There is a universe out there where Ant-Man and the Wasp, Quantamania, is a good movie. This ain't it. Director Peyton Reed is back for the third Ant-Man film. This time though, instead of directing around cities and different locations, it's all set within the quantum realm. A.K.A. a green screen. I'm old enough to remember when this trailer hit initially and I said, man, first reaction is not great. It looks like the whole thing's on a soundstage in front of a green screen and they're just splatting in VFX all over. It doesn't feel authentic. It doesn't feel natural or realistic at all. At which point, geniuses in the comments said, hey, Adam, how do you expect the quantum realm to look real? You can't make things look real when they're not. You actually can. That's what cinema is. That's what Hollywood magic's all about. Bringing the fantasy into reality. Convincing people what they're looking at is real. They stopped trying to do that anymore over at Marvel HQ. Instead, in my humblest of opinions, it feels like they're just making products now. Not so much cinematic films. In fact, while this nonsensical noisemaker was happening, I couldn't help but think back on Iron Man 1. When I saw that in theaters and how gritty and grounded and amazing it was. The effects, while not all the way practical, there was still plenty of CG. It felt real. It seemed plausible. But now here we are with Ant-Man and the Wasquantimania, with Thor, Love and Thunder, with Doctor Strange 2. It doesn't even have an inkling of authenticity to it. There's a little indie film called The Martian with Matt Damon. You may have heard of it. I think it won a couple of awards. It was filmed on Mars. Except for, oh wait, no, it wasn't because we can't actually put Matt Damon on Mars. You could have fooled a mass audience though, who actually believed it was filmed there. Why?  Because they use practical sets because they put some effort into things. And obviously it's a desert planet, right? It's a lot easier to fake it and make it.  With Ant-Man 3, let's really get into it without any spoilers at all. This is the same exact plot as Spider-Man No Way Home, where our supposed good guy screws around with things, tinkers, and he ends up causing problems. Problems that could inevitably lead to the ends of universes. So the main villain outside of Kang is Stupidity. On the behalf of the superhero, I'm really getting sick of this new trope Marvel has come up with, where it's actually the heroes that are causing the dilemmas that they're facing. When we meet Scott Lang now, he's enjoying his celebrity status. He's an Avenger. He saved the universe. And people are appreciating that. He's getting free Dunkin' Donuts. He's got book signings, yada, yada, yada. But one individual is not particularly impressed. And that's his daughter, Cassie. Now played by Catherine Newton. This is the third or fourth actress I lost count taking on this role. The one from Endgame that we saw that was of age. She, I guess, didn't get a callback. They needed someone with a bit more of that it factor. And they said Catherine's the one, the actress from Detective Pikachu. Here's the deal. I'm sure Catherine's a lovely actress and a lovely person all around. I do not like her as this character. She rubbed me the wrong way. She felt like she was smiling through every scene she was in. I had the same issue with her in Detective Pikachu actually. And you can, you can play back the tape. I said, she felt like she was acting in an entirely different movie from everyone else. And that's exactly how it is here. Although it's almost flipped. It seems like Catherine in this case is the only one acting in the movie that she's supposed to while everyone else is acting in something much darker, more dramatic, better. Because as it stands, Ant-Man and the Wasp Quantumania is basically a Power Rangers movie. It feels like a Disney film through and through. And I don't mean that as a compliment. I mean, this movie feels like something that should have been on Disney Plus. I know a lot of people were excited to see Jonathan Majors as Kang the Conqueror. Why? He's probably cool in the comics here though. Not, not impressed. Not really impressed at all. Actor does a fine job. It's just the material is really bad. The writing, the dialogue, the other Ant-Man movies are fine. They're perfectly serviceable films you can watch in and out. They got some good comedy. They're light. They're fluffy. Fine. That works for Ant-Man. But now we've put him in this big stakes, larger than life scenario. And we're expected, I guess, to be on board with the new style, the new approach. I'm not on board. And it's a little funny that some of the MCU guys said, this is the end game of phase five already right out of the gates. We're at end game again. This doesn't have that level of excitement of energy. It's given the illusion of larger than life, but everything feels so small. Ant-Pun intended, I guess. It feels green screen studio soundstage. The whole way through. And there are some moments that are so brutal to watch. Just cringe embarrassing. Where I don't know if it was the B team coming in, filming some reshoots, but damn, this looked terrible. The movie is a pepper over two hours long. That's the thing I just made up, a pepper over two hours and five minutes. You're not going to want to stay for the end credits. They're embarrassing. They're really bad. Especially the first one where... This third film is really about the PIM family. It's about family. This is the fast and the furious of the MCU now. Really a big focus is about hope and about Cassie and about dad, AKA Scott Lang, AKA Cassie yells his name out 3,000 times in this movie. Dad! Dad! And half the time there was no effort put into it. It's just dad! Dad? Dad. Dad. For me, this is easily the worst Ant-Man film and I don't have like glowing praise of the others. As I stated, they're very watchable movies though. They're fun, fancy free, getting get out sort of situations. This one at two hours long, it feels like a chore to sit through. It's lengthy at times. There's a ton of exposition. Half this movie is exposition. Every character I feel like has a moment or three where they're like, why don't you pull up a chair? We got some stuff to go over here. Remember the years I was at the Quantum Realm and we for some reason didn't talk at all about anything I was doing? I'm Michelle Pfeiffer right now. Yeah, I was holding on to some stuff. I got some baggage and we're gonna learn about it on the spot here instead of all that time we had to share. It's all coming out now. By the way, Michelle Pfeiffer, she looks so good still. She still can get it. Evangeline Lilly, however, wow, that haircut's awful.  I just got one myself. I think I'm pretty good with it. I'm pretty happy with how it turned out. Anyway, let's keep going. Since Ant-Man was always kind of your paint by numbers MCU movie where it had a lot of like lame jokes and like light action, it doesn't fit the molding of this big scale film. Scott Lane can't be as fun and as chipper and charming as he usually is and I don't want to see serious down in the dumps Ant-Man. Constantly like yelling at Cassie and wanting to save the world. That's not cool to me. That's not what Ant-Man is. Why is he going up against Kang the Conqueror even? Isn't Kang supposed to be one of the strongest, most feared characters? Didn't get that here. Didn't get that. Here's the bottom line. I was not expecting much when I went to this film. I thought it was going to be kind of mediocre. It was going to let me down in some places and guess what? It actually exceeded those expectations. It was way worse than I thought. Not horrible. It's watchable. There are a couple of jokes that land. There are a couple of fun characters that come and go. But the overall package is sorely lacking and this is such a far cry from where we started with Iron Man. Of course, things have to get grander. They have to go a little zanier, a little sillier. That's okay. That's all right. But we have to find a way to still ground them a little. We still have to find a way to have audiences connect to what's going on. Otherwise, you are truly just watching another dumb-ass Fast and the Furious movie. It's a popcorn vehicle explosion, extravaganza. And for a lot of people, that's plenty. They don't see a lot of movies. They don't get off. And they just want to be entertained and walk away. For me, I'm kind of at the point where I feel like the MCs started out smarter than that. Better than that. Felt like they cared more. But now I'm watching one or two people running in place while things blow up.  And then bridges are breaking. But I know that they're really not. And I know that there's no threat at all. And the people are like, whoa, I'm falling. But I'm just standing still. It doesn't work. It just doesn't. Not for me. Not for this guy. But I'm about one person. And I would love to hear from you. Let me know in the comments if you saw Ant-Man, Quantum Mania, and what you thought about it. Were you excited? Were you impressed afterwards? Did you love those end credits? Those tantalizing end credits that did absolutely nothing for me. Or are you like me?  What are we even doing anymore with the MCU? Please think about subscribing if you haven't. I would love to have you stick around. I post tons of movie reviews, rants, reactions, things related to the movie space. Sometimes they dip into shows. I'd like you to dip into this channel though. Stick around for a while. I'd appreciate it.


Hello, I welcome you all once again to my channel, Explore Education. I am Dr. Rashmi Singh, Assistant Professor, Department of Education, S.S. Khanna Galsruri College, University of Allahabad. And nowadays I am discussing over the issues of secondary school education, in which I am, this time I am going to discuss, empowerment of secondary school teachers, role of NCRT. You can say role of NCRT in the empowerment of secondary school teachers. The lecture will be in bilingual mode and it is useful for you all. First of all, NCERT. We all have heard a lot of words from our books, from our books, from our books. The word we have heard most of the time is NCERT. NCERT is National Council of Educational Research and Training. So first of all, what is an overall NCERT? So what is the vision of NCERT? To become a center of excellence, concerning research, development and training in the field of school education in the country. That is, NCERT is the vision. It is seen as a school education. In the country, the field of school education wants to become a center of excellence. It wants to become a center of excellence, related to experience, development, and education. That is, in the field of education, NCERT wants to become a center of excellence, through which research, development and training can be done in the field of education. So what is the mission for it? It is the vision that it wants to become a center of excellence. For that, what we do is a mission. So the mission is to improve the quality of school education towards building a society committed to constitutional values and contemporary concerns. It is said that students have to increase the level of education and improve it. And we have to build a society that is committed to your constitutional values, that is, to fulfill the commitment towards them and contemporary concerns. And the problems that we have are related to them. Working towards bringing out improvement in the quality of teacher education in the emerging demand of the school system. Because you are studying NCERT in secondary education, in school education. But even now, when you search for NCERT in the matter, you get a matter on teacher education. You will get a lot of matter on teacher education. So you will feel that NCERT works on school education, so why are you getting it on teacher education? What does teacher education mean? Trainees. Trainees of which? Trainees of B.Ed. Or B.Ed. So who is the B.Ed teacher for? For 6th to 8th, or you can say for 6th to 12th, B.Ed. So mainly 6th to 8th, by the way. So the teacher who is going to prepare for school education is the teacher education. That is why you get this rum somewhere that why is NCERT getting it on teacher education? The teachers who are preparing for school education are preparing for school education. That is why you have to understand this. So this works for whom? Working towards bringing on improvement in the teacher education will improve your school education. Because you will make a good teacher so that you can study in school. The quality of teacher education includes the emerging demands of the school system. That is, the emerging demands of the educational system, the new demands, they want to improve the quality of teacher education in their synchronization. And achieving the goals of universalization of elementary education and quality secondary education. Now that the universalization of secondary education is also happening, so this goal wants to be achieved. What are the objectives for that? What are the objectives of NCERT? It is the goal that it will do research, do research, develop, teach, and do research. For whom will it do research? Promote and conduct educational research, experimentation of innovative ideas and practices. That is, your practice of doing research, promoting it and using all the innovative ideas and practices. In the field of teaching learning. And what do you have to develop? NCF. You know, National Curricular Framework. When we will talk about all these in the future slides, we will do all that in the NEP 2020 slides. Now you cannot talk about the background of those 2020. Now you will not talk about NEP 1986. Why NPE 1986? Why? Because the National Education 2020 has arrived. Even though it has not been applied yet. We have to do everything within the National Education 2020. So when the National Education 2020 came, it said that you make a National Curricular Framework. For school education, for teacher education. Because it has given a huge amount of rights for school education. You will have to make it yours, for teacher education. And you should know that NCE 2023 has arrived. That means National Curricular Framework for School Education 2023. The day you get the chance, I will read it and discuss it with you. You will not get a chance. Because NCE FTE has not arrived. That means the National Curricular Framework for Teacher Education has not arrived. It is not old yet. But it has arrived in 2023. So what do you have to develop NCRT? NCEF, National Curricular Framework for School Education. Which it has done in 2023. To make textbooks. To develop teaching learning materials. To develop kits. To make training models and strategies. To make audio, video and ICT materials. All of this is part of NCRT. For training, you have to teach it. Pre-service and in-service teacher education. That means the people who are in the field of education. Before their service. That means the B-Edge level. And in-service, when they become teachers. Before the service. Then training of national and state level functionaries. And extension. That means collaboration with state, national and international organizations. So this is NCRT. What is NCRT? What else is NCRT? NCRT stands for National. National Council of Educational Research and Training. It was established in 1961 by the Government of India. GOI made it in 1961. Now it was made in 1971. I am so sorry. It is a statutory body in NCRT. It was made in 1971. See, when we didn't have a net. So it is not available in books. NCRT was cut. UGC was cut. So even if we were going through it once. It was very difficult to know. You had to google it. If someone had a laptop or computer. We used to say that when the NCRT was cut. NCRT was cut. UGC was cut. Now you can get any chance in your mobile. You can get any chance in your mobile. So it has become very easy to study. I remember this from 1961. That once we were confused about when to study. NCRT was cut. Then NCRT plays a significant part in influencing the educational landscape of India. NCRT is very influential in India. The following seven existing government organizations were combined to form the NCRT. They are saying that these are the seven government organizations that make NCRT together. Central Institute of Education Central Bureau of Textbook Research Central Bureau of Educational and Vocational Guidance Direct Rate of Extension Program for Secondary Education National Institute of Basic Education National Fundamental Education Center National Institute of Audiovisual Education You don't have to go into detail about it. You just need to know. You just need to know that many government organizations make NCRT together. There are many components of NCRT. Let's move forward. Now, what is NCRT used for? How to buy NCRT? To make a textbook for school education. It has to be given a guideline for curriculum. NCF or National Curricular Framework It has to be trained by a teacher. It has to be researched and developed. Additional learning resources. Additional learning resources. It has to be made by TLM, CAT, Audiovisual. It has to be made by TLM, CAT, Audiovisual. It has to be made by educational policy. Now, what we are going to talk about is our main topic. Empowerment of secondary school teachers. Now, we will know how NCRT works as an empowerment for students or how it increases their quality. Let's come to the topic. So, NEP 2020 has spoken about CPD. What is CPD? Continuous Professional Development of Teachers. NEP 2020 is saying that you should be aware that when we serve, we should pay attention to our professional development. Why? Because what we have studied will get obsolete very quickly. It will get useless very quickly. If we don't get new knowledge, then how will NCRT be able to help school education teachers? See this. NEP 2020 has said that it emphasizes CPD for teachers. It has said that you have to continue to develop professionally. Which teachers? Which head teachers? Which teacher educators? With what purpose? With an aim to provide them adequate opportunities so that we can get the opportunity for self-improvement. So, we can improve ourselves. And to learn about the latest innovations and advancements in their profession. So that in our field, in our profession, the new innovation that is coming and the new things that are coming we can learn from them. What else can we do? The policy also expects them from them to participate in at least 50 hours of CPD every year for their professional development. NEP 2020 has said that every teacher has to live at least 50 hours of continuous professional development. And what should this happen? How will it happen? Driven by their self-interest. In which we are interested you can do that. And with an understanding of the concept of learning as life-long process. And with this understanding we will all be educated teachers. What should we develop? Because the learning is that life-long process. This is the basic philosophy. So keep on studying. Then NCRT offers guidelines for the CPD of the year. So NEP 2020 has said that all teachers have to live at least 50 hours of CPD of the year. What NCRT has done is made a guideline for school education. How it will be of continuous professional development. These guidelines encourage teachers to engage in on-going learning, reflective practices and collaborative professional development activities. This guideline NCRT has made encourages teachers to learn on-going learning. They are involved in reflective practices and collaborative professional development activities. So what will teachers be empowered in this way? Why will they be empowered? Because they will be updated. So this is one way that NCRT updates and empowers teachers. Providing quality and empowered teachers to school system. The work of NCRT is to teach the students to learn on-going learning. So what they are saying is that technology-based comprehensive teacher requirement planning should be done in a forecasting exercise to see how much quality we need in the teacher. So how will teachers develop professional development? Or keep a merit-based and your track system so that we can tell them who is more empowered and who has more quality. Or we can see minimum 50 hours of CPG. So how will you assist who is better, who is more knowledgeable and who is updated. So they are saying that the assessment of performance of teachers will be based on peer reviews, attendance. Generally teachers study well and they have good attendance in their class. How much is the commitment they are showing for their studies? How many hours are they involved in CPG? And other forms of service to the school are developed by state or and based on national professional standard for teachers. What is NPST? National professional standard for teachers. Who has developed it? NCE National Counsel for Teacher Education has made it a national professional standard. You can check how good your teacher is. And a common national professional standard has been developed by NCRT, SCRT and other concerned institutions and with the help of teachers they have made it a NPST. You can check this. How much quality teacher we have. Now after NB 2020 after the present system there will be no talk of ICT initiative. So there are many ICT initiatives for NCRT. So you should know that ICT, SCRT, SCRT SCRT Everything is very interesting. We should know that a platform for showcasing and disseminating all digital and digitized resources for its stakeholders that is students, teachers, teacher educators, researchers, policy planners and parents. It will bridge the digital divide by imparting comparable quality of education and all. So part shala part shala e means everything is electronic. So this platform has made all kinds of digital and digitized resources available for all. So what did they do? They are empowering the teacher that you study and you study material and study. Similarly, there is another platform called DIGSHA. Look at the full form. Digital infrastructure for knowledge sharing. And for knowledge and SHA for sharing. Look at the name. DIGSHA. This is a national platform for school education. It is not for higher education. It is an initiative of NCRT under the Ministry of Education, G.O.I. This is also an initiative of NCRT. MOE, G.O.I. Launched in 2017. Luckily, it was launched before COVID. Because from 2014, digital India is going on. In 2017, DIGSHA launched by Honorable Vice President of India Shri M. Venkaiah Nadu. Look at DIGSHA. Almost all the states, union territories have adopted it. And during the time of COVID, DIGSHA platform was used. There is something new. The Honorable Prime Minister of India has launched NDR. Education in Architect. When did this launch? July 29, 2021. So, it has become even better. Core building blocks of DIGSHA comprised majority of NDR building blocks having enabled some successful use cases of NDR such as energised text books, online courses, content authoring, content sourcing, interactive quizzes, questions. There is a lot. You can google it and you can get everything from DIGSHA. So, this has become DIGSHA Digital Infrastructure for Knowledge Sharing. It is necessary to remember what DIGSHA is. NISTHA. Look at NISTHA. National Initiative for School Heads and Teachers, Holistic Advantage. They are saying that the head of the schools, the teachers, we have to do a holistic advancement. So, NEP 2020 has said that teachers truly shape the future of our children and therefore the motivation and empowerment of teachers is required to ensure the best possible future of our children. If we want a better future of our country, then teachers should be empowered. So, keeping this in mind, to realise the vision of NEP 2020 recently NCRD under the ages of the Ministry of Education, Department of School Education and Literacy, Government of India, in collaboration with State, UTS and Autonomous Bodies have initiated the NISTHA Integrated Training Program 1, 2 and 3 online for different stages of school education. See how much work is being done in school education. That we can educate our children and give them good education and update their knowledge. They can enhance their knowledge and make them a global level. In the secondary level, in which classes 1 to 8 you will get education. For the secondary level 9th to 12th and NISTHA 3 for Nipun Bharat from ECCE to Class 5 Early Childhood and Care Education Early Childhood Care and Education From Class 5 NISTHA 1, 2, 3 Classes 1 to 8 NISTHA 1 Classes 1 to 8 NISTHA 2 Classes 9 to 12 NISTHA 3 from ECCE to Class 5 This is how NTRT is initiated by school heads and teachers. What else? Sarthak and Prangya. When I searched Sarthak is Students and teachers holistic advancement through quality education. The students are saying that it is an implementation plan for the national education policy 2020. NEP 2020 told how to implement it. The department of school education and literacy has developed an indicative and suggestive NEP implementation plan for school education to health states to achieve the goals and objectives of the NEP 2020. In 2020, we will adopt it by 2025. We will achieve this by 2030. This is a suggestive plan and an implementation plan. What we can do and what we can achieve. There are hundreds of pages that you can document. I will go and they have to advance in quality education. This is also a way to empower teachers. NCRT is initiated by ICT and Prangya. In many schools across India, the MHRD has been funding computer labs, smart schemes, etc. 29 states or UTs are already onboarded digital platform. Almost everyone has adopted ICT, slowly but surely many teachers in several states are getting used to integrating ICT in the classroom process. You can say that almost all teachers are using ICT at school education level. What is Prangya? There are 8 steps for implementation of online digital education. They are asking how to use online digital education and Prangya is an abbreviation. Open it and plan how to use online education. Review it. Arrange the material. Guide the students and parents how to use it. Cheat on it. Sign a contract. See how much they are using it and appreciate it later. So online digital education is a way to successfully implement it. They have said plan, review, arrange, guide, talk. If you keep talking, Prangya won't be able to make it. So they have signed a contract and approved it. Okay? So it means taking the NCRT is a school education. For school education, NEP 2020 has also said that you are a good teacher. If the teacher is good, it will be good for children. For that, CPD, professional development, NPST, national professional standard for teachers, NCRT initiatives like Ratsala, Diqa, Nishtha, Sartha, Kragita keep on knowing and understanding and all this is the latest. 2020, 2022, even 2017 all these are the latest things so that we can be a better teacher so that we can get better school education for our students. Okay? So in this way you can cover the role of NCRT in the empowerment of angry school teachers. Okay? So thank you and don't call it a like and subscribe my channel and explore education. I have done from my side.


Artificial intelligence has been the field that has struggled to build computational models that can perform feats of intelligence, but it hasn't really been concerned as much with explaining brain or behavioral data. So it's really at the intersection of these fields that I think we can make progress. And it's kind of a long history of trying to meet more and more of these criteria. And these previous attempts have not completely succeeded, but I think now is a very exciting time to be a neuroscientist because we are in a position now to put these pieces of the puzzle together. And there's a particular modeling framework, neural networks, that I think will be key to all this. It's a modeling framework that's central to this interaction that has a long history in each of these fields. In the 80s it caused a revolution, a paradigm shift toward ideas of parallel distributed processing and cognitive science. It's got a long history in computational neuroscience at many different levels, some of which we heard about earlier today and in other talks at this conference. So a lot of the work in computational neurosciences is at a very detailed level with spiking models or very detailed biologically faithful models. But the kinds of neural networks that I'm going to be talking about are at a very abstract level. You could think of them as modeling the rate coding level. And these networks also have a history in artificial intelligence and they're currently, as many of you I'm sure have heard, revolutionizing really several domains of artificial intelligence. So they're very exciting from the perspective of what they can do in terms of task performance. So I think this is kind of a perfect storm for the next 20 years or so for us to really engage higher level complex brain processing with explicit models that perform the tasks. So I work in vision. So what does this mean for visual object recognition? Well, in my field, the goal is to build a biologically plausible network model that can recognize novel object images. So it has to be able to perform the task of object recognition. It has to be a computer vision model and predict their neuronal representations and human behavioral responses. So that's sort of the smaller story that I'm going to tell you is about how we got around to beginning to use these models in my lab and to test them with brain and behavioral data. So we present visual object images to our subject. So here's a set of 92 images that are isolated object images from lots of different categories. We present them to our subjects while measuring their brain activity. Subjects can be humans or monkeys that we measure with fMRI or cell recordings. And then we analyze the representations in those brains and the representations in computational models in a single analytical framework that I call representational similarity analysis or RSA. So here's how this works. We present each image to the brains of our subjects and in a given region of interest for each image we get an activity pattern which we consider a representation of that particular object. So for another object image we get another pattern here in the same region of interest and that's the representation of this object right and the difference between these is the representational difference in that brain region. We can play the same game for the models. The models have internal representations as well. So again for each image we get a unique fingerprint which is the activity pattern elicited in the internal representation of the model by that image. Now we want to relate these representations in order to address the question whether the model is a good model of this particular brain representation for example a visual area say visual area v4. This is difficult at the level of the original responses because we don't know the correspondency between all the units of the model and the measured response channels for example voxels in fMRI or the single cells in neural recordings. So one approach to this is to fit a linear forward model to predict each measured response as a linear combination from all of the units of the model. However as we engage very complex models they can have hundreds of thousands of units and then that is fitting a lot of parameters for each of your measurement channels. So this is the reason why we take a different approach. We look one level up at the level of the dissimilarities between these activity patterns. We make a matrix indexed vertically and horizontally by the stimuli where for each pair of stimuli we can look up the dissimilarity of these two stimuli in the representation. We call that the representational similarity and this is the representational dissimilarity matrix or RDM and this is our signature of the representation which tells us what pairs of images are similar according to this representation and what pairs of images are distinct. So what distinctions this representation cares about if you will. The signature of the RDM is easy to compare between different representations because it's indexed by the stimuli so we've abstracted from this confusing multiplicity of single responses and we can very straightforwardly compare different representations now between different brains, between different species but importantly for today's talk between models and brains. So we have a toolbox on this the RSA toolbox which provides methods for statistical inference on brain computational models. So how does this work? Here's a little simulated representational dissimilarity matrix so here you have all the stimuli or the other stimuli and the cold colors here are things that are similar so you see two clusters here in the simulated example and then we have a number of model representational dissimilarity matrices and now it's just about comparing these model representational dissimilarity matrices to the one that we measured in our brain region of interest. So we use a rank correlation to minimize the assumptions made about that relationship in order to assess the fit of each of these models and then we use permutation and bootstrapping techniques to do inference, frequentist inference on these comparisons. So you see that most of these except for the last one are significantly related to the simulated brain representation here and in addition we compute noise ceiling which tells us given the noise in the data and the intersubject variability what is the level of performance that we expect for the true model and this noise ceiling has a lower bound and an upper bound and these are lower and upper bound estimates on the performance of the true model and then we do pairwise comparisons so we statistically compare each pair of models usually by bootstrapping the stimulus set we consider the stimuli a random sample from a population of stimuli so we can do inference comparing these models and then we draw a horizontal line whenever two models are significantly different so here these are lots of lines because there are lots of significant comparisons there are some missing actually if you look very carefully but in general we have a lot of power there and we need to correct for multiple testing which here we've done by the false discovery rate. So with these tools we can address whether a model explains significant representational variance whether it explains significantly more representational variance than another model whether it explains all the non-noise representational variance so when it hits the noise ceiling then it represents the data rdm as well as we would expect for for the true model given the noise and the intersubject variability in the data and we use frequentist non-parametric inference controlling for multiple testing and random effects tests across subjects and across stimuli to generalize to the population of subjects because usually we're not interested in our particular group but we want to use our data also to support generalization to the population and also across stimuli because usually we're not particularly interested in the particular set of stimuli we've chosen but we're interested in making more general statements about the computational models and we want these computational models to perform for any random sample of stimuli and that's really the target of the inference. Also support searchlight rsa where we perform these region of interest based analyses all over the brain and do inference at the level of these maps again correcting for the multiple tests across brain locations in that case and we use a range of distance metrics for measuring representational dissimilarities including the linear discriminant t value and the cross nobis distance which bridged the gap between rsa and linear decoding analyses where you can think of rsa as kind of a generalization of linear decoding where you do the decoding for all pairs of stimuli so you get a very rich and complete characterization of the representational geometry if you will. So in 2008 we applied this technique to this image set that I showed you in the beginning and we got these representational dissimilarity matrices for human inferior temporal cortex and monkey inferior temporal cortex where human IT was measured with fmri voxels and monkey IT with cell recordings and you see these clear clusters of animates and inanimates there's a human face cluster here there's an animal face cluster here the animal faces all have different colors and shapes so this is less of a tight cluster that might explain the fact that it's not such a tight cluster and there's also very high similarities between patterns elicited when one of the patterns is elicited by a human face and the other by an animal face. So the question this post so this was this categorical structure was strikingly similar between human and monkey and all also within these categories there's a lot of correlation between these two matrices as we showed in the initial paper however the question that this really posed for the purposes of the present talk is can we explain the IT representation with a computational model. And to my graduate students say at Khaliq Ratsavi who's here in front of his his new home he's left the lab and is now a postdoc at MIT so he his PhD thesis was about comparing the IT representation in human and monkey to many different computational models so how similar are computer vision representations to IT that was the first question he asked so he took lots of models from computer vision different hand engineered computer vision features to compare them to IT. So here I'm going to plot for you the accuracy of human IT dissimilarity matrix prediction so as before that's the RDM correlation between the brain region and each of the models and let's look at one model so this is the gist model this is a particular set of computer vision features based on summaries of Gabor filters and you see that it performs it explains significant variants in the RDM of human IT it's highly significant so one could tell a story about this to some extent right this clearly shows that there is information about stimuli in this brain region you could use this information to decode this is a very popular method to decode information in brain regions and there is a particular encoding model here that explains significant variance however before we interpret this we should look at other models so here's 26 other models all computer vision features and we see that they all almost all of them perform highly significantly and they explain some explain a little less variance other explain a little more variance but overall they all explain some variance the important thing to look at is the noise ceiling which is much higher so this shows us that there's a lot of variance left unexplained by all of these models so our conclusion from this exercise was that all these 27 models fail to explain IT since many of the models explain significant variance we were interested in testing whether combining the models might help us explain the IT representation imagine each of the models has some of the features present in IT but none of them has all all of them so if we combined these features maybe we could do better so we remixed and reweighted the representation in order to best explain IT always cross validating across images it's totally uninteresting to us when we overfit to a set of images because this means that the model that we have doesn't really work as a computer vision model it wouldn't do the task on a new set of images so we always cross validate we use the separate image set to create these linearly remixed filters where we fitted three support vector machine discriminants to emphasize the major categorical divisions that are known from the literature to be prominent in IT and then we reweighted the representation using non-negative least squares where we assigned one weight for each of the model layers and one for each of the three SVM discriminants so that's kind of a low parametric way of finding the right mix of features to explain the matrix and again always cross validated across the images so when we did that this is the matrix that we got you can see that it gets this tight human face cluster just about right but it doesn't get any of the other sort of obvious categorical features here again for comparison the human IT and the monkey IT matrix so it misses a lot of these categorical divisions it doesn't get the animal face cluster where the faces are much more visually dissimilar and also it doesn't place the human and the animal faces in a single cluster together so this also didn't work suggesting that even with combinations of computer vision features it's not possible to explain IT so this was around 2012 when something happened in computer vision namely neural networks overtook computer vision features that are hand engineered at computer vision so just as a quick reminder neural networks have a long history started starting perhaps at least as early as the 1940s with McColloch and Pitz's binary neurons then in the 60s there was a lot of talk about perceptrons and their abilities and limitations in the 80s there was the big parallel distributed processing revolution in cognitive science with Rumelhardt and McLeland but in the 90s neural networks sort of lost steam they didn't work as well on real-world problems and computer scientists largely lost faith in this modeling strategy they thought other shallower machine learning techniques such as support vector machines had better mathematical theory and also less problems of training time complexity and work better in practical applications also but more recently there have been breakthroughs with deep learning in the mid-2000s Hinton Benjio and Lacan researchers who believed in the usefulness of deep hierarchies and the sort of ultimate superiority of deep hierarchies kept at it and solved the technical problems and it turned out that these limitations that people faced before were not fundamental limitations but they were just hurdles to be overcome and they were overcome in this period and in the last five to eight years perhaps with growing computing power and large labeled data sets this has led to major advances in computer vision and also in many other AI applications using both feed-forward and recurrent neural nets where increasingly you have these networks and technological applications and they're invading our our cell phones for for computer vision and language understanding and even translation at higher semantic tasks so here we tested a particular kind of deep neural network a deep convolutional neural net so how does that work it has these little filters and they look like the bore filters here so it takes a little local filter and then convolves the entire image with that filter producing a spatial map where each unit detects a feature of this shape in the image at that location and then this filtering operation is repeated for different particular filters so you get these multiple maps of the image that highlight different features then a static non-linearity is applied often that's a rectified linear activation function where the output of the filter is set to zero if it's negative and otherwise it's just passed through and that's often followed by some kind of pooling either local max or average pooling and local normalization and these steps together are considered a layer and then there's multiple layers of this type and the network that i'm going to show you there's five convolutional layers and then a couple of fully connected layers following that but people have since moved on to much deeper networks up to you know between 100 and 200 layers so here the results for the deep supervised convolutional network which was trained by Krzyzewski et al and this is the network that convinced people in computer vision to switch gears and start using neural nets and there's been no looking back in computer vision since so when we look at the first layer it has a very low performance at explaining the it representational geometry however it is significant but it's far from from the noise ceiling so it's clearly not not a good model for it so here to give you a bit more detail on the noise ceiling the upper bound of the noise ceiling is the highest accuracy that any model can achieve given the fact that what i'm plotting here is the mean correlation across subjects and all the subjects are different so there is some rdm sort of at the center of the cloud of points that correspond to the single subjects that has the maximum mean correlation and no model no matter what rdm it predicts can exceed that right so that's a hard upper bound and then the lower bound is just using the other subjects average as a model so that's basically saying let's use all the other subjects data rdms as our stand-in for the true model but because of because that's a noisy estimate it slightly underestimates the performance of the true model so it gives us a lower bound estimate on the performance of the true model so here for comparison again is the performance range of the computer vision features so as we go up these layers where we get into this range of the computer vision features and then we get significantly better in the higher layers of the network but we're still some ways away from the noise ceiling here so again we asked and and these these representations are significantly better than the the early representations so again we asked can we play the same game that we tried for the computer vision features and remix and reweight the features of this very rich representational space in the deep net to better explain the it representation so we followed exactly the same process we trained three svm discriminants on separate sets of images to emphasize the relevant categorical divisions and then we used non-negative least squares to train one weight for each layer and one weight for each svm discriminant so when we did this for the first time we got a matrix that looked really qualitatively very much like what we've been looking at for inferior temporal cortex for a long time here again for comparison the human it in the monkey it matrices you see that the major categorical divisions are now right the animal face cluster the overall face cluster the animate inanimate distinction and also within these categories there's a lot of correlation between these matrices so when we look at that model it still doesn't completely invade the noise ceiling here we don't have a lower bound because this model is overfitted to a set of subjects so this lower bound doesn't work for this but even if it did work it would be slightly slightly below right so there's still unexplained variants but this does better than anything we've we've ever seen and significantly so so just as a methodological note here all of these models explain significant variants and that's essentially asking whether there's mutual information between stimuli and responses of course there is and that's usually what we go for when we use decoding so from my perspective decoding is not very useful to learn something about computational mechanism the important stuff is up here this is what tells us something about the computational mechanism it's the comparison of the performances of the different models to the noise ceiling and the comparisons between alternative computational models so in the last minute I want to just show you briefly that we can also use these models to predict behavioral responses because ultimately of course we want to explain behavior as well and this is work from postdoc Jan Schauer who's now a lecturer at Birmingham University so Jan had subjects categorize our image set along several divisions categorical divisions I'm only going to show you animate and inanimate so he gets an average reaction time I'm showing you the subject average here we're looking at individual subject analyses and average analyses well for every image we get a reaction time in this animate inanimate categorization task and then our reasoning is that perhaps the way categorization works is by setting up a readout filter for animate inanimate categorization from the it representation and then accumulating evidence using that readout filter somewhere in the frontal lobe so for some images the evidence on this readout dimension might be strong leading to fast accumulation of evidence and for other images it might be weak so it might take longer for the evidence to accumulate and the reaction to occur so when we have a representational space here and this could be a measured representational space from ephemerio cells or it could be a representational space in a computational model we can fit this decision boundary we get a decision value for each of the particular images and this enables us to plot the decision value against the reaction time and we expect that when an image is far away from the decision boundary then the evidence should be accumulating very rapidly and the reaction time should be short so here i'm showing you this for human it measured with ephemerio and we see that there's a correlation here so the images the particular images that are far from the decision boundary in the human it representational space are the images that subjects can respond more rapidly to and that's a highly significant correlation we can play the same game for the deep convolutional network where we fitted a linear svm readout for the fine finally the final fully connected layer of the network and this works even better possibly because the measurements in the deep convolutional network are not noisy measurements but they're sort of perfect it's just computed from the images and we can predict the reaction times for individual images with even greater accuracy than from their the subject's it representation so just to sum up there's an emerging literature using deep nets to explain brain computations it started in 2014 with these three papers the other two are from Jim DiCarlo's great lab at at MIT and last year there was a very good paper from Marcel van Gervens group and there was a number of preprints floating around it's a very fast moving field and i want to leave you with one central message that i think of as most important from my talk which is about the novel feature of this literature i think the novel feature of this literature is that the models perform the tasks in this case object recognition under natural conditions explaining brain activity as well as behavioral responses thank you very much thank you nicko for a very elegant presentation beautiful slides um questions for nico perhaps i can start off and ask you one question nico about the neuro informatic neuro informatics uh challenges that you've been that are attending these kinds of approaches how much are you hitting ceilings in terms of computational capacity um computational capacity is not our main limitation at the moment so we bought a number of gpu's and they work very well for us so one interesting thing to discuss technologically is hbc versus gpu what is the trade off there for us so far it's looked for these applications like gpu's are the way to go for the near future at least so more generally i think we need to move away from the cottage industry style of doing neuroscience so in in my field uh we always do essentially the same experiment we show some visual stimuli and we measure responses and then we analyze them multivariately and more and more people in my particular field are realizing that this is quite a standard experiment so it's somewhat standardizable and making the data shareable so we're thinking about um how to move forward with this so that every lab doesn't have to necessarily do all of the steps acquire the data and analyze the data and do the modeling and draw the conclusions but we can come together as a larger group and pursue this collaboratively so we're very nice talk and uh i want to ask you most of the models the computational models that we use are quick forward right yeah so um we already know that the visual system is full of recurrent connections so can we really go to the high level of your the high level message that you're giving here can we really explain something about the brain did we ignore all of those recurrent connections yeah so the answer to the question as you posed it can we explain something about the brain is yes i think these these uh networks capture something about what's going on in the brain namely the feed forward sweep so they go some way toward explaining the feed forward sweep and behaviorally rapid categorization however uh recurrent processing indeed is our obsession in my lab in particular and we're training recurrent neural networks also this new engineering literature is very much driven by exactly this intuition and has had great successes for example machine translation and in a number of applications with recurrent neural network models the earliest successful model was the long short term memory but now there's gated recurrent units there is all kinds of more complex memory mechanisms that are the neural Turing machine that are being explored in the engineering literature and this is uh i think where the most exciting aspect of this whole new literature i would say so i very much agree with you that we need to incorporate that and that that is is the the grand challenge really yeah question do you think the some deep knit with models say anything about learning of object categories in the brain yeah so this is not how we use them because i don't study plasticity i study perception so i'm interested in how do these computations work when you see an object you know in the first second after uh the the object becomes visible to you that's my object of study so to me the way that these models are trained back propagation is might as well be just a hack for setting all these parameters and transferring the rich world knowledge that an intelligent mechanism needs to perform the task into the model right however there's also a very interesting literature on the biological implications of um different uh learning rules including more biologically plausible learning rules such as stdp and heavy and learning and you know unsupervised learning techniques but even back propagation right there there's so so this old debate is back propagation biologically plausible this uh there are arguments on both sides this is being revisited and my intuition is there will be very short sighted to dismiss it too easily this is a very complex and interesting uh discussion there is a great review that i would recommend by marble stone wane and curding on how the brain might use cost functions and if with using different cost functions in different parts of the brain to train itself in a way that is similar at least functionally to to back propagation and this links this engineering literature to the neuroscience literature in a very deep way i think okay i think we'll call it a stop there we have a lot of opportunities for questions in the discussion section once again thank you very much it was fascinating


Stand by for crime Chuck Morgan GOP newscaster talking Well, it was June here in Los Angeles, which meant that the countryside looked Just about as it does the other 11 months of the year You know the seasons pass you by out here was seldom enough change to let you know what's going on So in June, I always get a nostalgic feeling for the sight and smell of the countryside Which would justify the immortal lines of James Russell Lowell when he wrote And what is so rare as a day in June then if ever come perfect days? Which is why when I arrived at my office on this particular June day and found Pappy Mansfield owner of KLP in my blonde secretary Carol Curtis waiting with a surprise announcement.  Good morning Chuck. Say Pappy about that wetback story. I Will what goes you two been swallowing canaries again Chuck? How would you like to take a trip back, New England? It's June back there Chuck. Well that figures it's June here. So it must be June back there What's it all about Pappy? Do you remember that spy who was arrested in San Diego three weeks ago? The one who had been handing over information on airplane construction to the Russians Dr. Alexander Gad here Naturally that story was almost as hot as the Klaus Fuchs yarn. I ought to remember Gad here I interviewed him three times. Thank you to recognize him if you saw him again. I couldn't miss Gad he has escaped Chuck No kidding when about a week ago three days later. He was captured. He's being held now in the pocket Burlington, Vermont So so you're going back to Burlington to identify Gad here. Why me? That's all part of an elaborate plan cooked up by the FBI and our own secret agents You see the man they're holding in Burlington isn't Gad here at all isn't Gad now wait a minute Glamopuzz, what are you talking? Wait a minute? You'd better let me fill him in on this girl Yeah, will you stop putting him glamopuzz? I won't Go ahead Pappy. Thanks. The FBI has information that the spy ring is a rendezvous all the way from Washington up through New England With the last one at Burlington here Burlington's population contains enough people of foreign extraction So that most any kind of an accent would go unnoticed Also, it's a big enough place catering a good deal to tourists so that any infiltration of strangers wouldn't be noticed either I see Burlington's pretty close to the Canadian border too is not about 60 miles But those are details we can go into later Gad here escaped while being taken to Washington Our agents believe he's being smuggled into Canada They also believe there were so hot on the trail once at the spy ring pick one of their members Who looked like Gad here and let him be captured now? This would take the heat off for a day or two until the mistake was brought to light Which would get Gad here time to reach the border pretty cute So I'm to fly to Vermont tell the cops that Gad here isn't Gad here and then come home again, right? Well, there's a little more to it than that. Oh quite a lot in fact One thing I'm going with you.  Yeah, yeah, sure. Oh, I'm not convinced that you meant quiet glamopuzz What's the rest of the story Pappy? Well when you tell him Gad here isn't Gad here They'll apologize and let him go and they'll follow the man who was posing as Gad here to where Gad here is Hiding and that'll be the end of Gad here check. I think you'll find it won't be quite as easy as that Well, do you want to go? It'll be a hot story if you're in on the capture of the real Gad here I've arranged with the network for a TC broadcast of course. I want to go when do I leave in an hour your tickets hotel Reservations and instructions are in this envelope in an hour. Why that'll hardly give me time to pack. I Said that'll hardly give me time to pack. Oh Happy you wouldn't Chuck don't you want me to go with you? Don't start crying glamopuzz I wouldn't go anywhere without you. You know your tickets and hotel reservations are in the same envelope Carol So stop worrying A TWA constellation said glamopuzz and me down at LaGuardia Field, New York the next day at 3 o'clock 15 minutes later. We were aboard an Eastern Airlines plane bound for Boston Lampers was like a kid just let out of school Oh Chuck isn't it exciting with 3,000 miles away from home bound for a venture and it's June Yeah, but it's still June back in LA. Oh, but here. It's different. How is new life every way things are growing and Well, those things are growing Sometimes I think you're growing nuts and sometimes Chuck Morgan I think you haven't got a romantic bone in your whole body. Yeah, you did. Yeah. Come here. You Do it never mind. Yeah, she's probably We landed at Logan Field in East Boston 445 A taxi took us through the Sumner Tunnel down Beacon Street to Arlington and the Ritz Carton Hotel Happy to reserve rooms for us. Mine overlooked the beautiful public garden. I Stood looking down at them for a long time.  I opened it and Found the most beautiful girl standing there. I'd ever seen with the exception of glamour plus, of course Hello, hello May I come in of course by all means to be sure miss I'm Beth Hammond Your mr. Mansfield probably told you about me. No as a matter of fact, he didn't but don't let that bother you Can I fix your drink? Oh, no, no, thanks. I don't drink. I'm a pilot. Well, that's a you're what a pilot If mr. Mansfield didn't mention me by name, he probably spoke of Hammond Airlines. Oh, yeah, that's right He chatted a plane from them to fly us to but now don't tell me that that's right You don't mind flying with the female pilot. I hope no No on the contrary is just that it's unusual. Is that the word you're groping for mr. Morgan? Yes, and will you call me Chuck? Okay, Chuck thanks most people have the same reaction when they learn they've hired a female pilot So I always present myself first and offer them a chance to back out if they want no no no no Don't you even think about such a thing splendid my brother, and I own the airline and I have umpteenth flying hours to my credit and Think fast jucky boy.  This Curtis what the name of oh Miss Curtis, I'd like to have you meet miss Hammond miss Hammond. This is my secretary miss Curtis. Hello miss Curtis Hello, I don't want to seem too personal, but miss Hammond is a pilot of the plane Pappy chartered to fly us to Burlington tomorrow Oh, she is good old Pappy. I mean Pappy thinks of everything doesn't he I mean I know what you mean Well, are you ready to dictate those letters? That is what? Oh, I'm sorry. I should have known you'd be busy busy No, wait. No, I'm sorry too, but the letters really can't wait. They're terribly important. Of course I understand. I'll see you both tomorrow then eight o'clock sharp at pier 13 pier 13. Oh, I'm sorry I didn't mention it before we'll fly up in a seaplane that way I can put you down on Lake Champlain right at Burlington's front door. That's wonderful Well, we'll we'll see you tomorrow then at eight, right? Goodbye miss Curtis.  Why was she calling you Chuck Chuck now look glamour? I don't like that dame. She's too good-looking glamour. Puss. Can I help with it? But a wicked gleam in her eye She called you Chuck which was forward and she was rude to me. Oh, I give I don't Spiring or no spiring. I'm canceling that charter right now and hiring another Glamour Puss didn't cancel the charter. I didn't let her So the next morning at eight o'clock a cab dropped us at pier 13 on Atlantic Avenue And we found Beth Hammond waiting for us there with a speedboat She was dressed in a pilot's uniform and looked lovelier than ever. Hello there. Hi your head of time You will take your luggage. That's the plane out there. Are you sure you know how to fly it miss? What was the name Hammond death Hammond and you didn't worry miss Curtis I have umpteen flying hours to my credit.  I mean it. Make up your mind. Oh, all right I'm sorry miss Hammond That's better. Okay, Jim you grab hold of our luggage, huh? Glamour puss felt better and so did I when we got into the air We skimmed the custom house tower and headed northwest In no time at all we've flown over northeastern Massachusetts Cut across the southwestern corner of New Hampshire and it picked up the silvery trace of Lake George in New York State Directly ahead serving as a boundary between northern to Vermont and New York was Lake Champlain. I Was beginning to regret having blessed to glamour puss and was thinking of some kind of an apology when something happened Chuck something's wrong. Yeah, it sounds that way Any trouble miss Hammond? Oh Chuck are we going to crash? Don't get excited glamour puss. This is a sea plane. Remember right down there is Lake Champlain We can set down as light as a feather. Yeah, well, I don't like it Sure, we were all right. I knew that as far as crashing was concerned But I had to agree with glamour puss. I didn't like it either We hit the water with hardly a splash and taxing close into the shore Almost at once a motorboat put out from a tiny inlet The only unusual thing about this was that the shoreline was a veritable wilderness. What would a motorboat be doing here? There were four men in the boat three of them carried objects cradled in their arms Cradled is right. They were sub machine guns Now what all right big shots at right where you are Chuck she's got a gun.  Jack. Did you hear what she called me? Yeah, I heard it Well, aren't you gonna do something about it? Yeah, he's gonna do something get over and open the door big shot Yeah, of course, of course. I'm glad to accommodate you check for heaven's sakes. Are you going to?  Okay, you go down into the boat. Yeah, let me help you girl Jack, this is awful with being kidnapped. I think we better do as a man suggests.  We didn't have time to get better acquainted. Well, maybe it isn't too late. Sorry. Good-looking Well, you're going you're not going to have time for anything sucker Now the conclusion of stand by for crying Well, the men in the motorboat hadn't been sent out as a welcoming committee by the mayor of Burlington. That's for sure The three submachine guns kept at the same focal point as we climbed down from the plane. The focal point was us The man Beth Hammond had called Fred nodded to his seat Then looked up at the female pilot with the umpteenth flying hours. Okay, best take it away I don't be waiting for you at the island. They could kill our guests Fred. I'll see you wrong The motorboat swung away and had it for sure With the three submachine guns the constant reminder. This was no practical joke We went up a path and came to a shack that was completely concealed and some heavy timber and went inside The man called Fred pulled out a chair and sat down across the rough table from where I've been ordered to sit Well, everything came all fine quite according to plan you must confess you were neatly led into our trap I'm organ. Yeah. Yeah, okay. I confess it. Now. What's this all about a fair question? Good way to the point. How about five thousand dollars? No, that's a good round figure Who do I have to murder to get it? Nobody nobody at all all you have to do is turn around and go back to California Now please bear with me if I seem a bit dense, but just why do you want me to do that? Isn't the reason obvious? No, we don't want you to reach Burlington with the information you have I look one of us is a way off the beam. Just who do you think I am your Chuck Morgan newscast on KOP in Los Angeles Young lady with you is Carol Curtis your secretary. Well, you got me pegged. All right. Now. What's this? Information I'm supposed to have come on Morgan. You're not as naive as all that Well, how about it? I tell you I haven't got any information. I see Harry I Think mr. Morgan needs to be convinced that we mean business now That's enough for now Harry Do we understand each other now Morgan? Yeah Yeah, I think I'm beginning to get an inkling of your clan, but there's still just one trouble. Oh, what's that? I haven't got any information our investigators reported. We'd likely have trouble with you Morgan So I won't wait time off in your more money Harry hold it Well Morgan just how do you figure you're going to get away with this when that plane doesn't land at Burlington? Yeah, fool the plane will land at Burlington and there'll be two people in it two people named Chuck Morgan and Carol Curtis Fantastic, can you get how long do you think you can get away with that masquerade long enough to accomplish our purpose? I think like most people you underestimate the efficiency of our organization Well now that you have the facts of the case you want to change your mind look I couldn't if I wanted to Okay, Morgan. I Had hoped you'd be reasonable. This sort of thing is distasteful to me work. I'm over Harry Harry worked me over all right. He had a special technique. I Didn't lose consciousness for at least five minutes. I Had no way of telling how long I was out But sunlight was streaming through one of the cabin windows making a pattern of moving leaves on a plank floor I lay there tied hand and foot studying the moving pattern for a long time Trying to recapture all my senses Trying to figure this thing out After a while I looked around and saw I was alone But a moment later a door squeaked Someone came cautiously into the room I lay without moving but tending to be still unconscious Oh How did you get away from that big gorilla?  He didn't know I was holding glam up us if I ever kid you again about being scared You can hit me over the head with a poker Untie me will you? Take it easy What are the other men in a shack behind this one there's a radio transmitter there I think and there's something else to what an automobile. No kidding any chance of us getting it what they might be As soon as I get you loose you can take a man It was at times like these that I realized glamour bus was more than a beautiful blonde who played straight man for me She had what it takes We went out the front door and sneak around the cabin the car was there park beside the radio shack It was our only hope and a slim one I stood there Wondering how we could lure the man inside the shack outside and away from the car I'm thinking there wasn't much chance And suddenly it was done for us. I guess I didn't hit him hard enough. You did fine glamour bus Here come two of them Must still be inside. Yeah, but it's our only chance. Come on We took off of that automobile like a couple of jackrabbits praying the keys would be in the ignition We reached the car just as a fourth gorilla appeared in the radio Shack's doorway He didn't have his machine gun with him, which was fine with me into the car glamour bus My knuckles was sore for days after that one I heard the car motor start and I knew we got a real break because the keys were there Then things happened fast There was a yell from the direction of the cabin but Carol had the car on the way and I was in it We were a good 50 feet along the wooded road when the first burst of the team gunfire came Apparently the marksman was too excited for effective shooting because the bullets sailed merrily overhead Then Carol jerked the car out of sight around the curve and we were safe at least for the minute Keep it up glamour push. You're doing swell, honey. Jack. I'm scared. You drop nothing till we get a lot further way than this Come on. Give her the gun baby. Oh, what if they have another car? What if they have with you at the wheel? They haven't got a chance Please just because I had a man with the poke now. Now don't start bragging I'm not bragging. I'm just too scared to hold the wheel standing. All right glamour bus. All right. I guess we've got enough of a lead If we had time I'd kiss you Maybe I'd better anyhow So we changed places and a half hour later came to a gravel road. We turned north on this Apparently the gang back at the cabin only had one car and we were in it at least there were no signs of us being followed But they had the boat and they had the radio transmitter What you gave me an idea at the first farmhouse we came to I stopped What are we stopping you for you see those telephone wires running into that farmhouse? Well, I'm going to make a call. Well, who are you calling and why unless I'm greatly mistaken There's gonna be a reception committee waiting for us at Burlington and I want to be sure we're prepared for it But Chuck, I don't understand. How could you got time to explain now? I'll give you the details when I get back keep them on a running Well, I made my phone call and got through to the party. I wanted Then Carol and I headed north again. I Told her what my idea was and she told me I was smart to figure it out And I told her she was a brave good girl and we both agreed we were wonderful By that time we'd reach the outskirts of Burlington Burlington for months largest city and probably one of the most beautiful cities New England As we drove along the quiet Trisheted streets It was hard to believe that foreign intrigue of the worst sort was running a muck beneath the peaceful scene We reached the city hall and police station and pulled up near the park in front As we did so a young couple a tall distinguished looking man and a police lieutenant emerged from the police station It was perfect timing Karen I got a lot of our car and we all met on the sidewalk. Hello miss Hammond. We meet again I I beg your pardon recognize me. Well, I can't blame you for that Carol tells me your boyfriends were quite successful in changing my appearance, but how about Carol? Do you recognize her? How could she forget me mush mouth? I haven't the faintest idea what either of you people are talking about Officer, will you please just a minute miss Hammond if that's your name Is this the man that you just got through telling the police is not dr. Gad here How did you I mean? I still don't know what you're talking about. Oh, yes, you do You and your friend here just told the police and the FBI that you were Carol Curtis and your friend was me You were shown the prisoner and stated positively that he wasn't dr. Alexander Gad here Oh, it was an ingenious plan miss Hammond cooked up by you and your ring of spies to gain dr. Gad here's freedom Because the tall man standing next to you is no phony as the police thought. He's a real dr. Gad here My plan is still going to work run dr. Gad here dr. Gad here isn't going to run miss Hammond There's a policeman standing on the steps of the rifle aimed at dr. Gad here's head And there are other policemen all over the place waiting to pick up your pals who are going to try and help you affect this escape I don't believe you. You're lying. The plan couldn't fail. It was too foolproof You're lying both of you Fine, I know exactly how to treat this babe. Let me alone. Keep your hands off of me you Swine I'll take that gun sucker Burlington police did their job well Besides Beth Hammond that her friend they picked up five other agents will plant it around the station Ready to lend a hand in helping Gad here escape if it were necessary But they did better than that One of their planes scoured the east coast of Lake Champlain until they spotted a motorboat that looked like the one I described It contained Fred and his gang which added four more to the total take of enemy agents that day The glamour person I spent the night and the next day in Burlington, and I did my broadcast Two days later. We were back in LA reporting to Pappy Mansfield. Well, that was nice going kids But what I can't figure out is why those two who were posing as you Just didn't go up to Burlington and tell the police that Gad here wasn't Gad here without bothering to try buying you off Well, there are a lot of reasons Pappy in the first place They didn't know how much time they had in the second place if they could have bought me It would have been far less risky You see the fact that Chuck would recognize Gad here as Gad here and not a phony was the information that character Fred Was trying to buy from Chuck Chuck figured it all out when he was unconscious when he was unconscious She means of course after I return to consciousness. Don't you glamour? Yes, of course. Oh, I see Well, tell me what are you two so sweet to each other about all of a sudden Pappy I want to tell you that Carol is one of the bravest girls I've ever known one of the bravest I mean the bravest and I want to tell you Pappy that Chuck you boy is one of the Well, I mean the smartest man I've ever known. Well, my my Say Chuck how do you figure that spy ring knew all about our plans? Because their spies Pappy, that's a business now Don't ask me how many agents there are here in LA and who they are because I don't know you talk to the cops or the FBI or somebody But all I know is they have some beauties among them. Ah That Beth Hammond for one what a dish Chuck Morgan but glamour push. She was beautiful. She had to be What do you mean? She had to be glamour push She was supposed to be the secretary of Chuck Morgan wasn't she and isn't Chuck Morgan's secretary the most beautiful Girl in the world. Oh Chucky boy. You cute.


Favorite Christmas gift you ever were gifted, go. We got to go back to childhood pre-teens, probably 10, 11 years old, getting a brand new bike. It was nothing like getting a bike at that time.  So once you got a bike, a AK or whip, that was always the best gift you can have. So I begged my parents for that and they surprised. You know, parents always act like they didn't get you what you asked for, they surprised me with that bike. And what's special, they get stolen pretty shortly after, but you know, it is what it is, part of it. Did you get it back? I got a bike back, it wasn't my bike, but I got a bike back. Oh, so you stole a bike, okay.


it's father's day here in the US and a few other parts around the world too. Anyway, you know, it's just, the world is just so weird right now. Anyway, I started off the morning losing the back to one of my earrings on the floor somewhere in my bedroom as I was to be honest and graphic changing, taking my clothes off so I could get in the shower. I couldn't find it and rather than wandering around the bedroom naked I just said, forget about it. I grabbed a spare earring back, went in the bathroom, went to go put it on and then hop in the shower, dropped it and then couldn't find that one either so I got a third one. Took a shower, came out, the first one was right on the counter where I was standing trying to put it on, not the first one, the second one and then I grabbed that and put it away and then I went to go get in the closet to grab some clothes to put on and stepped on the first one. And since this whole like pandemic apocalypse thing started, I've broken like six dishes in my house and cups combined. If that's not a sign that changes the foot, I don't know, I don't know, it's just so weird. 20 seconds I think.  Anyway and top it off this year of course we have the apocalypse and the hospital I'm going to is a testing center and treatment facility for those affected by the virus. So yeah that's fun. We do normally at the house take all precautions. Everybody who comes in the front door is going to spray their shoes with Lysol and wash their hands and we really don't have anybody in the house who doesn't one need to be there and two we haven't already seen through this whole process in the door but today we will be doing all of those. No I won't film it. So yeah because it's a testing center because I'm nervous about coming in contact with the virus all of that stuff. So I'm leaving probably a few minutes earlier than I really need to leave because I'm not sure about getting into the hospital. Okay so as I suspected it took longer to get into the hospital than it did to actually get the test done so yeah that was a thing.  My anxiety about it doesn't usually let up until I get the results.  Okay guys so it's yeah 10-19 so I think my process process project for today is going to be this over here. So this is the printer office supply area of my art room and my husband's printer died a few months back and we've just been using my printer and when it dies we will just replace it and just use one shared printer which is working fine for us but what I need to do is I need to rearrange the supplies under the printer and next to the printer so that the paper supply and shipping supplies make sense to more people than just me. I also need to take that pile hold it and that pile and combine oh wait and that pile and sort through it get rid of what we don't need to keep and organize what we do need to keep over there because that paper stuff is from the husband's office so but anything that is printed intended for a printer doesn't need to be over there there's no printer so yeah that's my project for the day. All right I'll be back. Hey guys all right so we already have the mammography results from early this morning it is still Monday it's Monday afternoon it's I think just after two o'clock or something around there. I already have the results already and they're normal thank you thank you Jesus and thank you Legacy Health and the hospital and doctor system in Oregon everything pretty much is online you can get your test results very quickly they're very prompt unlike California where I'd have to wait a week if you got to call this quickly or notification this quickly it's because they found something wrong so yay so happy. Anyway I rearranged everything over here so fun fact my husband has a bad back he's got severe rheumatoid arthritis of the spine and so I kind of wanted to make it so that most of the time when he's using the printer he doesn't have to bend over to get paper so all the labels and printer paper and card stock are there on the top of the Akiha card the next shelf down is the small you can see in the back there's some small cards of photo paper and index cards in the front is more printer ink the postal scale and some tape the very bottom is sort of random office supplies including like scotch tape and paper for my pocket printer and stuff like that and then over here we have bubble small bubble mailers and spare tape for packing packaging electronics cables the box of cables I know right so thank you cards and things in the back is a a slide scanner and some slides I found a few months back before the pandemic started of they say my high school graduation so that's going to be a project that I'm going to try to scan them and maybe turn it into a video I don't know it depends on what I find on there and I did get a little scanner device for the slides for Christmas this last Christmas so I just haven't done anything with it yet so it's back there in the back of the yellow thing back there we've got the postcards the art postcards and post-it notes of which you get one minimum in every order when you place an order and then the cardboard mailers so I think that's going to work really well it's nice and sorted and there were a few things we needed to perjure get rid of that I don't yeah when you're questioning why you had it and where it came from needs to go away so I had some lunch I got my results yeah so now I'm going to work on that slow stitched canvas piece that I started yesterday that I was pulling threads out of I'll put a picture of that here somewhere and I also need to edit the vlog so I better get to it I'll be back yeah it's 85 degrees out it's in case it's peoply which it isn't right now I already have my mammogram results they're normal it's all good ending the day with a little bit of family drama on my side but you know it's the name of the game with my family so what are you gonna do yeah I think our cleaning lady is coming on Wednesday so that's a little bit stressful because of coronavirus but she's very safe I trust her and she takes all the precautions so I'm not too concerned and I appreciate her help keeping the house clean so there's that she does such a good job we love our letty um and at some point this week we'll have to go to the grocery store again so you know that's a thing but in the meantime I'm going to stay at home and get some stuff projects done some stuff for patreon hopefully and another video for my creative year I'm hoping to get both of those done tomorrow but we'll see and uh made myself a project list haven't done that since the coronavirus started so maybe it'll help me keep me focused anyway that's it for the moment we're gonna go walking I'll be back hey guys I don't think I vlogged today I have been filming again with my phone but I don't think I vlogged at all I filmed three videos for YouTube with my phone I don't know what the deal is with that since this whole thing started the pandemic thing um but I'm enjoying not having to get out the big camera to be honest um we filmed a few painting videos this morning in fact we have a 30-day challenge coming up there's a hint so stay tuned for that some of the content is filmed in such a way that I can create unique content for the patrons over on patreon they get more chatting and instruction from me then you all are going to get here on youtube sorry um at least right away I might release the talking versions at some point on youtube but at least right away so if you want to hear those or see content before it goes live on youtube you need to be a patreon uh whatever I filmed the July challenge for my creative year so that's done now I've gotta I've gotta go to the bank I keep forgetting I've had this check from a friend since May 25th is the date on the check um for he sent me some money to make him and his family some masks which was great thank you Andy um I would have done it anyway but thank you um anyway but I've never cashed the check so yeah so I'm gonna go to my bank has a drive-thru thankfully so I can stay safe and all of that so I'm gonna go do that I have a mask in the car I'm gonna use the opportunity to be out to go run and get a couple things at the grocery store that we just forgot we're completely out of and I don't know why we didn't have it on the list like subconsciously we just I don't know what the deal was so I'm gonna go grab those I might go grab some thread real quick at Joanne's and well we're not gonna go too many places because you know hello COVID-19 okay I spent the last I don't know a few hours chatting with friends rather than finishing the crackers which I have to finish I gotta get the dough out of the fridge um and I did but I did prep some fabric pieces for a couple of upcoming videos that have to do with mixed media stamping stenciling and all of the above stay tuned anyways um and I have um some more ideas of other things I need to do this week so I do have my to-do list I'm able to cross a lot of stuff off first thing I'm gonna try to do tomorrow well maybe not tomorrow maybe Thursday is I'm gonna try to film this week I want to film five of the videos for painting a day for 30 days I'm filming them ahead so that I can um maybe do them as a as premiere videos every day and I'll be like in the chat with you all as you're watching so anyway um I think that's it for the moment I'm gonna gather some laundry and start some laundry and then make my crackers and I will be back good morning everybody it is Wednesday June 24th 8 22 a.m don't get excited hello computer I'm checking you on youtube and having my breakfast cleaning off the table a little bit straightening things making piles my ironing board's now covered with project piles okay so I got the first two videos for the 30 paintings and 30 days challenge um edited and they're loading to youtube and scheduling out right now I am going to try to film most of them ahead I am sort of toying with the idea of doing the very last one live I'm not sure but let me out know what y'all think about that I might do one or two slow stitch lives to in July sometime I don't have anything set in stone or scheduled out yet so you guys let me know what you think I also got the July challenge video filmed and loading for my creative year and the last tutorial for my creative year for July uh June filmed and loading so there's six videos all together anyway I'm gonna try to get all the 30 day paintings or at least most of them done sometime between now and this time next week so it's going to be interesting and very busy so we'll see I am headed out for an evening walk by myself first time in a while definitely since this whole COVID-19 thing started Bob is at the gym he's taking all precautions but I don't feel comfortable doing that yet so I'm gonna go walking instead it is kind of late we haven't had dinner yet but it's all right it's all good we're gonna end up with a late dinner but that's fine and I've got a bunch of video loading to youtube and yeah I'll be scheduling posts after that's done and yeah it's all good I'm gonna listen to some music and go for a walk and all these hey guys it's after two o'clock I don't think I've vlogged it all today I went right away this morning upstairs and started working on the 30 paintings of 30 days project and getting that all straightened out which was for the next few days or a week or so it's going to be a lot of work but I'm going to get it done I'm doing it in a much shorter amount of time than hopefully you all are going to be doing it but anyways we also had my dad who came to pick his car up which was here and a couple of other things and we had to do it while being socially distant because he's been traveling and what that is I think Bill's he's been traveling and he had a COVID-19 test while he was gone it is negative but he says he had it out of an abundance of precautions but we gave him his car keys back very socially distant and I stayed in the house and my husband stayed six feet away and basically threw the keys at him so yeah it was good I talked to him through the window and he's he shouldn't be home by now so anyway I'm going to head in the house and sit down and watch some youtube while you while the videos I filmed this morning and edited are loading and just relaxing I need some water I'm like really thirsty so have some water in a snack and maybe go for a walk later and I'll be back well the lighting's kind of bad huh can you tell that it's a really bright and sunny out and hot um it is friday january june 26th I believe I think it's the 26th but I know it's friday um sorry I'm backing up I'm not having a good multitasking day it's just not not a good day for that today blaming it on a corona brain june 27th 11 26 a.m don't get excited computer hello guys we're at the ironing board I'm going to cut off about quarter inch cotton twill tape and I don't know if you remember but I have all of this camellia flower water I made a a few weeks back when I was eco dying with camellia flowers and I boiled the rest of the flowers I had and made this sort of pinky red dye so I want to take some of this cotton twill tape and soak it in here um overnight at least overnight and see what happens I'm going to take about five yards maybe a little more because there's a lot of water in here um and I'm going to soak it in there and see what happens I'm going to add some alum to the dye first because honestly I don't remember I don't remember if I did it already or not um but the alum will help set the color in the fabric so I was going to say if I can get the lid off holy moly it's not that way that doesn't want to come out let's try this way nope it's kind of bumpy put the lid back on give it a shake forgot to say make sure the lid's on good before you do this okay now I'm going to cut off I've got my yard stick out which I'll be honest it took me a few minutes to remember where I put it because I haven't had it out for so long I have oops I have a measuring guide on the on the art table so I usually use that and if this works I might also brew up some coffee and try this with some coffee and just soak the ribbon in the coffee actually I can I'm I'm going to do another one too I can put more than one in there need a paper towel okay and I have this giant spool of twill tape so there's plenty it was very inexpensive on amazon I'll put a link down below five so if this works I might be selling some of this dyed twill tape in my etsy shop along with some fabric scrap packs for lack of a better term I've I've had some on my in my shop already and they've sold they may be completely sold out there may be one I don't remember but usually a combination of found and vintage items things I've dyed things I've painted I think this is going to be one of them if it works I'm going to do one more piece okay we're going to just sit this in a corner and leave it for at least a day maybe longer let it soak up that color from the camellia flowers and then dry it we'll see what happens but you're not going to see what happens this week in this week's vlog you're going to have to stay tuned so you'll have to watch the next vlog so to see my adventures with dying the ribbon and whatever else I can figure out to stick in there you'll have to stay tuned so that's it for today and that's kind of a cool picture that and I might take a picture for that on instagram let people guess what I'm doing um okay so that's it for today and for this week it's been a fun one it's been a weird one because you know a whole lot of apocalypse and all but um you know we're still chugging along and we're being creative and we're thinking of new things and they're having fun doing a lot of stitching so there's that plus getting back into watercolor a little bit so it's all good um if you have any questions comments or concerns please leave them down below don't forget to like share and subscribe if you have a way to support the free content here on youtube and over in the facebook art groups I sure would appreciate that there is my etsy shop link paypal tip jar if you want full tutorials on some of the videos that are speed through here on youtube the full tutorial videos over at patreon it's only two dollars a month so you might think about supporting over there and not just for me though there's a lot of really great creatives on youtube and over in the facebook art groups and I know they're all looking for ways for you to support them whether it's buying a piece of art from them through their etsy shop or um taking a class from them joining their patreon something like that so you know if you can't figure out how to support them check it out uh check out their video descriptions maybe send them an email and ask and I know they I know I would appreciate it I think they would too so that's it this week stay safe stay healthy stay creative don't forget to go out and do something nice for yourself because you deserve it and I'll see you later bye guys


Yeah, but why are you? What movie okay, tell me about it. Okay, but what about the reserves? That makes sense, but what about under the legacy system? No more about continuation pay options. Who would I actually talk to? Yeah, thanks, I'm gonna go now No, thanks