 So indeed, thank you so much for this opportunity to be here today and to present on the AI Forward Forum initiative and the activities that we organize around it. So AI as a term it evokes many different images to people and there's at least two topics that are hotly debated that are in the public eye, so one is this idea about the future of work, the future of humanity. Another hotly debated topic is the role that algorithms have on the way we communicate what information would get exposed to etc. And of course there's more we just talked about ethics right so there's many more issues surrounding this technology. But the thing that doesn't get that much limelight is this idea that over the course of the past almost 70 years, the field as a whole hasn't gotten that much closer to solving intelligence, and this is what the founders of the field set out to do, they wanted to understand intelligence and to develop it to engineer it in machines. And actually, as look at the then and the now people are still very much they have very obscure idea what intelligence is as they had at the time and what algorithms do we need what modules do we need to combine to develop a truly intelligent systems. So, as it was pointed out by the founder of one of the founders of this fields of this field, you are bound to be thinking about this question if you if you want to develop intelligent systems. And I almost hear you say, that's not important. We already have a lot of systems on which our many apps are based right so for image recognition for speech recognition for translating between languages, and they all seem to be doing pretty good job. Yeah, that's fair enough. But if you look at some other domains saw in robotics and education health care, their people would benefit from having general purpose systems. So if you take robotics. There's a robot that is able to go and work on a building site outdoors. So that's robot will need common sense will need very developed understanding of the environment and the ability to cope with changes in the environment. And that's with health care. So if you want to have a social robot in a health care scenario. You need to equip that show what with the ability to pick up on the very subtle cues in human communication, and the ability to derive the meaning from those cues. On this, the robot is supposed to base its behavior as well right so all these they are very much open questions and unresolved questions and very difficult questions to tackle. Perhaps they are difficult because we don't have a theory of intelligence. And this is unlike in other areas of research. So if you take physics but that's a general relativity biology has evolution chemistry has a periodic table. The AI doesn't have a theory, a set of guiding principles about what intelligence is. And some people are saying that, Okay, you only need a lot of data, you will be applying machine learning on it and you will be scaling towards general purpose systems. And this is also a hotly debated topic, whether that's true. And to why haven't we built then the general purpose systems already because we do have a lot of data for certain tasks for certain problems. And so this is one issue, another important issue actually. There is this sometimes misguided thinking that you only need computationally savvy people so computer scientists, perhaps neuroscientists. They are the ones that are truly posed to be solving intelligence and understanding it better. And I think that actually, all the people across the board are very important so historians anthropologists artists are equally important. And with this idea in mind we started the AI for form initiative as a platform for different people to come together, and to exchange ideas, perhaps develop a new set of ideas combined set of ideas that could be useful in thinking about intelligence and developing a general purpose systems. And I know this sounds like a super ambitious goal. But I think, first of all, what you want to do is just raise awareness why this is important. And now you did that will talk about the activities that we organize around this idea to advance this course. Yes, thank you. So, just let me repeat that the goal of this airport forum is to bring these fundamental questions regarding intelligence that we had talked about the table and enable people from different fields to join the discussion. So designing and thinking about technology should not be the domain of the technology can be inclined. It is a fundamentally human issue. And therefore, this crosstalk between those who work in computer science and feel as diverse as anthropology arts culture biology psychology to name a few is crucial. So how actually we try to achieve it is the organized monthly talk series where we invite researchers and practitioners from diverse fields to present their work. So far, we already had a broad range of guests from different domains and even different countries and culture backgrounds, such as professor Cecilia here as we presented her work on social cognition than a high tech fashion designer, who introduced us to the emerging field of fashion tech, which is a real combination of fashion design and engineering. And for example, Mark Serpent from Facebook AI who joined our discussion on the superpower subhumans and machines and what it takes to create intelligent AI. So we invite our speakers from different areas because we truly believe that this diverse knowledge and expertise could enable a scientific progress in a way that it wouldn't be possible only in an arrow field. And to illustrate my point I would like to tell you an example of what is possible to achieve if you combine the knowledge from different fields, and this example is from our talks. This is hosted at all given by Professor George Barnard and Professor Michael Levin. So George Barnard is an excellent expert in computer science and his research centers on evolutionary robotics evolutionary computation and physical simulation. And in his lab they use an evolutionary algorithm to create thousands of designs for the new life forms. So the computer would over and over assemble a few hundred simulated designs into different forms and body shapes. And for one of their experiments we use basic rules about the biophysics of what a single frog skin and cardiac cell can do. So the more successful simulated organisms were kept and refined and while failed designs were tossed out. So this is where usually the experiments would stop. However, in collaboration with Professor Michael Levin and with Microsurgeon Douglas Blackstone, they managed to transfer these simulated designs into life. So firstly gathered actual stem cells harvested from the embryos of African frogs and then we separated them into single cells and left to incubate. So once the cells were cut they joined them under a microscope into a close approximation of the design specified by the computer from Professor George Barnard's lab. And this assembled body forms never seen in the future. They started working together, allowing these tiny cells robots to move on their own. So this is a fascinating thing and I think it wouldn't be possible if they wouldn't combine this knowledge between different domains. So of course this is just one of the examples out of many and we also think that it applies to the progress of AI. So that in mind, just one more time to repeat myself that we aim to actually form an effort of people interested in creating a roadmap or kind of a guideline for developing these machines that are social context I bear and able to generalize or what you would say it's more related to general intelligence. And now, just about the talks itself. We have a short video of our speakers and some great ideas from our speakers, just to have a sneak peek. So, general intelligence is not only primarily social in its functions, but also that it's primarily social in its origins, that we get it, primarily from other people, rather than from the genes. So if you don't have a certain enzyme in your in your genome, no amount of bioelectric signaling is going to get you that enzyme right if you need to turn chemical a into turn chemical be and you don't have the enzyme bio electricity signal is not going to help you do that. But, but things like wings. I mean, we haven't done wings, but but it should be no problem because as long as the wings are made of the same kind of stuff that you at the building blocks as long as your building blocks are present. No problem. Because you might have some digital agents that need to express or communicate certain messages from their face across a longer viewing distance or a much noisier channel. And so you'd want to amplify those signals and they can apply that we can do that. They can we can just make them have like popping eyes or very, you know, large movements and so then humans will be able to see these from longer distances so might serve them well. I mean, when kids see my dress they always what I think is interesting they look at how it works, and adults like to embrace the magic effect of what it does, and they don't really want to know how it works as early if you have the children at the child and it comes up to the challenges like where are the sensors, how does this work, you know, where's the battery they all have these very technical questions which I think is funny because you could expect that from a mature person and mature person would be like oh this is just a magical device. So that was just a glimpse of how our talks look like what the discussions are like in the Q&A sessions. And then just a couple of brief sentences words on the website that we run so basically Marie has done a good job of explaining it in a way because she mentioned that yeah we have a newsletter that we send every yeah every two weeks. And just to add to that it's a theme driven newsletter because every time we tried to touch upon a different topic so we had newsletters where we discussed genetics then the last one was on why AI is actually hard, why building intelligence into machines is hard. And also for all the, if you would like to check out all the talks that we so far had in the forum, so we can do that. And in addition to that you will find the record so first of all the recording of the talk is online. In addition, there's transcripts of the Q&A sessions and also some resources that accompany each talk if you want to deep dive into a particular topic. And as regarding the upcoming events so yeah the next one is in December, December 16 with Dr. Daniel White from currently based at the University of Cambridge. He's a cultural anthropologist and he will be talking about his ethnographic fieldwork in Japan. And it's a very fascinating topic too. And also we are all set for next year so for the first two months of the 2022 so I hope you can join us and if you want to, you can scan the QR code that is on the slide. And you can also get in touch with us via this email and yeah to exchange also maybe your thoughts about what we just discussed what we were telling you now. I guess just sort of to finish it up, I think me and Yudita would like to wish you to be as those kids as you saw in the video as discussed by Anuptip Rekt. So the ones that are really curious and asking questions and trying to figure the stuff out. So thank you so much for listening.