Hello everyone. Today we will discuss a big collaboration called Trostwerty AI Developments, Mechanisms for Supporting Verifiable Claims. So it's a collaboration led by Miles Brundage, Shahar Avine, Jasmin Wang, Haydn Bedfil and Kretschen Kruger, with a couple of other researchers from OpenAI, Montreal, McGill, EPFL, Ecole Normale Supérieure and Google Brain, etc. And so we will follow the structure of the paper more or less in this discussion, where we first will discuss the institutional mechanisms for Trostwerty AI development, then we'll move to software mechanisms, and finally hardware mechanisms and specifications. Who wants to start? Maybe Le or Louis on the institutional mechanism? Yes, one of the important problems is of course how to verify and audit people who are designing AIs and how to make sure that these AIs are constructed with the right interpretability, privacy, no bias conditions and so on. And in order to do this, the first section of the paper focuses on institutional mechanisms and what kinds of structures should be put in place. And the first great point is the third-party auditing program. So this is what happens in the banking industry. You have this auditing made by outside companies that can be governmental. And maybe for AI, even probably surely, we need some external auditing of some sorts to understand, to better track what the algorithms are well designed. And if they verify the specific conditions, we want them to verify. Yeah, if I have more comments on this. The problem is that of course, as we already discussed, this raises a lot of different problems in practice. I guess there's some legal problem. This probably needs to be legalized for this to be done consistently. It's hard to think of other mechanisms through which this can be implemented for actual companies these days. Another problem is that there's going to be a need of a collaboration of the other company to make available what it is that needs to be audited. Actually, there's even the question of what it is that we're auditing. Do we audit the data? Do we audit the learning algorithms? Do we audit the algorithms that have learned the objective function of the algorithm to steal another thing and arguably the most important to us. And so far, I don't think it would be very easy at all. I think it would be a huge enterprise, even for, especially for big companies like Google. They probably have very intertwined code and not ready to be audited. But this could be also interesting to push them in this direction, because it would maybe force them to have a more structured code. Well, I don't know, but they could maybe it's already very well structured. But if you're trying to make transparency, you have an additional incentive to make your code documented and clean. So, yeah, I think everybody agrees that there needs to be some sort of better auditing like this, to which extent and how the devils in the details, I guess. Yeah, something to add about third party editing. Otherwise, you can explain the next one. So, yes, auditing the auditors. That's in the fifth point. That's what he discussed at the end, I think. The second recommendation of the program to work on at the level of institutions is recommending more routine exercises. So, routine exercises consist in trying to attack the system that you are designing yourself to test for failures, to test for lack of robustness resilience. And so, the author recommended that developers of AI would learn better skills to perform these kind of exercises and to be able to catch before it happens the possible problem that would arise with their software. One thing we can think of is, for example, last time we discussed about DP3T. And the authors writing the framework of DP3T, they really made the extra effort to think of how different agents, different stakeholders, could try to attack and break the system. And this is extremely important to ask this kind of questions because otherwise you couldn't end up in a case where your system doesn't work and you did not anticipate how it would go. And the author found that this sort of skill, like threat modeling, is lacking within AI developers and should be more motivated. And this is also something we can recommend to AI developers not doing only research but working in large companies. And one thing we, a second example that we often talk about is simply the effect of maximizing engagement, which led to a lot of biases and polarization on social medias. This is also a kind of flow that AI developers should try to see how people will react to their system and anticipate the worst cases that could happen. So yeah, I don't know how much we can stay on this point, but I wanted to comment a bit on the interpretability part of the, so the report has the usual calls for interpretable, transparent algorithms. I know that Le has a lot of caveats to add to interpretability. Well, yeah, it's just that it's ill-defined. And so as I like to say, interpretability requires interpretation. And a sense in which the algorithms could be transparent is if its code is open, but if it's a neural network, its code is like the weights of the different snapsys. And actually, you want to learn, well, you will learn a few things by having this data, but it's not explaining a lot of the algorithm. So there's actually research to be done about what kind of interpretations of the algorithms can be done. I think it's also very tied with human psychology, like what it is that we find will increase trustworthiness in the algorithm in terms of data interpretation. The risk with interpretability, so calls for interpretability are fair. And I think we should always have some level of interpretability. There's also evidence, research evidence that adding interpretability constraints improves, actually improves the algorithm. You can see it as a sort of regularization. So if you constrain the algorithm to be in addition to be accurate, to be interpretable, so naturally you would force the training to select less complex models and regularize somehow by interpretability. You can see it as some sort of sophisticated Occam's razor. So it has benefits. There's empirical evidence for the benefits of having interpretability constraints, besides interpretability itself. Now the KBS is that there are risks of making up a story or of cherry picking one explanation of the algorithm when you start having interpretable or trying to make it interpretable. So if you say, you take a neural network as an algorithm, so obviously this is the algorithm you deploy. You deploy, so most like many times people, reviewers especially, they don't think of neural networks as algorithms. They talk about the model, the model, the weights, the parameter vector, but they forget and or sometimes it happens to us that we tend to forget that at the end of the day this parameter vector would be deployed itself as an algorithm. So we think of, let's say, the training algorithm as the algorithm, so SGD, gradient descent as the algorithm. So that's the algorithm we use to train the model or the parameter vector. But once we're done training, we deploy something called the parameter vector or the model. And we tend to think of it as a model or like a set of parameters and not as an algorithm. It is actually, it is an algorithm you execute it. So it tells you by how much you multiply this and by how much you multiply this and how much you add this with this. And at the end, this is how you decide that the photo contains a prisoner or someone who is not likely to be a prisoner. So you take an obscure example from judicial deployments. And if you sometimes, if you start cherry picking explanations, say, oh, it's predicts that this person is likely to be a prisoner because it wears this thing that has been observed in all the groups of these bands, this criminal band, or it has this tattoo, the person houses, she or he has this tattoo. And we know that the tattoo is present in all the members of this criminal group. Fine. But you might start cherry picking, imagine a lot of pathways in the neural networks, you might start cherry picking pathways to make up an explanation. Yeah. So it's like, I think we should think of these algorithms, especially large neural networks as complex systems. And when you're analyzing a complex system, especially if you have motivated reasoning, there's a risk of, oh, yeah. Maybe I can comment more on that because I mentioned the word pathway. And so this kind of objects, a graph with a lot of nodes and a lot of paths. So you can see a neural network as a graph. And then you have a lot of neurons and then paths. So you have features. This is the moustache feature, the trouser feature, et cetera. And then you have pathways between them and the output. We have the same thing in biology in metabolic networks. So you have a lot of a graph of reactions between metabolites. You have this gene and this piece of RNA is a catalyzer for this protein and this protein is, so it expresses this protein and this protein is internal catalyzer of this reaction. And for the early days of bioinformatics, once we started sequencing the genome, you had a lot of speculative interpretation where people would start talking about, oh, this is the gene of, to cite a recent controversy, this is the gene of homosexuality. We now know that this is not such a thing as a gene of, or like this is the gene of this trait. And now we know that it's not about this piece or that piece or this pathway. It's like, it's way more complex. And if you start analyzing a complex system, a complex graph like this, you, the probability that you are just cherry picking something is close to one. So you shouldn't absolutely try to explain a complex system by just like taking a small pathway and say, okay, look, there's this tattoo and because the trouser is blue, then therefore this is the prisoner. Yeah, it's a, even more general question about epistemology or what it is that we mean when we mean an explanation. And it's a complicated question. I think the philosophers debate this. So, yeah, interpretability really needs interpretation and it's a hopefully better interpretation rather than interpretation. Another point I want to mention about interpretability is that there's the question of interpretable to whom because some explanations are perfectly valid for computer scientists. So if you tell me that a university assignments a system where students are assigned to different universities, if it follows the game shape algorithm, that's fine for me. I can understand. But if you tell that to the general public or just non computer scientists, you know, yeah, it's no longer clear that that interpretable, even though it's not a very complicated algorithm. And when you're talking about DP3T or like these algorithms that are slightly more complicated than the game shape algorithm. Yeah, the question of interpretable to whom is really becomes a big problem. Like he can spend hours and hours explaining the algorithm. Maybe you actually need hours and hours to explain the algorithm to the layman.  So in the end, these algorithms are somewhat opaque to people who don't spend enough time thinking about them. Yeah, the author of the paper mentioned some research done with data scientists or computer scientists where explanation were generated and they were asked to actually interpret the explanation to somehow these social studies to explain, to better understand what are good interpretability methods. But overall, they recommend more work on more research work on interpretability because as of today, there is no frame or clear definition of what interpretability means. Do you think it's an important work for AI safety landscape? So in a sense, this is kind of the job of science communication, just explaining explanation is also the job of science communicators. And I spend a lot of time trying to do this. And it's just very, very hard. And the more complex things are to very complex algorithms. But if I struggle to understand it, we have an even harder time to explain it. And yeah, it's just very, very hard. And there's no simple path. There's no clear guideline about like, this is how you should explain this concept in science. When concepts are completed, there are multiple ways to go, you actually need multiple ways to go. And in the end, even if you take the best science proposal out there, it's still possible that they will convey more misinformation than actual information, even though everything they're saying is right. Because it's very hard, like people misinterpret things and it's all like just like very simple steps, like the Newton's law, F equals MA, even this is very hard to get through. You really have this inner feeling that, yeah, it's acceleration that's affected by forces, not speed or velocity. Even this is very hard to communicate. So yeah, try to explain the neural networks and how we can go wrong and it gets very complicated. All right. This was actually one of the points they make in the second section, which is discussing software mechanisms to improve trustworthiness in algorithms in general. So to go back on the first point, which were institutional mechanisms, two other ideas that we did not mention yet is the first one giving bounties for finding bias and safety issues in algorithms. This is something that is already done a lot for usual programming bugs in software. But it's not done for an algorithm that starts to recommend very problematic content or the machine learning algorithms. This thing is not well spread yet and they hope that by putting in place some sort of bounties, it will motivate the general public that is knowledgeable and machine learning to look for these bugs and report them. So one thing we can think of today is that if someone finds a bug on the YouTube recommender system, there is no reward into simply revealing this bug. But what someone of today could do is simply use this to generate ad revenue instead of telling me to Google and helping to fix it. So that's why this kind of bounties can be useful. Yeah. So as the software gets more and more complicated, the testing of the software gets usually overwhelmingly more costly than the production or the development, the initial development of the software. And yeah, there needs to be a lot of testing, like huge incentives, financial incentives, social incentives as well. This works a lot in the open source community to find bugs and create bugs. And yeah, this is an important task. And this is all the more important for machine learning algorithms that have been shown to have a lot of hidden vulnerabilities. Well, maybe he knows a lot about this. It's his base. I was saying in the discussion that I find more reasons to have both bulky programs in machine learning than in traditional software. For traditional software, there is basically a piece of code that is way more interpretable because it was written by hand or by many hands. So you could imagine a setting where many hands were reviewed as code and audited. And sometimes for the client, it's safer to do this auditing internally and not leave it in the public domain for bug bounty. Of course, in bug bounty program, you don't share the code necessarily. But for example, you can do something like penetration testing. So I don't share the code, but I can invite you to try to penetrate my website. And then if you find a vulnerability that allows you to penetrate the website, then you can win the bounty and especially if you can explain why and help you fix the vulnerability. So there are reasons. So in case of websites, obviously the website is already there. So there is incentives to have bug bounty programs on, for example, penetration testing. So according to a friend who is a penetration tester for a company that deals with banks, like this is one of the largest markets they have is just penetration testing of online banking websites. And then the thing is already there. It's already public. It's already there. You can make it a bug bounty program. For most of traditional software, you don't want to expose it to external reviewing. So there are incentives for clients not to go for bug bounty programs and keep all this in internal. Now, for machine learning, there is one thing that makes, that is very different from business software is that you have the software, so say the weights of the neural network. And then you don't want, you don't know how it behaves on every data point.  So if someone outside the outside world has some set of data set or distribution of data that you never have accessed, then you would be happy to audit your software with their data set.  And then someone has a data sets of people living in India or in Japan. And they can, they can, they can test it on your software and say, okay, look, you have a bias against this or this fake feature, but it's not very present in the American data set. So this is, this is, this is something where, where, where you can see beneficial, beneficial outsourcing of auditing. And especially like, for example, auditing for bias, auditing for bias, or even for inaccuracies that, that people do not necessarily call bias. So you just for like, for even for like robustness and, and, and accuracy of the algorithm, you can, you can see a lot of reasons to, to adopt both multi programs for machine learning. But yeah, in terms of vulnerability, as you said, and in general, like, there are like, several other reasons why machine learning should probably embrace but multi spirit is that the space of vulnerability is extremely high. We deal with very high dimensional spaces, data and of models. And you can just, you can just do it the, the, all the way or like just audit things and start reviewing them, etc. So maybe like crowd sourcing audits or multi programs could be, could be, could be, could be efficient. I think related to this is the fact that machine learning, especially neural networks or anything that's a gradient based. So it's not very necessarily neural networks, but I think, I think that's gradient based is more vulnerable if the attacker knows the code or the weights in particular, because then he can compute the gradient and, and he can have this so-called evasion attacks. So transparency opens the vulnerabilities. And so there's, yeah, there's a, it's not clear that what should be really transparent and what should not be transparent for safety reasons. And I'm just not thinking about interglow city here. So this should be also, I guess, something to be, yeah, better understood and see what is the optimal tradeoff here between transparent like security. Like, I think the overall goal here is basically if it's a security and address worthiness, like, it's not clear that a transparent differentiable model should be made transparent. Yeah, we discussed this topic in the first episode of the podcast. Discussing accountability of the black boxes. Yeah, another thing we did not discuss earlier is the, having the, like the data or the logs of what has been done by the algorithm can still be extremely worthwhile, especially if you think in terms of the, the YouTube newcomer system. Understanding what is recommended, for instance, by the platform right now is impossible for outsiders. It's very hard for outsiders.  Yeah, it's not easy. And so if there were more, like collaboration between YouTube and research entities, and because there's also this GDPR problem, because the data is very hard to make them both useful and differentially, like really private. We talked about the example of Netflix, the Netflix prize, where Netflix gave data, made public some data and anonymized it, but like it was de-anonymized afterwards because you could relate this to, you could connect this with what people commented on some other website, like IMDB. And so just this thought about how to better understand what is done by the YouTube recommender and how to allow only researchers, for instance, and not the general public to export this data, to better understand and better audit and better, and improve the algorithm if something is very bad. Yeah, this should be thought carefully. It's not easy, but this sounds like something quite desirable. As opposed to today, some researchers, what they do is what they try to find some API. It's much harder to get this ribbon data, for instance, to understand the radicalization paths on YouTube as has been done by one of our colleagues at DBFL. What you mentioned is one of the recommendations about software mechanism, leaving audit trails. This is the term they use. So one thing they recommend is also to do research on what kind of logs would actually be more meaningful. I guess there are many ways that algorithms can keep track of what it is doing and if there are some that are more meaningful for auditing later on. One reason it's really related to trust is this comparison they do with how airplanes are working. In an airplane, there is a flight recorder that is keeping track of everything that is happening in the airplane, of the mini-metrics. These are a part of why we really trust planes. Thanks to these flight recorders, for the few accidents that planes have had, we could get back to the source of the accident and fix them. I think a plane would be a lot less trustworthy if when there was an accident, we could just say we don't know what happened and stop there. This would be a very charitable system. These logs definitely exist in YouTube or Facebook. Yeah, maybe part of these logs will also be true because the logs of YouTube and Facebook are definitely absolutely huge. Maybe for the planes, they measure some other kind of metrics and I think we could come up with some metrics that make the log more easily understandable. So for example, YouTube could log every video that is recommending, but it could also log the ways with which it takes decision over time. For example, if it has a vectorized parameterization of every video and we know that these parameters depend on some dimensions, like what topic the video is, how long the video is, etc., logging this kind of metric could be better to simply audit the algorithms and simply logs of what the algorithm did. Okay, okay, should we move to hardware or do we want to still discuss something? Okay, let's go for hardware. So one of the points they raise in the article, which is a very natural comment to raise, is that you have to make sure that your hardware is secure in the sense it is running what it is supposed to run and only what it is supposed to run. We briefly mentioned that in our previous episode where we mentioned maybe something that is not present in this report, which is the very low-level vulnerabilities you can have in the CPU. But again, when discussing the paper of Trust 40 AI, we more or less all agreed that the threat model for most machine learning, there is so much threat on the high-level software part that this is probably not one of the top priority in terms of risk. For now, I don't know what you recall the discussion on that. And this is also the, this was a topic of your PhD that even though one third or 20% of the hardware that you were using got hacked and start behaving in a different way, still implementing systems that are still robust and resilient to this kind of situation. Yeah. Yeah, so I think we can think of different attacks on hardware. Like the worst kind of attack would be a backdoor installed by the designer, as we discussed. But yeah, this is very hard to defend against. And maybe there are other priorities. For a field that is still struggling with the average and the median, I think this is not yet. The most likely cause for vulnerability. We still have way more obvious vulnerabilities in machine learning. Yeah. Yeah. And then there are other attacks, maybe some part of the hard work can be hacked and well, even this is not the most likely threat model, let's say. But one that's much more reasonable is that part of it crash. And this happens probably all the time and especially if you have bigger and bigger systems. But yeah, you can also mitigate this with the research as done by MIDI. So nevertheless, one of the topic they mentioned is that something we can do to make hardware more secure is they call it... Trusted Execution Environment? Yeah, exactly. So in this Trusted Execution Environment, it's well known how to make them and use them for usual hardware, like CPUs that we use all the time. But machine learning uses more and more specialized hardware. And we can expect that in the future, there will be new specialized hardware designed specifically for machine learning and artificial intelligence systems. And the problem is that creating this secure environment incur a cost. And what they think will happen is that this cost is... As we update the software we use, this cost will need to be paid every time to create secure environment on this new type of software. So yeah, the authors recommend that we should work towards building this kind of secure environment on the hardware. But they realize that it's also a costly thing to do specifically for AR, which updates its hardware often. Yeah. So one of the people who had our discussion shared a couple of papers that show that there is ongoing research in designing, in particular GPU, so graphical processing units that are used to parallelize the computation in neural networks learning. This is research into making this more into a trusted execution environment. Yeah. Another thing that was discussed in this section was providing academics with more hardware. So typically, to test for the idea is like to test the YouTube algorithm for instance, all very big things like this, you're going to need to test through a lot of angles and this requires a lot of computational power. Also, like if you want to just keep up with the latest developments in AI, like these days, the big papers, things by open AI for instance, represent, I don't know, was it thousands, hundreds of thousands, maybe millions of dollars? There's maybe a connection to be made with... So recently there are calls for reproducible research in NL and we see in the submission platforms of NRIPS and ICML which are the two major venues for machine learning. But you have no incentives to explain how reproducible is your results and your code, et cetera, and you have to provide some helpful information for people who would like to produce your work. Two years ago, one year ago, an effort led by Joel Pinot on reproducibility was to take codes or papers, codes and et cetera from these papers and use it as students' projects where they would verify the reproducibility of the work. And within this discussion, so many people, a member suggested that, so like I remember, like when Joel Pinot was giving this talk on reproducibility at EPFL, I asked her, why want you, so if you want to have some built-in reproducibility guidelines, checkpoints in the submission and also for the reviewers to check the reproducibility, why not provide computing power to academics who would like to verify the reproducibility, especially that like we have some papers, of course, this is not yet the average paper but some exceptions. Recently there was a paper from, I think DeepMind and Ben Resht was ranting and explaining that to verify these experiments, you need one million dollars of money and you can't expect academics to verify claims or results. So one thing you can imagine is that you could impose or incentivize companies to provide computing power for academics when reviewing their work. But again, I don't know what's the cost benefits for this, this could be an idea. Yeah, but it's not clear, how are you going to need some legal thing? I think legal and the financial parts, there is also the career incentive part. I would see any academic incentivized by their employer to spend their time verifying the reproducibility. We are not even incentivized to verify the reproducibility of other academics. So I don't see a word we would be incentivized to either produce the company's work. It's not even our own work. So yeah, I think beyond the financial and legal parts, I think the real bottleneck is academic incentives and career incentives. That would block any hope for this part. Yeah, yeah, there's like this reproducibility concern but I guess there's also a competitiveness concern and also a concern for the ability to audit large scale systems. Yeah, and just to get this computing power, like it's going to be costly at some point. But yeah, it will be like a lot more costly than because like in computer science, most of the cost of an academic is I'm guessing still like wages. And as opposed to some other fields where like in biology, they have these very expensive equipments. Like compared to this, I don't know how computing power would compare to a new MRI machine or stuff like this. I don't know. I think it's about equal. I know some colleagues at PFL who spend about the same number of CHF in computing power as they are on the salary. I expect that for people working with an even bigger system than the price of the salary doesn't matter compared to the price of the hardware they use. Yeah. Okay. Do you want to talk about a specification? Yeah. So auditing auditors. Oh, you can talk about. So yeah, now I guess we're going to talk about things that are a bit beyond the scope of the paper. So one thing we discussed is the problem of auditing auditors. And I think it's neglected the challenge because it's, I don't think it's very easy at all, like to understand like if auditors are doing what they're supposed to do, but also if they are actually auditing the things that they should be auditing. So we're talking about trust. There are two issues. One is like, is the person willing to do what we would want him to be doing? And the other question is, is he competent enough to do the things that we want him to be doing? And I think both should be evaluated at some point. And yeah, so to understand if auditors are doing the right job, like we ask a few questions about the banking industry or the restaurant like health care industry about the standards of restaurants or hygiene in restaurants. And it's not clear that auditors are doing actually audit as they should be. There's this importance of having a good relationship with the person you're auditing. And it's actually like an instrumental to do good auditing. Like you want to create this trust between the auditors and the oddity. And because of this, then you have a lot of sorts of human challenges, I guess. And in the case of algorithms and additional time, is that you can actually wonder if what the auditors are searching for is actually what's most relevant. For instance, to take a very concrete example, for contact tracing, there's a lot of very diverging opinions. And for instance, what WTO is kind of recommending is not necessarily aligned with what the Human Rights Council is saying about this technology. There's a tension here. And it can be very, very complex if you move to a system like the YouTube algorithm. Like what should it recommend? Like for instance, or for LinkedIn, for instance, should it recommend as many times this kind of job to men and women, even though in this kind of field, like in, for instance, in programming, it turns out that there are a lot more men than women. So does this mean, well, yeah, just the tension in fairness, well-known between individual fairness and group fairness. Yeah, it's not that easy to know what should be done. And this, I guess, leads me to the other point, which is the point of specifying what it is that we want for these algorithms. And I fear it may be neglected by, at least for the paper that's not talked about this, and like I can understand that it has had other priorities, but I feel personally that specification is a huge part, like is a critical part of building trustworthy algorithms. Like if you want to have a collaborator for a given project, you want to, like if you want to trust him, like you want to make sure that his incentives are somewhat aligned with what you want to do. I think it's part of trustworthiness. And if he has hidden motives, then it's not going to be good for you. Then so the answer to this is that so that you trust your collaborator and instead of giving her or him, so yeah, instead of giving her or him, like the mindset of the company or the spirit of your intentions, you start giving them more and more detailed to-do lists of tasks. And eventually this becomes a very costly to do. And you'd always look for aligned collaborators, not for collaborators for which you spend time. For each of you actually, you and him or her, spending a lot of time just specifying tasks and exact intentions. Yeah. I feel like the people may have started with the assumption that the specifications are unknown or not too hard to specify. And then it's mostly a matter of auditing and verifying that they are satisfied. But the specifications are a huge mess. If you think about concrete problems, like even like contact tracing, but especially if you move to recommender systems, just specifying what it is that we want these things to be doing is huge. And any specification is going to be extremely complex. Just writing down the specification is probably going to be impossible. And even if you write it down, if you write an algorithm, probably there's going to be specification gaming. There's a recent DeepMinder paper about this. So it's also called a reward hacking. So if your specifications are somewhat what you want, but not exactly what you want, then the auditory can fully satisfy all of them, all of your specifications, and turn out to do something that it is not what you wanted to be doing. So I feel like thinking about specification in general is neglected. Okay. Okay. Good for today. Yes. Cool. Thank you. And so next week, what will we discuss? So thanks for listening to the podcast. Next week, we will discuss the paper titled Closing the AI Accountability Gap, defining an end-to-end framework for internal algorithmic auditing. Because today we talked a lot about auditing, but it's still unclear what auditing actually means. And so that's why I'm looking forward to this interesting discussion on how to better audit algorithms and maybe do it with the right specifications. Bye. Bye.


Lux presents Hollywood. The Lux Radio Theatre brings you Loretta Young and Miriam Hopkins in The Old Maid. And as our special guest, Miss Zoe Akin's author of The Old Maid. Ladies and gentlemen, your producer, Mr. Cecil B. DeMille. Greetings from Hollywood, ladies and gentlemen. Tonight we bring you the drama of a strange masquerade. The story of a woman who remade herself for the eyes of the world, who suppressed forever the yearnings of a great heart and lived a magnificent lie. Charlotte Lovell was called an Old Maid, but tonight we reveal her romantic secret. We go back to the days when she was young and beautiful and discover the reason for the deliberate sacrifice that her life became. The Old Maid is an intimate play, so intimate that it could never have been written by a man. It reveals too great a knowledge of the feminine mind and heart. And to this knowledge, Miss Zoe Akin's, who adapted the play from a story by Edith Wharton, brought a deep human sympathy which gave her characters a universal appeal on both stage and screen. The Old Maid was a Pulitzer Prize drama on Broadway and recently became a memorable Warner Brothers picture. In studying the script for the first time, it occurred to me that the term Old Maid is gradually passing from use. Today we have bachelor girls and single women, but few Old Maids. Perhaps that's because the words denote a rather drab appearance. And nowadays a dull drab appearance is old-fashioned when the help-like-luck's toilet soap is available. In tonight's play, the roles of Charlotte Lovell and her cousin Delia are of equal prominence, which presented an unusual casting problem. We needed two actresses who could be convincing both as girls of 20 and women of middle age. And we needed stars who were willing to appear unsympathetic at times for the sake of the play. And we found them in Loretta Young and Miriam Hopkins. And now we begin a story as old as the human race and as new as the sacrifice some mother must have made today. The Old Maid, starring Loretta Young as Charlotte and Miriam Hopkins as Delia. The curtain rises on Act One. In the past hundred years, the tempo of life has quickened and the ideas of a century past have been laid aside with the quaint dress and customs of their day. But one great ritual has remained almost constant. The wedding. Then as now there was the same hurried attention to last-minute details. Then as now the bride waited in her room, waited for the knock on the door which would summon her to her bridesmaids and the groom. The year is 1833. Our bride in a lovely old-fashioned wedding gown turns this way and that in front of her mirror, while her maid smooths out the lacy folds of the long white veil. You don't seem a bit nervous, Miss Delia. Nervous? Oh, no, I don't think I am. I wish I wasn't. I wouldn't be so clumsy. Landud think it was me getting married. I mean, my hand shakes so. Oh, take your time, Nora. We've still a few moments. You was always one to take things calm. You're not superstitious either, are you, Miss Delia? Oh, not very. All the same, you ought to have something borrowed and something blue. I know something old and something new. Well, my lace is old and everything else is new. But I've nothing borrowed and I've nothing blue. I'd feel easier in my mind if you had. Oh, Nora, then you'll have to lend me something. But what if I got missed that you'd wear? No, let me see. A garter. Lend me a garter. Gracious. Do you mean it, Miss Delia? Yes, of course. Why not? Well, please to look the other way then. Go ahead. I don't see you, Nora. It's your cousin, Miss. Charlotte. Oh, of course, Charlotte. I have something for you, Delia. Oh, how pretty you look. Did you, Nora? No, Miss. I could hardly believe me eyes. It must be the dress. No, I, of course, is the dress, Nora. I don't often have a dress that's been made especially for me. Here's the garter, Miss Delia. Thank you. This is something borrowed, Charlotte. You haven't anything blue I could carry, have you? It's odd you should ask me that, Delia. Here. This is blue. What is it? A cameo. It's a present for you, Delia. A present? From whom? From Clem Spender. Clem Spender? Here we go, Nora. Yes, Miss. Charlotte, tell me. He asked me to give it to you. But I thought Clem was in Italy. He came home today just in time for your wedding. He hadn't heard you were going to marry someone else, Delia. He thought you must be ill because you'd stop writing. Oh, Charlotte, I'm afraid... Of what? Of Clem. Of what he might say or do. Oh, you know Clem. There'll be champagne, and if he should take a glass too much, we'll watch him, Charlotte, will you? And be kind to him. I don't see how anyone could ever be unkind to Clem. Oh, don't. Please don't. Why, I didn't know you cared that much to me. You knew I loved him. I knew you told him so. Why, I wasn't crying. No, no, you won't cry. You won't cry if you keep saying to yourself, over and over, I'm marrying a Ralston. I am marrying Jim Ralston. Oh, yes, I'm marrying a Ralston, and I'm glad. Yes. You know, you know, Delia, when Clem went to Italy to study painting, you promised to wait for him. I did wait. I waited almost two years. If Clem wanted a wife, he should have stayed here and made something of himself. Oh, Delia, why didn't you have the kindness, at least, to write to Clem that you were going to marry someone else? I intended to. I... I tried to. But you were ashamed. No. No, I wasn't ashamed. I... Oh, I'm fond of Jim, and it seems so hopeless to wait for Clem. I couldn't bear to be an old maid, Charlotte. I shall be an old maid, because the man I love doesn't love me and not for any other reason. Charlotte? Oh, I'm so sorry. I didn't know there was anyone known as ever known. But I would have waited for him all my life. You think so, but life doesn't stop. Oh, a woman gets lonely. She wants children in a home of her own. I could have waited. Oh. There's the wedding march, Delia. Yes. I'm ready. All right. Remember, watch Clem. Oh, I'll not forget. Here's your present. Something blue. Oh, Charlotte. I'm trembling, so... Let no man put us under. I now pronounce you man and wife. Wasn't she beautiful, Miss Charlotte? Nora, where is Mr. Spender? Where did he go? He went by through the hall. Miss Todd, the balcony. Oh, thank you, Nora. She seemed very happy, didn't she? Yes. Well, why not? She's married to a fine person and rich. We mustn't forget that, must we? The Ralston Millions. How important they must be to her. Oh, Clem, please don't. Oh, don't pity me, Charlotte. I don't want pity. I'm glad, because you don't need pity, Glen. If Delia wanted the things that, well, Jim Ralston could give her, if she wanted them more than she wanted you, you could never have been happy, could you? I might have been. I might have made her happy, too. Why didn't she give me the chance? What will you do now? No. Go back to Italy, I suppose. There's a packet sailing next week. Oh, is that wise? I mean, to go off alone? You shouldn't be alone now, Clem. You should have friends around you now. Someone who could, well, help you forget, perhaps. Who? Well, I could try, Clem, if you'd let me. Oh, I'd like to try. Well, Charlotte... Oh, please. Please don't look at me like that.  Charlotte, forgive me. You're so very sweet. And you could help. I'm sure you could. Oh, Clem. Let me through, please. Here's the moment, lady. All visitors are assured. Yes, I know. We're sailing in ten minutes. I know, I'm a passenger. Oh, good evening. Go round aboard. Oh, thank you. Mind that hoist. And it's dark. I'll be careful. There are the decks there. Do you want someone hurt? Move along now, ma'am. The tide's over the shore from the hour. You weren't going to make it? Well, I had to wait till they were all asleep. Charlotte. Yes? Are you quite sure you're not sorry? Oh, no, my darling. I'll never be sorry. Well, I'll let you unpack your things. All right. I'll be out on deck. Clem. Clem, I must write a note to Aunt Carrie. Of course. I'll have a boy come to pick it up. All right. Goodbye for a while, darling. Kiss me. Oh, Charlotte. Goodbye, sweetheart. Dear, perhaps someday I may be forgiven for what I am doing. I love Clem Spender. I have always loved him. And I must try to make up for the hurt he has suffered. I believe I can. By the time you receive this, we shall be far at sea. Please tell Delia. Explain to her that... Oh, yes? Who is it? First mate, ma'am. Well, come in, please. Sorry. Are you Mrs. Spender? Why, I... Is anything the matter? Quiet, ma'am. Quiet. Well, what is it, please? Mrs. Spender, there's been an accident. What? It was unavoidable, ma'am. The boom swung fast. And I guess he didn't see it. Where is he? He hurt badly? Oh, please, ma'am. Well, tell me, please. Ma'am, your husband's dead. Dr. Lanskill is here, Miss Delia. Lanskill, how are you, doctor? Good morning, my dear. And many happy returns of the day. Oh, thank you, doctor. And many happy returns from me, too, Miss Delia. And thank you, Nora. Miss Delia. Miss Delia. I've been married exactly six years, and Nora still can't get used to it. Perhaps because you're still as lovely as a bride. Miss Delia. Oh, doctor. Sit down.  You seem to be looking quite well. Oh, it wasn't for me. I wanted to speak to you about Charlotte. Oh. What's the matter with Charlotte? Well, I don't know. Perhaps nothing, but... Doctor, you know she's going to marry Jim's brother next month. Of course. Why? Well, I've been Jim Ralston's wife for six years. When Charlotte becomes a Ralston, she'll learn they're very exacting about some things. Go on. Doctor, is there any reason why Charlotte shouldn't marry Joseph Ralston? None, whatever, so far as I know. I'm talking about her health. You remember about five years ago, she took a trip south. She was gone almost a year. Yes, sir. Lung fever. Yes. Oh, but you may take my word, Delia. There's no danger of a recurrence. Charlotte is perfectly well. Oh, thank you, doctor. I am relieved. But I thought she was looking badly lately, and I was afraid. Well, I imagine it was just overwork. I don't see why she does it, really. You mean the nursery? Yes. Taking a lot of strange children for charity, say. Seems to me they'd be better off at their homes. Perhaps, but she's doing a fine work, Delia, and she likes children. You go there often, don't you? Yes, I'm going today as a matter of fact. Charlotte asked me if I'd drop by and look at one of her little girls. Doctor, do you suppose she'll miss it after she's married? Miss the nursery? Because she'll have to give it up, of course. Joseph will insist. Will he? Oh, if I know Charlotte, my dear, she may do a little insisting herself. Oh, wow. Hi, children. Hi. And remember, be early tomorrow. We will. Good night, Miss Charlotte. Good night, dear. Good night. Good night. Good night. Bridget, Bridget, where are you? Coming, Miss Novel. Tina, supper's on the table now. Oh, thank you, Bridget. Tina, dear, your supper's ready. I want to play. I know you do, darling. But aren't you hungry? No, ma'am. Well, just the same. You must eat. I'll run along in the kitchen. All right. That's a good girl. Bridget, did you give Tina her milk today? Oh, yes, ma'am. Oh, thank you. I'm afraid Tina needs more attention than the others. Yes. I noticed she was a little pighead. Well, it's not only that. She's had a cold, of course, but, well, the chief difficulty is that she's very shy and the other children like to tease her because... because she's a foundling. May I come in? Oh, Dr. Lanskill, of course. I have brought some very distinguished guests to see you. Well, here, let me light the illuminating gas in the passage. Too late, Charlotte. Delia. Well, how nice of you to come. And, Joseph. Good evening, darling. I told Delia I was stopping by to see you and she wanted to come. Oh, not of her own free will. She's never been here before, you know. But that doesn't mean I haven't been interested, Charlotte. Of course not. Where are all the children? Oh, they must have gone by this time, haven't they, Charlotte? Well, all but one. She, uh... She lives here with Bridget. She lives here? Yes. Oh, may we see her? Well, uh... Oh, yes, of course. Uh, Bridget, will you bring Tina in, please? Yes, ma'am. How is her cold, Charlotte? Well, I thought you might look at her, doctor. Oh, come in, dear. This is Tina. Good evening, Tina. Don't be shy, darling. This is Mrs. Ralston, Tina. And how what do you do? Creepy. Oh, that's right. Tina, Tina what? What's your other name, child? Clementina? No. Your other name. She doesn't understand, either. How old are you, Clementina? You're five, dear. Five? And don't you know your parents' name, Clementina? No, she doesn't. Oh. Bridget. Bridget, you better take Tina now. Yes, ma'am. Oh, no, let her stay a moment. Do you want to stay, Tina? Yes, ma'am. I'd like to stay if you do. Well... Oh, affectionate. Yes, ma'am. I've never thought I'd take to anybody like that before. She's very sweet, isn't she? Tina, Bridget is waiting there. Good night. Good night. Good night, Tina. Oh, she's a lovely child. I wouldn't worry about her cold, Charlotte. She looks much better today. Delia, I'm afraid I've got to run. Can I drop you at the house? Oh, so soon. Well, if I must. Goodbye, Charlotte, dear. Goodbye, Delia, and thank you for coming. Good night, Charlotte. Good night, doctor. Are you coming, Joseph? I'll see you later. All right. Good night. Well, are you ready, Charlotte? To go? Oh, no, I have some things to do yet, Joseph. Can't they wait, dear? I'm afraid not. Charlotte, do these children need all of your time? Well, no, not all. Why? Well, no, I should think you could spare some for me. I'm very jealous, Charlotte. Jealous of my waves?  Well, you can give me what I want most for them. What is that?  My presence? Where? Here, Joe, with me. Charlotte, you're not really counting on still coming here every day after we're married. I certainly. It never occurred to me that I wouldn't. It never occurred to me that you would. Well, why shouldn't I? Why should you? Well, you know because of all these unfortunate children mean to me and all I mean to them, because it's my duty. Duty? A married woman's first duty is to her husband or home. Joseph, you mean because I'm making a good marriage, I'm supposed to get rid of these poor darlings as if they were dolls I'd finish playing with? Oh, no, I can't. I won't. Well, they need me, Joe. And I need you. Oh, but it's not the same. I'd be giving them only a little part of each day. Why can't I do that? Because people might think it is. Oh, people, that shows your roast and Joe, afraid of everything that everybody else doesn't do. Charlotte. It's not fair of you to ask me to give up these children. Very well, it's not fair, but I ask it all the same. Joseph, let's not talk anymore. Not now. Goodbye, Joseph. After that, after all, what I'm asking isn't much. You, you'll see that. I'll be waiting for you to send for me when you do see me. Did you call, ma'am? Why, I, oh, no, I just wanted to see Tina to, uh, to kiss her good night. Here she is, ma'am, already prepared. Good night, Tina. Good night.  Why, Charlotte? What are you doing here at this hour? May I speak to you, Delia? For certainly, darling. What's the matter? Delia, I want you to help me. Help you? With Joe. Delia, he wants me to give up my children. Oh, well, of course he does. Not any man would. But I can't. I told him I can't. Well, now you must try to be sensible, Charlotte. Whether you agree with Joe or not, you must realize you'll never have another chance like this. After all, one's own babies have the first claim. That's just it. How can I give up my own baby? Yours. Which of the poor waves do you call your baby? I call my baby. My own baby, Delia. Your own? Yes. My little girl, Tina. No. Yes, she's mine. She's mine, Delia. Oh, my poor darling. Oh, Charlotte. Tell me everything. Oh, what do you want me to tell you? That's all you need to know. You, you love someone. Oh, my poor Charlotte. I can't give up my own baby, can I? I can't, Delia. No, no, of course not. We must think of some way. Hold it. Delia, if you could talk to Joseph, persuade him that no harm can cover my going on with my nursery. Oh, please, Delia. Oh, don't cry like that. I'll do my best to help you. Delia, if only you can. If, if you could just tell Joe. No, I couldn't. He'd never forgive me for that, you know that. No, you're right. You couldn't tell him, of course. Charlotte, when was it? When you went south that time? Yes, Delia. Then Dr. Lanskel knew, didn't he? Yes. Yes, he was very kind to me. But he can't help me now. You're the only one I can turn to, Delia. Oh, if I only knew what to do. If there was only something I... Well, Charlotte, we're talking as if, as if there went someone else to consider Tina's father. Does he know? No. You never told him. Well, I couldn't. Why? Why, darling, you? Because he's dead. Dead? Yes. Charlotte, don't you want to tell me who it was? Oh, it doesn't matter, Delia. You wouldn't understand it. But how can I help you if you don't trust me? What happened? I told you all you need to know. How did he die? Was it six years ago? He was killed. An accident. Delia. Clem, Clemspender. Yes, you and Clemspender. Oh, Charlotte, how could you? When he didn't even pretend to love you. Oh, I knew you wouldn't understand. I shouldn't have told you, Delia. No, I don't understand. Hadn't you any pride at all? Well, you needn't be so contentious. I've never been sorry. Not really sorry, Delia. Now you've come to me. Well, what do you expect me to do? Oh, have you forgotten so soon that you were going to help me? Well, what can I do? Do you think you can marry Joe with a thing like that on your conscience? But what he doesn't know, what nobody knows, can't hurt him, Delia. And I'll make up for it in other ways. Oh, he won't be sorry he married me. But you don't love him as much as you love Clemspender. Well, I... I love him differently, perhaps. As you love Jim. Oh. Differently. And I need Joe's love. Well, I... I don't know what to say. I can't think. You must give me time. No, there is no time, Delia. I must decide now, tonight, and I can't think either. Please, dearly, do something. Whatever you do, you can't make things worse. Whatever happens... Yes? You'll have to trust me. Leave it entirely in my hands. To do it, I think, best. Yes. Yes, sir. I trust you, dearly. That was the first act of the Old Babe. Act two sets the course that determines the rest of Charlotte's life. But during this intermission, Mr. Ruick has a short presentation for you. Before our stars Loretta Young and Miriam Hopkins return in act two of the Old Maid, let's hear what our trio have on their minds this evening. Hello, Mr. Ruick. What's on your mind? Hello, girls. Well, first of all, I've been wanting to ask you what it was you were humming just before the show tonight. Oh, everyone's humming it, Mr. Ruick. It's called an 18th-century drawing room. Oh, yes. It's a very romantic song about two old-fashioned lovers, the man in silk and lace, and the girl. Yes, the girl. What was she like? Well... Here is one thing that we know This must have had a smooth complexion It is natural to assume For she was enchanting in that 18th-century drawing It was in love with her name today Gentlemen still prefer... Girls, you've turned the description of a beautiful girl in an 18th-century drawing room right into a message to girls in 20th-century living rooms Well, we want to remind them how lucky they are to have swift, easy complexion care We want to be sure they appreciate luxe-toilet soap Most women do because luxe-toilet soap is real beauty soap It makes your skin feel so much softer and smoother That's because luxe-toilet soap, active lather, removes every bit of dust and dirt that might otherwise remain to choke your pores Active lather gives you the protection of perfect cleansing And every woman knows how important that is to complexion beauty Don't take any chances on losing your complexion appeal Use the soap that nine out of ten famous Hollywood stars use every day And never forget that three-minute luxe-toilet soap complexion care at bedtime Smooth, soft skin is so important to your appearance Your charm Why don't you decide now to get three cakes of gentle, white luxe-toilet soap tomorrow? Our producer, Mr. DeMille Starring Miriam Hopkins as Delia and Loretta Young as Charlotte with Helen Wood as Tina Only a few minutes have passed Shocked and jealous at the news that Tina is the child of Charlotte and Clems Bender Delia is determined to break up the marriage Downstairs in the dim candlelight of the drawing room She faces Joseph Ralston but cannot bring herself to tell him the truth Perhaps in her place, Joseph, I'd feel exactly as she does And since it seems to be a matter of deciding between you and the children Well, I don't see what you can do if she chooses them If she chooses them, it's because she thinks she's doing what is right In that case, I must be the one to give in Joseph, you mean you've decided that Charlotte's to do exactly as she likes in this matter? Exactly as she likes so long as she doesn't overtax her strength And I'm going to tell her so at once No, it's no use What?  But why, why wasn't I told at once? Well, because, because Well, such things aren't easy to tell Charlotte ill Good evening Fine concert tonight, Delia You should have been there Is something wrong? Dr. Lansko Yes? When Charlotte went south five years ago It was for lung fever, wasn't it? Oh, I thought everyone knew that, Joe That disease is in her family, isn't it? Everyone knows that, too Why do you ask? Suppose she began coughing again Has she coughed? What would it mean? It would be a very bad sign, of course That's all Good night What did he mean, Delia? He's not going to marry her But I, I don't understand this Dr. Lansko Charlotte didn't go south because of her health I know why she went That's why she mustn't marry Joe Who decided that? I did With or without her consent? Without You've taken a great deal on yourself, Delia You think I've done wrong? I think it's a sacrilegious thing To lay so much as a finger on another person's destiny But she came to me herself to beg me to help her So she wouldn't have to give up her child This was the only way And I can make it up to her I'll take the baby into my own home Delia And Charlotte, too She'll have a proper home here Clemspender's child must have every advantage Clemspender's child I think I understand Good night Dr. Lansko Yes? You're right I've done a sacrilegious thing Deliberately The wisest of us make mistakes, my dear Sometimes we never learn Whether or not they were mistakes I hope yours wasn't Speak to Joseph Charlotte, you can't marry Joe, can you? And keep little Tina Well, not keep her with me No, but... Old dealer You're trying to tell me I must give her up I promised to help you to die But I didn't promise that you should marry Joe, too Well, I've done the best I could You and little Tina, she'll be together always But Joe, you didn't... You didn't tell him, Delia I couldn't bear that What did you tell him? That you were coughing again Oh, Delia Well, I had to tell him something If you were to keep the child Engagement had to be broken So you frightened him away I see He's dreadfully unhappy, but He accepts your decision My decision Well, mine then And if I don't accept your decision What if I tell him the truth? What if he should say he'd forgive me? If there'd been any hope of that Would you have come to me? Or give up these foolish thoughts, Charlotte And try to realize what it's going to mean To be with your child always That's what you wanted most, isn't it? Well, yes, but... Well, I've done what I could I've spoken to Jim about it You had to come here You and Tina Here? In this house? Yes But not as her mother Well, that would be impossible You must never know You realize that, don't you? Yes Yes, I do Don't shout at me Don't Everything will be all right Oh, I promise you You must have the best of schooling The very best Certainly, Mrs. Rom She must be treated as my own daughter Of course London and Rome I think that's sufficient For Miss Tina's first trip abroad You will do nicely, Mr. Harvey Thank you Oh, Nora, you've forgotten One of the candles On Miss Tina's birthday cake She'd never forgive me, Mum 18 of course Tina plays beautifully, doesn't she? Charlotte, did you hear me? Yes, Doctor I was saying, Tina plays very well Yes, she does But you'll be late for the ball Unless you start soon You know, when you consider How pleasant life is in this house You must feel a deep satisfaction In having managed to give Tina such a home If Tina's life has been pleasant in this house It's Delia who's made it so, not I She calls Delia mother We won't talk of your own sacrifices For the child Charlotte But Delia's done her best too Her best to spoil her From the day Jim died 12 years ago There's nothing now Nothing at all that Tina doesn't have Or can't have I ask him for it I must be going Doctor, are you leaving? Yes, Delia I have a patient to see on my way home Good night, Doctor Oh, Tina Let me see you You're very beautiful tonight We're going to a ball Doctor Lanz, can you know landing? Of course How do you do, sir? May I help you with your cotton? Thank you Well, good night all Good night Tina Yes, Charlotte Tina, the carriage has been waiting for 20 minutes And horses don't like standing on a cold night like this They're going now, Charlotte Yes, we're just going It doesn't matter if we're late at a ball It's not unbecoming to anyone To make a habit of being on time I know The early bird catches the worm Take care of the pennies And the pounds will take care of themselves Tina I see nothing to laugh at People who have no regard for punctuality Usually have no regard for other things And Charlotte, please Must always find fault with me You're not perfection, my child I know I'm not perfect Tina, dear, you'd better go I'll wait outside, Tina Very well Oh, mother, I wish you were coming too But this is a young people's ball doubt You're a young mother And Charlotte doesn't mind being alone Tina, please Mother, do you like my hair like this? Lanning said it's lovely I hope everyone likes it Tina, why must you always think That people are interested in you? Why shouldn't I? Aren't they? My dear, what would people think of you If you talk like that?  Now go Good night, mother Good night And don't be too late I suppose you felt you had to scold Tina Because of the way she spoke to me I wanted to realize what it is to be respectful She thinks I can't understand her Because she considers me an old maid Yes, a ridiculous, narrow-minded old maid What else can she ever think of? Oh, my dear Oh, you needn't pity me She's really mine I do scold her But I don't want to be hard on her As it is, I always practice what I'm going to say to her If it's anything important So I'll sound like an old maid Aunt talking and not a mother I know, Charlotte, I know Delia, have you noticed that Tina has changed? Changed? Since when? Since Lanning Halsey has been coming here so often It's very natural for him to come often He's very much in love with her Yes But do you dislike it? I mean as a husband for Tina Tina, I cannot afford to pick and choose But there's nothing against Lanning, is there? Except that he seems uncertain As to his choice of a profession Uncertainty about a profession May cause uncertainty about other things, too I don't understand you, Charlotte He hasn't asked Tina to marry him How could he? He earns nothing and his allowance will be stopped If he marries against his parents' wishes Oh, but Charlotte, you... Unfortunately, Delia, my girl has no fortune and no name And if the careful mother we know Has warned her son against becoming interested But after all, Tina's happy with us She doesn't need to marry anyone Tina, an old maid? Never! My child shall have her life! Charlotte, why do you twist my words? I mean that Tina's young, she isn't 20 yet She can wait No We've waited long enough You're thinking of Charlotte What are you going to do? I don't know You'd better go to bed, Delia There's no need in both of us waiting up for her And it's my duty, not yours Very well Good night, Charlotte Good night Probably walk through the snow What, Tina? You walked home with Lenny Why, you imprudent child in this wet snow You shouldn't do these things, Tina You were always a delicate child Do you know how late it is? Tina You must go, Lenny Not yet No one's about This is very wicked of us Is it? Why? I don't know Oh, Lenny, I loved it The snow and the moonlight And icy trees in the square And being with you last all alone What are you doing?  You won't, will you?  Oh, I'm sorry, Miss Lovell I was speaking to Tina And Charlotte, stop Tina Oh, Mother I've not been able to sleep I heard you come in It's late I know, I am sorry You'd better go now, Lenny Yes, Mrs. Roche Don't scold him This is Tina's fault, not his Any boy would have done the same As she'd permitted No, no, it's not her fault Tell you Tell him not to come here again It's your house or I'd tell him myself And Charlotte, take that back Mother, tell Lenny he's to come When he wants to Tell him, Mother It is your house, not hers Don't bother, Mrs. Lawson I'm going away soon in a week, probably So it doesn't matter very much Whether I'm forbidden this door or not I'd only have come again to say goodbye In any case Goodbye I'm sorry, Tina Lenny You see You see what she's done, Mother She's driven Lenny away Oh, no, my child I've not driven him away If he's not coming here again It's because he finds it awkward When he has no intention of marrying a girl Who is so free with her kisses That's not true That's not true You know it's true Your aunt's right, Tina If Lenny goes, it's because he doesn't care As much for you, don't you think?  You've been good to us, Tina But I understand my duty now If I am to save my child, I must take her away Charlotte, what are you saying? I must take Tina away from here We must go somewhere we were not known We wish to live among plain people Leaving plain lives But she can find herself a husband And make herself a home You take Tina away from me now I'm not ungrateful, Bea Oh, don't speak of gratitude What does it matter Whether you're grateful or not It's Tina I'm thinking of Of course it's Tina you're thinking of Tina and Clemspender You're insane, Charlotte I've not thought of Clemspender for years Oh, but you have You have, Delia You've thought of him and thinking of Tina A woman never stops thinking of the man she loves You can't forgive me Because Clemspender didn't quite break his heart over you That's why you were like keeping me at your mercy And taking his child from me And suppose that's true She's your child too And to take her away now From the life you've made such a sacrifice To give her that would be too cruel Even more cruel to her than to me What's ahead for her here, Delia For a girl without a name Or a penny among cautious people Like the whole of Lismarckine You've done all you could for her But you see what's come of it so far Now I... No, I've not done all I could But I'm going to if you'll let me I'll adopt Tina legally Adopt her? How could that help?  My pride? Why, what pride have I accepted my child? Then you are going to sacrifice her When she might have everything she wants Even landing Halsey for her husband It's just as she said He will love her if she wants him to And if she has money of her own And my name while the Halsey's wouldn't dare Find her a bad match for their son Charlotte, give her this chance What mother wouldn't? If landing takes her away from us both Well, it won't be like really giving her up Couldn't we just go on loving her together? Or couldn't we, Charlotte? Mother, are you coming up? I've been waiting for you Charlotte, go up to her You're her mother now It's on the second act of the old maid But the love Charlotte feels for her child Cannot be denied so easily As we see in a moment When our stars return for act three Right now, here's a gentleman with a brief and pointed message Miriam Hopkins, Loretta Young and Helen Wood Will be back in a moment for the third act Of the old maid But before they do I'd like to tell you about some conversational bridge That I overheard the other day Conversational bridge? Why, what's that, Mr. Rui? Well, my wife was having a bridge party But the ladies didn't seem to be playing cards Raisy, but I've been done Did you see her in when tomorrow comes? She always looks so pretty I'll bid a heart I'll pass She is lovely, but my favorite is Madeleine Carroll Wait till you see her in honeymoon in Bali What beautiful hair and skin Even in the close-ups Sure, bid dear But did Jane bid hearts or clubs? Say for sure, gorgeousness You want to see Joan, Bennett in the man and the iron mass Can that girl wear stunning costume? Well, anyone is good looking as she is Can wear a bungalow apron and look like a million See here girls, can't we just review the bidding a minute? You see, no matter where you are You'll hear women discussing the screen stars They admire them, often follow their lead In the kind of clothes they wear Or the beauty care they use The makers of luxe toilet soap take real pride in the fact That nine out of ten screen stars use luxe toilet soap And they're just as proud of the fact That thousands of charming women all over the country Agree with the stars That fine white luxe toilet soap Is the right soap for their complexions Now here's the reason luxe toilet soap Gives your skin such gentle, beautiful care It has active lather That removes dust and dirt Every trace of stale cosmetics Active lather gives your skin the protection Of thorough, perfect cleansing It isn't only screen stars who must pass close-up tests Every woman knows that the eyes of her friends The eyes of those who love her Notice how she looks They notice whether her skin is smooth and clear Or dull and unattractive Use cosmetics all you wish But don't take chances with improper careless cleansing Use luxe toilet soap regularly, every day Make it your bedtime beauty care You want your skin to stay smooth and lovely Because it's nice to hear people say How attractive she is Start your luxe toilet soap care tomorrow, won't you? We pause now for station identification This is the Columbia Broadcasting System We continue with Act 3 of the Old Maid Months have gone by And Delia's plans for Tina have borne fruit It's June now, the evening before Tina's marriage to Lanning In the drawing room, the last preparation for the wedding Are being made Everything's done that can be done tonight I've been to the kitchen, I told Melissa Grimes She'd better count on, or 200 plates of ice cream I can't think of anything else, neither can I And Charlotte, you've been wonderful Haven't you, Dr. Lansko? Very impersonally, I think the only thing left is to go to bed You must be tired, Charlotte The bride's the one who should be sent to bed Say good night, Tina Yes, Tina, you should go to bed now But I'm not sleepy, Mother You can rest, whether you sleep or not Yes, you should go up now, Tina All right, but will you come up and say good night to me, Mother? Of course, because tonight... Oh, Mother, I'm so happy Too happy, too happy, dear Oh, Dr. Lansko Lanning says we've all been straws Whirling around on a sunlit torrent Ever since Mother adopted me And his parents said he might get married They've been sweet to me But if Mother hadn't cared enough for me To give me her name and the money They'd have not wanted him to care Oh, not since...  Aunt Charlotte gave me all her grandmother's jewels Oh, you're a lucky girl, I've seen them I know I am And my wedding veil She wanted me to wear the wedding dress That she was going to wear once, too, and didn't It seems odd to think that Anyone ever wanted to marry Aunt Charlotte Tina, you must go off to bed Now, hurry, dear All the same, I won't go to sleep Until you've come in and said goodnight I've told you I'll come Now run along Good night, Dr. Lansko Good night I must go, too You know, I think poor Charlotte is happy at last I hope so Doctor, I know you thought I took too much on myself When I kept Charlotte from marrying Jo that time I've not forgotten what you said About meddling with another person's destiny But I know that you've paid for your mistake If it was a mistake Good night, my dear Thank you Good night, Dr. Lansko Yes, Charlotte You're going up now to speak to Tina Yes Unless there's something you want to talk to me about first I told her I'd go up to her I understand But please understand me, too, if I ask you not to No, I don't I understand you, Charlotte Surely you realize on the night before her wedding A girl ought to have a mother's counsel Naturally That's why I must be the one to talk to Tina tonight Just tonight, I am her mother Charlotte, you're not going to tell her that Not now You hate me so much for it is all that Hate you?  Did you?   Well, sometimes Sometimes people don't think Sometimes they're selfish But you remember And you try to make a glad of the choice she made Without letting her know I asked you to I've always been so horrid And there's one thing more Yes When you go away tomorrow, at the very last moment You understand, after you've said goodbye to me And to everyone else Just as Lanning helps you into the carriage Yes Lean down And give your last kiss to Aunt Charlotte Don't forget The very last I won't forget And I'll go to her now Aunt Charlotte You didn't come to say good night to me No, Tina, I didn't So I've come to you Good night Oh, Aunt Charlotte I'm so grateful for everything And I love you so very, very much In a moment our stars, Loretta Young and Miriam Hopkins Are returning for a curtain call First, however, I'd like you to meet the author of tonight's play There are two groups of playwrights Who've contributed to the vitality of the American theater The first group is made up of those who write one successful play And are never heard from again In the other category are those who've written not one but many hits Writers like Zoe Aikens, author of The Old Maid And our special guest tonight At the last count she had seen 22 of her plays produced And there's a brand new one called Starvation on Red River, soon due on Broadway Miss Aikens, you should be used to taking curtain calls by now Unfortunately, there are not always curtain calls, Mr. DeMille Sometimes one looks round for the closest exit And runs instead of walks But I do remember one night The first time I heard an audience shout, author And found myself somehow out on a stage I don't know exactly what I said now But it must have been something intensely original Like, thank you Not being exactly a silver-tongued orator And I do remember also the little speech The producer made afterwards in the wings This is wonderful, he said, if that audience is on the level You say you're not a silver-tongued orator, Miss Aikens And I've noticed as far as politics and economics are concerned You keep your characters off the stump, too Yes, I've never gone in for propaganda in my plays That's a rare virtue When so many dramatists are going in for propaganda instead of plays You regard the theatre solely as a place for entertainment, then Oh, not at all I think good entertainment is a very rare and beneficial thing But I think the theatre, to be important Must be more than merely enjoyable A play is only important When it widens the horizon of the mind By enriching it with emotional experiences Other than its own And so gives light as well as pleasure Make them laugh, make them cry and make them remember Yes, make them remember if you can What is easily forgotten doesn't count The emphasis you place on human values in plays Has made you something of an authority on feminine psychology Whether you welcome the distinction or not I decline it with thanks After all, a psychologist is only a person Who can guess what others feel and think And I like to think my guess is as good about a man as about a woman Wasn't the old maid criticized as too sentimental? That is, before the audience flocked to the box office Yes, it was And I don't know why Unless it's because the ladies were hoop skits instead of slacks There isn't a sentimental line in the play To my way of thinking You know the Oxford dictionary defines sentimental As being prone to shallow emotions And after all, jealousy and love and hate Are the strongest stuff of life And when I first read Edith Wharton's story I thought it was intensely modern In the way it dealt with these forces The critic of the English Manchester Guardian said This might well be called the story of a crime Being the story of the murder of one woman's happiness by another And that hardly seems a sentimental tale to me But I think I've said enough And I know we all want to congratulate Ms. Young and Ms. Hopkins You are playing Ms. Aiken's figure in universal language And here are Loretta Young and Miriam Hopkins No longer in the period of candlelight and carriages Thank you, Mr. DeMille. It's been a real adventure being here tonight But before I go, I want to say something about the product behind this program Candlelight and carriages may be outmoded But there's one American fashion that will never change And that's the fashion of having clear, smooth complexion And part of it is the regular use of luxe soap I've been a luxe soap booster for a very long time now I agree with Loretta. Luxe soap is a definite help to a good complexion Now, won't you tell us what the Luxe radio theatre play is going to be next week, Mr. DeMille? Next Monday night, we're going to present Only Yesterday And our stars will be Barbara Stanwyck and George Brent Only Yesterday is a love story of unusual tenderness A drama of a woman's love which endured through every hardship that time and circumstance created Everyone who likes a real love story will thoroughly enjoy Only Yesterday Oh, it's a beautiful play and a fine cast, Mr. DeMille Good night, and thank you again Good night, Loretta And it's also been a great pleasure to have you with us, Miriam Thank you, Mr. DeMille. I've enjoyed it, too Good night Good night That was fine acting we had tonight Our sponsors, the makers of Luxe toilet soap Join me in inviting you to be with us again next Monday night When the Luxe radio theatre presents Barbara Stanwyck and George Brent In Only Yesterday Mr. Cecil B. DeMille, saying good night to you From Hollywood This is Melville World Inviting you to enjoy the increasingly popular Luxe daytime program The Life and Love of Dr. Susan In today's episode, Dr. Susan helped perform a delicate operation on her own son Tomorrow, she will learn the startling result Listen then For the time and station, see your newspaper The Life and Love of Dr. Susan comes to you in addition to the Luxe radio theatre Heard in tonight's play, where Juanita Quigley as Tina as a child Doris Lloyd as Nora Jack Lewis as Dr. Lanscale Fred Shields as Clem Spender Fred Mackay as Joseph Ralston Harold Daniels as Lanning Halsey John Fee as clergyman Lou Merrill as mate And Howard McNair as seaman Loretta Young's current picture is Walter Wanger's Eternally Yours Miriam Hopkins appears through the courtesy of Warner Brothers Pictures And is now appearing in their production of The Old Maid Lewis Silver's is from 20th Century Fox Where he directed music for drums along the Mohawk This is the Columbia broadcasting system


the heck do these go together?  Now I'm gonna be honest here I have some conflicting thoughts about these because when I first saw them I was like oh my god these are absolutely amazing the pieces are like so intricate so beautiful and then it's like well how do these go together? What do I do? How do I even start something like this? Now hit that like button if you're a fan of wooden puzzles and let me know down below what is your favorite wooden puzzle brand? So considering that not only am I again excited about trying it out but I'm also quite terrified I figured the best puzzle set to start with this you know first time wooden puzzle journey would be this particular one that was sent to me by Kathy S and this one I believe she said she got from Timu and it's a pretty small puzzle count here I don't know exactly how many pieces are in here but we got a really cute image of a turtle and I absolutely love turtles turtles were like one of my favorite pets aside from frogs I love frogs too but anyways let's get back on the topic here now with it being a very small puzzle in general I mean I kind of feel like this is the best way to introduce myself to this kind of madness and I have to say I mean one of the things that I absolutely love about wooden puzzles so far from from what I have here is like the piece shapes some of these are absolutely adorable and again quite terrifying because it's like how the heck do these go together now obviously I haven't the slightest clue how long this is gonna take me to kind of even figure out how to go about starting it now who knows I might end up putting this together fairly quickly because you know it's a small puzzle count or you know I just might end up pulling my hair out and just never wanting to work on wooden puzzles again I don't know but I'm sure either way it's gonna make for a very interesting experience now if I end up being quite successful with this particular set and it takes me less time than I think it will then I'm kind of considering also starting on the owl set that was also sent to me by Kathy S and again I'm not sure what the puzzle count is for this but this one is obviously a lot more pieces than the turtle honestly I don't know if you're actually meant or if you are even able to sort these kind of things I haven't the slightest clue okay I need to stop getting ahead of myself here I think I'm I'm going a little overboard here let me focus on one thing at a time let's start with the turtle first let's see how long this is gonna take me and if I feel pretty brave enough at the end of this one we're gonna go after that owl and then we'll really see what I think to wooden puzzles you know guys let me put on my big girl pants and let's get started all right so let's open this first one up here because you know we've got to start small first before we start jumping into the bigger set so here's the reference image that the little set comes with so that is really good and obviously it's the the puzzle itself is not this big so let's put these pieces out and let's see how these are now I know I'm flipping these the other way but I mean I just kind of want you to get an idea here you can kind of see it better against the table color but look at these piece shapes this is amazing now like you do have your like animal shaped pieces here which is this is wild to me honestly and I'm already dropping them and you do have kind of like the non-animal shapes here as well now they look like they do have a little bit of a glossy finish to them but it doesn't look too intense honestly the colors on these pieces are amazing the colors are not bleeding into each other at least from what I'm seeing on these pieces so far it looks absolutely fantastic and they're so cute now I know I've said this a million times already but we have a small puzzle count here but I still feel like this is going to be a beast of a set to put together now again I am both nervous and excited because this is a whole new world to me oh boy I don't even know how to go about starting this but you know what I'm never gonna get this one finished if I don't finally get started so let me stop procrastinating and let's get busy here here's what I did so far in terms of trying to start this I did my best to kind of group certain pieces together according to like their color patterns and as you can see here I like have all these kind of similar pieces these over here these light green ones we have the reddish orangey ones purpley and so on so this is kind of like how I'm figuring is gonna be I guess you can say the best way for me to start so I don't know hopefully this works hopefully this gives me kind of like a a good head start so let's continue and see if I can figure this out oh I think I figured out the first piece oh look at that I'm so proud of myself oh you know what wait a minute oh I put those three together fairly quickly this seems promising this is the turtle's head there you go look at that we got the head done oh this is exciting all right let's continue and quite honestly I'm glad that I kind of separated or sorted I should say the pieces the way I did I feel like it definitely gave me a great head start and made me feel very optimistic for the rest of the completion process so if you ever feel like you're stuck trying to figure out how to go about starting something like this I highly recommend sorting it this way try to group the pieces according to the colors or the patterns because it could really give you that jump start you need to get going but one thing I am noticing is that these kind of fall apart pretty darn easily so you have to be super gentle on this but so far I'm gonna be honest I am quite enjoying this so let's continue yeah overall the hold wasn't that great it's very loose sometimes if you nudged it in the slightest or touched one of the nearby pieces the wrong way while placing another one in it would just come apart but it's not surprising really when you're dealing with shapes like these and some of them are actually really small most of the tiny ones were the pieces that filled in the gaps between a lot of the animal shapes they would fit into each other in the most interesting ways you have to be delicate with this and I'm sure it's also because it was a smaller count set so there wasn't that much there to keep hold together anyways and I was quite amazed at the cut really and how precise it is on all those tiny thin edges and grooves I became more and more fascinated with this the more I piece these amazing shapes together this puzzle took me about 38 minutes to complete and I was shocked I did it so quickly I'm really glad I did this set first now I'm not very well versed in the world of wooden puzzles but I thought the quality of the piece was nice and solid and the color print was great yeah the holds sucked but I think this may be a common thing with these kinds of puzzles it was easy and most importantly fun and it was a great way to introduce myself to this kind of challenge so if you're looking to start your first wooden puzzle adventure look for a small set like this one I'm super proud of myself I did it now I feel brave enough to do the next one let's try out the owl all right so once again I kind of did my best to try to group together anything with like similar colors and patterns so as you can see we kind of have like this pattern going on here we have kind of like these colors going on in the same area um this area here is kind of a bit of a mess but you know I can kind of pinpoint ones that are like a darker red tone we have the orange tones and anything that has any other colors similar to each other along with the red or kind of like uh scattered about we have some of these here that are very similar and obviously we have the purpley blue tones the blue tones and then this particular pattern here so is this the absolute greatest way to sort wooden jigsaw puzzles absolutely not I have no idea this is this is what I noticed worked for me with the turtle so I'm trying it again and see if I can you know be as quick with this one as I was with the turtle which is most likely not the case because obviously this is a lot more pieces but anyways you never know right so I'm actually really excited to put this one together because I was like super happy putting that one all together so let's see how this one turns out and how much more difficult is this one gonna be we shall see all right let me shut up let's continue and what do you know it gave me a great head start again I think I'll continue starting out my wooden puzzles this way and yeah I know sorting is not for everyone but you know what it works for me so I'll stick with it now both sets did have a very slight glossy finish to them and I had to change my lighting or sitting angle at times to get a clear look at whatever I was working on but I didn't feel like it was enough to take away from the whole experience surprisingly the owl pieces held on way better together compared to the turtle and I don't know why maybe that was because there were more pieces involved or what I don't know again I'm not sure but I'll tell you what if I just so happened to put two of the wrong pieces together it was a bit difficult to take them back apart you'd risk messing it all up but what I really loved about this particular owl set were the colors they had this deep darker tone to them and the more this all came together the more I loved it the colors just seemed so rich and I felt like it really looked nice against the color of my puzzle table it was beautiful I was blown away by the finished image this puzzle took me about two hours to complete and I wasn't surprised that there were a lot more pieces to play around with and I was still getting acquainted with this type of challenge this was fun and it's a shame I can't really save them but that's okay that means I could work on them again and I'm surprised to say that because I was really timid in the beginning but now I can say that I am very much looking forward to working on more wooden jigsaw puzzles so like I said before if you are new to the world of wooden puzzles start with something small and try out my sort of sorting method if you have a hard time figuring out how to get started it helped me now I also have two more sets one of them actually being a Wentworth that I plan to work on soon but let me know if you know of any similar brands out there thank you again Kathy for sending me these two awesome sets and for finally getting me started on my wooden puzzle adventures and if you're new here be sure to subscribe so that you can see me start up on other new puzzle adventures and hear what random stuff I have to say about them well guys as always thanks for watching take care and I'll see you in the next one