So it's a distinct honor to be here, and I am delighted to be here to have the opportunity to participate in an inclusive event which has been really a transformative program at the National Science Foundation, and I check in my time, so I still haven't started just in case. So it is sort of a complicated task to figure out what exactly you're going to have to say. I want to say a few things. I know this is an emphasis in data science. Now my visit to Brown is co-supported by the Data Science Initiative. I don't know what they thought about doing that, the way I was chosen to, but so I have to talk about data, so I already did, right? So let me see if I can go with this. All right, so first, you know, there are at least two ways of doing science that you should be aware. It's not just as Senior Google, and he will tell you. But there is a third way of doing science, and I think it was better highlighted by a New York Times op-ed article by my former colleague Steve Strogett, and in fact he just wrote a book about calculus that the white calculus, I forgot what exactly it was called, but it says anything that he writes is worth reading, and it has some excellent interviews so you want to see them. And what he talked about that is the two defining events that took place exactly in, I think, 1953, and those were the discovery of the chemical structure of DNA by Watson and Crick, but also the invention of the computer experiment. We're not talking about simulating a partial differential equation or anything like that, but the use of the computer as an exploratory tool and with this, a third way of doing science. And then over the last couple of decades, a fourth way of doing science has come up, and that has to do with data science, which is how to develop these ways of analyzing data that comes massive amounts of data, and how can we actually explore and search for patterns among these data, and what can we say about this.  So that's what this guy said, and then they make some predictions, and then somebody had the bright idea, let's put it on a computer, and they put it, and they realized that they were totally wrong, that they have no idea what was going on, and that was the birth of the computer experiment by these individuals. If you see at the advances with the Human Genome Project and all the advances that have been in the social sciences, none of that could have happened with the great advances that have taken place in computer science, not only in the development of hardware, but also in the development of hardware. A few years ago I visited Orange National Laboratory, I can't correct the word national yet, but and then I looked, so somebody took me to see what it was, the fastest computer at the time, and then I took a picture with Titan at the time, I was very happy, the fastest computer, and about ten days later China announced a faster computer, and I think now we have the fastest computer for the next two hours or something like that, but this is just the advances that take place. Data science provides some really dramatic challenges, because we generate data velocity all the time, in fact we do it over our cell phone all the time, we do it with streaming this presentation, so the amount of data that is generated is immense, and then the question is, since you have also tremendous volume, that's another challenge, where you put it, the velocity, where you place it, so these are some of the challenges, and also in addition to the fact that all the data that we generate is on different formats, that's what diversity is called about, and therefore how to be able to use all these data that comes from different different formats, from different different purposes, how can we actually make use of all that, so there is a lot of challenges associated with data science. One of my areas, and more importantly, is essentially epidemiology, mathematical epidemiology, and it's interesting to see who developed mathematical epidemiology and why, and most of them were physicians. Now I think that we have somewhat forgotten what is the connection between science and physicians, and I think a lot of the medical pre-college med programs in fact make it almost impossible for many of them to become curious about science, about evolution, and everything, but that wasn't so in the past. So Daniel Bernoulli, who comes from my family, a prestigious mathematician, was also a physician, Euler was also a physician, mathematicians don't like to say these kinds of things, but bringing them out of the closet, Sir Ronald Ross was a physician, and then Anderson MacKendrick, and Kermak, one was a physician and the other was a mathematical statistician, and I get confused, and I won't remember, but between then they developed the field of mathematical biology. The most important work really is the one for Sir Ronald Ross in 1911. He wins the Nobel Prize for his work on Malaria, and then one of the challenges that he has to do is now that I know the transmission process of malaria. How can I use that information, not only to take care, better care of my patients, but how can I use that information to ameliorate the impact of malaria in my community, in cities, in the whole world? That's the challenge, right? That's a predecessor of what data science is about, right? Exactly. How can we use information at low levels of organization, and how can you use that information and those patents to understand patents at larger levels of organization and how you can change that? He had won the Nobel Prize, so he said, all we have to do is there is this tipping point, he didn't call it that way, and if you bring the mosquitoes below some level, then that would really make a huge difference, and they didn't believe him, so then he writes on mathematical equations and then they believe him, okay? So that's the power of mathematics, so probably a good scientist had a good idea of what had to happen, and then he does, and he writes these deterministic equations where the terms actually make sense, which is different from the statistics. So you see, for example, that M over N is the host vector ratio, which should be important, right? You have a trillion mosquitoes per person, you are more likely to get beaten, that you have only five, okay? So this is information that we have there, and then he talks about probably the most important process that taken science and is to differentiate when you go from no growth to growth, and of course then the most important function of mathematics is the exponential function, and what does exponential function mean? It means that you have a pattern of growth, or in terms of processes, for example, of infection, that means that if I infect more than one person in this room before I leave, and I just travel from Providence, then I will have generated an epidemic, but if I infect only half a person, then that won't happen, right? So that's the general idea, that's the basic reproductive number, so if it is greater than one for mosquitoes and for humans, then the mosquitoes will generate secondary cases on the mosquitoes via human, and the human will generate secondary cases on the humans via the mosquitoes. So that's the general idea, and just to, and essentially these ideas were used by Sir Ronald Ross, and I think it's important to analyze why was his holistic perspective. This is, it is interested in the public good, after he made these scientific discoveries, can malaria be controlled at various levels of organization? And this represents a challenge of scales, he understands how the transmission process takes from human to vectors and vice versa, how can you use that information to understand the patterns of spread of malaria at a high level of organizations? Do I have to incorporate every bite of every mosquito on every person, or do I use less information to do that? The mathematics help, yes, identifying thresholds, right? Also the prestige of mathematics play a role here. And finally, can we use these models to study other phenomena? Does the abstraction of mathematics, can mathematics be useful to address other types of problems? And of course, that's the case. And in order to, to, to explain you what they did, I'll use the simplest possible model, which now I'm happy because that's not taught also, and sometimes in calculus courses, certainly in differential equations courses. And this is the simple model of epidemics, which is the SIR model susceptible to infected and recovered. And in this case, we have three classes susceptible to people that don't have the disease. Then you have infected individuals. And they come into infections because they interact with infected humans. Here in this model, they interact at random. This is a simplified model. And then eventually they recover after some time the infection doesn't last a lifetime. So this could be a case of one outbreak of influenza. This case, there is no births and deaths. So this time scale is short. So you see how all the elements associated with what is mathematics are used for are clearly delineated when you postulate a model, because you have to be very precise and clear about the assumptions that you make. And then you write those differential equations. And the only nonlinearity here is S I over N, which is that the susceptibles, you know, might be infected depending on the proportion of susceptibles of infected individuals, which is I over N. And then you make some magic. You say, what happens at the beginning when almost nobody has the infection when S is equal to N? So you cancel the S and the N. In applied mathematics, you can do everything. And then you write this equation, D I D T is beta I minus gamma I. And then bingo, that's the most important differential equation that you have. The only one that you need to know how to solve. And then the solution is the exponential function. And you see beta I minus gamma is greater than zero. You have exponential growth or the beta divided by gamma is greater than one. You have exponential growth. And that's what is called precisely the basic reproductive number. So essentially beta is the transmission rate and one over gamma is the time that you are infected, the window of opportunity to you to pass the disease. And if that's greater than one, then you get exponential growth. And you have an epidemic. So this is basic. This is what happens almost any physical phenomena. For example, what is the transition point from boiling water to steam from ice water to regular water? I mean, these are transition points that take place. And there is a tipping point there. And in these cases, when you go from not growth or for an exponential decay to not growth and then to exponential growth. It has been used to study gonorrhea, for example. And some of the way gonorrhea gets treated today is the work of Herbert Headcott, which applied mathematician, Kenneth Cook, which played both teams. And James York, which is a person that introduced the word chaos, a very personal and dynamical systems. And the changes that they made about gonorrhea had transformed the way that we treat gonorrhea. And then you have people like Ed Kaplan at Yale, which also used these ideas of roles to study the dynamics of the needles, to study needle exchange programs on HIV, the needles were the vectors, the humans were the humans. And then there are needle exchange programs. So there is dynamic in the needle, there is dynamic on the humans. And he studied this to figure out if needle exchange programs actually work and reduce in HIV. All these ideas generated by the work of Ross. Now there is epidemics in complex communities. And we will talk about the case of Episemes. This is Sara Del Valle. She participated in my summer program. And I will talk briefly about that afterwards. And she came with me when she was a graduate student. She was at Iowa. She had worked in my program. And he says, I know you're going to be Ulaanba Scholar and Osalamus. Can I come with you? And I said, sure. In fact, one of the practices that I make every time I visit some place, I take students. I don't go to spend a considerable amount of time at a place without taking the students. So at Brown I have right now one grad student and two postdocs in residence and I bring in two more. And then including two U.S. Latinas that are coming to the program. It's very important. I was at MIT and I took again three U.S. Latinas to that, including Camila Young-Quest as a postdoc that visited when we was at MIT. And so forth I was at SAMHC and again we took six or seven graduate students. So this is very important. And let me tell you, they changed the dynamics of the place. They changed the dynamics of the interactions and they changed also the understanding of what underrepresented groups can do. So Sharad El-Bagi got involved in a study that was created before. It was a PCIMS, which is like SIMS series. Creating this series in this case was to reorganize series so that they could save energy. But now they also used to study epidemics. And the example that I have here is has been highly developed by, particularly by Steve Eubank. And they involved a million and a half people running around 180,000 buildings and then tried to understand how these parts of movement might affect the part of the spread over a single day. This is important because unless you are in a college campus, at midnight most people are at home sleeping so there is not much movement and afterwards the movement starts to stack. So you don't have a fixed network, you don't have a scale free network, you don't have any of those networks, you have a real network and that network changes over time. So this is an example of, I guess Alicia commented to the conference before there was sunlight and everything else to be sure that everything was going to work out. And then we studied all those parts, we started by activity, we started by social activities and we study all these kinds of networks, they are dynamic networks, they don't scale free networks, they change over time and so forth. So we published a paper by that and also Steve Eubank has applied this to the study of epidemics and behavior in the left. You have an epidemic of influenza where everybody continues to say hi to each other, give Biden kisses, whatever they have to do to go on online, on the other side they say no there's an influenza epidemic, this is the strong arm of Pelosi that says I have a call, I just read that today, did you expect that? And then you see that you have more cases where people are friendly, less cases where people are not friendly. So this is the best, so this is the best case against friendliness. Okay, but you see the epidemic curve is just a typical curve, it's a very complex of interactions but the epidemic curve is very simple, it will use simple statistical analysis and fit curve, we can do a perfect fit, but use a simple model, it doesn't tell us anything unless we interact the complex patterns of spread and what are the critical points of this network. So we have to be careful also about that, so making use of a whole lot of data and not just the aggregated patterns is very, very critical and that science has played a role on that. Here is the same epidemics, but a study over the whole United States, I believe 300 million people were moving around and again people have studied these patterns of spread and understood essentially. So you can imagine the amount of data that you have, how do you sample this amount of data to do some statistical approach to have some understanding of what's going on, what are the most important places of transmission and so forth and you know, so this requires new algorithms, new ways of thinking, new ways of doing science. Okay, so let me go back to the tipping point and the tipping point is according to Merriam-Webster dictionary, which I have never seen, but I saw it on the Mr. Google, the critical point in a situation, process or system beyond which a significant and often unstoppable effect or chain takes place. That's one definition. Mr. Google says the point at which a series of small changes or incidents becomes significant enough to cause a larger more important change. All right, Malcolm Gladwell became famous for this. It's a Canadian journalist that writes beautiful, he doesn't quite understand no linear process, but he changed, he changed, I think he had a tremendous positive impact by talking about tipping point, but the tipping point is that magic moment when an idea, trend or social behavior, that one sounds like a political speech, right, crosses a threshold, tips and express like wildfire. Just as a single sick person can start a flu epidemic, so too can small, but precisely targeted, precisely that's the key part, right, push, cause a fashion trend, the popularity of a new product or a dropping crime. So this is and he has made millions of understanding exponential growth. So guys, this is your chance. Okay, and now that's used everywhere, right, at tipping point in education, at tipping point this, at tipping point that now has become now part of the common and essentially the question is why high school students can't quite understand exponential growth, because probably they don't use these ideas to explain exponential growth. They probably just write the question and digraph and things like that. So this is an area. Now what happens in an epidemic, see, so what you have is, in this case, tipping point is being beyond one, initially you have exponential growth, eventually you start running out of people to infect, so it saturates and you have a limit, and this is the curve. And if you are below the tipping point, then nothing takes off. So this is the typical epidemic pattern that you see, it's called a transcritical bifurcation. What happens when you have social processes, like drug addiction, etc., and some complicated transmission processes when you have multiple modes of transmission in the spread of diseases. You don't get that. You don't get this standard. Certainly you are above one, you are always going to get an outbreak. Certainly. In this case, the outbreak is actually a lot faster than exponential. But what you see here is in that wide, wide region, that if you have enough people that arrive there, that are infected, they can generate growth even below the tipping point. In addition, most of the practices that people use to control the diseases is to bring, right, is to bring the process from exponential growth to exponential decay. It's not even the process so that the parameters that operate in the process now operate below exponential growth. But we see, in this case, that that's not possible unless you go well beyond the wide line. So controlling a disease, controlling any kind of behavior becomes a lot more complicated. That's why social processes like drug addiction or others, they are resilient communities that are very difficult to change, right, because they get structured in such a way that the process of conversion is continuous at all times. There are many, many layers that we operate, many non-linear times that operate that generate these kinds of curves. I'm going to use this to talk a little bit about meritocracy. And meritocracy and social justice is important. See, the promises of our democracy cannot be fulfilled selectively. Therefore, a social justice educational model that does not account for the following factors, is not a social justice model. Initial conditions, family background, quality of school, access and resources. And you can see the recent scandal with people buying themselves into colleges makes this incredibly evident, right? Yet many people talk about, tell people that we actually do live in meritocracy. A model that does not eliminate the deleterious impact of preconceived notions of ability and talent that are pretty disempowered those with a history of exclusion. A model that asks us to wait until care through education is perfect. Of course, they don't have the children in those schools. And it must be put out of commission as no family, white, yellow, brown, or blue, I forgot green, can possibly have said the sacrifice of a single generation delays in access are simply unacceptable. A system where inequality is pervasive cannot be called meritocracy. And I think this is very important that this gets stressed over and over and over. And I think if this recent scandal tells us and in addition to all the legacy admissions, it just proves conclusively, conclusively that we live in any kind of model except a meritocracy. True, there are some schools that are doing a marvelous job but that's not the rule. That's the exception. So I'm going to tell you a model that or how we should do this and hopefully we can apply to this. These models can also be used to create communities of mentor people, resilient people. This is about mentorship. So at one point I call this the Richard Tapia model. Okay, we can call it any way you want today. And essentially, we have susceptibles. So that's people that don't understand the importance and value of studying mathematical epidemiology, right? So we put in a community that they talk about this all day. And there are some people that are key, they semi-convinced the people say, yeah, epidemiology is cool. But sometimes they say, well, maybe not so.  And you have the brainwash. Okay, the people that say, no, this is absolutely. But if you live in a community that interacts like that, right, you have second order effects, second two modes of transmission. And if you do that, right, do you get a situation where conversions are generated by a typical mentor obeying the basic model? If R0 is greater than one, you have a mentor community. Otherwise not. We get that. And you can see in this case, these believers, these exes. And the answer is no, you get this. You get this. You get faster growth. You get a community. And you get a community that there is very difficult to destroy. This is the this is the power of collaborative learning. This is the power of having communities that interact. This is the power of clearing opportunities for global communication and learning. This is the power of these communities. And this is what we have developed on the Mathematical and Theoretical Biology Institute. They started in 1966, 1996, where essentially we have a model, but we have a common language. Students select the questions so that we don't have the answers. We cannot look at the back of the book. So students realize very quickly that they are smarter than most, not all of the faculty, and that that's not too hard to accomplish. And then they realize that when we are put in a situation where we don't know what to do, we could be called all sorts of things that we should not be called because we should be respectful, but that often takes place in the classroom when they want to discourage people about the ability to learn something. But that happens there. And we have done this. And out of that, the first paper of women asked this question about bulimia. They published the first paper on the dynamics of bulimia in 2003. They have, and they even made a model of this, this resilient community right talk, that was appeared in discrete and continuous dynamical systems B. And there is a lot of proof that in fact that's what we create. So for the ones that think that this is just a nonsense actually, there is mathematical proofs that talk about that. We have written lots of articles about that, more about writing about that. I think that there is a volume in a book where we wrote also something about that. This is the first group of students that participated. And what you see is a lot of people, for example, in the bottom you see Erika Camacho, the first student admitted to the theoretical, to mathematical and theoretical biology institute. Now she's my colleague and she just won a Paesmana Award. You see the late George Casella sitting in the first row. One of the big drivers of Bayesian statistics, which is critical to the study of data science. And you see many others. A lot of those people that are there are mathematicians that now have positions on the right side, Jorge Velázquez Hernandez, who was my post doc and later on, the first biologist to become the president of the Association of Mathematics in Mexico. They must not have enough mathematicians. So rightly so they chose a biologist, right? And so forth. Anyway, this is the, when I went to Los Alamos, I took all these students and all of them have tremendous career. They have Erika Camacho. She was my post doc for some time then. She's now, as I said, Paesmana Award winner. She's Daniel Professor Ariel Sintron. He's Associate Professor of Eastern Tennessee University. Gerardo Chaval is the chair of Epidemiology at Georgia State. Steve Wilkos is my colleague at Arizona State. Fabio Sanchez likes to travel. He's, so he's a Daniel Professor at Costa Rica. Miriam Nuno, Daniel Professor at UC Davis. Karen Rios-Osto, Full Professor at University of Puerto Rico and Macau. David Murillo, I think he's now is VP with American Express. So here are some. This is Sara Del Valle. I talk about this. Kevin Flores, Professor at North Carolina State of Individualized Medicine. Johnny Guzman. He's a Daniel Professor at Brown. Maybe that's what they invited me, okay? It helps to have good students, right? And then you have Brisa Sanchez, University of Texas El Paso, Full Professor at Michigan. Now she has a Center at Drexel University that she just started. Here is again Miriam Nuno. She's a Daniel Professor at UC Davis. And she crossed the U.S. Mexico Desert at age 15, right? And her contributions to our community and our society have been immense, immense. You have Daniel Romero. I met him when he was chief manager at Wendy's at age 19. I asked him, how much do you pay? I pay you the same and you become a full-time student of math. It took me 10 minutes to figure a guy that this was good in math. 10 minutes. No SAT, no ACT, nothing to me. And now he's a Professor. He began to just let a diverse student in Arizona State. And he wanted to study with me. And I said, no. And he asked me, aren't you good enough, Carlos? And I said, you are correct. I'm not good enough. You have to go to... You have to really get out of Arizona and try another one. So he... So we made an agreement. You try several universities. If you like one, you stay there. You know, I'll sacrifice myself and take a really good student. Eventually, he went to Cornell where he graduated. Now he's Assistant Professor at the University of Michigan. Emilia Huerta Sanchez, the woman that wrote the paper of Bulimia. Now she's my colleague at Brown. Another reason why I was invited. Now I have two votes. Okay. And then Gerardo Chaguel. He is now again Chair of Epidemiology at Georgia State, Karen Riosoto, which she was about to get married in Puerto Rico. And her husband to be said, you can leave Puerto Rico. And she said goodbye. Now she has a beautiful family. And you have Ryan Hernandez. He was at UC San Francisco. Now he took a position on McGill. So we're in bed in Canada. And then Daniel Riosdoria. He always wanted to go in business. He wanted to be at Disney. He is there. Another Peruvian, Carlos Torres. He is now Vice President of JP Morgan. He picked me up with a $200,000 card the other day. Indirect effects. We have managed to establish the Blackwell Tapia Award that has been done. All these winners we have in some sense facilitated the knowledge of the great people that our communities have created like David Blackwell and Richard Tapia. We have won awards and we have written lots of books. And thank you.


Welcome back to the Breakfast on Flots TV Africa. So earlier on top trending we had mentioned about how EFCC boss Abdu Rashid Bauer had mentioned that a particular female minister by the name of Desyani Alice Madduke had laundered money and actually paid about twenty million dollars in cash. We had it run as he said two million Naira, but that actually is twenty million dollars in cash that Desyani Alice Madduke, according to Bauer, had paid for that property. Yes, it's now time for Off The Press with Mr D. D. Johnson. Good morning. Thanks for joining us sir. Thanks for joining us. Good morning. Good morning Justin. Good morning to all of you as well. Yeah, good morning to you and we will kick off from the nation newspaper the band headline for this morning at the EFCC. We recovered six billion Naira, two hundred and sixty one million dollars houses in one hundred days. With the right that there are rise in cases of cyber crime, a worrisome, one thousand five hundred and two Yahoo boys held in six months. Above the must head of the nation newspaper, APC awaits presidents not to fix convention dates. Buhari orders troops to intensify Boko Haram battle. NPO calls for hold to press council bill consideration. Buhari praises Kaunda as ex-Zambian leader dies at 97. Other stories on the nation newspaper this morning, teachers pupils abducted in Kebbi for Chinese in Oregon state. Okay, just below the pictorial there on the red strip, reps to amend the IC Act, Nigeria earned thirty four point two two billion dollars from oil and gas in 2019. And those are the stories are making headlines on the nation newspaper this morning. All right, moving on now to the next newspaper. Let's take a look at the Daily Independent. And the headline reads MNPRA 2021 guba poll. Obia not six alliance with APC as abgac crisis festers. And PDP says they're unholy alliance wouldn't produce any results. Above the headline on the Daily Independent and Sars Zitini gives birth in prison. IMF says Nigeria's real GDP recovering unemployment inflation still elevated. Also on the Daily Independent, you can't sack chief judge. Court tells Governor Akiri Dulu, as Jusson strike ends, EFCC storms court with 800 litigations. Don't allow terrorist Britain space. Buhari charges troops. Air peace aircraft to arrive Nigeria end of 2022. That's according to O'Neill Ma. Also bandits kick attack Kirby College kidnap scores of students and teachers. Gone men abduct four Chinese Q Policeman in Oregon State. Now APC Governors pass vote of confidence in bunny led committee. Zambia's founding president Ken Carunda dies at 97. Those are the stories on the Daily Independent. All right. Our next support of all is the punch newspaper making banner headline this morning. NBA or your Ford Buhari's grazing roots. Gazette with the riders land on the state governor's control, not president say NBA and senior advocate of Nigeria. Western region created farm settlements, not cow roots. That's according to the governor of your state marking day. Beside that the must head two stories, the EFCC goes after Oji Carlo again sleeps on Shema or Hakim or those cases. Don't give terrorists or those breathing space Buhari tells troops above the must head of the punch newspaper this morning. We have some more stories there. A peak 2023 presidential candidate from the south. Shikarao tells the APC new electricity law underway says Senate return of fuel subsidies disturbing IMF tells federal government above those stories and Nigeria made 34.22 billion dollars from oil in 2019. That's according to an 80 just below the pictorial there. Bandit of a power policeman kill cop abducts calls in KB federal schools. NPO stakeholders tackle reps over a press regulation bill. Colorado cases rising in seven states says NC, DC. Aquabomb general oversea beats wife to death over alleged infidelity. Ghani Adams writes U.N. U.S. others over rising in security in southwest.  Those are the stories on the punch this morning. All right let's look at the Nigerian Tribune. The headline reads money laundering. AFCC eyes real estate car jewelry businesses on covers newly looted 6 billion Naira from state government. Above the headline on the Nigerian Tribune, bandits abducts teachers, students, kill policemen in KB kidnapped four Chinese working on Legosi but on rail. Electricity reforms federal government unveils plan to transfer equity to state. World Bank four Nigeria's inconsistent electricity tariff policy says country may incur 3 trillion Naira shut fall by 2023. Zambia's founding father Kenneth Kaunder dies at 97. Presidency doesn't feel the impact of Naira devaluation, PDP governors. If by done violence, Maki says we'll review park management system. Read Iwo Road of courtest. Allege sex for max. Aquabomb varsity sex 14 lecturers. Stakeholders won NAS over proposed draconian law against social media says 5 million Naira fine, three years imprisonment, two punitive. Buhari in Borno tells security chiefs to be harder on terrorists. Court stops Akira Deleu from sacking on Doe Chief George. And lastly, Hashim Bajo says reps to review NSAS judicial panel reports on public brutality. All right from the Nigerian Tribune to the Guardian. Nigerian business is shown IPOs raised 3.09 trillion Naira via commercial papers. CP's dominance may worsen drought of new issues in stock market. Operators of blame weak macroeconomy and others. Edge government to tackle insecurity provide tax incentives, reduce issuance costs. Our most stories on the Guardian this morning. Nigeria ranks 146th on global peace index. Eighth list peaceful in Africa. COVID-19 causes global rise in civil unrest. Violence costs 14.96 trillion dollars to global economy. Could take poorest countries a decade to recover from COVID-19 impact. All right, just beside that one, subscribers may pay more as government moves to levy airtime. And Zambia's first president dies at the age of 97. Just below the paper down the blue strip, Abuja court stops Akira Deleu from sacking on Doe CJ. And on the red strip, there are seven students, four teachers of federal government college burning Yari kidnapped.  And those are the stories on the Guardian newspaper this morning. Okay, let's begin with those. Good morning, once more, Mr. Jilly Johnson. Good morning. It's a pleasure to be with you. Fantastic. Let's start with the story of ESTC and that they recovered over 60 billion Naira in 100 billion dollars. Every time we have flooded with stories of money recovered with no accountability for the money recovered, with no justification or proof to justify the money recovered. If we take the stories we have read in the newspaper with respect to ESTC since 2015 to date, I'm sure we must have recovered over 600 billion Naira in terms of recovery. Yes, Naira has recovered money, stolen funds, and yet we are borrowing money to do projects from compensation funds from Chinese from left right to center. I don't seem to be able to conduct the dots with ESTC claim on fund recovery from corruption and Naira inability to get funds to do projects. So, we just come on the pages, we just release figures, figures that are not validated by the account. A general office, either by the ministry of finance or water. If they recover the money, who do they give the money to? Where do they return the money to? What measure of accountability? What process do we put in place so that recovered money are not re-uploaded? So, these are we already taken and then every ESTC chairman will always make reference. There's a reference point for them. Every river says always make reference to designing. If die and zoning could make such a amount of money from government debt. Was he the only minister in government debt? Was he the only minister? That means that a lot of people must have looked at this condition. Dishonour has become their cash cow to use put on code for them to see they are doing, they are fighting, they are fighting corruption or they are making recovery. Any fight against corruption with a prosecution. I have said it, let APC do what is APC? Arrest, prosecute and convict. Not only arrest and recover. Arrest, which is a prosecute and convict. Criminals and people that have looted this country. Let them do the needful. Not beyond calling press conferences and telling them we recover $600 billion, we sold $200 million cash, we sold this, we sold that. There's no corresponding effect from the office of their country general or the ministry of finance. If they recover the money, I think the money will turn back to expression, then there should be coordination between ERCC, central bank and the ministry of finance with respect to whatever. Other than that, all we just see is just fighting corruption and recovery of money on the prisoners of mystery. And I've said that over and over again. It's evidences. Only fools argue with truth and evidences are proof. Evidences making. All right. All right, Mr. Jileh Johnson. All right, because let's quickly go to this other story in KGB State. It said, bandit strike abducts students and teachers. We don't know just the amount of students and teachers away. We were dealing with Boko Harami, the North East. We are not solved that problem. Then we moved to Edas Farmer's crisis. It was the tale of the last time of the president in play through being with Nasalawazis. Then now we have come to the North West as we come the bastion of banditry from Zamfara to Kassina, Kassina to Kaduna, Kaduna to Niger. Niger now to KGB. KGB used to be extremely peaceful. We never had anything concerning that concerning KGB. And schools have been, when you have a system that lower criminality, when you negotiate with bandit, instead of calling them terrorists, this is what you have. Because it is easier to make money now when people are abducted and a lot of people have resorted to self-help because state institutions are filled in dealing with the issue. Let me link this to you with a particular story. An exas protester gave birth in prison in 18 year old and 18 year old was detained in prison just last year until she gave birth and she was pregnant. And here we have a system whereby you negotiate with bandit. You even see Govno with their security detail talking to bandit. Those ones are now arrested and he's a protester calling for good governors that is arrested and detained to the point that the protester gave birth in prison. It tells you the security architecture and it tells you what and the way we treat criminals in this particular country. Four Chinese were abducted in one state.  You must provide security. So there's a lot of things that cause concern with respect to our approach towards security, approach towards development of our security architecture. Everybody is now resorted to self-help. You want to travel now. You have to look at the security situation, go on your knees and pray and then hope that the roads are safe. Another story I want to talk about is the NPU and stick order rejection of the backdoor draconian policies in the NBC amendment act that the government is trying to pass when the physician. One thing I tell people is and I will link this story as well. I will link it with the date of Kenai Kaulda. Where's Kenai Kaulda is gone? But I'll tell you that what is later part of his life compared to the earlier part of his life. When we are growing up, Sambia was synonymous with we thought Sambia could never exist without Kenai Kaulda. Kenai Kaulda with his white anchor ship. Every time he goes to stadium, you know in the 80s and in the 80s Nigeria played Sambia a lot in a lot of football games. Just as they denied us in Colify for 1986, Nishosko, I still remember him. Kenai Kaulda would go away to work and she would go away to work for Kenai Kaulda. We thought Kenai Kaulda would have lived in Uganda. He made a lot of laws, draconian laws to affect the system. But when he died, he died quietly. Why? We have thought that Sambia would be money in everything. So it's a lesson for those that are empowered today that whatever laws they make today to stifle or position might come to them in the future. Nobody owns the world forever, nobody is ever, is only God and is forever. So that law, it's like some stakeholders have said is passing the antisocial media be through the back door, through the back, in posting files, democracies have opened up a lot of people. I said it's a function of censorship. Censorship versus guilt keeping. Government has no control over the flow of information except you infringe on the right of others to privacy, infringe on their right and their external brother can deal with that. The law, the law of life and slander and sedition can give you talk of this issue. You don't even need to set up a committee to be stifling people's access to the media space. In democracy, democracy is about access. It's about participation of the largest number of the people in this side. So let's wait and see, let's wait and see, but they should be careful that whatever law they are trying to put in place, they might be the first victim of that law they are putting in place because nobody no party and nobody remains in power forever. They should understand, they should understand that. All right, Mr. Johnson. And I support what the stakeholders are doing and I'm speaking against, in totality, against that attempt to stifle the public domain and people access to the public sphere. All right, Mr. Johnson, let's cross over to the puncher. The Obama headline for this morning, NBA or your Ford's Buhari's Grazing Roots Gazette. Some writers there say land under state governor's control, not president. That's the NBA and the senior advocates are saying, how do you react to that? You know, on the literature, it's a common structure. But if we look at the, this land juice decree of 1977, which has become the component part of our laws in Nigeria, which was written by the former B.C. of, former B.C. of Inappurasa Deliyo Motala, Wanda Abbasadu administration in 1977. The exclusive right to who gives land resides with the children of the South State. In Abuja, it is the minister of federal capital territory. So the president does not have powers to give, to give, to give land to people. And like the young governor said, that those who are farm settlement, farm settlement were created, not grazing roads. If you, if you recall, we go to the campus of federal, federal legal state polytechnic in Khorotu. It used to be a farm settlement, farm settlement of, of western, of western region in the past. The one we have, you know, here, in, in where you have the, the largest abatio in, in northern west Africa, used to be a farm settlement of western region. So then I don't know why the president would be concerned about the businesses of just one sector in the agricultural sector. One sector, which is animal husbandry. It's not the one animal husbandry, it's cow hearing. We are not talking about poultry farmers. We are not even talking about people that are into fish farming. We are not even talking about people that are into, into pigry. We are only talking about cows. It's just, it's just one of the value chain, even in the animal husbandry. So why is the president much more bothered about people's private, private business that a lot of energy is put into that and a lot of resources and a lot of state, state, state effort are put into that. You put that effort into, into Ikeja industrial estate, into Ikeja industrial estate, to Agbara industrial estate. All the companies that, if you put that effort into power generation, you know, most of the companies that have left Nigeria will stay in Nigeria. Most small and medium-skilled enterprise will develop. A lot of small and medium-skilled enterprise spend a lot of resources on power, buying diesel for their generator to power their business, which increases their overhead, which reduces their turnover and limits their capacity to, to, to, to recruit more people to expand their business beyond what they are doing. So by should look at ways of improving the economy, not trying to take political decisions to please some certain group or segment of, of the nation. So I agree with the NB. The president does not have right over the lands of, over the land. It's the governor that gives us the right of land, the certificate of compassion, signing it belongs to the governor. So any attempt by the governor to give that, any attempt by the president to do that, we challenge the governor. And we saw what the court has done in Abuja, saying that the governor of undue still does not have the power to send the judge. I thought that the supreme court should have done that too. When the president made that attempt, some of us said the president does not have wants, he wants an appointment that has been made.  Look, who swears in the governor himself. The governor himself cannot become a governor until he takes an oath of office administered by the chief justice of the federation, a chief judge of the state street or any other judge assigned to that duty by the chief judge of the state. That's what democracy is interdependence between checks and balances. And the governor would not wake up to serve the president, so he's in, into office. It's, it's, it's antithetical to democratic spirit and it's not limited to those things. So governors have done that and on to this institution, which is the institution of the judiciary, which is the institution of order, bringing about order in the chaotic democratic system. That's where you have the judiciary and you must wake up to your responsibility. That's why I saw, I saw a little bit of the courage of that judge in Abuja taking that decision. You should not be limited to those things. You should be a standard to other things. We saw what happened in, in course reverse, in the Ayati, where, where the, we've seen in many cases in the most states across Nigeria where governors would just wake up one day and they say they have sucked their, their, their chief judge. I see the chief judge is their pointy. Once you have nominated a chief judge and then the, the, the, the, the, the legislature has screened the chief judge and he has been appointed. Every other thing is left on the judicial council and not on the president or the governor to undo. So I salute that courage. And we must do that to deepen democracy. The judiciary must, our way to his responsibility belongs to deepening, to deepen. All right. Thank you, Mr. Johnson. Yeah. All right. So still much more interesting stories to look at, but I think that's the much we can take on of the press for today. Thank you very much, Mr. G.D. Johnson, chief lecturer at the Nigerian Institute of Journalism. Have a great day. It's a pleasure to be with you. I've been blessed a weekend. You too. All right. We'll take a quick break and well return. I learn your face. Thank you very much. Thanks. All right. We'll take a quick break. And when we return, we'll be looking at what happened today in history. In a moment, don't go away.


Mefanya, Evelyn Wanjiru, Alefanya. True to you. Alafu ni monegi ni apu. Yes, kuna mwikletu wa Alex Karani. Yes, Alex Karani en Grace Mwai wa mefanya kava ya. Kila mtu wa li kwa mefanya da song alia. Denu na fanya kava nao a collab remix of onu imba wimboya, onu na mi na imba wimboya, kwa then we mash it up, live recording. Ime kwa Osam sana. Pia tu mekuwa na msani, piya na itwa profound Kenya. Goma ni itwa badai, goma noma sana. Alafu kuna onni yu bai umsom mwuguna featuring movie. Tu mekuwa na andume number one. Ya ama number one. Masterpiece piya kuna goma metua un piya inaitua. Chekyju an Osam Jam palaivi. Alafu ingine live recorded shot by GK inaitua nema anaitua nema njeri ingoma inaitua Sifa Zote. A very Osam Jam. Live music. So, samu na na gila metua new song Zivo. Yes, yes. Namre mwa mwa mekuja kutulete kwa whatever is happening. Mwa piya iwi ki eh whatever happens inakwa nga story kama oku na eventa hapa. Eh, lea oku na event gani tiziyu juzi kuna event gani oku tiziyu na anya liye na kapara na anya. Ya, wakapanda nege wakapanda tiziyan tiziyu li anya mwa chatwambi. Yao. Very good afternoon to you. Na my name is Lynn The Brand and of course mki ni ona hapa kama kawahida inakwa nga it's all about gospel trends ones trending in the gospel industry na vichu zime kwa ziki happened for the past one week. Na sosa since it's an all request Sunday, did you any experience? Yes, request ya kwa. I have a request. Yes, yes. I have a request. Ya, na uchikweni kumura. Yoyote ikona jabidi. Oh, yoyote ikona jabidi. Nadu me play but due to public demand. Oh, ok. Public ni mimimi sosa ni kwa apa. Sawa zan, taku tizia tumetizia dingdi dingding na tumetizia ilia mpiya so to tizia ida boiaga kuna akuna azo. Ita kama ita kama. Kichu yoyote ikona jabidi ki inani barikingi sana. You are sorted. Yes, thank you so much. And before I start na takaku wish fan, the biggest fan of J.C. Circle, the biggest fan of DJ Nix, the biggest fan of Miriam Masaba, the biggest fan of Lynn The Brand, the biggest fan of Ghost Portrends, ya ni uyera kwangatu fan weitu ni family ni nata sijwinta tatu miya wadzi gani but she's celebrating her birthday today an ito of Inka Jaki. Oh, sa. Yes. Nani li kwa nata kaku wahimi zata fadani. Muki mui mpiya tuta shuku. Tuki maliza tuta maliza I think na happy birthday song. Wei. Do you have that? Kwanza September babies. Yes. Wakuri. Yes, please. Happy birthday to you, Pinka. You have always been a blessing to date na celebrate a special day na wewe. We appreciate you so much. We love you. May the Lord continue showering his blessings upon your life. And now we have some good news, some bad news. Wachatransera, good news. Dimigate is back. Ali Rudi on Monday and he's fine. He's good. He's doing great health wise. Ako sawa kabisa hindi tango to give God all the praise and honor for his health, for restoring his health. Na, silo na that Dimigate story. On Thursday, he announced his retirement from music. So Dimigate anata kuimba officially and he said that he will be taking a new career path. So we still don't know what that is but it's official anata kuimba. He shot his last video in India. Akiobadu India kumatibabui juzi juzi blialyenda. So he shot his last video which he'll be releasing it on 1st of October na Ali sema yondita kuwa Sonya kia muisho kabisa. So it's it's kind of sad kwe tumafanswake but still we are praying for him, we are praying with him na tunawomba anything any God America palembele in store for him may it come to pass. We wish you all the best apart from Gospotrens from JC Circle we wish you all the best in anything in yutakuna plan kuhu ingia into. We wish you all the best you've always been a blessing and I believe that you'll still continue being a blessing even in that other area. Yes and moving on we have an artist who has been more than an artist to us his family an ito xp so xp has been battling with a condition for quite for quite some time for a long period of time nivili anakwangam strong strong but he's not been well and on Friday he underwent surgery in Kenyatta Hospital and which was successful but still too I'm requesting you guys kindly to please please please while praying pray for xp pray for his health pray for good health speak healing upon his life and all shall be well xp it is well when himshindi tu biloratombianga kwa songiako and you shall imagine a wina I believe that the Lord is preparing something for you atatumia konditioniako kuku fanya ukwe unapiawa to testimony through it Yes and next we have LG Masai LG Masai is a gospel artist when you see she wanted to namjua and he recently lost his grandfather whom he was so close to a hasan Olekamuaro passed on in United States while receiving cancer treatment and it's so sad it's been a difficult time for him so let's pray for him let's pray with him let's pray with the family preparation zabariol zina ndelaya kuna meetings piya zina ndelaya is concerning the same so you can always follow us on our social media platforms to ndelaya kuambia place meetings ziko what you have to do how you can be able to support him and stand with him but for now we are praying for you LG Masai and we are praying for God to give you strength and peace that surpasses human understanding through this difficult time yes last but not least took on an event nimeskia kina miriamome he said maapa yes, I am excited because ni kama 28 what you are saying ni kama 82 so Alice Lisa this is just a reminder I mentioned it last Sunday Alice Lisa is having a thanksgiving concert this afternoon ita kwa pala koma rock ah no ni koma rocka mani kaiolik ita kwa inaitua Calvary Church pala koma rock kwa yes no one has ni confiiskaiole koma rock ita kwa po koma rock Calvary Church from 2pm to 6pm entrance ni 100 shillings per ke of course have performances from various artists special guests special performance the biggest performance of course ita kwa elin the brand ntsikosa i and we'll have DJ Gigi on the decks we'll have redemption performing we'll have Zablon Deflamer performing we'll have very many artists performing and of course we'll also have a very young DJ Nix ata kwa pala kwa decks so let's all go to Calvary church this afternoon entrance mkumu ketu ni ni sa wa tunabure 100 shillings per ke ake so let's all meet there this is Lin the brand you can always follow us on our social media platforms at Gospotran's Ent across all platforms and at Lin the brand thank you


Hello and welcome. It's March 13th, 2024, and we're here in Active Inference guest stream number 75.1, Active Inference with Empathy Mechanism for Socially Behaved Artificial Agent in Diverse Situations with Tata Yuki, Matsumura, Kanako Isakai, and Hiroyuki Mizuno. So thank you all for joining and looking forward to hearing your talk and discussion. Okay, thank you for inviting us to such a good opportunity. And I'm Tata Yuki Matsumura and my co-researcher Kanako and Hiroyuki also joined this discussion. We are developing AI, especially AI which is inspired by human-like autonomous agents to make social artificial agents. Recently, we published a paper related to the idea in Journal of AI Life. The title is Active Inference with Empathy Mechanism for Socially Behaved Artificial Agent in Diverse Situations. First, I'd like to explain the paper. This is the motivation of this research. The title of the paper shows the motivation of this research is to develop Socially Behaved Artificial Agents. We believe that sociality becomes an important point to apply AI in our daily life. The difficulty to realize social agents is that an appropriate social behavior depends on a given situation or culture. As described in the bottom figure, we have sociality even in just walking case. For example, how fast should we walk or how much distance from others? So if we implement sociality with rule-based program, we have to design a lot of magic numbers for each situation. This is impossible because there are a wide variety of situations. Due to this, unified mechanism for principle for social behavior is required. This is the challenge. We assume that we humans have such unified behavior model because we humans can behave socially in diverse situations. We develop social agent inspired by human behavior model, namely free energy principle and active influence. In this research, we use the active influence for the basis of the behavior model of human life agents. As you know, active influence is a concept proposed in the free energy principle. And free energy principle is a unified process for our active cognitive activities. In the FAP, our cognitive activities are explained by such. But we have internal models for predicting future of surrounding environment. And second, we always try to minimize the free energy of the model. Free energy is defined by this equation and it intuitively represents uncertainty of prediction. The interesting point of the FAP is that the internal model is not only the hidden state but also actions for the agent. That is the action to be taken is the action which minimizes the free energy of the agent. This is the basic idea of active influence, as I understand it. Intentional actions also can be explained in the active influence. In this case, we consider expected free energy, which is the free energy for the future state. When considering much time step ahead, expected energy at much time step ahead is considered as described in the right figure. Based on the expected free energy for each action, the distribution of the action is determined such that the smaller the expected free energy, the higher the probability of selection. When we consider intentional behavior, intentions are considered as components of future observations. And we want, according to the future observation, encode it into the last equation. These are my understanding of the standard active influence and the behavior model of the human life agent based on it. We extend this active influence to generate the social behavior. The idea of the extension is explained using the situation in which there are two agents called I and other. The agent called I is the subject of the action in this explanation. The biological agent and the free influence makes predictions about the environment and takes some action to minimize the uncertainty of the prediction. In this figure, I predict future behavior of the other because the other is an environment for I. Namely, I act to reduce uncertainty for the other. So far, this is a standard active influence. Here, if we consider that the other is also a biological agent, we can assume that the other also acts under the active influence. Under this idea, we can assume that the other also predicts the future of behavior of me and the other tries to reduce the uncertainty of the prediction about me in this situation. In this understanding, we notice that our action can reduce not only our own free energy but also that of the other. This is because my action affects not only my own free energy but also the free energy of the others. If I can predict other's prediction for me, I can act to reduce the free energy of the other. This is the idea of the proposed extension of the active influence. We extend FEP and active influence from an individual idea to a collective idea as described in this image of concept. The agent marginally shares the brain and they try to minimize the free energy of the shared brain. To reduce the free energy for the shared brain, we act not only for me but also for the others. Because of this action for others, we expect that this idea can be used to generate social behavior which is a behavior for us, not only for me or only for you. To realize this idea, it is necessary to predict other's prediction about myself. This is implemented based on the mirror system and simulation theory. I process for the mechanism of empathy. In the mirror system and simulation theory, it is said that we understand others by simulating others by own bodies. We consider the idea of using our own body to simulate others as using our internal model to infer other's prediction. The overview of the mechanism is described in the figure. The agent has the internal model to predict future observation for his own observation. Moreover, we use the same internal model to predict other's prediction about myself. Although the model is the same, the input to the model is changed to the observation of the others. For this, the process of inference of other's observation is included. For example, when the observation is an image, no overview synthesis, which is the problem of generating images from different viewpoints, can be used. In the other case, as we will discuss in the following evaluation, if the observation is the location of the agent, for ordinary transformation, transformation can be used. Based on this inference procedure, the expected free energy is extended as described in this equation. As with the encoding of intentional behavior, we add the term to the first term that is intention to response to the expectation of others. If there are multiple others, the sum of each other expectation is considered. We call the proposed idea empathic active inference. We evaluate the proposed idea using the control path of an autonomous mobile agent. In the evaluation, multiple agents move to each target point. The shortest path of each agent intersects each other, and agents are required to take collision avoidance behavior. The player, his red agent, is controlled by the proposed method, and the other agents are controlled by the social force model. Social force model is a simple social walking model. Roughly said, it is a dynamics model in which the walking speed and direction determined by two forces. The first one is driving force to the goal, and the second is the passive force from the others. In this evaluation, the weight of these two types of forces are changed to evaluate different situations. There are two types of others, selfish or altruistic others. When the force to the goal is larger than the force from others, the agents take behavior in selfish. On the other hand, the force from the others is larger than the force to the goal. The agents take behavior in altruistic. These are examples of the behavior of others under the control of the social force model. The player gets stuck with randomly moved, and the others, green, blue, or orange, are moved with social force model. The top picture is an example for selfish others, and the bottom picture is an example for an altruistic others. The difference is from the difference of the magnitude of the two forces. As you can see from the figures, when others are altruistic, the other will take the path which has more margin with others. The setup for the player is simply described. The action space is defined in discrete space. Movement for five directions and weight current position, totally six actions is assumed. A simple variational autoencoder-like model is used to the internal model, and the model is trained in the offline model. The model is used to predict the expected free energy for each action. First, the variational results when there is only one other besides the player are shown. We discussed the sociality of the agent based on the point. The first one, if my travel distance is longer, it is social behavior. And the second, if the other travel distance is shorter, it is social behavior. Third, if the minimum margin among agents is larger, it is social behavior. Fourth, if the inequality of the travel distance is smaller, it is social behavior. Inequality means that the difference in travel distance among agents. As a comparison, we also showed the result of the standard active inference cases. The two figures in the left side of the case, when the others are selfish, in this case, the player with the standard active inference moves straight to the destination because the player acts optimally only for its own purpose. On the other hand, the proposed agent moves to avoid the other, as well as the other, because the player predicts that the other expects the player to avoid collision and the player tries to respond to it. As a result of taking this detour path, the sociality of the behavior is increased. Namely, the travel distance of the player increased compared to the standard active inference case. At the same time, the travel distance of the other decreased and the minimum distance between the player and the other increased. In addition, the inequality of the difference in travel distance is decreased. Next, the case where others are altruistic is shown on the right two figures. In this case, the same tendency is observed, as in the case where the other is selfish. But the player moved a path with larger margin to the other compared to the case of selfish other. This result indicates that the proposed agent can behave in a social manner that is beneficial to the other, even if the result is advantageous to itself. Another important point is that the difference of the social behavior does not depend on any reward designing as in reinforcement learning. Namely, the sociality depends on the character of the other around the player. The other around the player is not just an obstacle for the agent, but as a teacher for the social manner. In the case where three others besides the player are similar results, observe in the altruistic other case. Namely, the travel distance of the player is increased and that of others are decreased and the minimum margin is increased and inequality of the travel distance is decreased. On the other hand, in the case of selfish others, a collision occurred. This may be due to high density in the center point. In this case, the proposed agent can avoid collision by waiting the crossing of others. I repeat the important point. There is no need to design reward for penalty or sociality in each situation. A social behavior realized by responding to the other expectation. Finally, I would like to summarize the explanation of the paper as I said at the first slide. The motivation of this research is to develop a unified mechanism for social behavior. The answer for the motivation is that running an internal model to predict the future behavior of others. This is the same as standard free energy principle or table inference. Moreover, predict others' expectations about me with the same internal model and try to respond to the other's expectations. This process makes a cyclic process and it encourages the agent to behave like surrounding others because due to mirror system, other expectations to me are same to my expectations to others. This mechanism encourages the agent to behave like surrounding others. This summary shows our idea for what is social behavior. The answer is that social behavior is a behavior like surrounding others. The others are not obstacle but teacher for social behavior. This also means that we are teacher of AI or robot for the future society. That's all. This is the explanation of the paper. I'd like to discuss about it. Thank you. Would either of the other authors like to say hello or to give any remarks? Hello, I am Kana Paesaki, also a researcher at Hinaji. I have been studying on control of systems, especially systems with hardware. My recent interest is how to apply the activity of robot systems. Hello, my name is Hidoki Mizuno. I am responsible for this research. Thank you for giving us this wonderful time. Thank you. Okay, many interesting questions but definitely very striking results about the tolerance to a variety of different social settings for the active inference algorithm to by nature of always updating at each time step based upon the expectations able to kind of deal with a range of different settings like different numbers of agents and the different behavior types for the agents. I'll ask some questions that were submitted by... Can we talk to you for now? No? No? Or okay now? Okay. Okay. All good. All good. Okay, I will read some questions that were submitted by email by Shohei Wakayama and also will look to the live chat. So I'll start with the fourth question he wrote. I would like to receive more detailed explanations of no operation action, the NLP in general. Because I noticed also in the graphics that when there's going to be a collision that the active inference agent just stops. So how did you model or calibrate that decision to make a move at all versus which way to move forward? Okay. First, I'd like to answer what is the NLP operation? I mean, NLP is just no operation. So in the evaluation case, NLP means wait the current position. The agent take no movement action. The agent stage the current position. This is a no operation, no operation. And the question is very important because this is a critical problem to realize our method. The problem is that we want to decide an action. But the procedure we proposed requires to decide action. As described in this figure, the lead circle action, actions for others. We decided to action of the agent, but it requires the deciding other action. This cyclic requirement is very difficult problem. So we have to set some actions for the other. I think there are three solution strategies. The first one is assume random action, raising the result of the randomly selected actions. The second is predict action, running and predict using policy network. For example, AlphaGo used this strategy. And the last one is we selected the third strategy that assumes specific actions. No operation means I don't take any actions. And you act as a priority, please. I mean nope. I mean this idea for assuming nope. So I believe that humans have such sociality in nature. This is the reason why I used this third idea. Does it make sense? Yes. Okay. Next question. How do you tune or calibrate the model so that it's in an area of state space that's pro-social? Like maybe you could have a generative model that just only did nope. And it was just paralyzed. Or another one just goes straight forward no matter what. So how do you explore that state space and choose which parameters are open for updating? And which parameters are going to be like explored during the design phase and then fixed at runtime? At runtime. I don't adjust any parameter at runtime. The model is aligned and offline manner. And the parameter model is explored and searched at the training phase. But the parameter space is not so huge in this case because the model is very simple. The observation is just two dimensional information X and Y. That means location of the agent. So in the case of four agents, there is just eight dimensional space. But we need some time steps. So even that case, the dimension is not so large. So the hidden state is also not so large. Can you understand? Yes. The evaluation situation is very simple. So the model is also simple. So I don't search a parameter with a lot of independent. That is not tough work. Simply just try to some parameter and I pick up a best parameter set. So this was an in-silico software only simulation, right? Yes. This is just a simulation. So how did you tune it? And then I guess a bigger question and definitely one that a lot of people are interested in learning active inference is like, how do you go from having an in-silico simulation to bringing that on to a physical robot? Like what are the steps in the process between having the situation in the simulation and bringing it into like a room? Yes. That's a very interesting point and it's very important. And Kanako tried to apply the active inference into the real robot. So how about do you think Kanako? So I also tried in a small space to apply the active inference to the robot. So I have just an idea that maybe the continuous space is important key from simulation to the physical world. And in general, the problem is also the problem in reinforcement learning, I think. So the techniques in the reinforcement learning field also can be applied into the active inference cases. For example, simulation to real. I don't remember the name of the technique, but similar to real. Maybe such techniques can be applied in the case of active inference. But I don't know there is the active inference specific technique. I don't know such technique. That's interesting about the continuous state spaces because in the discrete state space where you have the Markov decision process, you get the big planning trees. But our experience like walking through a crowd or maybe waiting at an intersection, it's not necessarily based upon planning. It's a lot more on the path and the curvature of the path that's planned and maybe the paths that others are expected to take as they move through space too. So that's possibly like a simpler representation that could give natural movement behavior with just a few parameters. And there is another problem for applying this technique to a real world. That is the computational cost because we use the Monte Carlo simulation to start the best action. I think this is not suited to the real situation. So if we try to apply this technique to the real application, we have to solve the computational cost problems. I think so. Yeah. Another part even outside of the decision making computational costs would be like going from the video camera data. When you're getting the exact location data perfectly, you're in the minimum. You're kind of playing chess in the state space versus to have noisy sensors. There might need to be a lot of observation model here, a lot of layers before the simple kind of active inference could even happen. Okay. I'll ask another question from Shohei. So question five, he wrote, study the behaviors of the standard active inference agent and the empathetic agent are compared. I would like to know why you did not compare the proposed method with other game theoretic approaches because the metrics like travel distance can be applicable to these methods as well. The reason why I compared the proposed method with active inference is that the aim of the paper is to extend the active inference to the social behavior manner. So I think it is natural to compare the proposed method with standard active inference. But the question is that why we don't compare the proposed method with the game theoretic method. I'm not sure what this means, the game theoretic method. But if that means reinforcement learning method, the reason why I don't compare it is that just it needs a lot of time to compare it. And I think it is important, but the main focus of this paper is to evaluate the quantitative future of the proposed method and the qualitative evaluation is not so important for us at the moment. But I think it is important and I have to compare it in future work. So there is no real reason. This is just, we don't have much time to compare it. Thank you. Make sense. Thank you. Another question. So the movement was being influenced by the relative ratio of the F other and F goal? Yes. So here F other is kind of like a electrostatic repulsion from other agents. How does it come into play that the red agent is entertaining beliefs about what other agents think? Sorry, can you repeat the question? So here the two forces acting on the red agents are the gravity for the goal and the repulsion for the proximity of other agents. So where or how does it come into play that the agent is actually thinking what other agents are going to do? Like the birds flocking simulations that have the simple rules like go in the same direction as the other birds and maintain a distance between them. But that doesn't require a theory of mind for the other birds. You mean how the agent knows the cause from the other birds? You mean how the agent knows the cause of goal of others? Yeah, it's just running away from others or is it actually imagining what they think? That's a good question. In this simulation the answer is that the destination of the other is given. I don't remember the detail of the evaluation. I think the agent don't have to know the information. At first I think I give the information to the agent but that information does not need the agent. So the goals of the others are not explicitly given but with internal model the agent can predict the path, the greater path so we can predict the goal. That's not explicitly. Does the red agent explicitly model what the others are going to do? Or it just has heuristics that it follows when they move? The former one only predict the action, the recent action and the future. The agent just learns the future position of the others. This is all of the tasks of the internal model. So there's no need to give the goal of the others. Yes, I think this is very interesting aspect. Like to what extent the social behavior rests upon simple rules like etiquette, like passing on the right or on the left that don't require knowledge about the internal states of others or even what kind of thing they are. Whereas the experience of sociality and a lot of the psychology of sociology focuses on very cognitive mechanisms that are very expressive. Thank you. This is because the action is repeatedly decided in short time duration. So this is future of the proposal method as you said. Thank you. I don't notice that that is important. Where are you going to go in your continuing research or development directions? Sorry, in future work, I think an important point is regulating the empathy. And we have a presentation the last year conference about artificial life. In the presentation, we discussed how regulates emotion are empathy. This means that the people we introduced is that the expected free energy is extended to the including others. So D called D my plus the others. And there should be weight of empathy. That means W in this equation, we have to regulate this parameter. I don't consider the parameter in the journal paper, but we have to regulate this parameter because there is diverse others. There is good person or there is bad person. We should not empathy to the bad person. So this is an important point. I will emphasize this. And we tackle this problem with the emotion. We tackle this problem with the emotional context. This is a future work. We have a presentation at the current conference. So if you have some interesting, please read this paper. That's very interesting. Like in a lot of the active inference and social sciences conversations that we've had here. There's a lot of discussion about how do you balance the coefficients in the free energy calculations for self and others. Because you're making decisions at a very short time scale like speech and second by second. But then sociality is very long term. And so you could make a decision that is aligned locally but then very out of alignment at a longer time scale. Especially if these other four have complex relationships, then action is not very simple. Yes. Is there any other things that the other authors want to say or anything else you want to talk about? I don't have cool. Well, thank you very much for joining the discussion. Thank you. Yes, good luck with the ongoing work. And I hope to stay in touch and hear more about the research in the future. Okay. Thank you. See you later. Bye.


Hey, good morning. David Russell here with you today with Josh Elmore. We're in Jefferson County and we're looking at treatment options for Chinese privet. Chinese privet is in the category of a semi-evergreen. We all know it as a brush type species in most instance. It is an undesirable weed in both non-crop areas and pasture lands along your fence lines anywhere that is a productive system and even like this case in an understory woodland where we're trying to control privet. It becomes really difficult especially whenever we have desirable species around. You can see the leaves of privet there in just a few short years privet can get to a height that is really hard to treat. Once it gets six plus feet or better, you can't get over the top with many of our control treatments and so it becomes really hard to kill and not only that, it chokes out a lot of our desirable forage species that we're trying to grow. So we're here today just looking at many of our treatment options to number one control the privet and number two maintain our desirable grass and forage species.