New post-war old Dutch cleanser famous for chasing dirt present Nick Carter famous for chasing crime Every week at this time two great names are joined as new post-war old Dutch cleanser brings you one of the most resourceful and daring characters in all detective fiction Nick Carter master detective Hello, Patsy. How soon can you meet me right away Nick? What's the matter we've got a job of extricating to do Yes, there's been a murder and right smack in the middle of it up to his neck is a 16-year-old boy Now for the case of the boy who got lost today's adventure starring Lon Clark as Nick Carter It is early evening and in the back room of a slum district pool room two men Stan Powers and Crow Jackson are in the midst of a business conference with Billy Turner a 16-year-old boy when Hey Billy I want Billy he's busy now beat it get your coat Billy. We're going home. Maybe you didn't hear what I said Take it easy crow. This guy's my big brother. Huh? Yeah, how'd you find out about this joint Roy? I followed you. I promised mom.  I don't want to get tough suppose you do Roy What do you think that'll get you? What was that mr. Powers a threat?  Yeah, I know all about you too. Uh-huh Close the door crow. Okay, Stan What do you know about me Roy? You've been in jail a couple of times Your pool rooms are hang out for crooks and gangsters and kids that's gone wrong. Listen Roy if you're talking about Billy, I'll handle this crow This guy goes out Okay, Stan Look out Roy.  He's just taking a nap. He'll be okay. You shouldn't have hit him crow He thought he was doing me a favor. He was button-end. I know Stan, but look Billy I thought you wanted a crack at the big time. I do no more of them tin horn jobs on little candy stores for me Okay That's why I picked you to go along with us on this job tonight. Sure Stan. I I know I know Then relax kid Have a smoke. Yeah Thanks, Stan Here, I'll give you a light. Sure. That's Roy Stan. He's coming to Get into the closet Billy. What for in there? I don't want him to say yeah. Oh sure sure What do we do with this guy Stan? Nothing.  No crow. He's got nothing on us Where's Billy he went home Buster? Home. Yeah, that's right. He didn't want to be around when you came to he's afraid of his big brother here You kidding me? All you've got to do is go home and check You don't see him here. Do you? Sure sure go ahead look around and you can check the pool room on your way out Help him up crow a man's going home Oh, why didn't you come to me last night Roy instead of waiting until this morning? Well, I didn't think about it I thought maybe Billy did leave the pool room and and maybe he was stalling some place Afraid to come home because of the beaten crow gave me you wouldn't have stayed out all night. Oh, don't I know it well? Listen mr. Carter. I'm gonna tell you something Last night when Billy didn't show up by 12 o'clock mom couldn't stand it anymore So I went out to look for him I went back to the night. Oh, hello Patsy I'm glad to see you.  They got away with seven thousand dollars of the police Go ahead Roy and the police what? Have they found out about Stan Powers? What about him? He was murdered what's that? That's what I started to tell you mr. Carter When I went back to the pool room to look for Billy I went into the back room Stan mr. Powers was on the floor With a knife in his back with an eye Well, didn't you report that to the police? Well, I was afraid mr. Carter all I could see in my mind was was Billy mixed up in a killing The police come into our house to ask a lot of questions Mom Roy do you think Billy did it? No, I know he didn't At least I don't all right all right then come on Patsy. We're going down to Stan Powers pool room I'm going with you mr. Carter. Oh, no, you stay right here Roy. I'll bring you back a full report as soon as I find out What's what? Yeah, the porter found powers Nick He came in to clean up about an hour ago and boom what time was it killed Maddie? Connor said between 11 and one o'clock last night. Uh-huh any idea what the motive might have been yeah Patsy, do you think it was a double-cross? Oh We found these slips of paper on the floor cash register receipt From the 9th Street supermarket right the job was pulled by Stan Powers and these gang these receipts prove it Oh, but why should Stan Powers have been murdered that open safe should tell you why Patsy. Oh, I Didn't notice it Nick those crooks got away with seven thousand bucks last night But there ain't a nickel in that safe or on Stan Powers now Nick somebody somebody one of Stan's gang Probably one of that money and kill stand to get it right Right if I look at the body do what you like Nick, but make it snappy the boys from the morgue will be here any minute to take it away Hey medic, I'm here. Yeah, what's the matter? Thank you. Good. Look at the handle of this knife. I want to do the killing I Already did it's a horn-handled knife when there's white powder in some of the hollers Well, what do you suppose it is looks to me like it's face powder Patsy And you think that a woman sure I do now not so fast man Men use powder after shaving. Huh?  What about it? We found it on the floor in your stand's body Billy what'd you say Nick said Billy the name engraved on this letter Now look Nick I ask you would a mug be seen with a dainty little lighter like this Well, that's a good point. Maddie. So I figure Billy could be a woman's name. Do you it could be Nick? Yes, it could be come on. That's right with you. Hey. Hey, what's the rush? Only two hundred ninety nine days to Christmas Maddie. We've got some shopping to do Do you think that cigarette lighter belongs to Billy Turner? I let you know after a phone to Roy at the office Okay. Oh, there's a phone booth over there. Oh, no that's you. I want to use a phone It's not too close to the scene of this murder This is Nick Mr. Carter, what'd you find? Roy tell me did Billy own the cigarette lighter? Yeah Describe it. Well, it looked like it was made of silver and it was it was small and pretty fancy He left it on the table once in the house. I I asked him where I got it. What's his name engraved on it? Yeah, why mr. Carter the police found it and stands office near stands body What didn't you see it there last night? No, do you think I'd have left it there if I did I saw the guy on the floor dead So I beat it Look, mr. Cut Other cops saying Billy killed Stan power. No, not yet. They're looking for someone else Now tell me did Billy own a horn handle jackknife? No, are you sure? Well, I'd have seen it if he had one mr. Carter. All right. Now, what can you tell me about crow?  You know his real name Stan or Billy mentioned it. No, no, they just called him crow. Okay. I'll talk to you later in the meantime stay in my office. Goodbye Well, Nick ladder belongs to Billy. All right. Well, that's bad Yeah, the police will trace it assured we're standing here and that'll lead them directly to Billy Nick Are we going to look for Billy too? No, no, right now. I'm more interested in the powder on the handle of that knife and a guy named crow Nick sets out to find a man called crow and learn the meaning of some white powder on a knife handle Meanwhile Matty and the police are tracing a cigarette lighter that will surely lead them to Billy Turner We'll see what happens next in just a moment Now back to the case of the boy who got lost today's adventure with Nick Carter Nick Carter and Patsy Bowen faced with the possibility that Billy Turner a 16 year old boy May be implicated in the knife murder of Stan powers a gang leader are trying desperately to run down the identity of crow The dead man's lieutenant and to solve the riddle of the white powder on the handle of the murder knife In the meantime in crow's apartment near the center of town Billy is having trouble Just let me call my mom crow.     I tell you I caught you bending over him. Didn't I I told you crow I was looking to see if he was dead look Billy. I'm your pal You don't have to lie to me. Why should I kill Stan? There's a lot of dough and Stan's office last night. Maybe you was planning on getting it for yourself Nuts I never went near that safe. That's because I walked in on you I didn't have to swipe the dough we were gonna split that seven thousand bucks in a couple of days There is more than seven grand in that safe. There was another thirty five hundred you wanted that too, didn't you? Hey How do you know how much Stan had in his safe? Huh? Yeah, how come you know all about that? What are you getting that kid you killed Stan you did it now wait a minute.  No, nobody can make me talk. Nobody's gonna get the chance I Protect you and that way I'm protecting myself. I don't need protection from nobody except you What's the idea you've got to learn to appreciate your pals kids slapping me around ain't gonna change nothing. Look, I ought to Not answer that kid okay answer it Yeah talking what They know anything else No Okay, tell the guys not to worry. They'll never get them Yeah, yeah, you can take my word for it That was bad news Billy. What do you mean your names in all the newspapers? What are you giving me crow the cops have got your cigarette lighter? I got my lighter. Yeah, they found it in your Stan's body They're looking for the owner she they'll say I killed Stan. You can't let them pick you up Billy You got to find another hideout. What am I gonna do? Don't worry kid. They'll never find you Never Nick you must have questioned a hundred people trying to find out about that guy crow At least a hundred and nobody admitted even heard of him. Well, maybe maddie will be able to tell us he can't perhaps somebody else here Please headquarters can seem to their only chance Never mind I've just been trying to get you Nick. It's on your mind Maddie. We traced that cigarette letter Oh, you did huh? Yeah, a lot easier and we thought bought in a store on the west side At least I'm glad it was bored. You're not gonna like this Nick But we've got a warrant out for the arrest of Billy Turner. Oh, but sergeant. He's only 16 16 or 60 murder is murder Patsy Sorry about it Nick. I found out he's the brother of Roy Turner one of the kids in your boys club Then you've been to his home. Yeah, I talked to their mother. Oh that poor woman Well, I guess she had to know sooner or later. I suppose so, but why would Billy want to kill Sam? Oh, I know is that the safe was open and that the 7,000 that gang took from the supermarket is gone. Maddie, that doesn't if you asked me Nick We'll find that the guy who killed Stan and took the dough is Billy Turner. Maybe. Oh by the way, Maddie I found out what that white powder was. Yeah, little chemical analysis reveals that it's common ordinary flower Kind to make bread with oh your nuts, Nick Why should there be flour on the handle of that knife? It's a question. I can't answer yet, but that's what it is Look, don't tell me Billy Turner is a baker as well as a crook Billy's no crook sergeant even I can see that Well, I can't say Maddie. What do you know about a man named crow? crow Crow who if I knew that I'd be on my way to meet him I look what's he got to do with this member of Stan's gang and Roy Turner saw him in Stan's office last night Nick have you been holding out on me? What was Roy doing there trying to get Billy to go home with him Crow knocked him out with a blackjack. He blacked out Roy. Yeah, why didn't you tell me this before Nick? Well, does it matter much? I'll say it does Crow that name must mean something to you Maddie Why don't you ask me to remember a guy named Joe might be a lot easier. All right I'll be saving time by asking you but now I guess I'll have to get Roy down here for a look at the rogues gallery If he saw the guy face-to-face the ought to be able to pick him out Oh, but that'll take hours. I know it. I know it May save Billy Turner a lot of years if he happens to be innocent That's him mr. Carter. This is a picture of crow and I was just beginning to give up hope Roy You're sure this is crow. Yeah. Yeah, I'd know him any place well Maddie This character is Philip Jackson alias Jack Phillips alias crow Jackson and he's the guy who killed Stan power. Hey wait a minute Roy How do you know that well because he tried to kill me isn't that enough now? You went to Stan's place to get Billy didn't you? You told him mr. Carter. I had to go all right all right.   Is that your logic Maddie? No He just didn't notice his kid brother's light. Oh, you're not I take it easy. Take it easy Roy Listen Maddie I want you in this picture a crow to take a trip with me Yeah, where to down to the supermarket I'd like the manager to see this picture then you may have a reason to go after crow for armed robbery at least okay Okay, but this boy stays here. Is he under arrest sergeant? Not yet Patsy, but I want to know where he is if I happen to need him All right, mr. Is the man in this picture one of the guys that held up this market last night well I I think so officer, but can't you be sure I'd be plenty sure miss if the color of his hair was different colorless hair You mean it wasn't black. No, sir. No, sir. It was white. That's it Maddie That's why there was flour on the knife handle come again. He didn't have a knife. He had a gun No, we're talking about another crime mr. Jones, and thanks very much. You've been a great help. Goodbye. I don't understand Well, all we've got to do now is to get crow Jackson or whatever other name. He might be using this week Nick what's this about you know why there was flour on the handle of that knife Maddie flour is much better than face powder for making black hair Look like sticks better. Why sure crow used it as a disguise Well, all right, and it's going to land him in the hot seat if we ever find him I don't know where to look for him now look Nick I don't carry every crooks address around on my notebook. Maybe not but the probation department does yeah Yeah, yeah, yeah, you're right. I was just gonna fool. Yeah. Yeah, that's the spirit Maddie You arrest crow for robbery and I'll see that he's convicted for murder Seems as though crows playing hard to get Nick Well, it's only a door and we've got a warrant. Come on Maddie. We need muscles. Yeah, okay. Let's go Hey We should have known that a guy with a murder rap staring them in the face would lamb out of here Just the same I want to look Nick. I'll get out of general wait a minute. I want to look around Well, you never can tell what you'll find okay. Okay, but if you think now wait a minute wait look Two people were in this room and they left home a few minutes before we got here brother I've seen you pull some robbers out of a hat, but now you're trying look I'm calling headquarters Maddie look at that cigarette in the astra. Huh? Why it's still small okay? Okay, Nick. How do you figure there were two people here because there are two different brands of cigarettes in the Astray, yeah, yeah, Billy Turner smoked one of them. Oh, but Nick, you don't know what brand Billy smokes. I'll have to know that What do you mean? Look here at the underside of this half smoke cigarette? Somebody wrote Billy on it in pencil and it was Billy who was here with crow Looks that way. Yeah, wait a minute I don't watch this is here on the table crow probably left his forwarding address. No, he didn't but somebody left this drawing here Funny kind of drawing if you ask me it's doodling if you ask me. Yeah. Yeah Look at this odd shape figure here, Maddie and this thing inside it Maybe somebody's idea of a tree and look at this other thing in this a circle with a swirl inside. Yeah Maybe that odd shape figure could be a tree on an island of course and the circle Maddie this is a message knots this drawing the one you call doodling will never win an outcast No, but it tells me exactly where crow and Billy went Well, I got a rest for a minute keep rowing kid You'll have plenty of time to rest later. You think the cops got to your apartment by now if you're wise guy Brother told him about me. They have I should have knocked him off. He was only trying to help me crow Give him over the left or kid The left or pretty dark. We're heading for the island the way we're going Making a detour Billy if I do like you say we'll head right into the whirlpool you catch on kid And that's where you get off Oh, you ain't planning to that's right kid The cops will find you floating around in a couple of days and they'll close the books Huh? I'll say you couldn't face a murder rap.  I Got the or see and I got this blackjack here Billy. Why you lousy a dark night And we're out here in the middle of the channel. Nobody can see us So it looks like you go overboard when I say so get it Turner heads toward death of the channel and no one to whom he can call for help is within hearing distance We'll see what happens next in just a minute Now for the conclusion of the case of the boy who got lost today's adventure with Nick Carter It is a few minutes later and Nick Patsy and Matty are in a voter police launch heading toward the island Their eyes fixed on the water, which is lighted by the both searchlight Keep that searchlight moving ready. Yeah, it must be here somewhere There's the island up ahead Nick But I don't see them they must have used a motorboat have to be a fast one Patsy they couldn't have been more than a few minutes ahead of us. Hey, you sure this is the right place Nick Do you know of any other whirlpool around here? Well, no, but there could be one maybe You know of any other island shape like the drawing Billy left well No, no, I don't make but that doesn't mean there ain't one when you put the island in the whirlpool together You have to admit sir. Okay. Okay. Okay. You win This must be the place. Come on. Come on Matty. Can't you get a little more life into this tub? Look, she's riding the top speed now Nick after all she ain't a Find out yeah, sure Nick that must be them they are in that rowboat. Yeah, there are two men in it. I'm not fighting speed it up Matty That robot's moving toward the whirlpool. I'll try Nick Nick they've stopped fighting and one of them got a gun and he's aiming.  Well, that's what it looks like. Hey Nick take the wheel. This is a game to can play. I got it Matty He sure is a lousy shot. Yeah, but the searchlights in his eye. Well, I'll give this cookie a lesson in how to shoot Shooting Matty, I only hope he's alive when we get our hands on him The idea of crow taking Billy out to drown him in the whirlpool What some people won't do for an alibi Yeah, that's even it was all wasted motion after crow gets out of the hospital is gonna stand trial for murder and after that He'll never need money again That's $10,000 the police found stuff in his pocket should be enough to convict him Yeah, and the flower particles we found on the inside of his coat and on his shirt collar, right? Oh What do you think will happen to Billy? Well, he admitted taking part in the supermarket hold up and a few others Great Billy is going away for a while. It's a shame to a 16 year old kid. Well, that's he I've got an idea that Billy won't be gone too long And when he comes back, you want to feel the way his brother Roy does about what's right and what's wrong I hope so he wait and see we'll have him in the boys club yet Nick what about next week's adventure just a minute Mike. I want to ask you a question When are we going to announce the winners of the new 1948 Ford sedans in the first new post war old Dutch cleanser jingle contest? Nick, I'll have the names of those winners next week For fine. I hope everybody will listen. Oh, so do I now suppose you tell us something about next week's show Well next week Mike Patsy and I get mixed up with a gang of racketeers a kidnapping and a particularly brutal murder And Nick might not have solved it if the killer hadn't forgotten to leave a clue You mean you caught him because there wasn't any evidence Nick. That's it Mike. That sounds like a new twist What do you call the story Nick? I call it the case of the absent clue Nick Carter Master Detective is presented each week at this time by the Cuddehy packing company It is produced and directed by Jock McGregor and is copyrighted by Street and Smith Publications Incorporated Charlotte Manson is featured as Patsy Matt. He is played by Ed Latimer today's script was written by Stedman Cole's Original music is played by Henry Sylvone This program is fictional and any resemblance to actual persons living or dead is purely coincidental This is Michael Fitzmorrow speaking This is the mutual broadcasting system


Now, there is a controversy to be addressed here. What exactly does John mean by confess? Is it, you know, what's the format of confession? Does he mean like a prayerful confession unto God? Or does he mean verbally confessing before men? What is the repetition of the confession? Is it just like a one-time confession? Like, you know, when John the Baptist baptized and people confess their sins, but then what, if it's one time, what happens if we sin after this confession? Is it an ongoing confession? What about the sins of ignorance? The thoughts of foolishness is sin, unpresuctuous sins, honest mistakes, struggling against the flesh, okay? If it's ongoing, you know, how does that all fit in there? Now, confession as a word in the Bible, interestingly, isn't actually as common as you might think. It appears just over three dozen times in all its different forms, like past tense, present tense, you know, its different forms. And the underlying Hebrew word, you know, if you look it up in a concordance, it can also mean like, if thanks or praise in English because the root word is to throw or cast, okay? And also confession doesn't always refer to sin either.  You know, yes, I did it. Or, you know, bring it to a tension that you did something. So let's start with a one-time confession. Could it mean a one-time confession? This is where we'd normally refer to baptism because in Matthew 3-6, it's explained that the people who were baptized by John confessed their sins, right? Baptism is a one-time event that ought to occur at or shortly after the time of conversion. And in Acts 2-38, baptism is explained to be the representation of the remission of sins, the forgiveness of sins, okay? So with this context in mind, people would say that confessing sin is an initial confession to admit that one is a sinner or, you know, if you're a sinless perfectionist, then, you know, repent of your sins for the purpose of believing on the Christ and getting saved, right? Because this is a one-time event, advocates would say that if you do some kind of an ongoing confession of sins, you're essentially denying forgiveness because you're not believing that Jesus Christ has already forgiven all of your sins, you know, past, present, and future. That's the terminology that they'll use, right? But then, you know, we're just asking the questions. That's all we're doing right now. What about ongoing confession or the confession of somebody who is a believer? So for example, James 5.16 instructs us, as brethren, to confess your faults, now the underlying Greek word is the same for sins, to one another. So that's a man-to-man confession for the purpose that you may be healed of that given sin. In Acts chapter 8, 22, Peter tells Simon this also, to repent of a specific wickedness that the thought of his heart could be forgiven and that that's a very specific issue. But this was not in a salvation context because Simon already believed and was baptized before this in verse 13. Now in Psalm 32.5, David declared, I will confess my transgression onto the Lord. So that's man-to-God. Now, a lot of brethren have a problem with this concept because it sounds like Jesus hasn't really forgiven us our sins if we have to carry on confessing them. But really, if you read the Psalm though, and just be honest about the language that David uses, he already starts this Psalm saying, blessed is he whose transgression is, present sense already forgiven and onto whom the Lord does not impute sin. And then in verse five, he says, I have acknowledged my sin onto these. So that's like, it's already happened. And that's quite an important interpretation. I think that'll probably give you a picture of what we're talking about. He also says, you forgave my iniquity, past tense. So that's a past tense verse right there, but he's still confessing his sins or he's still saying, I will confess my sins in the future, right, you know, future tense. So according to Psalm 32, it's not unheard of or unbiblical to confess sins to God, even though he already forgave you because that's what David did and your transgressions are forgiven, right. Now, but then we do raise another problem with this. If it does mean ongoing confession, and perhaps, you know, you might think that this is verbal confession, many people object to this because of the following problems it presents with.  There's the first problem, right. Secondly, the Bible says we have secret sins, like Psalm 98, sorry, Psalm 90 verse eight. We may have sinned in ignorance, not realizing that something is a sin. Leviticus 4 talks about that. And under this point, you can see that there is a distinction between sins of ignorance and willful presumptuous sins like in numbers. And because of this, some sinless perfectionists might say that they'll just say that you have to turn from unknown sin.  Think how Chafia said something like that. Some sins are open to interpretation about whether they really are sins. So, you know, drinking wine, if you sober and you don't actually get drunk, that's obviously something where Christians will disagree with that, or something like smoking where the Bible obviously doesn't mention the practice. Even the thought of foolishness is sin, right? And, you know, Proverbs 24, nine, and we all go through hundreds, if not thousands of foolish thoughts. You know, we all go through thousands of thoughts every day. So, you know, you think at least a handful of them are foolish. So then it does present us with this problem because what happens if we don't confess the sins, right? Well, now let's consider the language of verse nine. So it doesn't strictly say whether we confess our sins before men or before God. It doesn't give us that context, right? Confession is in the present tense, which you might say, if we were just to be unbiased about it, you would say that it perhaps makes a stronger case for an ongoing confession, rather than a one-off instance, just because of how we use that tense there. But also as well, remember that the word confess can actually itself have multiple meanings or applications. So, obviously it can mean to verbally disclose guilt, even if somebody didn't actually ask you to confess. But then on the other hand, you could just admit it if you were questioned about it. You know, even if it's not a moral issue, did you do this? Well, yes, either you confess you did or you deny, right? And so this is important is that it can mean just to acknowledge or admit a particular belief and actually very similar to the word profess, but with more honesty and sincerity. So obviously you could profess that Jesus, Lord, or you could confess that Jesus, Lord. The Bible uses both of those terminologies and obviously profess is more of a negative term, confess is more of a positive term, right? But what many people overlook is there's the blatantly obvious about these verses as I myself did, okay? We can easily define the word confess by its antonym, deny, right, their opposing words, confess, deny, they're the opposites. So either we confess or we deny, they're the two options, right? It's not, there's a third option. We just don't confess and don't deny. Either we deny or we confess, right? Likewise, we can easily define then by the same logic, we can define what John meant by confession because of how he contrasts it with its opposite, right? The opposite of if we confess is if we say that either we confess our sins or we say that we have no sin. We say that we have no sin. So then because of this perfectly simple dichotomy here, we can assume that confession in this context, it's not whether you've personally got down on your knees and confess, it's about what we admit. It's about what we acknowledge in our collective belief about our sins and how this relates to the Christ. So confessing our sins is very simple. It's the opposite of saying if we say we have no sin and we have not sinned, that's what it means, okay? So let's just try to illustrate these verses that are in the present tense without adding any presuppositions or doctrinal assumptions in their day-to-day application, okay? So it's if we, so I'm gonna illustrate this with a group of people, if we, okay? And then on any given day, because it's in the present tense, we don't need to look at past, present, and future. We just look at right now at any given time, whether you're watching this today, whether you happen to be watching it the next day or you watched it yesterday, that's not the point. On any given day, day one, day two, day three, we just apply this verse in its present tense state, okay? If we confess our sins, right? Or if we say that we have no sin. Now, in this wee crowd, on any given day, what happens if any man sin, right? If we, and if any man sin, whether it's him or whether it's him or whether it's her or whether it's him or whatever, well, if we confess our sins, he's faithful and just to forgive us, right? If we say that we have no sin, that's what we say, well, we deceive ourselves, they're the two options. So you see how simple that is, if you just take away any sort of predispositions that you already had about this passage. So really, you see how quickly and just how simple John's statements really are. There's so much controversy about how we interpret verses eight to 10. You know, these little verses, it's really quite simple. And just to clarify, if this is not already obvious, I am not suggesting that to obtain forgiveness, you need to be aware of and confess in heart and in word every single tiny microzin that you ever do and fall in your knees and absolute tears in it for reasons that were already given earlier in the study, such as the sins of ignorance, okay? That's not the point, that's not the point that I'm getting at. The point is the mentality, the belief that you have about your sins. Just acknowledge that you still struggle with your sins in your mortal body. And just a simple acknowledgement that you are not a sinlessly perfect, self-righteous I am, okay, because Mike Krakowski is an I am and Abide in the Word is an I am, okay? And Proger Frogger is an I am. That's all I'm saying.  Then the point then is about the mentality that we have towards our sins, which fundamentally revolves around what we believe about salvation, okay? Do you acknowledge that you still sin in ignorance or just unintentionally? Do you just acknowledge it if confronted? Do you acknowledge that even the thought of foolishness is sin? If somebody confronts you about an obvious sin or a blatantly false claim that you made about the Bible, would you come up with a million and one excuses for it or just ignore the accusation and pretend you didn't hear it or would you turn it on the person confronting you and make it about them being a bad person? Or rather, would you just rather confess it and say that you were wrong? I mean, it's a lot easier that way really sometimes. Now, there will be certain circumstances and there's kind of a disclaimer that where a brother or a sister in Christ denies certain sins and does justify oneself. And yet the opinion of another brother or sister is that he did indeed sin, right? Now, this doesn't undermine his salvation because actually the Bible does prepare us for that to happen because in the New Testament, there's frequent places where Jesus or the apostles give instructions on how to resolve conflicts with one another and the church is required to settle these issues and were required to forgive each other, right? Because there will be conflict. There will be denial about certain sins even among safe brethren, right? That's why the church is there to resolve these disputes because the reason why we have a dispute is because one person's claiming that a sin happened and they were wronged and another person is denying it. So, you know, but we don't need to get too wrapped up in the legalistic thing of all our sins that we confess, but it's about the mindset that we have of acknowledging our sin generally, okay? So wrapping up chapter one then, we see how simple and comprehensible John's statements are really there are simply two types of people and either we end up fellowshiping with one type or we fellowship with the other type. So either we say we have no sin and we have not sinned or we confess our sins, they're the two opposites. So either we say we have not sinned and this could apply to both sinless perfectionists and those who deny being sinners or we just say, yes, we have sinned or we can say we have turned from all of our sins and we don't sin or we can just confess that we struggle with sin or we can say, well, we're not trying, we really have turned from our sins, we're still good people or on the other hand, we can say, we are trying most of us at least but we still fall short of the glory of God. And if somebody confronts us about our sins, well, either we end up in this camp where we just end up saying, la, la, la, I can't hear you, you're a false accuser or that's not really sin, God is okay with it or we just, on the other hand, we can say, yes, I was wrong, sorry about that. And consequently, either we lie and do not the truth, we walk in darkness, we deceive ourselves or we have fellowship with him and we walk in the light and he is faithful and just to forgive us and to cleanse us and we have an advocate.


People hate politics. Hate it.  You can do it. We have the power. Some of us vote. A lot of people don't. But it doesn't seem to make a difference either way. We're the people. We have the power. We unite. We own everything. We have the power. Unless you can make a big donation, nobody cares what you think. Money has completely corrupted American politics. All our legislators do is raise money. That's all they do. They don't legislate. At the end of the day, that's really what bribery is. But it is done every day. No, it is corrupt. The system is corrupt. The system isn't corrupt. No, no, no. The system has been utterly overrun by moneyed interests. Your Congress is bought. Your Congress is incapable of making legislation on healthcare, banking, trade or taxes because if they do it, they will lose their political funding. If it feels like the system is broken, that it's stacked against you. And the question is, how do we fix it? It doesn't matter what you care about, whether it's climate change or small government. The first issue that we've all got to come together and deal with is getting money out of politics. The answer is simple, but not easy. We have to get past the old left and right labels and unite behind a new political strategy. We need tough laws at the local, state and federal level to stop lobbyists from bribing politicians and secret money and create citizen-funded elections. The American Anti-Corruption Act can do that, even under the current Supreme Court. We've done the polling and people love it. When it gets on the ballot, it'll win. I'm just really concerned about our country that we're not addressing really serious problems because our government is bought. I really like to see everyone come out and reclaim our democracy and pass the American Anti-Corruption Act so that we can have a say in who we're represented. We're standing up because if you want to create change and you don't have millions of dollars, you need millions of voices. Anti-Corruption activists stages a demonstration. Representatives of represent us protesting money and politics. We represent the first wave of a growing movement that's tired of sitting on the sidelines. It's time to take matters into our own hands. We are calling for lasting change that puts people first, that makes our leaders represent us, the American people. Here's how you can help. Right now, committees all over the country are working to pass local anti-corruption laws. Go to volunteer.represent.us to join them. This movement can't grow without you. Take a moment right now to tell one more person about the American Anti-Corruption Act. And of course, share this video.


Welcome to the 3a Institute. My name is Rob Hanson. I'm a senior honorary fellow here and today I am very honored to have the delightful Laman Nukman who's a fellow at Intel and also the director for the Anticipatory Computers Center. Did I get that right? Computing lab. Computing lab, sorry. And we're gonna have a conversation today about all the fantastic work that she's doing particularly around sensing and sense making So we might start there actually. Do you want to tell us a little bit about the lab and sense making and what that's all about? Sure. So the lab is actually a multidisciplinary lab. So we have people in user research, ethnography, design, machine learning, embedded systems, software, hardware and really what we're trying to do is make technology more aware of people, their situations, you know, anticipate what it is that they're trying to do and then assist them in different facets of their lives. So we typically look at different types of applications where we can apply that intelligence towards and try to build these experiences in a way that at the end of the day it's really helping people accomplish what it is that they're trying to do in a more delightful way from a human machine interaction. And of course, you're famous for helping Stephen Hawking. Can you tell me a little bit about that? Sure. So actually, that's that's really an interesting application of these technologies. Right.  Right. So it takes Stephen quite a bit of effort to, you know, type one letter. So one of the things that we take for granted when we think about computer interfaces is that we assume that people can easily type or move a mouse or do any of these things. Right. And when you're really, when you have very limited ability to actually do these things, you have to make the system much more intelligent in anticipating what it is that you want. So today, we all think of, for example, word prediction. Right. And word prediction is a very obvious example. Right. You don't have to actually type all the different letters. It's going to assume what it is that you want to do. Now think about anything that you might want to do with your machine. Right. Being able to anticipate based on your context of use, what it is that you might want, and give you a few set of options rather than have you go do it. Right. Makes it much, much more efficient to actually use a system like that. Right. So with Stephen, for example, you know, anything that he's doing, not just typing letters, right, but like opening files, surfing the web, doing all of these things, what we ended up doing was, you know, watching him use his machine for a while, we understood what are the things that he's trying to do. And then we would understand from a lot of the clues of what he's doing on his machine, what he might want to do. And then we would go and surface a few set of options for him that he could select from rather than go and scan the whole screen trying to figure out what point on this, you know, 2d space he's trying to actually reach, which is kind of ridiculous if you think about it. The other thing that we also did was, so Stephen used a sensor, an infrared sensor that sat on his glasses to detect essentially his cheek movements. So every time so imagine a system that's constantly actuating things. And when the thing that he's interested in is actuated, he would move his cheek. And that sensor will detect that the cheek was moved and will trigger whatever is on the screen. The problem with this is that Stephen had very different motions depending on how tired he was, whether it's morning or evening or stuff like that. So one of the things that we did is we actually created an adaptive sensor that understood his range of movement and then was able to filter a lot of the noise that was happening in a way that, okay, now, you know, you don't need people to constantly be adjusted, adjusting with the threshold of that sensor is, for example. I'm interested in the enabling technologies behind that. So my understanding from some of the things that you've been saying and from some of your work is that it's only being because of recent advancements with technology that we've been able to have technology that's been, first of all, that sensitive, but also then able to have the smarts to have that adaptability. Could you talk a little bit about that? Yeah, sure. So I mean, if you think about it, being able to create systems that are contextually aware, what do you need to do that, right? You need sensing. So we've seen a huge advancement in terms of the fidelity of sensors, the size of sensors, the power consumption of sensors, right? I mean, sensing has improved dramatically over the last 10 years, right? So now you see it embedded in everything and it's, you know, it's not costly in terms of cost or power. The second thing that needed to happen was to have enough computation so that you're able to actually model the sense making part of it, right? So a lot of these techniques use machine learning to look at all of, to, you know, ingest all of the sensor data and then create, you know, models that can actually comprehend out of that sensor data what it is that somebody's doing. So that advancement in compute, in computation, right, allowed that to happen again at very low power, right? Because a lot of those things sometimes they're embedded in our everyday life, right? You're not assuming like supercomputers sitting somewhere doing that, you know, stuff. And then the last part of it is connectivity, right? Because a lot of times what you're really trying to do is bring data from other places and you're trying to actuate things that are in the digital world. So being able to actually use that so that you're able to have that connection means that you really need good communication. So I would say that the advancement in all three is what's really enabling us to see tons of usage of these technologies today. You seem very motivated around disability. What sort of things drive you? What sort of things interest you about the ability to use this technology in these new ways? So, I mean, one of the things that I think is a really special time that we're in is that, you know, if you look at what we've instrumented to be able to control the physical world, and we've done a lot of that, frankly, not because of disability, right? I mean, we as a society, but because of convenience, right? So the idea that you're able to control, you know, there's home controls of everything, right? You're able to control anything in your home from your smartphone, right? It's something that people have done because of convenience and that works because of like the economy of scale, right? But if you think about it, given the fact that now all you need is an interface to a computer, to control your physical world, that has a huge potential for people with disability, right? Because now everything that you need to do is just solve that first part of the problem, which is how do you enable someone with disability to actually control a machine? The minute you do that, you've enabled tons of independence, tons of independence, right? They can control anything in their digital world, I mean, in the physical world through that one interface. So to me, that was a big part of the thing, right? How do you enable people to continue to be independent even with, you know, all of these disabilities and hindrances? And it's actually kind of interesting because like, to me, the really amazing thing about these technologies when we think about contextual awareness is if you think about them as a spectrum, right? Because I would argue that each one of us has certain contexts where we're disabled, right? When you're driving a car, you can't be, you know, distracted, you can't be looking at your phone, you can't, I mean, I do this all the time, but I shouldn't be, right? So if you think about it, depending on what part of our lives, you know, we're engaged in, there is a notion of disability in there. And for technology to be able to actually comprehend that and tailor itself in a way that is the most suitable for the context, that enables people at a whole range of abilities to actually be able to, you know, interact with that world without actually having to create something specifically for the disabled, right? That's very powerful. What excites you about the future of technology and where that might go, particularly in that regard? What do you think it might empower and enable us? I mean, I think, you know, in general, if you think about where technology can go, right? I mean, there is really no limit to it understanding everything that's surrounding, that's, you know, surrounds it, and then really facilitate everything for us that we need, right? So if you think about it today, we make a lot of assumptions about how we need to interact with technology, right? I mean, in some sense, I keep joking that the technology really trains us to interact with it in a way that it can understand us, right? And as you see improvement in these technologies, I mean, we've seen a lot of improvement in these technologies. I mean, let's, I think the best example is actually speech recognition, right?   They had a huge potential because as I mentioned, you know, you're driving a car, you're doing all of these different things where you can't actually be typing anything. That's a great interface, right? But the way we spoke, right, we spoke so that we can be understood, right, with these machines, the way you actually gave commands, which is not necessarily about speech recognition, but either, even NLP and command recognition, we had to essentially almost act like robots for these systems to understand us, right? Now, if you start to think about where technology is headed around autonomy and, you know, autonomous vehicles or whatever example that, you know, you see kind of where the world is headed, you know, robots that are acting on their own and working with humans, it's impossible for that to scale if you don't solve that human machine interaction problem, right? It's impossible. So to me, if you think about human to human interaction, it's so contextual in nature, right? You don't repeat everything that you assume the other person knows, right? So in my mind, where technology really need to get to is to the point where it understands what it is that you know, it understands what it is that you want, you can understand what it is that it knows, right? So there's a transparency part of that problem, right? And then really interact in the most, you know, efficient and pleasant way that one can have, just like you would be interacting with a human. That's really fascinating. I'd like to use this as a moment to sort of transition to something else. And it comes to the point that a lot of people are talking about what we call AI or artificial intelligence at the moment, and really it's a cluster or constellation of technologies, as you mentioned, there's natural language processing and machine learning and all these other things. And often we hear people say, I need more data, I need better data, I need higher quality data. And in fact, they have access to that data. And you've done some really fascinating work in this space pointing out that actually, there's a lot of stuff we're currently collecting that we don't know that we're collecting. So if you could let us know a bit more about that. Yeah, so, so I mean, I think, yes, there is a problem of needing a lot more data. But that problem really comes from the fact that we're using a lot of techniques that are very data hungry, right? So if you think about a lot of the advancement that happened in perception recently, right, whether it's, you know, being able to detect objects or people or whatever. There was a huge advancement because of applying deep learning towards that technology, which is awesome. But deep learning requires tons of data, right, to go train. Now, to me, what's interesting about this problem is there are a lot of things that are enabling us to get a lot of data. But it's so much easier to get data from the digital world, right?  I mean, Google doesn't have to work very hard to understand what is trending. Everybody's using a search engine to look for something, right, or Facebook or whatever, right? The whole advancement in terms of identification of faces and objects and things like that. Well, I mean, what was Facebook doing, right? They had people tagging tons of pictures, right, with all the things that they want. Like it's they solve the ground truth problem by having people do that for them, right? And now you have massive data that's tagged that you can easily train these systems. So in some cases, yes, I think we have tons of data to enable us to do really much better models and things like that. The problem is when you go into really the messy physical world, right? When you start to talk about something like, you know, autonomous vehicles, for example, you have to get data from the physical world. And yes, you know, so many companies are actually trying to collect data from the physical world to train these systems. But if you think about it, being able to capture the cases that these things will break down, you have to have an unbelievable amount of data to find all that long tail of all of these different problems that in general, you're going to have to encounter after millions and millions of hours of driving, right? So I think, I mean, there is a problem with data, but there is also a quality issue. There is also a ground truth and annotation issue, because it's not just sufficient to actually just have data, but you have to have some, you know, some cleaning of that data, some ground truth, blah, blah, blah. To me, one of the things that I believe is really important when you start to build these technologies is to actually get data from the physical world that is realistic. Because with a lot of the contextual awareness, capabilities and things like that, one of the problems that we always run into is you do these things in the lab, you take it out into the real world and everything breaks down, right? So it's really important when we're actually doing the data collection to collect it in a place that is realistic to the thing that you're actually trying to use it for. And that requires a lot of effort. That's not something that's usually available when you're collecting a lot of data from the physical world, but it's not something that we can do without. So sometimes the senses out there, though, are actually capturing far more than we plan for them to capture, aren't they? And so we can actually do things with that. So there's some interesting work you've done in that space. And I think there's a lot of potential there. Could you tell us a bit about that? Sure. So one of the things that I think, you know, when we think of sensors, we usually think of things that we're built for the purpose of sensing the thing that we're trying to sense, right? So a great example of that is an accelerometer, right? It's actually measuring vibration. That's what you want to sense. You want to translate that into either an orientation or a human motion or whatever that is, right? So, you know, we understand that as a sensor. However, if you think about it, there are so many things that we end up impacting in the real world as a result of our activities, motions, etc. that can be then used as sensors. So there are multiple examples of that. I'll use wireless as an example. So if you think about wireless, we think usually of wireless as a communication mechanism, right? Your Wi-Fi is used to actually communicate data. However, Wi-Fi signals get impacted by people, right?  So if you start to think about that and say, well, can I now use the information that's coming back from the wireless, right, in terms of signal strength and things like that to try to model human motion or trying to understand in a space how many people there are, well, that's possible, right? You just need to be actually looking for that noise in that signal rather than what these systems typically do, which is try to suppress that noise because if you're trying to communicate, you don't want your communication to be impacted by how many people are in the room. But if you're actually trying to sense how many people in the room, you want to amplify that part of the problem. And we see that in many different things, right? I mean, you know, we also use things like RFID where people think of RFID as a mechanism to identify an object, but also when a person touches an RFID tag, it totally changes the behavior of that signal, right? So again, you can actually build a detector for human motion using RFID. And the nice thing about this is RFID can be totally passive. So you don't even need to power these things. I mean, imagine a mug that you just put a passive RFID tag on. And now you can absolutely figure out what once somebody's actually drinking, right, without even having to have a camera or anything. So a lot of potential here for utility in spaces that we hadn't thought about previously. Does that mean that we need to think about the ramifications of that and the impact of that? Yes. And, you know, this is always kind of the interesting side effect of that, right? I mean, being able to extract information out of something that people have not necessarily assumed that that was possible, that ends up really opening up a lot of concerns about privacy and leakage of information and things like that, right? I mean, it's actually even really interesting. I mean, like recently I've been thinking about even something like, you know, security issues, right, with computers, right? The fact that the temperature or the energy profile or whatever says something about what's happening inside that. I mean, that is a signal that one can use to actually try to gain more understanding of what a system is being used for, right? So it's in some sense like we constantly leak information in the way that we interact with this world. Now, one of the things that I like about having all of these different options, for example, if you're able to detect human motion or falls or something like that through wireless signals, one of the nice things about it is that, you know, while this opens up things that you can know about the person that you might not have been able to know, it also opens up an interesting space of trading off what type of technologies do you use for sensing. So one of the things that we work on, for example, aging in place, right? So you're trying to enable elderly to stay in their homes for longer. But there are a lot of concerns, right? I mean, people can have more accidents, they can fall, all of that stuff. Now, a lot of times what people think about this, well, let's put a camera in that space. We know from a lot of the research that we've done that, you know, people don't want to be monitored with a camera, people worry, I mean, like in many of the cases, we will just turn off the camera, right? So if you're able, for example, to detect these things through something like a wireless signal, well, in some sense, that's more privacy preserving than if you were actually trying to do it with a camera, right? So in some sense, you know, what's really important is disclosure, because people really need to know that a wireless signal can be used for the purpose of detecting the faults. But on the other hand, it enables people to select what are the set of trade-offs that are more comfortable with, right? That seems to strike at the heart of another issue. And that's around the concept of a cyber physical system. So it's a term which we use to talk about how the digital world is impacting on the real world and how they relate to each other. And in that space, we're noticing that when people look at a physical cyber physical system, so the embodiment of it, they often have an emotional response. And sometimes it's fear and sometimes it's something else. But when it's more behind the scenes, when it's something that might have a real impact on their life, but it's a series of zeros and ones, it's digitized, that people seem to be more oblivious to it. They don't seem to have any real sense about it or any sense of fear, particularly when it could do something wrong in that space. I suppose one of the challenges in this discussion is the lack of definitions. I mentioned AI before and artificial intelligence isn't really defined, not in a common way and not in a concrete way that we can use in that discussion really well. Cyber physical systems is an area which we've taken at the Institute as being this is a term that we need to really come to grips with. And there's a lot of push around the world. I don't suppose you'd have a stab at trying to give that a definition, would you? Well, I can try to give it a definition. I think, as you mentioned, a big part of it is really being able to connect the physical world to the digital world. I think, I mean, I tend to think of a cyber physical system as one where the physical embodiment of that system is a part of that picture, as opposed to one, you know, what one might think of any of an IOT systems or things like that, where, you know, that could be something that's actually totally, you know, people are not aware of the fact that it even exists, right? So in some sense, you know, it's almost a more specific, right, or a more targeted than in general anything that connects the physical world to the digital world. That's how I tend to think about it. I've actually seen it used many times. I mean, this is one of the problem with these terms, right, is that people use these terms in very different ways. And as you said, even AI, right, I mean, you talk to three different people, you're going to get three different definitions. But I've seen it used to simply say it's something that connects the physical world to the digital world. I tend to think of it as, you know, there is some form of physical embodiment beyond the fact that it's just that.  But I would argue that the world is actually changing quite a bit and people are starting to really worry, especially with all the latest stuff that's been happening and with Facebook and so on, right, that people are really starting to worry about these systems that actually don't have a physical embodiment that are actually impacting every single aspect of our lives today, right? I mean, and in the US, that is definitely something that's top of mind for, you know, the majority of the people today. So, you know, it's, I think that to me, it was really interesting about these systems that do have embodiment is more about, like, how do you actually create that interaction? And what kind of emotional responses, right, does it because it's kind of interesting, right? I mean, if you think about a robot and, you know, an embodiment in a robot of some intelligent system, I mean, we know from the research that, for example, children interact with these things very, very differently than if you were, say, doing, you know, the same capability in a tablet, right? I mean, we know that from the research already, right? That it's very, very different. And it's, they start to assign qualities to it that are human-like where you don't necessarily see that type of behavior, even if it's doing exactly the same thing for them from, like, a tablet. So that strikes at another thing, which is very important to this whole topic that we're talking about, is diversity. So the way that people interact with technology depends on their cultural background and who they are and all that type of thing. So I was wondering if you would share your thoughts about when it comes to designing these systems, what we need to think about in terms of getting those datasets right and doing the appropriate research to actually build the tools that will have these good outcomes. Yeah, so that's actually a great question. And I think that touches on so many different points, right? But let me maybe try to address a couple. One, I mean, use the word diversity. And if you think about it, a lot of the, I mean, today, the fact that many of those systems are about, you know, training all of these deep networks to, you know, come up with something or some decision or whatever based on that data, really the ability of these systems to behave in a certain way is all dependent on what type of data have you thrown into it, right? So as we start to build these systems, we really need to be thinking about, are we actually sampling the right part or are we, are we, do we have a reasonable representation of what we want that system to work for? That's not necessarily saying that I want any system that I go develop to work for every single thing, right? Because typically any product that you develop, you will think about developing that product for a certain use for a certain application, sometimes in a certain culture, right? In a certain setting, all of that. So, and because these decisions will impact cost will impact what it is that you're trying to do, right? So I'm, I don't think that the answer is to say, well, everything needs to work everywhere. I think the point that we really need to be cognizant of is that you don't want to design a system where there is an unintended consequence that you haven't thought through, right? If you're trying to design a system that would work in terms of, you know, let's say being able to detect, I don't know, being able to detect that a person is there, right? And you haven't really trained it on all sorts of different people and all sorts of different races and all sorts of different, you know, ages and things like that, then this system isn't going to work if it encounters people that are outside of what you've just trained it for. You know, so that is an unintended consequence, right? Because, you know, you need it to work irrespective of, you know, that constraint. Would you agree with the the saying that algorithms have a country? So if they're made by a particular people, particularly if they're made by a sort of a homogeneous set, so a group of like-minded people of a particular subculture together building something, that that thing that they make, in this case an algorithm, has a particular country. It's come from somewhere, so it's reflecting those views. And then, perhaps unconsciously, it's then reflecting or enforcing those in another part of the world. That's absolutely true. And I think it's, you could think of it as a country, you could think of it as a gender, you could think of it as, in so many different ways, right? I mean, the, the, I think in everything that we designed, right? If we don't bring a diverse set of expertise, race, culture, gender, all of these things, right? Then what you're really doing is you're introducing blind spots. And it's not that, you know, it's what I was trying to say earlier is that we have to know what we are missing. And if you don't have the right people to know what you're missing, you won't know what you're missing, right? So these things will go unnoticed, right? And that, I mean, that isn't just about AI and systems like that, right? If you think about designing a product, any product, right? Even if it has nothing to do with data, right? If you don't bring in enough diversity on the team that's creating that product and you are trying to appeal to a diverse set of users and not the users that look like the people who were designing the product, you're not going to succeed, right? So in the same way, in the case of designing AI systems, that translates to what data am I sourcing, right? And where what am I missing, right? What data am I not sourcing, right? So there is a specific instantiation of that that has to do with AI systems. But I think in general, it's been proven that you can't really get as good of outcomes if you don't bring the diversity of your user space into your design space. What are the enablers for that? What helps you get to that point? What helps you get to that point? Yeah, in terms of having diverse teams. Yeah. I think we have to start to solve, you know, the education system to begin with, right? Because I mean, part of the problem I would argue is I mean, there are so many different problems that I can get into. But, you know, one of the things that, for example, at Intel, we've been really quite focused on is to try to improve diversity. And in fact, our CEO, I think three years ago or three and a half years ago pledged that the Intel population will mirror market availability by 2020, right? And at the time, it was like such an ambitious goal, right?  So what was interesting was that, you know, once that became the goal, everybody started to run towards that goal, right? And that mobile. So you start to really think about, I mean, the nice thing about it is then you get data to understand where you are, you figure out how far you are from there. And then you start to actually come up with actions to make that happen. What's interesting to me is the last numbers that I saw, it looks like we're going to make this a year ahead of time. Really? Yeah. So that's awesome. But if you think about it, that's mirroring market availability, right? Now you look at market availability, let's look at take a gender, for example, look at gender. So you're talking about somewhere in the span of 22 percent, right? As market availability as comes out of college. So to me, what's really interesting, it's awesome that we're committing to that and solving that problem. But to me, the more interesting problem to solve, once you've solved, once you've metered market availability, is how do you get market availability from 22 to 50 percent? Right? It should matter the population. So that's where, you know, a lot of the efforts around really understanding what's impeding women to get into these fields, for example, and become really important.  And one of the things that I mean, I was talking to Eleanor about this earlier, but one of the things that really bothers me is that there is this undertone that says, well, that's not women. These type of topics, women are not interested in. Women are not capable of doing math and science and all of that stuff, which, in my opinion, is not sure I want to use that word. Let's say it's ridiculous, right? Let me use the polite word. I think it's absolutely ridiculous to say that. And I think in all of my interaction and my discussion, you know, with girls in schools, with, you know, women in colleges and things like that, is that a lot of these things are about what they are envisioning themselves to be like and to do, right? So I think it falls on all of us to try to bring a very different perspective of what a computer scientist and engineer and, you know, anyone in this field to look like and what type of jobs they do. And it also to me what's really interesting is that what we're realizing is that what's really important is that transdisciplinary approach, right? So if you take that, right, we know, I mean, in many of the characteristics that women do tend to do quite well actually in working across disciplines, right? In bringing people together from different perspectives. So that becomes a really key thing, right? So it's like as we move towards that world where we're trying to solve ethics and AI and all of these different problems. How do you do that? If you don't bring that diversity of disciplines and expertise and skill set to the picture? It's also more than a pipeline issue, though, isn't as well. There's some other issues that need to be done in order to foster that diversity. Could you speak about that? Say more. So with respect to the pipeline that you were talking about, helping students understand from an early age that this is a career option for them. My understanding is that whilst that is part of the issue, and is an area that is often focused upon from policy and other spaces, is that once we start to try and get diversity in the workplace, there are certain factors in the landscape, as we know it today, that is adverse to that. Oh, absolutely.  But like there, I think there is this assumption of what is acceptable behavior, right? So I think it's it's really kind of interesting. If you think about how men tend to think about what a role that a woman should play, I think there is that somewhat disconnects between how they see women in the home and how they see women at work. So as a result, you tend to always hear one of two things. Either women are not aggressive enough, right? They're too timid, they're not aggressive, blah, blah, blah. Or they're too aggressive.  And in fact, you see a lot of the data that shows that a lot of the complaints about women in terms of the, you know, what do you call it, like reviews and things like that tends to surface around behavioral issues, right? And for men, they tend to be around skill issues and technical issues and all of that. So like you have to ask yourself the question of and most of these reviews are being done by men, not by women, right? So so if you think about that, I mean, like, there is definitely a lot of these biases in the way that people think about it that are very unconscious in nature. I mean, a lot of people have unconscious bias. There is no question about that. But to me, this is why, you know, I was having this conversation with Genevieve and, you know, one of the things that I believe is while we talk about algorithmic bias all the times, like, oh, my God, it's important to actually train systems that are, you know, in ways that that data is representative of the world. In some sense, there is a nice thing about being able able to reprogram something to not be biased. And I would argue it's much harder to actually reprogram humans, even when they know that there is a bias, because by definition, if it's an unconscious bias, it's very hard for them to see it in the moment. So that's a great point. I just want to underline or underscore is around data sets and training of algorithms and AI. And so we're seeing already that for job candidates that using historical data sets is not always preferable in its ways because you tend to get more of the same. And that goes in bad places. And so if we start to see more of these decision making tools brought in into circumstances like you were talking about, if we're not wary about what we're doing, if we're not mindful about how we're applying that, we can amplify more of the same countless. Absolutely. I mean, it essentially the bias that exists in the world will get reflected in your data set and you'll re you'll amplify that bias over and over again. The nice thing about having these systems is that you can actually do all the analysis that you want to understand that there is a bias, right? Whereas if it's just based on human judgment and whatever, right? But then it's very hard to even quantify that bias, right? Because you could say, well, why is it the fact that we get, you know, that percentage of women into these jobs versus men? Well, one could argue that, oh, well, maybe the woman that we got didn't have the right skillset or whatever. If you actually have all of the stuff being done in an, you know, some form of a system, you can actually query that system to try to actually prove out some of these assumptions, right? Which is very hard to prove out in people's brains, but it's much easier to prove out in a system. So in some sense, you want to use it diagnostically, right? You want to use these systems to understand where there is bias, you want to then take that into account when you actually design these decision systems, right? Because then you wanted to actually compensate for these biases. Yes, I'm glad that we've found a sense of hope in that part of the conversation. I am an optimist. So that's good. I think where I'd like to land is to really draw out some more hope and talk about what excites you about the future with respect to technology. So where do you have hope? Where do you think things are going to go that are going to be really good? You know, I have a lot of hope for technology in the future. I think a big part of my hope for the future is that technology can help with equity. I see technology as a place where, as a thing that can be totally utilized to level the playing field, whether it's about economics or whether it's about physical ability or whether it's about cognitive ability, right? I mean, if you think about it, there are all of these potentials for these type of technologies to come in and help augment whatever gap there is from a human capability perspective. And that to me is really the biggest hope that we have for these technologies. And I think we sometimes, and again, I am an optimist, not to say that we shouldn't be wary of technology because it could always be used to do the opposite, which is amplify the inequity, right? But it's really up to us to make it turn out into whatever way that we wanted to turn out, right? And it is a tool that can really help bridge a lot of these gaps, right? Because it changes the way that it changes everything. It changes. It's so disruptive in nature that you could actually start to think about how do you use it to actually improve that equity in your society, right? So that is what I'm really mostly excited about. That's excellent. I think that's a great place to leave it. Lama Nakaman, thank you very much for your time. Thank you.


Hello, Psych2goers! We just want to take the time to thank you for all your kind support. We are a team of dedicated, passionate, and hard-working individuals who come together every day with one goal in mind. We want to make psychology come alive for you. We are self-funded and recently launched our Patreon. If you enjoy the work we do and find it meaningful, please stick around until the end of the video for more information on how to get more involved with us and all the cool rewards you can get from us. We hope you enjoy this video. It's actually quite easy to tell when someone has fallen in love with you, but due to our tendency in human history to complicate things by overthinking, we may not believe it at first. Thanks to hookup culture, men especially have a bad rep for being non-committal players, but this doesn't make anyone immune to catching feelings. Are you wondering if someone is beginning to fall for you? Here are six clear signs he's in love with you. Number 1.  Although teasing is a common form of flirting in the initial stages of dating, over time when they get to know and fall in love with you, they start to come up with inside jokes only you understand that make it more exclusive and special. People in love tend to be more in tune with their playful youthfulness. It's important to be able to laugh with your partner so you can handle all life's curveballs thrown your way. Number 2. He opens up about things that are hard to talk about. This is especially true if your partner is more shy or reserved. When someone is slow to open up and selective about who he lets in, pay attention to what he reveals to you. Does he share embarrassing stories or things that are hard to talk about? If he lets his guard down and shows his vulnerable side, it means you're becoming someone he can trust and depend on. Number 3. He pays attention to details about you. It's common for people to remember things about you when they're initially trying to impress you, but when they're in love, it goes deeper than that. People in love pay attention to the details, big or small, because they genuinely find you interesting and enjoy learning everything about you. If you think your partner is beginning to know you better than you know yourself, there's a pretty good chance he's falling for you. Number 4. He becomes more open-minded about trying the things you like. Whether it means going to a restaurant you've been dying to visit or trying something as bold and dramatic as skydiving, people in love are willing to step out of their comfort zone to make unique memories with you. Even if he's not always a fan of the things he tries because of you, just being open-minded about your interests and hobbies is a clear sign he cares about you enough to support them. When someone is in love, the last thing they'll do is be dismissive or judgmental towards you, so don't worry about embracing your true self. Number 5. He's protective of you and helps you get through your bad days. It's easy to love someone at their best, but the ultimate test of love is being able to accept one another at each other's worst. In relationships, your challenge should become a better person. When someone is willing to help you get through your bad days, not only is it a sign of maturity, but it shows that you have a stable relationship built on trust, compassion, and understanding. If he sees the potential in the two of you being long-term, then he will continue to work on making that happen. Number 6. He's at ease around you and doesn't feel pressured to change. We're rarely ever our true selves when we're dating someone in the first few months. It kind of feels like a prolonged interview, but when you're in love, dating stops feeling like a performance and more like home. You'll notice that he'll stop feeling self-conscious about himself, whether that means having bed hair in the morning or just dancing in front of you. When the walls start coming down, that's when you know you've truly reached his heart. Do you agree with these signs? Please share your thoughts with us below. Also, don't forget to subscribe for more content from Psych2Go and check out our Patreon. With your contribution, you can receive our PSI pendant, t-shirt, issues of our magazine, and many more membership benefits. Your funding will help us produce quality script writing, voiceovers, and animation that makes our content both entertaining and educational. Thanks for watching!