass Boston,

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Speaker 1: where you have an institute, an AAI center the teachers.

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Speaker 1: This money will fund a program to get Boston teachers

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Speaker 1: to spend some time this summer doing.

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Speaker 2: One So the million dollars is not a huge amount

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Speaker 2: of money, but it's enough to do intensive teacher training

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Speaker 2: this summer. We're going to select I hope to announce

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Speaker 2: in May that Mary Skipper, the Visionary Superintendent of bost

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Speaker 2: Public Schools, will announce the selection of the teacher Ambassadors.

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Speaker 2: There the one teacher from each of the high schools

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Speaker 2: in Boston, and they will be undergoing intensive training with

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Speaker 2: u MASS Boston's AI Institute, which is a three year

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Speaker 2: old program this summer, and then instruction will begin in

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Speaker 2: the high schools in September. So it's really a very

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Speaker 2: aggressive timeline. Yes, But the way to think about it

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Speaker 2: is these kids are already using AI. They're kind of

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Speaker 2: figuring it out for themselves at home. We want to

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Speaker 2: put a little bit of structure behind it to teach

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Speaker 2: these kids things about like the ethics of AI, things

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Speaker 2: about when AI is biased, why is it biased, how

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Speaker 2: to detect that, when AI hallucinates, when to trust AI,

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Speaker 2: what other tools you should use it with. And we're

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Speaker 2: trying to put just a little bit of formal academic

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Speaker 2: rigor around AI. So these kids graduate either with AI

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Speaker 2: knowledge which will help them in their college careers, or

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Speaker 2: for the kids who don't want to go to college,

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Speaker 2: not of our needs to go to college these days.

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Speaker 2: If they take a job at a local company in Boston,

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Speaker 2: how they can use their AI skills at Boston Public

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Speaker 2: High School to improve business for any local business in Boston.

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Speaker 1: Well, you said it's aggressive, It certainly is aggressive. I

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Speaker 1: would say it's on a fast track. If September is

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Speaker 1: what four or five months away to have these teachers

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Speaker 1: from each high school in Boston trained up and ready

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Speaker 1: to get the the classes that resume in September. That's

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Speaker 1: going to be great. The AI Institute at you Mass

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Speaker 1: Boston was funded by you three years ago, if I'm correct,

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Speaker 1: and if I'm wrong, correct me with five million dollars.

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Speaker 1: When we get back, I'd like to talk about what

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Speaker 1: that institute, which is, you know, which is a little

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Speaker 1: bit more heavily funded, that is intended to be there

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Speaker 1: for a while, and I want to find out from

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Speaker 1: you what the goal of that is. And also again

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Speaker 1: it raises the identity of school that I feel close to,

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Speaker 1: and I know you feel very close to you Mass Boston.

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Speaker 1: My guest is Paul English. We have to take news break.

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Speaker 1: Paul at the bottom of the hour, see what's going

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Speaker 1: on in the world. If you'd like to join the

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Speaker 1: conversation six one, seven, two, five, four ten thirty six

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Speaker 1: one seven, nine three one ten thirty. I think that

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Speaker 1: having a guest like this talking about something that is

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Speaker 1: very much futuristic and will affect all of us in

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Speaker 1: some way or another, is invaluable. I appreciate his time tonight,

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Speaker 1: and you are more than welcome to join the conversation

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Speaker 1: and ask questions and make comments. We will be back

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Speaker 1: right after the news and we'll talk a little bit

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Speaker 1: about the Institute at UMass Boston, which is also supported

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Speaker 1: and funded by Paul English, dealing with artificial intelligence, and

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Speaker 1: we're where we're going to be five ten years from now.

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Speaker 1: I mean, it's sometimes some people thinking it's scary. I

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Speaker 1: see some reports that say that, well, we will be fine.

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Speaker 1: I'll be interested to see what Paul English thinks, because

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Speaker 1: he knows a heck of a lot more about this

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Speaker 1: than I do. We'll be and most of you as well,

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Speaker 1: back on night Side right after this.

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Speaker 2: With Dan Ray on Boston's News Radio.

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Speaker 1: We're delighted to be joined by tech entrepreneur as John

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Speaker 1: Cesto in the Globe described we're talking about his donation

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Speaker 1: of a million dollars to help kickstart an artificial intelligence

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Speaker 1: program for Boston public school students. Prior to that, I

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Speaker 1: believe you donated five million dollars to U Mass Boston

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Speaker 1: to file to establish an AI center. What what is

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Speaker 1: the goal of the AI center? Obviously, this this training

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Speaker 1: for teachers in Boston is going to be incorporated in

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Speaker 1: the U MASS Boston AI Center this summer, in anticipation

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Speaker 1: of teachers starting to teach this September. What is the

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Speaker 1: goal of the AI center at you Mass Boston and

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Speaker 1: are there other schools in the area who are doing

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Speaker 1: the same thing or is the program at U MASS

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Speaker 1: Boston unique at the moment?

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Speaker 2: What we did We started discussing plans for an AI

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Speaker 2: curriculm that you Mass four years ago, actually before AI

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Speaker 2: got super super hot. We saw that a big changes

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Speaker 2: were coming. We wanted our students U MASS to be

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Speaker 2: prepared for this. The thing that was unique about U

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Speaker 2: Mass as the largest research public research university in New

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Speaker 2: England and the most diverse college in New England. But

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Speaker 2: what we saw is that an opportunity to teach AI,

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Speaker 2: not to compete as science students, where traditionally you had

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Speaker 2: to study computer decigence to learn about AI. We wanted

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Speaker 2: to teach it to every student in your mouse, whether

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Speaker 2: you're a nursing student, education student, climate student, And we

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Speaker 2: wanted to experiment if we could teach every student across

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Speaker 2: the campus, anyone who wanted to study AI. We had

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Speaker 2: a course for them and then we put these students

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Speaker 2: together in teams. What could they invent? And so U

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Speaker 2: Mass has been running a hackathon every semester. We are

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Speaker 2: student teams and there'll be a nursing student with a

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Speaker 2: marketing student, you know, with a math student, and they're

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Speaker 2: building apps and it's incredibly impress of what's getting done

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Speaker 2: the other thing. In addition to focusing on cross functional,

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Speaker 2: collaborative team work with AI to all students, UMass also

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Speaker 2: has a really big focus on the ethics of AI.

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Speaker 2: You know, when should AI be used? When should it

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Speaker 2: not be used? People cheating when they use AI? How

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Speaker 2: can AI lead you down the rung paths sometimes? And

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Speaker 2: when teaching the students that U MASS how to have

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Speaker 2: a critical eye and know where and how they should

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Speaker 2: use AI to become better students and to become more

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Speaker 2: valuable employees when a enter the workforce.

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Speaker 1: Wow, let me get a caller a two in here,

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Speaker 1: and then I want to get from you your view

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Speaker 1: of is AI something that we should look at as

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Speaker 1: a threat or as an opportunity? Let me get a

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Speaker 1: caller of two in here. Just to incorporate some of

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Speaker 1: the callers, let me go to Maureene in Winchester. Maureene,

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Speaker 1: welcome your first This Hour with Paul English on Marien.

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Speaker 1: Where wouldn't Maureen be Rob.

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Speaker 3: I just want to say that we are so lucky

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Speaker 3: and fortunate to have people like you, Dan and Paul

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Speaker 3: to be part of the Boston Saying to help innovate

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Speaker 3: and and help everybody grow. I think AI has more

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Speaker 3: benefits than negatives. I do support AI. I use it

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Speaker 3: a little bit. My sister is a computer scientists. Since

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Speaker 3: the eighties, she made me use computers, learned computer Since then,

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Speaker 3: I've gabbled a little bit en coding myself, and there

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Speaker 3: are she's leading the AI efforts at her current employer,

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Speaker 3: and there's definitely more pros and cons and I think

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Speaker 3: we shouldn't be afraid of it. We need to learn

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Speaker 3: how to embrace it. And like Paul was saying about

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Speaker 3: the ethics. So that's that's my opinion. And I think

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Speaker 3: it's really great that Paul is doing tons of social work,

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Speaker 3: philothropic work and work with the homeless. I just learned

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Speaker 3: somebody in my church in Winchester was homeless and he

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Speaker 3: does the walk, you know, for the homeless. And another

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Speaker 3: lady goes to South Station every Wednesday night she collects

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Speaker 3: items from people in our free cycle and donates it.

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Speaker 3: And a lady that in another church. She goes and

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Speaker 3: counsels to the homeless and South Station and Harvard Square,

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Speaker 3: and I've connected with some of those people on Facebook,

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Speaker 3: and you know, sometimes they pass away and I've been

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Speaker 3: invited to some of their funerals. I haven't been any,

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Speaker 3: just more to timing, but they do need somebody to

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Speaker 3: talk to. And one thing that haunts me to today,

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Speaker 3: I've noticed Boston's gotten rid of a lot of chair

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Speaker 3: I mean, you know, seating like in downtown Boston.

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Speaker 1: You mean like bench benches and things like that.

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Speaker 3: I was walking back from the Boston bor Holiday party

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Speaker 3: a few years ago, and it haunts me to today.

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Speaker 3: There was a man that was shivering on the concrete.

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Speaker 3: It's at Walgreens, and I wish to God i'd given

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Speaker 3: him my coat. I was two blocks away from the car.

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Speaker 3: It was really wendy. But you know, that's why I

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Speaker 3: think that it's just great to have so many people

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Speaker 3: taking initiatives to help other people out, because that's what

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Speaker 3: we're here on this earth to do.

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Speaker 1: Okay, Paul Say had him Maureene from Winchester. She obviously

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Speaker 1: is a big fan of yours in terms of doing

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Speaker 1: anything for the city. I stand way in the background.

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Speaker 1: This guy isn't uh is in the foreground and is

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Speaker 1: and is making an immense difference. Mareene Paul Say had him.

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Speaker 2: Maureen Marverne, thank you for coming in and I appreciate

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Speaker 2: your car men. I think you know where Dan has

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Speaker 2: us on a number of topics tonight, So this is

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Speaker 2: going to be exciting hour on the topic of homelessness.

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Speaker 2: I mean, it is something that touches many, many, many families,

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Speaker 2: and if they're our way for people to get involved,

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Speaker 2: either by volunteering at a soup kitchen or a food

436
00:25:20,160 --> 00:25:23,240
Speaker 2: pantry or a shelter, there's a lot of different ways

437
00:25:23,240 --> 00:25:25,280
Speaker 2: for people to get involved. So I really appreciate that

438
00:25:25,680 --> 00:25:27,520
Speaker 2: you have gotten involved in a lot of people in

439
00:25:27,520 --> 00:25:30,080
Speaker 2: Winchester doing so as well.

440
00:25:31,720 --> 00:25:35,080
Speaker 3: And our mess Withist Church in Wuburn also feeds the

441
00:25:35,680 --> 00:25:39,680
Speaker 3: homeless in Mouburn. There's a we need to I think,

442
00:25:39,800 --> 00:25:43,280
Speaker 3: take these empty office spaces and convert them into micro

443
00:25:43,359 --> 00:25:47,600
Speaker 3: apartments to help people. I think California has some program

444
00:25:47,640 --> 00:25:50,840
Speaker 3: going on. I saw it on TV. But anyway, thank

445
00:25:50,880 --> 00:25:54,320
Speaker 3: you for the time, and I'm really excited to see

446
00:25:54,520 --> 00:25:56,480
Speaker 3: about this how this AI probe is going to grow.

447
00:25:56,520 --> 00:25:58,240
Speaker 3: And I think it will really help out the students.

448
00:25:58,400 --> 00:26:00,840
Speaker 2: So thank you, Paul, Thank you, You're very welcome.

449
00:26:00,840 --> 00:26:05,639
Speaker 1: Thanks Mario, Paul. Coming back to and this is a

450
00:26:05,760 --> 00:26:10,800
Speaker 1: question that I want to get into you. Obviously, in

451
00:26:10,840 --> 00:26:15,199
Speaker 1: the nineteen eighties saw something in terms of the computer revolution,

452
00:26:16,000 --> 00:26:19,360
Speaker 1: and you immersed yourself in that and it worked out

453
00:26:19,400 --> 00:26:23,480
Speaker 1: great for you. When most of us weren't thinking about

454
00:26:23,600 --> 00:26:28,080
Speaker 1: the computer revolution and the internet, you perceived that you

455
00:26:28,119 --> 00:26:31,399
Speaker 1: saw the advantages to it. As you look at AI

456
00:26:31,640 --> 00:26:35,960
Speaker 1: artificial intelligence, now, do you see it as something that

457
00:26:36,960 --> 00:26:39,399
Speaker 1: somehow has to be controlled or do you think that

458
00:26:39,680 --> 00:26:43,879
Speaker 1: on its own people will learn about it and be

459
00:26:43,960 --> 00:26:46,520
Speaker 1: able to incorporate it into their lives. I mean, there

460
00:26:46,520 --> 00:26:48,320
Speaker 1: are some people out there who think it's going to

461
00:26:48,320 --> 00:26:52,359
Speaker 1: be a disaster for the country because jobs will be replaced.

462
00:26:52,359 --> 00:26:57,400
Speaker 1: And I'm sure you know that those theories better than anyone.

463
00:26:57,520 --> 00:26:59,360
Speaker 1: And yet there were some who were saying, hey, look,

464
00:26:59,400 --> 00:27:02,800
Speaker 1: we've had things like the internet before, for that matter,

465
00:27:02,880 --> 00:27:06,520
Speaker 1: the assembly line. When Henry Ford invented the assembly line,

466
00:27:06,560 --> 00:27:09,359
Speaker 1: everybody was afraid horses and carriages were going to go away.

467
00:27:09,440 --> 00:27:12,280
Speaker 1: But are you an optimist on it or.

468
00:27:13,680 --> 00:27:15,240
Speaker 2: I am an optimist If you look at if you

469
00:27:15,320 --> 00:27:18,280
Speaker 2: study the history of technology going back a thousand years.

470
00:27:18,359 --> 00:27:21,159
Speaker 2: It's true the things that you mentioned and the factories.

471
00:27:21,320 --> 00:27:24,080
Speaker 2: Every time there's a new invention, people think job loss,

472
00:27:24,280 --> 00:27:27,199
Speaker 2: but economies continue to grow. I do think AI might

473
00:27:27,240 --> 00:27:28,800
Speaker 2: be a little bit different than some of the prior

474
00:27:28,840 --> 00:27:33,240
Speaker 2: things because AI really involves thinking, not just physically doing labor,

475
00:27:33,320 --> 00:27:36,600
Speaker 2: but how people process information that AI can do in

476
00:27:36,640 --> 00:27:39,679
Speaker 2: many cases better then people can do on their own.

477
00:27:39,840 --> 00:27:42,720
Speaker 2: But the thing about education is AI is here. It

478
00:27:42,800 --> 00:27:45,520
Speaker 2: is not going away. If we tried to regulate and

479
00:27:45,520 --> 00:27:47,600
Speaker 2: slow it down in the US, the other country is

480
00:27:47,640 --> 00:27:49,600
Speaker 2: going to raise ahead of us. So what we need

481
00:27:49,640 --> 00:27:53,200
Speaker 2: to do is train our students how to use it ethically, responsibly,

482
00:27:53,320 --> 00:27:55,440
Speaker 2: how to use it accurately, how to use it to

483
00:27:55,440 --> 00:27:58,680
Speaker 2: become a better student. We expect that when these students

484
00:27:58,800 --> 00:28:02,720
Speaker 2: in BOSS public school learning AI, informed by the work

485
00:28:02,800 --> 00:28:06,080
Speaker 2: done at UMass for the last three years, that they

486
00:28:06,119 --> 00:28:09,480
Speaker 2: will go home and after school and they'll teach their

487
00:28:09,520 --> 00:28:12,159
Speaker 2: parents how to use AI. When they get hired in

488
00:28:12,280 --> 00:28:14,879
Speaker 2: local boss of businesses, they'll teach those businesses how to

489
00:28:14,960 --> 00:28:16,960
Speaker 2: use AI. So I really to look at this something.

490
00:28:16,960 --> 00:28:19,399
Speaker 2: It's great for us students, it's also gonna be good

491
00:28:19,440 --> 00:28:21,840
Speaker 2: for their families and good for their employers, so I'm

492
00:28:22,160 --> 00:28:25,720
Speaker 2: excited about it. We've had my collaborator on this effort

493
00:28:25,840 --> 00:28:28,280
Speaker 2: is Ellen Rubin, who's a three time CEO in Boston,

494
00:28:29,359 --> 00:28:31,800
Speaker 2: very well known in the tech scene here, and Ellen

495
00:28:31,840 --> 00:28:34,080
Speaker 2: and I have been everyone's been ringing off the hook

496
00:28:34,320 --> 00:28:36,960
Speaker 2: since we announced this two weeks ago with the mayor,

497
00:28:37,680 --> 00:28:40,480
Speaker 2: and there's other cities now who want to join in.

498
00:28:40,840 --> 00:28:44,959
Speaker 2: So we're now looking at collaborating with some other organizations.

499
00:28:45,600 --> 00:28:50,840
Speaker 2: We have big companies volunteering to give resources to Boston

500
00:28:50,840 --> 00:28:54,240
Speaker 2: mobile schools to make it so they have computing available,

501
00:28:54,280 --> 00:28:57,200
Speaker 2: So we're trying to figure that all out. And what

502
00:28:57,240 --> 00:29:00,560
Speaker 2: I've been telling other people this week of it last

503
00:29:00,560 --> 00:29:03,880
Speaker 2: week is we're going to open source our project. What

504
00:29:03,920 --> 00:29:06,440
Speaker 2: that means is we have a website which will be

505
00:29:06,480 --> 00:29:09,720
Speaker 2: launching soon. We're going to show the curriculum, the courses,

506
00:29:09,840 --> 00:29:12,800
Speaker 2: the tools we use, the budgets, how much we're spending

507
00:29:12,840 --> 00:29:14,840
Speaker 2: on each thing, and we're going to try to put

508
00:29:14,840 --> 00:29:17,000
Speaker 2: it out there as a template so that other cities

509
00:29:17,120 --> 00:29:19,560
Speaker 2: might wander the same thing, will be informed by what's

510
00:29:19,560 --> 00:29:20,400
Speaker 2: working in Boston.

511
00:29:21,520 --> 00:29:24,080
Speaker 1: It's pretty exciting stuff. Paul English is my guest, we

512
00:29:24,160 --> 00:29:26,040
Speaker 1: have a final segment coming up. If you let to

513
00:29:26,280 --> 00:29:28,440
Speaker 1: ask a question, make a comment six one seven two

514
00:29:29,200 --> 00:29:32,960
Speaker 1: thirty six one seven, nine three one ten thirty. You

515
00:29:33,000 --> 00:29:36,040
Speaker 1: know when you were talking about people who have come

516
00:29:36,080 --> 00:29:42,520
Speaker 1: along and with open a with aill will teach the children,

517
00:29:42,840 --> 00:29:47,040
Speaker 1: The students will learn, and they'll teach older young adults

518
00:29:47,080 --> 00:29:52,440
Speaker 1: and their and their parents. I mean, I learned as

519
00:29:52,520 --> 00:29:55,520
Speaker 1: much about computers probably from my kids as they learned

520
00:29:55,520 --> 00:29:58,720
Speaker 1: from me when when they were in a place like kindergarten,

521
00:29:58,800 --> 00:30:01,360
Speaker 1: the first and second grade. Got to know your alphabet,

522
00:30:01,440 --> 00:30:03,440
Speaker 1: got to know your numbers, got to know this, got

523
00:30:03,480 --> 00:30:05,280
Speaker 1: to know how to add, got to know how to subtract,

524
00:30:05,400 --> 00:30:09,280
Speaker 1: pretty basic stuff. My guess Paul English. He makes it

525
00:30:09,320 --> 00:30:13,959
Speaker 1: sound simple, and it may be simpler than we've been

526
00:30:14,040 --> 00:30:16,360
Speaker 1: led to believe. We'll be back with Paul English a

527
00:30:16,440 --> 00:30:18,120
Speaker 1: final segment coming back on night Side.

528
00:30:18,160 --> 00:30:23,440
Speaker 2: Right, It's Night Side with Dan Ray on w Boston's

529
00:30:23,480 --> 00:30:24,120
Speaker 2: news radio.

530
00:30:25,200 --> 00:30:29,560
Speaker 1: My guess is Paul English. Paul, just since you have

531
00:30:29,600 --> 00:30:33,120
Speaker 1: a much better sense of a grasp of AI than

532
00:30:33,160 --> 00:30:36,959
Speaker 1: I'm sure ninety nine point nine percent of my audience,

533
00:30:37,040 --> 00:30:39,760
Speaker 1: certainly better than name. Look. One of the concerns I've

534
00:30:39,760 --> 00:30:41,719
Speaker 1: had and I'd love you to address it. Is that

535
00:30:42,320 --> 00:30:45,560
Speaker 1: AI and I think about like chat, GTP and all

536
00:30:45,600 --> 00:30:50,480
Speaker 1: of that GPT. I should say, it is only as

537
00:30:50,520 --> 00:30:54,040
Speaker 1: good as the information that is submitted and is ingested.

538
00:30:55,280 --> 00:30:57,520
Speaker 1: How do we you know if people are going to

539
00:30:57,600 --> 00:31:01,960
Speaker 1: relate rely on AI to get information? I mean, one

540
00:31:01,960 --> 00:31:04,320
Speaker 1: of the concerns that I have is are kids going

541
00:31:04,360 --> 00:31:06,959
Speaker 1: to use it to write, you know, term papers? And

542
00:31:07,080 --> 00:31:10,880
Speaker 1: how will we know that their actual work? I'm sure

543
00:31:10,880 --> 00:31:12,960
Speaker 1: that takes you back to your Latin school days and

544
00:31:13,360 --> 00:31:17,080
Speaker 1: me as well. But how do we control what goes

545
00:31:17,120 --> 00:31:21,160
Speaker 1: into artificial intelligence? Who's going to monitor the information that

546
00:31:21,240 --> 00:31:22,680
Speaker 1: people are going to have to rely upon?

547
00:31:24,080 --> 00:31:25,880
Speaker 2: A couple of things there. First of all, in the classroom,

548
00:31:26,080 --> 00:31:30,960
Speaker 2: the way teachers can prevent students using AI to do

549
00:31:31,000 --> 00:31:33,800
Speaker 2: the homework is simply some of the test given classroom

550
00:31:33,960 --> 00:31:36,720
Speaker 2: with laptops clothes. So if your kids are writing big

551
00:31:36,720 --> 00:31:39,400
Speaker 2: papers but they're not to answer questions of class, they're

552
00:31:39,400 --> 00:31:42,800
Speaker 2: probably using technology not really learning the materials in their own.

553
00:31:43,160 --> 00:31:46,240
Speaker 2: As far as how AI gets information, the most interesting

554
00:31:46,240 --> 00:31:49,320
Speaker 2: thing happening in AI this year is the rise of

555
00:31:49,920 --> 00:31:53,800
Speaker 2: the agents, and the agents are a way to use

556
00:31:53,880 --> 00:31:58,760
Speaker 2: AI to plug into different systems. So the first versions

557
00:31:58,760 --> 00:32:02,800
Speaker 2: of chat GPT was trained on Wikipedia and Reddit and

558
00:32:02,880 --> 00:32:04,920
Speaker 2: new sources and everything you can read on the internet.

559
00:32:05,200 --> 00:32:08,040
Speaker 2: The aid is being built today in twenty twenty six.

560
00:32:08,560 --> 00:32:11,720
Speaker 2: People are building these custom agents that will go out

561
00:32:11,800 --> 00:32:15,160
Speaker 2: and read your email to find something you need to

562
00:32:15,200 --> 00:32:17,720
Speaker 2: be reminded of. It will look at your account, it'll

563
00:32:17,720 --> 00:32:21,440
Speaker 2: make appointments for you, it'll look at documents on your

564
00:32:22,160 --> 00:32:25,680
Speaker 2: drive and help you. It'll take meeting minutes. It really

565
00:32:26,080 --> 00:32:29,520
Speaker 2: interacts with every type of communication you're doing. You can

566
00:32:29,560 --> 00:32:32,360
Speaker 2: build tools that work for you, almost like as a

567
00:32:32,440 --> 00:32:35,160
Speaker 2: chief of staff. They can look at all the information

568
00:32:35,240 --> 00:32:38,240
Speaker 2: of AI, but plus being plugged into any live system

569
00:32:38,240 --> 00:32:38,960
Speaker 2: on the internet.

570
00:32:39,760 --> 00:32:43,360
Speaker 1: So in effect, you have an administrative assistant. How does

571
00:32:43,400 --> 00:32:47,840
Speaker 1: that administrative you know again a theoretical administrative assistant. How

572
00:32:47,840 --> 00:32:53,840
Speaker 1: does that administrative assistant have access to your records and

573
00:32:53,880 --> 00:32:57,280
Speaker 1: your appointment calendar. I assume you would go out and

574
00:32:58,600 --> 00:33:03,040
Speaker 1: hire some company to When you say agents.

575
00:33:02,680 --> 00:33:04,760
Speaker 2: People are doing this. People are doing this in their

576
00:33:04,760 --> 00:33:07,880
Speaker 2: own Yeah, it's called agents. And when you install an

577
00:33:07,880 --> 00:33:13,320
Speaker 2: agent on the computer, whether you're using Gemini by Google Cloud,

578
00:33:13,400 --> 00:33:18,360
Speaker 2: by Anthropic chat Top, by open ai, whichever language model

579
00:33:18,400 --> 00:33:20,720
Speaker 2: you're using. There's a way to plug in agents on

580
00:33:20,760 --> 00:33:23,440
Speaker 2: top of that. When you install an agent, it's the

581
00:33:23,480 --> 00:33:25,800
Speaker 2: system is going to say, do you want to allow

582
00:33:25,880 --> 00:33:28,920
Speaker 2: this CRM to access your calendar U, S and O?

583
00:33:29,280 --> 00:33:31,080
Speaker 2: And you have to give permission for the agents for

584
00:33:31,160 --> 00:33:33,960
Speaker 2: what it can access. But when you if you're careful

585
00:33:34,360 --> 00:33:37,120
Speaker 2: about selecting which AGENC you want to install or what

586
00:33:37,240 --> 00:33:39,720
Speaker 2: agent you might be building in your own, by giving

587
00:33:39,720 --> 00:33:43,440
Speaker 2: them some access, you get tremendous return on investment.

588
00:33:44,240 --> 00:33:49,760
Speaker 1: The the the acronym CRM. What does that's when.

589
00:33:49,800 --> 00:33:53,080
Speaker 2: Oh, customer relationship management. It's the tools that like hab

590
00:33:53,160 --> 00:33:56,240
Speaker 2: Spot here in Boston is the number one CRM company

591
00:33:56,640 --> 00:33:59,880
Speaker 2: h for midsize, small and mid sized businesses. They have

592
00:33:59,880 --> 00:34:05,400
Speaker 2: a incredibly great agentic platform they built about how people

593
00:34:05,400 --> 00:34:08,360
Speaker 2: can build agents on top of HubSpot and which integrate

594
00:34:08,400 --> 00:34:09,040
Speaker 2: with HubSpot.

595
00:34:09,800 --> 00:34:13,400
Speaker 1: Okay, and then one final question which I think people

596
00:34:13,440 --> 00:34:16,839
Speaker 1: probably are thinking about. One of the things I was

597
00:34:16,880 --> 00:34:19,319
Speaker 1: I was looking at a video the other night that

598
00:34:19,400 --> 00:34:22,000
Speaker 1: popped up so much stuff pops up on your your

599
00:34:23,320 --> 00:34:27,520
Speaker 1: computers and on your cell phones, and it was a

600
00:34:27,640 --> 00:34:31,200
Speaker 1: video of of It's a fake it was a fake video.

601
00:34:31,239 --> 00:34:34,439
Speaker 1: It's a video of a deer with a small deer

602
00:34:34,480 --> 00:34:38,520
Speaker 1: whose hoof was caught in the middle of a highway,

603
00:34:38,680 --> 00:34:41,440
Speaker 1: a two lane highway, and they were cars speeding by,

604
00:34:41,560 --> 00:34:43,520
Speaker 1: and the mother deer was sort of trying to be

605
00:34:43,600 --> 00:34:47,759
Speaker 1: protective and also seeming Finally someone stopped and they got

606
00:34:47,760 --> 00:34:50,839
Speaker 1: out of the car and they helped the little deer

607
00:34:51,920 --> 00:34:56,759
Speaker 1: escape and everybody was happy. Now that's also known as clickbait.

608
00:34:56,880 --> 00:34:58,440
Speaker 1: I mean, that's the sort of stuff that a lot

609
00:34:58,440 --> 00:35:01,880
Speaker 1: of kids are going to sit and watch. Mh is

610
00:35:01,880 --> 00:35:03,800
Speaker 1: is your do you think that you'll be able to

611
00:35:03,840 --> 00:35:07,080
Speaker 1: convince these these kids and say, look, you look at this,

612
00:35:07,320 --> 00:35:09,400
Speaker 1: and you say this is horrible. This poor deer was

613
00:35:09,440 --> 00:35:12,239
Speaker 1: sitting there with the baby deer stuck in the middle

614
00:35:12,280 --> 00:35:15,320
Speaker 1: of the highway and cars were buzzing back. Why didn't

615
00:35:15,320 --> 00:35:18,279
Speaker 1: the person who was taking the video of this help

616
00:35:18,360 --> 00:35:20,080
Speaker 1: the baby deer? If you think about it for a

617
00:35:20,160 --> 00:35:22,680
Speaker 1: second and you know at that point that it is

618
00:35:23,960 --> 00:35:27,480
Speaker 1: it's a deep fake or whatever term you use. Is

619
00:35:27,520 --> 00:35:30,319
Speaker 1: that the sort of knowledge that young people have to

620
00:35:30,800 --> 00:35:34,600
Speaker 1: understand that hey, I'm looking at something that's that's not

621
00:35:34,800 --> 00:35:37,920
Speaker 1: real and I'm wasting my time? Is that right?

622
00:35:37,920 --> 00:35:39,640
Speaker 2: I mean, you have to you have. You have to

623
00:35:39,640 --> 00:35:41,759
Speaker 2: trust your sources. You got to think about what source

624
00:35:41,800 --> 00:35:43,759
Speaker 2: do I trust. I know a lot of people turn

625
00:35:43,800 --> 00:35:46,960
Speaker 2: to you, Dan for your interpretation about what's happening in Boston,

626
00:35:46,960 --> 00:35:49,120
Speaker 2: what's happening in the news, And if they read something

627
00:35:49,160 --> 00:35:53,360
Speaker 2: from BZ, they trust BZ because it's a known media

628
00:35:53,520 --> 00:35:56,279
Speaker 2: entity that's been as a lot of reporters. I'm doing

629
00:35:56,280 --> 00:35:58,400
Speaker 2: work for years, and so I can trust something I

630
00:35:58,400 --> 00:36:01,160
Speaker 2: see on BZ more than I trust some random thing

631
00:36:01,200 --> 00:36:03,320
Speaker 2: I see on TikTok or on YouTube. So we have

632
00:36:03,360 --> 00:36:05,759
Speaker 2: to figure out where we're getting the source from. But

633
00:36:05,960 --> 00:36:08,200
Speaker 2: what I want to teach these kids in Bossby schools

634
00:36:08,239 --> 00:36:10,960
Speaker 2: is one how to tell when something is a deep fake,

635
00:36:11,520 --> 00:36:14,640
Speaker 2: and two how they can use AI to create their

636
00:36:14,640 --> 00:36:18,080
Speaker 2: own content. And I'm not opposed using AI of great videos.

637
00:36:18,080 --> 00:36:20,600
Speaker 2: I want it labeled that AI created this video, or

638
00:36:20,640 --> 00:36:23,719
Speaker 2: AIR was used as one tool for helping assemble a video.

639
00:36:24,160 --> 00:36:26,840
Speaker 2: But I think if you give kids these tools, and

640
00:36:26,880 --> 00:36:32,800
Speaker 2: you allow anyone to compose music, write poetry, write papers,

641
00:36:32,840 --> 00:36:36,200
Speaker 2: create videos, it's going to be a really exciting set

642
00:36:36,239 --> 00:36:37,600
Speaker 2: of years we have ahead of us.

643
00:36:38,600 --> 00:36:40,880
Speaker 1: And you're going to be in the forefront, that is

644
00:36:40,920 --> 00:36:45,160
Speaker 1: for sure. I so appreciate the time, Paul. This was great.

645
00:36:45,880 --> 00:36:48,879
Speaker 1: I have a much much better understanding of several things

646
00:36:48,880 --> 00:36:51,320
Speaker 1: as a result of our conversation. I hope my audience

647
00:36:51,360 --> 00:36:54,359
Speaker 1: does as well. Wish all the success in the world here,

648
00:36:54,400 --> 00:36:58,480
Speaker 1: and I hope I hope that all of these programs

649
00:36:58,520 --> 00:37:02,120
Speaker 1: that you're getting involved in and beer fruit because you

650
00:37:02,160 --> 00:37:07,120
Speaker 1: were very dedicated to anything that you undertake from the

651
00:37:07,120 --> 00:37:10,719
Speaker 1: time you're at Latin School right right through your professional career.

652
00:37:10,760 --> 00:37:12,359
Speaker 1: I just want to say thanks for spending the time

653
00:37:12,360 --> 00:37:16,239
Speaker 1: with us tonight. More information about AI tonight on this

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Speaker 1: hour than I've had in months here on Nightside. I

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Speaker 1: just want to say thank you very much.

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Speaker 2: Thanks Anam.

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Speaker 1: We will We'll see you again, and I like to

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Speaker 1: periodically maybe have you come back, particularly when the program

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00:37:29,280 --> 00:37:32,719
Speaker 1: gets started this summer at UMass Boston, and want people

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00:37:32,719 --> 00:37:35,200
Speaker 1: to be aware of it even more. We'll talk again.

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Speaker 1: Thanks so much, Paul, appreciate.

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00:37:36,480 --> 00:37:37,960
Speaker 2: It, right, Thank you, You're welcome.

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00:37:38,080 --> 00:37:40,440
Speaker 1: We're welcome. When we get back, we're going to talk

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00:37:40,480 --> 00:37:45,719
Speaker 1: about less pleasant subjects, which are going to include what

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00:37:45,840 --> 00:37:48,840
Speaker 1: may happen to around tomorrow. We'll try to get the

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00:37:48,920 --> 00:37:53,200
Speaker 1: latest on what is going on around the Globe tonight.

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00:37:53,680 --> 00:37:57,399
Speaker 1: The President and others had some comments today and we

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00:37:57,440 --> 00:38:01,720
Speaker 1: will we'll get to all of them before the before

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Speaker 1: the evening is out, we're going to open up phone lines.

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Speaker 1: We have the great news this weekend of the of

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00:38:09,200 --> 00:38:12,640
Speaker 1: the return of the two American pilots safe and sound

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00:38:12,760 --> 00:38:15,480
Speaker 1: on Easter weekend. It was indeed an Eastern miracle when

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Speaker 1: you hear what the second pilot went through. We're coming

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Speaker 1: back on Night's side right after the ten o'clock news