You know, help us out, do the thing you do to goose the algorithm and like and subscribe so you can find out about next time we're live. I think air greetings. There's the feedback and we are ready to start. Our guest today is Cade Metz. He is a technology correspondent within New York Times covering artificial intelligence, driverless cars, robotics, virtual reality and other emerging tech. Before that, he was a writer at Wired Magazine. His new book, Genius Makers, The Mavericks who brought AI to Google, Facebook and the world was just released by Penguin. And Ashley Vance, the New York Times bestselling author of Elon Musk, Tesla, SpaceX and The Quest for the Fantastic Future calls it one of the most surprising and important stories of our time. Cade, welcome to the IRM Media Podcast. Glad to be here. Now today, the state of artificial intelligence in three acts will start with a discussion about what AI is and what it isn't. We'll talk about what it's really capable of and who's most likely to benefit from it. Then we'll talk about the ethical challenges and dangers that AI poses and how likely they are to come to pass. We'll wrap it up with a discussion about the impact of public relations and IRM Media on the technological development of AI with New York Times reporter, Cade Metz, author of the new book, Genius Makers, After This. Act one, these days it seems like every software product or app maker out there is promoting themselves as having artificial intelligence. The term has become so overused and widely misunderstood, but what really is artificial intelligence anyways? I'm here with New York Times reporter, Cade Metz, who has a new book out on artificial intelligence called Genius Makers, the Mavericks who brought AI to Google, Facebook and the world. He's been tracking developments in this space for over a decade. Cade, why do you think Ashley Vance called Genius Makers surprising? What surprised him about the book? Well, it's about an idea, a particular idea in artificial intelligence that dates back to the 50s that even people in the field were skeptical of and thought would never work. And then around 2012, it started to work. It's called a neural network and we can talk about what that is, but basically the single idea started to work about a decade ago and it is now moving into so many of the technologies we use on a daily basis and changing the way we use our phones, the way we use robotics, what those services and systems are capable of. It's something that was surprising even to the makers of the technology and that's what the book is about. Why now?  Why did you write this book now? Well, because that change had really happened. It's a very real change. And I like that you talk about AI being this term that is overused and applied to everything. It's confusing for the layperson what is and what is not AI. What I wanted to do with this book is push all that aside and show people what has really happened, the way this single idea has actually changed things. And I wanted to do it through the people who built this technology. Any good story is about people and that's what this book seeks to do. It seeks to go back to the roots of this idea which goes all the way back to the 40s and the 50s and follow that up to the present day through these key moments around 2010, 2012 in particular where that idea started to work and follow it up to the present day and look at how it might change our world in the years to come as well. So the term artificial intelligence truly is as broad as the term medicine, right? I mean, what is artificial intelligence? It is or maybe it's even broader. Like it's so difficult to define. What I often say is that the original sin of the founders of this field, the people who gathered at Dartmouth University in the late 50s and coined the term, the original sin was that they called it artificial intelligence. It's immediately misleading, right? It's certainly misleading the 50s when we were nowhere close to having a system that behaved like the brain and it's still misleading today because we don't really have a system that behaves like a brain. We and I often underline this for people, we don't know how the brain works. Even now we don't understand the brain. So recreating the brain is from the get-go quite a task. We can mimic the brain in very specific ways, limited ways, but we can't mimic everything that it can do today. That term though implies that we can. It implies that we have systems that mimic the brain in extravagant ways, right? It brings up these visions that are lodged in the back of our mind from science fiction movies and books, right? We hear the term we imagine how in 2001, at least subconsciously, or so many other tropes from science fiction. And that's not exactly what's happening. We're moving in that direction in some ways and you can see sort of sparks of that, but we're a long way from that as a reality. So what is artificial intelligence? When someone says, oh, my software has artificial intelligence or my app has artificial intelligence, what claim are they making? Well, I don't wanna speak for a minute. They're making all sorts of claims that in many cases are bogus, but let's do this. Let's talk about a subset of that and talk about what is actually working, okay? Let's talk about that idea that I mentioned, the idea of a neural network. That is central to so much of what has happened over the past decade and it's very easy to understand. A neural network is a mathematical system that can learn skills by analyzing vast amounts of data. So the example I always give is, if you have thousands of cat photos, for instance, you can feed those into a neural network and it analyzes those photos. It looks for the patterns that define what a cat looks like. And in that way, it learns to recognize a cat. That's what drives the system on Facebook, for instance, that can recognize faces and photos. It's what drives the speech recognition service on your iPhone when you speak commands to Siri and Siri can recognize the words you say. That's also a neural network. It's trained in the same way. You feed thousands of hours of spoken words into a neural network and that's usually old tech support calls of all things. It analyzes those spoken words, looks for the patterns that identify particular words and it learns to recognize those words. That basic idea is used in so many other things that we can go into during this conversation. But the reason that is important is that the system is learning the task. It can learn the task by analyzing the data much quicker than engineers could ever tell the system how to complete that task. It can learn it as opposed to engineers line of code rule by rule defining what a cat looks like. That is too difficult for engineers to do. Even a cat photo is so complex, you can never define all the possibilities, all the different breeds of cat, all the different angles you can shoot a photo from. All the imperfections in the photo and the movement, you can never define all the possibilities. But if you give a neural network all those photos and let it do the analysis, look for those patterns, it can learn that skill on its own. That's a powerful thing. And that's- So is a neural network machine learning? Is it the same thing? It's a subset of machine learning. Machine learning is another general term. It just means a machine that can learn a skill by analyzing data. But this is something very different from the machine learning we've had in the past because it learns from such large amounts of data and it learns these very, very powerful and specific skills. It learns from more data than we humans could ever wrap our heads around, right? We're not seeing all those patterns in the cat photos. It looks for and finds those patterns on its own and learns that skill largely on its own. It's a powerful thing. But it's much easier to teach a machine to do one thing over and over again than it is to get a machine to think like a human, right? So what is this concept of AGI? What is artificial general intelligence? Well, people have dreams of taking this idea and expanding it to the point where you have a giant neural network that can essentially analyze everything that we would experience in our daily lives and somehow from that build a system that can do it all, right? So think of it this way. Now, I'm just speaking in broad strokes here, but if you could model the world digitally and then you could have a neural network analyze all the patterns in that model of the world, people believe you could reach a system that could do anything. I'm speaking broadly, but that's the basic idea here. We're still a long way from that. There are two labs in the world who say that their stated mission is to build AGI, a system anything the brain can do. That's DeepMind in London, OpenAI in San Francisco, but they don't necessarily know how to get there. That's gonna take a lot of doing. Right now we have systems that work in very specific ways, speech recognition, image recognition. You're starting to have systems along these lines that can analyze vast amounts of written text. So digital books, Wikipedia articles, other content from the internet and learn how we humans piece English together in other languages. And it can better learn to not only understand the world recognize the words you're saying, but understand those words, actually understand what you're asking for. For instance, if you're talking to a machine and then respond to it, we're seeing a lot of progress there. See a lot of progress with robotics. If you can recognize a cat in an image that can help a robot learn to recognize a cat, learn to recognize other things around it and respond to it. We're seeing progress there as well. So there are these particular areas where we've seen a lot of gain over the past 10 years and we'll continue to see gain. But that doesn't necessarily mean we're gonna see a machine that can do anything that human brain can do anytime soon. Like it's one thing to create a robot that can sort products in an Amazon warehouse and pack an order. It's another thing to create a robot that could have this conversation with me. So when you talk about these two labs in London and in San Francisco, which you write about in the book who are focused on achieving artificial gender intelligence, you use the word dream. You use the word hope. Almost as though it requires blind faith. And in the chapter on that in the book, it's titled religion. So why is the AGI argument a religious argument? Well, like with any new and ambitious technology you need belief in order to build it, okay? If you're gonna build Facebook you've gotta believe that you can do it. If you're gonna raise the money and attract the talent, you better believe it. This is that same attitude but applied to just enormously more complex and ambitious project. So if you're gonna build AGI you better believe that you can do it. And a lot of people really do have that belief but that doesn't mean that you're gonna do it as soon as some people say or may think, right? It's believing is one thing and actually doing it is another. And what you do see is that there are people in the field who really believe this is gonna happen.  You get a real disagreement here. And it is almost like a religion where you have this kind of spectrum of belief even where there are various places along that spectrum where the belief rises and falls. So in his book, your colleague, Thomas Friedman in his book, several years ago, thank you for being late wrote an entire chapter on Moore's law. And Moore's law basically says that microprocessors double in capacity every 18 months and they also half in price. So that's exponential growth from a processing power standpoint. Yet, we hear from the folks in crypto that the ledgers take incredible amount of processing power to maintain. We hear from you here in your book about the incredibly intensive process of computer processing demands to satisfy AI. But does Moore's law somehow ensure that AGI is right around the corner? Some people argue that it does that as we get a greater and greater processing power that we're on a path to that. I mean, generally speaking, of course, the way this works today is the more processing power that you have, the more data you can analyze and the faster that idea that I've been talking about can progress. And that's what you're seeing now is you're seeing these very large companies apply more and more processing power to the problem and you see it improve. You know, a really good example that I've been writing about recently at the times is a project called GPT-3, which came out of open AI. The lab in San Francisco founded in part by Elon Musk. It's that giant neural network that analyzes all that digital tech. So all those books and Wikipedia articles and other content from the internet, it spends months analyzing all that data. And we're talking about hundreds of chips in a giant data center that crunched through all that data. What we have seen with projects like that is that the more computing power you throw at the problem, the better the technology gets. So there is some truth to that. That doesn't mean that we can scale this all the way up to AGI at the same rate. We're already starting to see limits to that particular idea. We're running out of juice when it comes to analyzing digital texts in that way. Will it work as we acquire more data and model the world the way that I was talking about? We'll have to see. But you do see a growth curve there. Talk to us for a minute about whether or not we should be worried that AI is gonna take our jobs. Like obviously I understand if you're sorting products in a warehouse, AI has already taken your job. But for those of us who are knowledge workers, should knowledge workers be concerned that AI is gonna take their jobs or is it already taking their jobs? Well, when people talk about concern over jobs going away, the real worry there is whether or not it's gonna take them away in the short term, right? Before you have time to train the population for new jobs. We haven't really seen that. And actually, the warehouse problem you talk about is can be illustrative here. So yes, we now have robots that can help in the warehouse, help sort through all the goods that come into an Amazon warehouse and need to go back out. In the past, it's always humans that had to sort through those bins of random stuff. We're starting to see systems that can do that. Now, you might say, well, that's gonna take away all those jobs. But if you step back and you look at the situation, what we're seeing, particularly amidst this pandemic is that we're relying more and more on Amazon. Amazon needs to grow these warehouses. It's having trouble staffing those jobs in a lot of places. I've been to these warehouses in areas where they have trouble getting the human labor as that market grows. So as the robots come in, they're not necessarily replacing people. In some cases they are, but we need to think about this in a broader sense. Another good example is the trucking industry. We're on a path towards self-driving trucks. And you can say, well, that's gonna take away jobs for truckers. And in the long term, it certainly will. But at the same time, the number of truckers is on the decline. That population is aging as well. And so what we're not seeing is vast amounts of jobs suddenly go away. And what we're also seeing, and I wanna underline this too, in all these areas, the technology is still flawed. In many cases, it can't necessarily replace a human or it needs to be used alongside a human. And so even in these areas where it's working well, you're not necessarily seeing a huge threat. We now have these systems that can generate language on their own. Like GPT-3, that's one of the things it can do. It can generate blog posts and articles. And in some ways, they're remarkable. But it can't do it every time, as well as a human reporter can, a human writer. We still need people to do that sort of thing. So in the long-term, machines are gonna do more and more. But that kind of moment when all the jobs vanish, we're not seeing that yet. We're not seeing evidence that will happen anytime soon. In his book, AI Superpowers, Kaifu Lee published this risk of replacement for cognitive labor graph. And in the right, he says, he calls it the safe zone, the upper right. And the jobs he says that are safe from being replaced are a concierge, a social worker, a psychiatrist, a PR director, a criminal defense attorney, a CEO. And then the danger zone jobs, he lists our customer service rep, radiologist, personal tax preparer, insurance adjuster, consumer loan underwriter, telemarketer. Any thoughts on this? Have you seen this? Did you give this some thought as you're writing your book? Do you agree, disagree? Yeah, I mean, generally speaking, that sounds about right. That second group, those are the areas where machines are getting better, right? We're seeing machines that can better understand language and better respond to it. And that fits into so many of the categories you talked about. Machines already can deal in simpler ways with Excel spreadsheets and other simple forms of technology and they can replace humans who have done that in the past. We're seeing that. You mentioned radiologists. So that's one place where these neural networks are really powerful. They can look at medical scans, whether they're x-rays or CT scans, eye scans and look for signs of disease and identify those signs. That can help do the job of a radiologist and other doctors. But even then, countries, hospitals have been slow to adopt that technology because we wanna make sure they do the job well. Typically they're used in tandem with a human. And for the foreseeable future, that's the way it's going to work. Where they can really be powerful today is where you may not, in countries where you may not have a doctor on hand and the machine can be the first line of defense, so to speak, you identify a possible problem, you alert the doctor. So in the long run, in the distance, Kaifu Lee is right on all those areas but that doesn't mean all those jobs are just suddenly gonna vanish tomorrow. The ethical challenges and existential threat of artificial intelligence with Kate Metz, author of Genius Makers, after this. Kate, is the public perception of what's possible with AI real or overblown? Well, the answer to a lot of these questions is a little bit of each, right? It is overblown, especially as people like Elon Musk stood on their soap boxes and said that we're on a path towards AI destroying humankind if we're not careful. That gives a false impression. It makes it seem like that risk is close. But there are areas today where we're already seeing issues with the technology and areas of real concern. These systems do learn from enormous amounts of data and that means that we're not always gonna see everything they learn, realize where they might learn things that we don't want them to. And the prime example right now is these systems can be biased. They can be biased against women and people of color. If you give them data that's biased, they are going to exhibit that bias. These systems like GPT-3 that train on all this text from the internet. The internet for anyone who's used it, right?  It includes hate speech. It includes language that you don't necessarily want your machines to learn. But the way these systems work, they do learn those flaws as well as the positive aspects that you might want them to learn. That's an area of concern. Another, I mean, there are so many other areas we could discuss. Surveillance, these technologies are particularly powerful when it comes to surveillance. In China, this type of technology is already used to identify an ethnic minority. That's a real area of concern for a lot of people across the globe. Autonomous weapons, you know, we're on a path towards that sort of thing. You know, if a self-driving car or robot can recognize the world around it, so can a self-flying drone. That can be a way of targeting things on the battlefield. You know, these are things that we need to start thinking about now, separately from this idea that AI will somehow turn on humanity and destroy the world. You know, the section of the book on autonomous warfare is just fascinating. Talk to us about Project Maven. What is that? Well, it's what I was just talking about, right? It's an effort by the Department of Defense to build a system that can identify objects in drone footage. So identify cars and people or buildings. That's a way of doing surveillance for the military, but it's also a path towards autonomous weapons. That same technology can be used on a system that has a weapon on it. There were many tech companies who started working on this project with the DOD. What you see in the book is that the people who specialize in this technology, they were mostly academics. They went to work for these giant tech companies about 10 years ago when the technology took off. And so that's where the DOD went for the expertise. All right, these technologies were being built inside the Googles and the Microsofts and the Amazons. And those are the companies that went to work on this project. At Google, there was an issue. Several mini Google employees were upset by the fact that Google was working on such a project and protested the effort. Google ended up essentially pulling out of that project and left it to other companies. It's an example of where this concern can pop up, even inside the companies building the technology. And where are we today with drone warfare? I mean, do killer robots exist? Well, we're not there yet, but we're on that path. What we now have, I just did a piece in The Times about various companies that are building self-flying drones. And these drones are very good at flying on their own and identifying various objects around them. The companies building this technology are willing, many of them say, to put a weapon on it. So they see this as a path towards that type of thing, what people call killer robots. What they mean is an autonomous weapon. We're not there yet. The DoD, I spent a lot of time talking to people inside the DoD as well. It's something that they are working towards, but we're not quite there yet. What we do need to do though, is as we work in that direction, think about the consequences. When do we want a human in the loop to make sure these are used in ethical ways? If I remember correctly, at one point in the book, there is a Shanahan who is a military official who's quoted as saying, we don't think any defense systems moving forward should be without an AI component. Absolutely, but again, that term can mean a lot of things. These are powerful technologies. It's no surprise that the military want to use them. That doesn't mean we're gonna put systems out into the field that are making all the decisions. We don't have machines that can do that yet. You very much need human help. And a lot of the thinking is that that will continue to be the case. You will continue to have a mix of machine and human, but we do need to keep an eye on how this progresses. You talk about how we need to start thinking about these things now. Are there any proposals for regulating AI that have promise? There are. It's a difficult thing when it comes to regulation. The technology is improving at such a rapid rate. The problem is often you get the regulations out in the technology changes. We just had last week proposed rules for regulating AI from the European Union. And they're very broad. They look at restricting face recognition in public places. They look at restricting the way these systems can generate disinformation. We've talked a lot about how these systems can recognize sounds, images, or text. They can also, if you flip them upside down, so to speak, they can also generate all those things. If you can recognize a cat image, you can generate a cat image. And what we're seeing is these systems can generate blog posts, videos, even the sound of your voice. That's a danger. And the EU rules also seek to restrict them, force companies to identify what they call deepfakes. These false images and texts are created by machines. That's an area of concern. There are other areas where not just the EU but other governments are starting to think about how we can regulate these things. But it is very difficult. I do wanna say that. If the EU cracks down in some ways, that's not going to prevent other countries, companies and other parts of the world from doing similar things. There are some of these issues where we need to think about this globally. Autonomous weapons is the best idea, right? If you crack down on that in one country, that doesn't mean it's not gonna happen elsewhere. What do you think of Shoshana Zuboff's recommendation that it's really data collection at the top of the funnel that needs to be regulated? Well, I mean, we've spent this podcast talking about the fact that these systems learn from data. It's fundamental. And you see this in so many ways. In the book, you see this pop up time and again where these efforts to collect the data and it's the big internet companies that have that, right? That's the real currency. And that's how they're able to build so many of these systems. You and I contributed to that on a daily basis just by uploading all this data, these services or another example I often give, anyone who's ever used an internet service and it wants to make sure you're not a robot, right?  Odds are anyone listening to this, watching this has gotten that capture that says, show me which pictures that I'm putting on the screen that include a car or a stop sign. And we do that. What you're doing is you're identifying the photos that Google can use for a neural network to train itself driving cars. That's what you're doing there.  Later that gets fed into these neural networks that can learn to recognize those objects. So on a daily basis, we're contributing to these systems. It's using our data and our brain power in building these increasingly powerful systems. Kate, you write about how Google invested in AI before Microsoft for many reasons, but you mentioned that Microsoft's more mature status as an enterprise may have held them back. Will Google and Facebook reap the rewards of AI or is there a whole new class of startups out there who will somehow inherit the mantle and displace them? It's hard to see startups displacing them at this point. They are in such a powerful position. The currency here is not just the data, it's the processing power as we talked about. Those are two things that the big companies have. They also have the money. That means they have the talent. They have a real advantage right now. There are cases where startups can succeed. Those drone startups I talked about are a good example. They're pushing forward in that area, looking to work with the government in that area. But when it comes to really pushing these types of systems we've been discussing, the big companies have an advantage. What about China? Is the US in an AI arms race with China? In a way it is. I think this is another thing that's often misunderstood, but as you see from the opening scene in my book, from the moment this idea started to take off, a Chinese company was there, Baidu. They were there alongside Google and Microsoft. And you see this as a real area of interest in China. And as Kaifu Li has mentioned in others, China can potentially have an advantage in the long run because it has such a large population. That generates more data in this day and age, in the internet age. In theory, they can produce a greater number of researchers. That's also important. But the landscape is more complicated than people might expect. In the past, new technologies were built inside government labs and held secret. The way this technology is developed is a little different. For various reasons, as you see in the book, the big technology companies openly published their latest results. It's academics who believed in this idea, who came into these big companies, they wanted to keep publishing. The Googles and the Facebooks keep doing that. What that means is that the latest ideas are available to anyone on earth, including rivals of the US. So you can't necessarily crack down on your exports to certain countries to bottle the technology up. At the same time, and this can be hard for people to understand as well, the US really relies on immigrant talent in this area, including Chinese talent. You talked about Project Maven. There were Chinese nationals who worked on that project at Google. People should realize how important immigrant talent is to the US, particularly in the science and technology fields. So you can't just shut down your borders to foreign researchers, right? You end up shooting yourself in the foot. So there's a careful balance that goes on here when it comes to the competition. The concern here in the US is that the US is not keeping pace or won't keep pace in the long run with its rivals, that so much of the talent and the progress is inside these big private companies, excuse me, public companies, and not in government labs, not in universities, and they wanna try to correct that balance. They don't want Googles, say, driving everything because Google's aims and ambitions might not align with the US government, right? Google is a company headquarter in the US, but it's a multinational company, and it's driven by other motives. And part of the concern is that, you know, the progress is happening there and not happening in government. In China, the situation is completely different, right? You have a synergy between government and industry that you don't necessarily have here. Can AI solve the fake news problem and the role of PR and technological development with Cade Metz after this? We're back with Cade Metz. He's the author of a new book, Genius Makers, the Mavericks who brought AI to Google, Facebook, and the world. Cade, talk to us about Apple for a moment because they're pushing the AI envelope as well with Siri, right? Absolutely. What people may not realize, though, is that Siri debuted with a different technology. You know, it didn't work as well as it does today. The neural network idea came in after Siri's introduction, and what you see today is a more powerful version of that. Apple comes at this a little differently than other companies. There was this real race for talent between Google and Facebook and some of these other very big players. Apple approached it a little differently. They're not as concerned with jumping on the big names as maybe some of these other companies. But it has started to hire some really important people of late. John G. Andrea oversees their work now. He once was head of AI at Google. Ian Goodfellow, another key player and character in my book, is now at Apple. They see where all this is going, and they certainly want to roll this technology into so much of what they're doing. But you're right, Siri is at the heart of that, right? The areas where this technology is starting to work are areas that can benefit a digital assistant like that. And can AI solve the fake news problem? No, so you need to think about... People need to think about this a little differently. I know that Mark Zuckerberg has gotten, you know, before Congress and said such things. Identifying fake news is difficult even for a human, right? It's a judgment call. You and I may disagree on what is not fake news, or to speak, and you see this all the time. If we as humans can't agree on what is not fake news, or what is not hate speech, how can you build a machine that can do that? It's a difficult problem even if you're talking about humans doing this. And right now it is humans who make those judgment calls and those judgment calls get disputed. The other thing is that these same systems that can recognize, say, fake news, can also generate it, right? I talked about that. If you can identify what a cat looks like, you can generate an image of a cat. And that's what we're seeing, too, is these systems that can generate fake images and fake videos. So on the one hand, it can help solve the problem. On the other hand, it's creating the problem. It's another arms race. This is a real area of concern that we're pushing towards a point where the systems can generate fake news at a scale that humans never could, right? If they can do it as well as humans, then you can do it much better than humans, just because machines can do this at a much higher volume. That's when it gets really scary. We're all looking at these news feeds all day. And obviously, that takes the concept of if it bleeds, it leads to a whole new level. And as a result, the news has become more sensationalized than ever. And you talk about how the media has overblown the promise of AI. But our incremental technology gains enough of a scoop to garner coverage in this age of sensationalized news media. Or our reporters and journals really being forced to gin up their stories and make them seem as scoop worthy as possible, just to get them printed. Well, I mean, obviously, there's a lot of that. A lot of the coverage in this area is not that great. But I mean, I can only speak for myself and my colleagues at the times. We're looking to tell people the reality here and what is really happening and not gin things up as you say. It can be easy to do and to get attention to your story by ginning things up. But that's not my aim. It's not the aim of the times. I'm not going to deny that it doesn't go on elsewhere. But I think that people need to realize that there are news outlets that are certainly working hard to tell you the reality and not just the hype. Who are some of the news media outlets that you read that you pay attention to that you appreciate? Well, I read my colleagues at the times. There are certain people who I like in this area at the Wall Street Journal. There are some talented people at Wired where I used to work. It's a tricky thing to cover. You really have to understand it at a deep level, but then be able to relate that to the lay person. So you need those two skills. You have to use different parts of your brain. You need to understand what's really going on, but then you need to step back from that and explain to people at a level that anyone can understand what's happening. In the book, you write about public relations and how a lot of the AI organizations and individuals in the space used PR to their advantage in some way. Is there any sort of generalization or general statement you can make about how PR benefits organizations that are developing AI? Well, we talked about being the call, this term that gets thrown around a lot. If you just apply that term to whatever you're building, that might be able to help you. But as you look at what plays out into the book, it shows you in some ways how Silicon Valley works in general. Silicon Valley is often built on that sort of hype. If you want to attract the talent you need to build something, if you want to attract the money, you do need to have that belief and you need to voice that belief and tell people that your technology is around the corner. That's how you attract the money and the talent. And you see that in the book, even with these incredibly ambitious projects. Sometimes it's conscious, sometimes it's unconscious, but that's definitely part of what goes on here is that people over promise. And some people may know they're doing that. Some people may not. Some people may see explicitly the power of that or they may not, but it's happening. That's a very real phenomenon, which I think you can see in the book. Kate, you write open AI said to the press that it was too dangerous to be released. Was that a PR stunt? And if it was, what were they hoping to gain? Well, I mean, I don't know if it's a PR stunt or not. You can never get inside someone's head. But a lot of people accuse them of that. And that is another phenomenon in this world that is counterintuitive. That if you say something is dangerous, it makes it seem more powerful than it is. If you say it's dangerous when it's not necessarily, it makes it seem more powerful as well. And there's a chapter in the book called anti-hype when Elon Musk comes into the picture and he's telling the world that this technology is dangerous. One of the effects of that is that people assume it's more powerful than it is. And that can be kind of this strange inverted PR tool. Again, who knows how conscious that is. So there is this great scene in the book where Musk and Mark Zuckerberg sit down to dinner and even the people at dinner aren't sure what Musk is really thinking. What are his aims here when he says that AI is going to, you know, achieve human levels and potentially turn on us? They're not sure whether or not he really believes that.   It has played out time and again, and you see that in the book. You quoted Musk as saying, we're headed towards an existential threat or civilization ending. Obviously, you know, we're not in his head. We don't know what he meant, but I mean, you spent a lot of time thinking about it. Do you have a sort of gut feel about, you know, what he's trying to do? Can you explain that thinking in any way or unpack where he's going with that? Well, you know, again, this is tied to this AGI idea that, you know, that we're working towards a system that can do anything. The human brain can do, but be backed by, you know, enormous amounts of processing power. You know, there is this, there is this fear that once it is, it is more powerful than we are, that it will turn on us, that we will, you know, we will build these systems without realizing all the unintended consequences. They will learn things that we don't want them to learn, that they will be motivated to do one thing and we won't see sort of the related motivation that's built in there. A lot of people, you know, do, you know, it's not too strong a word to say scoff at that idea. You know, people, you know, who were at that dinner with Elon Musk who do scoff at that idea that we're, that's not something we need to worry about today. But, you know, it's something that a lot of people in the field feel differently about and do sort of voice many of the same fears and concerns that Musk does. Again, you can't get inside your head, but there are a lot of people who see that. So, you know, obviously, you know, for global news, The New York Times is a gold standard brand for tech news, Wired Magazine is a gold standard brand. Could you have written this book and gotten the same level of access if you weren't a Wired Magazine or New York Times reporter? You know, probably not. That doesn't mean it's easy. It's years of talking to people, getting a little piece of information and taking that information, getting a little piece of information to another person, seeing if they can build on it. You know, as you build on it, going back to the first person, it's a hard thing to do, particularly when you're writing about characters that are inside these very big companies where it can be harder to get them to talk. You know, it does help to have The New York Times name behind you, but it also helps to have a track record of stories and relationships. So it probably helps some to have those names behind me, but make no mistake. It's a hard job on a daily basis at the time. It's certainly hard to build a book like that. Okay, that's author of the new book, Genius Makers, The Mavericks, who brought AI to Google, Facebook, and the world. Thank you for joining us. Thank you.


Thank you, Dan. And I'm going to try to get everyone's attention. And I may lose my voice in the process. So my thanks to everyone who's already joined our next entry campaign. We are going to need help from all of you. We appreciate your support. It is tremendously important to all that we do. And so thank you in advance. And for those who have already been helpful, we so appreciate it. So before we end our program, I have to say that my husband, Don, and I think he's here, but there's so many people and it's actually hard to locate folks. So my husband, Don, and I have been so welcomed and so thrilled and honored to be part of this fabulous community. The students, the alumni, the faculty, the staff who are part of the Ford School family are just fabulous. And we couldn't accomplish all that this school accomplishes that our network and that our combined family accomplishes without all of you. And it is an honor for me to be here and to serve you as dean. And I wanted to take this opportunity to say that. Would like a glass, has a glass, and how could you not want a glass to toast at this very important moment? Thank you, Cliff. And so together in a toast on this very special milestone, please raise your glasses to the Ford School family. To our first 100 years and to our next century. Cheers! Cheers! With mighty men we cheer Hail to the victor's riot Hail to the conquering heroes Hail, hail, to me she come The leaders and best With pride we hail To the victor's riot Hail!


Now, um, software defined E1, yeah, so, um, as I already introduced a bit, well, E1, T1, TDM is good, all the ISDN technology, where, uh, signals arrived reliably and in time and synchronously and, uh, we didn't have lost packets or reordering or jitter or anything like that, um, and those interfaces are 2 megabit or 1.54 megabit in case of T1 synchronous full-duplex interfaces and they're not much used anymore in telephony since everybody has this interesting tendency to move to SIP, um, since apparently the quality of ISDN was way too high, uh, we had to move to something that, uh, loses, uh, the guaranteed quality at least, um, however, it's still used today quite a bit in 2G, 3G cellular networks even in 2018, so many people don't know this or assume that this is, uh, gone, but in reality it's not. So, where is E1, T1 used in GSM or 2G, 3G networks? Well, basically traditionally everywhere. So, ABIS was over E1 using LAPT-MS at layer 2, A interface was over E1 using MTP, ISAP, uh, between the MSCs, uh, is over E1, uh, then of course the MAP protocol stack is over MTP, over E1, um, and also the GB interface from the PCU to the SGSN was over E1 using frame relay as a layer 2 and, uh, then IUB, uh, over inverse ATM multiplex was also spoken over, uh, for bundled, uh, E1 interfaces to the RNC. So, we have, uh, E1 everywhere, um, in classic GSM architecture. And today, on the ABIS side, uh, not so much anymore, uh, it's, uh, much on the decline, uh, because of course, uh, you have an existing 2G side, you put a 3G or 4G cell at the same site and then you put an, uh, Ethernet backhaul there for your LTE backhaul and then you don't want to have E1 and Ethernet at the same site because that's double the line in infrastructure cost and so on. Um, but lots of BTSs interestingly still have physical E1, not just old equipment from the 90s that basically nobody runs anymore in at least, uh, developed countries. Um, but still, um, you have even if I, what I showed you, even, uh, Ericsson, uh, 2G equipment from a few years ago still only had E1 as a backhaul option. Um, and, uh, then basically, uh, you have this equipment that you need to, uh, uh, convert and that's where this SIU, the site integration comes into place. So you basically have an E1 link of one meter or half a meter between two units at your BTS and then you have IP. So, uh, that's basically what, what ABIS or E1 looks like. On the A interface, it's also very much on the decline. Um, we see this from the kind of requests we get from operators. Uh, I don't even remember how many years ago we last received a request from anyone who would be interested in having a physical E1 interface for A in, in, in the Osmo Com stack. Um, so, uh, 3GPP has introduced A over IP, uh, quite some years ago as an official standard of how to speak the A interface over IP and that's basically what the modern MSC implement. Uh, so if you have a Huawei or Ericsson or whatever MSC today, it's implements an A over IP interface and Osmo BSC now supports this. So, um, we can basically interoperate and there was some posts on the mailing list. You may have seen that some people completely independent of Osmo Com developer community have basically taken Osmo BSC and, uh, uh, established interop tests with a Huawei MSC and it appears to work to some extent, at least. Um, we haven't received any serious bug reports or so. Now in the core network, it looks a bit different. That's actually quite a lot of E1 and T1 still in, in core network. Uh, that's, uh, um, lots of legacy switches out there. Uh, not everyone has moved to soft switches and as well for STPs. Basically, um, we receive a lot of questions about Osmo STP in recent years, uh, because, uh, people want to move to virtualized environments and they can get a soft MSC and they can get everything basically implemented in software, but the STPs, uh, are proprietary hardware from the vendors and some of them are no longer supported these days. So there's, there's E1 lines around there. Um, and, uh, um, also I know that, uh, at least, uh, I think like two years ago or so still new MVNOs in Germany would be attached over real E1 interfaces with the MNOs and not over sick trend links or something like that. So it's still a very common, uh, even for modern virtual operators that they don't use virtual E1 links or something like that. No, they use real physical TDM E1 links to interoperate, um, on those interfaces. So there it's quite, uh, prevalent still. Now in, uh, uh, the lip Osmo Avis, we always had this interface for MSC and in Dadi as we just mentioned, uh, you get PCI and PCI cards, which are still surprisingly expensive. So if you buy even the Chinese clones of Digi Digium cards are rather expensive and original Digium cards are ridiculously expensive, which is maybe fine if you need one or two of them in your, in your core network, but it's not so fine if you need a hundred of them in your radio access network, uh, to attach all the BTSs. And then of course, the PCI card, you need a rather large computer. Uh, the smallest we could find is something like the Circus devices that have one or two PCI or PCIe slots, but even they are rather expensive and it's actually not really needed. Um, and if you look at what this E1 interface looks like, you have your magnetics, you have then, uh, um, a line interface unit, then you have some TDM controller, which attaches over parallel bus to some bus bridge and the bus bridge goes to PCI or PCIe. That's basically how your typical E1 card looks like. And this, uh, TDM controller, how I called it, of course, it's not just a TDM controller, but it even does things like line equalization as elastic buffers for jitter compensation. It has CRC handling, framing, HDLC, uh, processors that do the, the HDLC bit stuffing and so on and the synchronization in there. And, um, well, now we know there are all these, uh, traditional BTSs out there, whether it's the Ericsson, uh, RBS 6000 or other devices, which we could use at a low price, but we don't have a low cost and, uh, easy to use E1 interface. Um, right, uh, because in the end, the E1 card and the PC are more expensive than your BTS. And I think that something is wrong, uh, in that situation. So you want something like an Ethernet or a USB interface between that E1 adapter and your, um, your, uh, your Osmo BSC and, um, that can be used with a laptop if you're doing development or debugging something that can be used with a Raspberry Pi or a Beaglebone or, uh, uh, whatever orange pie or a, um, whatever the names of all these, uh, embedded boards are these days. Um, you just attach that and you have E1 interface and then you have a really low cost, like a sub $100 solution for running, uh, such a BTS together with a BSC or together with a media gateway, um, on, uh, that board. Um, so how do you go about this? Normally you would say, well, okay, let's just use one of those chips here, uh, like the line interface unit and a particular TDM controller, um, and put them on a custom board. Um, uh, the problem is many of those controllers are already end of life for many years. So they're very popular, um, uh, Infineon, um, uh, Falk or QuadFalk chips that you find, uh, on many devices. They are EOL for many years because ISDN is not so popular anymore.  Um, and they're rather expensive, uh, and rather expensive means easily 30, 40 euros, uh, a unit, uh, for a chip that basically does nothing, uh, in, in terms of processing or, or logic capacity or something like that. And then they come in very arcane packages and, uh, like, I don't know, 200 or more pin, uh, uh, TQFP packages, like, uh, six or seven centimeter long, uh, uh, chip, uh, um, packages, uh, really, really old. And, uh, so the plan is to establish a software defined, uh, TDM or software defined E1, where we simply use a line interface unit, which is, uh, this guy here in the middle, which does nothing else, but converting this HDB3, this, uh, ternary, um, uh, polarity neutral signal, uh, that, uh, you speak on the E1 line to a serial bit stream on the other side. It doesn't do anything else, but converting the HDB3 to, uh, normal binary bits. And then we go into some microcontroller of whatever nature and then we go over USB or Ethernet into a Linux system. And the idea is that in here you basically do nothing in the microcontroller. You just take buffers of bits and you stuff them over USB or Ethernet and vice versa, and you do all the logic like the HDLC controllers and so on in, in software and you don't bother with link equalization or whatever on, on the physical layer because well, we're talking about one meter of copper between the BTS and the E1 card. It's not about a hundred meters or whatever, uh, where you actually have to worry about, uh, reflections and, and, uh, whatever, uh, cross talk and, and all these things. So what kind of options do we have? Option number A is, uh, Ditas preferred method, uh, is, uh, using a PRU. Um, that basically, uh, not sure if everyone is familiar with what a PRU is or what it does. Um, I'm not the best person to speak about them here. I think we have our resident PRU expert, uh, sitting in front of me, but just very quickly it sort of allows you to do a high speed real time bit banging. You have two separate or one or two separate CPU cores inside, uh, like an AM335X or other TI arm processes, a separate core with a very limited instruction set that you can use for doing basically such serialization, de-serialization jobs. And then you exchange in the end memory buffers with the ARM cores on the Linux side.  And in the end, you have some memory buffers, uh, uh, DMA like or memory mapped that you use on, on, on the Linux side. And this would mean you could basically use the BeagleBone uses such a processor. So you could have a cape, actually, we could have an E1 cape for the BeagleBone, um, that you just stack on it and, um, you get, uh, support for that. And the, the nice part is the BeagleBone could actually run the entire Osmo BSC and Osmo Media Gateway two, uh, and use A over IP as a backhaul. They have a very nice self-contained small, um, solution. You don't need any, anything else, uh, in, in that approach. Um, my, uh, uh, approach would be, uh, to go for an XMOS device. Uh, I spoke a bit about XMOS controllers at previous years. We've now done, uh, our first, uh, a board design with an XMOS, uh, in, at SysmoCom unrelated to this E1, uh, idea. Um, and, uh, XMOS, uh, basically has, uh, an interesting, uh, architecture in which they have no hardware peripherals in the controller. So there's no I square C controller or no SPI controller and nothing like that. But you implement all of them as soft cores and you have, uh, uh, high speed CPU core at a risk core at 500 megahertz. Um, that has programmable serializers and deserializers and clock blocks around you. And basically you could do the serialization deserialization, um, in those, uh, in those, uh, serdes blocks. And then, um, in the, in the XMOS core, you again, uh, just do your, your, uh, basically collect a couple of those buffers and then send it over, over USB or Ethernet both are available in, in XMOS controllers. But then you would still need an external, uh, like an X86 or ARM board. Next to it attached over USB or Ethernet, which then does the processing. So the, the beauty of the, uh, PRU approaches on a BeagleBone that you really have all in one device and you don't have two devices in the end, even though, well, the strictly speaking, of course the Cape and the BeagleBone are still two boards, uh, that work together. Well, of course you can also go for programmable logic, but then it's not really software defined anymore, right? Um, depending on how you define software, uh, these, uh, defined and you need to work. And I think it's just overkill for a slow, for today's perspective, a two megabits per signal, uh, uh, two megabits per second signal. Uh, you can do that with big, big bit banging and, and, uh, and an XMOS or PRU or something like that. Yeah. So there's some, uh, references. If somebody wants to follow up on, on, um, reading on this, uh, what's the state of it? Well, originally we wanted to start this in, uh, quarter one this year, but as you can realize, quarter one is over by now. It has not materialized yet, but, um, I think we have to do it soonish, uh, because I think the window of opportunity for being useful to people closes. So now we have all this Ericsson equipment out there for a little money. Now we still have people, particularly in, in less developed countries who have an interest in deploying 2G networks. Um, and if we can have a, uh, because so far the Osmo com stack has been used always with rather small cells or femto cells, right? Um, maybe up to 10 watt, uh, uh, uh, and, and single or two TRX, but with this equipment we could really go for a six TRX, eight TRX, 12 TRX, whatever, a large macro cells, um, and, um, uh, have a, uh, sort of fill the gap in there, uh, for a very low cost and help people, the gap filler. Yeah. That's a different, yeah. Who, who still remembers the gap filler in here? Yeah. Yeah. Okay. Well, they at least had boards. I'm just doing vaporware here, but, uh, yeah. And that's, uh, still so, so Tita and I want to look into this, uh, soon and, uh, implement it. And the large part actually will not depend whether it's XMOS or PRU related because all of the software is just software in the end, uh, which controller or which hardware interface you use to get these raw bits of the line into your memory. That's then a small detail. Um, and it's, it's actually the, the CRC for processing the, the TDM frame generation, the, the HTC controllers and so on. That's all software anyway that you can run with any, uh, hardware interface. Yeah. Questions. Um, it's, um, they are the, oh, sorry. Yes. The question was, do I have a link to the, uh, specs of E one? And my answer is no, I do not have a link right now, but, uh, I think it should be in, if you check, maybe the Osmo-com issue or so, I definitely looked at them. They're ITU specs. And I think it's, it might be something like G seven or three or something like that. I don't remember right now, but definitely, uh, the, the entire, whether it's a E one or E two or E three or T one, and all of these, they're all, um, uh, uh, public ITU specs that you can just look at. Let me just, maybe I can find it here. Yeah. It's G seven or three, mainly I think. Um, so it starts with G seven or two, but, um, G seven or three is really the one. Uh, so basically here, this is the physical electric logistics of hierarchical digital interfaces. It doesn't say E one anywhere in the title. Um, but here, actually it, um, if you look at the year here, you see there's the, uh, 64 kilobits and then the, the T one 1.5 megabits and you have the higher bit rates. And then you have the E, uh, yeah, whatever why they call it E 12 here. I don't know. Um, but, uh, this is basically the, um, the series of specs, uh, seven or two, seven or three specs is, uh, what do you want to look at? It's not really not much.  Uh, one, one aspect that I didn't mention yet, maybe it's worth mentioning, but you always wanted the BTS of course is also a stable clock. So if we actually go ahead and building this, then I think, uh, it would be worth to put a GPS DO on the same board so that the two megabit signal of the E one is really stable. And then you have no clock issues on your BTS as well. So, um, yeah, maybe we can recycle some clock tamer there if you're interested. Um, okay. So the comment was, uh, they're doing a remake of the clock tamer. So, um, okay. Um, good. Any questions, comments? Yeah. Peter has a question if somebody could pass the mic. Oh, Keith behind you.  No. So how, how quick is the turnaround? How, how much time do you have for processing, uh, of bits before you have to turn around? Uh, there is actually nothing really fast. I mean, I studied the specs and I couldn't actually find anything.  And so you have 30 other times lots that pass until you, you could respond in theory at the first time and the timeouts are rather in the hundred milliseconds or so. So it's clearly buffering should not be a problem there. Um, uh, at least not the kind of buffering you have on, on USB these days. Um, I like the PRU approach a lot. I think the PRU is, is fun. Yeah. As I said, I mean, I'm not emotionally attached to XMOS. I just think it's a good technical match for the problem as well. Um, but as I said, the software will be independent of that anyway. So in the end, we could have both or whatever. I mean, we have to have to software implementation. And I think the particular hardware interface will be, uh, there's, there's many options. I mean, some people could even do it with an FTDI or something maybe, uh, uh, if they wanted to. Okay. Good. Um, then if there's no more questions that concludes the software defined E1 talk.


Welcome once again to the breakfast on plus TV Africa is now time for off the press and where we have a quick review of the major stories making headlines across Nigeria today We're starting with the punch newspapers and we'll be introducing our guest for Thursday morning in a bit on the punch there you have it platu binway gunmen kill 44 youths deposit corpses at government house Eat dead bodies if you can't protect us protests in platu youths tell government officials Protesters destroy governor's billboard the long green poses 24-hour curfew Also on the punch grazing reserves presidency knocks autumn cause governor a serial defector NDA gunmen target five apartments federal government incompetence as can CNG and others and Also telecom Nigeria's data usage rises by two hundred and two percent in three years Still on the Pontius one and super TV CEOs family petitions IG six new probe into murder Kidnapping cultism Ogun's biggest security issues as a biode CBN threatens prosecution of Forex policy default as again dare devil robbers attack bullion van kill two in on Do and also terror sponsors turn in Nigeria into Taliban countries as a ex-naval chief and NPC rejects customs proposal on petrol smog smuggling stoppage I think that's all we're gonna be taken on the punch. Let's now move to the daily trust newspapers And so what we can also quickly find here the big one there on your screen says youths dump over 20 corpses at to government house Naguta leaders accuse her as of killing 37 members. We are not responsible says the full any group 24-hour curfew reimpose on just north 10 suspects in custody NDA leaked army memo confirms daily trust report and lose fence Bandits abduct widow in Katsina 24 hours after husband's death Also on the daily trust why NNPC retail stations in neighboring countries won't stop smuggling says carry PDP crisis governor's BOT take position on Secundice and Also Delta confirms outbreak of bird flu 164,000 birds reported dead and DLE a sax 35 for corruption and desertion and now on the Guardian newspapers this morning NDA attack army sets up inquiry presidency assures of security Ex-nevo chief alleges sponsors of insurgency known to government attack wounds dampen military action against criminality and Also, PDP charges National Assembly to summon Buhari demands forensic investigation Bandits may extend attack to DSS facility housing canoe IPO be alleges Also on the Guardian this morning just in 24 hours shot down after fresh attacks leaves 36 dead houses burnt youth storm assembly in government house with victims bodies The long updates Buhari says all corporates must face justice Declare state of an emergency northern Nigeria says Zamfara governor We can also find on the Guardian this morning NNPC invests 2.7 billion dollars in Dango Terry finery gunmen murder eight abduct one in Bainway as Daria kills eight in Kogi again gunmen attack bullion van killed driver and policeman in Undo and How Nigeria's poor airports killed over 30 airlines in 30 years? Finally on the daily independent this morning Residents flee Josh City after gunmen killed more than 63 people burnt houses Morners take victims bodies to assembly security council We could also find insecurity caught shot your vacation someone Buhari PDP charges National Assembly Calls an NGF and others to rescue Nigeria Attack on MDA or NDA beg your pardon can't Dampen morale of our military Says the president Buhari gunmen kill eight persons in Bainway community PDP governor some on emergency meeting of a party crisis and also presidency carpets or Tom over comment on grazing roots family petitions AGF DSS police and others over a tag as murder I Think we're just gonna squeeze in one more experts differ on what you portfolio diversification of pension fund investments Good morning to our guest mr. Ezekiel. Yeah, I talk Thank you for joining us All right while we wait to connect with the mistake I talk this morning remember it's off the press and plus TV Africa and Some of the stories that we've read this morning basically repeated across the papers the big ones of course from Plateau State and The Nigerian Defense Academy Incidents the presidency's response and of course the Plateau State governor one of the papers says the governor You know sends a report or you know, this said the president to Give a give a update rather. Yes as a long updates Buhari says all corporates must face justice And that's on the Guardian newspapers this morning is one of the things that I had earlier spoken about You know how you know, this is you know, this seems to be the modus operandi for Nigerian governors these days when there's a crisis in the estates You know, it doesn't seem like there is anybody from the presidency who you know visits these troubled areas or you know This you know center representative if the president doesn't go by himself instead. We find governors Using of course state funds to fly to Abuja and meet with president Buhari and Shadda show pictures or you know Give updates on the situation and bump elbows, you know, because we're in a COVID-19 era And you know that that's really what it is and I'm not sure how a lot of Nigerians see these things You know what this really, you know represent or what this tells of the reaction and you know How the Nigerian government really feels about some of these issues and happened, you know when the Lagos state governor of course also took pictures of The destruction in Lagos state back then in 2020 to the president to of course do the same thing You know bump elbows and you know show pictures. Good morning, Mr. I talk. Thanks for joining us Good morning. I hope I can be heard. I've hardly heard anything so far. Oh, okay Yes, we can hear you can you can you hear me at all? Yes, I can hear you now. Let's start with your reaction to the crisis in Jaws Plateau state the governor Simon along has declared another 24-hour curfew in Jaws north and You know, of course, there were protests yesterday 20 of these dead bodies were taken to the state government house, you know in protest So let's start with your reaction to that first Well, I think it all comes down as much as possible to the concept of in security and the question is why do we have this constant in security? It's either what they call the head of a clash or they talk in terms of them, which goes into religious and aspects or they are talking in terms of You know communities clashing and that's what just has been said this day And the question is why can't the solutions be arrived at and from all the analogies that I've looked at from all different sides I think politics plays a major role in all these but at the end of the day, human lives are lost And it all comes down in my opinion I always bring it to one which is that we don't pay attention to the essence of government and governor And I keep I couldn't quote this section in all which says that the security and welfare of the people the security and welfare of the people shall be the primary purpose of government and We really need to wake up to this reality because we'll keep having these analyses the moment with we have a governor structure in place that prioritizes the Security and the welfare of the people generality of the people then we'll be able to address some of these issues that of just is It is annoying in more ways than one if some of those that are older Always be just as one place that anybody would want to go and be you have the one investment Destination in this country that would have done everybody so much good But because of it because of the weather they have but because of insecurity Just has become a shadow of itself So for me, I really don't know what more analysis to do than to say that we should refocus on what we understand as Governments and governors and then our leadership recruitment criteria Where people who get into office think more in terms of themselves and what do you benefit them and bring them back to power And sustain them in power not minding who them Who which price has been paid that we are looking at it I wish I wish I'd followed you in the preliminary analysis. Well, Miss. I I talk if you're talking about the leadership recruitment process That happens every four years. Basically. Yes, there should be a build up to that But you know, we've not seen a lot of people either resign or get impeached or you know get Recalled from these positions. And so Nigerians have to leave with those consequences for four years the challenge that we you know, I believe we should focus on now is how These Nigerians can survive until the next leadership recruitment process And so I want you to you know focus, you know a little bit on the reaction of the governor of Plateau State Imposing a curfew. There was a curfew before the skillings But he's once again, you know re-imposed that curfew a 24 hour curfew in just north and also has gone to Sorok and giving updates to President Obama to Buhari So I want you to quickly respond to the reaction of the governor who should be the chief security officer of the state and also President Muhammad to Buhari the number one thing is this concept of government dog not as then Chief security officers of the estate have generated a whole lot of controversy Because you can't be a chief security officer when you do not control the Operatives of enforcing the law. How do you enforce what you have no control over? So I think that's one area that everybody's clamoring for please and all that and Within that context, I can feel the frustration of the governor if we are on the same page Because like I said, I seem to be answering questions. I seem to be setting for myself as I'm not hearing too well Well because of the time that we have we probably would just touch on two stories So I was you know asking that you you know speak with regards the Plateau state governor and his reaction to these killings Imposing a curfew on just north and of course are sending updates to President Muhammad to Buhari You had earlier spoken about, you know, the failure of the, you know You know him being called Chief Security Officer when he doesn't have the apparatus, you know control over security agencies But do you think that's a big enough excuse as to why the people of Plateau State are not properly Protected. Mr. Ayanato, can you hear us? I think we will of course apologize for the challenges with the sound this morning it has been off the press where we have of course a review of the major stories across Nigeria and You know, of course try to share as many of them from some of the, you know, news headlines In the country today, some of them very very sad Of course, we shared with you the crisis in Plateau State and the reaction of the government Also the PVP crisis and governors and the Board of Trustees Still meeting to try to resolve some of those issues We also spoke about the Nigerian government and the Nigerian Defense Academy and some of the reactions to that also Ex-navo chief has also been in news Sharing what his thoughts are concerning the attack on the Nigerian Defense Academy Sometimes I'm on a breakfast this morning. We're gonna be speaking with someone from Plateau State the director Center for Nomadic Education in the University of Jaws and also the commissioner for information In Plateau State who will be joining us sometime on the program this morning to share his thoughts on the current issues in the state Mr. Ayanato, can you hear us? Okay, well Since that's obviously not working. We'll take a short break and when we come back We'll be sharing with you things that happened on this day in history the 26th of August. Stay with us


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So Nexo just had its first governance proposal and this is what you need to know before you start investing with Nexo. Welcome back to my channel. My name is Johannes Bottle and I read the news so you don't have to. Nexo is a lending institution in the digital finance industry which allows you to generate income from your Bitcoin or in general cryptocurrency holdings. By the way, this video is not sponsored by Nexo or any other company. I simply want to inform you about the changes they recently made within their company and their interest rates. If you hold any cryptocurrency, that is also available on Nexo. You can then put your cryptocurrency into a Nexo wallet and then stake, freeze the coins to generate income. The interest is paid out daily in either the coin you're staking or Nexo token. If you are a US citizen, you cannot earn Nexo tokens. Anybody else, including myself, can benefit from a 2% higher interest rate. For the past couple months, I've received Nexo tokens on a daily basis and this is what the token has done for me so far. Increase my interest rate on my Bitcoin holdings. Depending on your Nexo holdings, you will advance in different tiers, starting from base, silver, gold and platinum. The higher you are, the better interest rates you will receive. It allowed me to receive a dividend payment once per year from Nexo. So this used to be a one-time thing. Once a year, Nexo paid out a dividend to their Nexo holders. It's just an incentive to hold more Nexo tokens. Now, here's what changed June 8th. Nexo started a governance vote to see if people are interested in getting paid interest on their Nexo tokens instead of a once a year dividend payment. 90% of the people who voted wanted to see interest rates on their Nexo tokens, which automatically removes the once a year dividend payment. So Nexo said they're going to pay out one more dividend payment worth 20.5 million dollars. This sounds like a lot of money, but if you only hold a handful of Nexo tokens, this will be a very insignificant payday for you. Let's do the math. There are 325 million votes for yes and 38 million votes for no. So I'll just assume and say that there are no more Nexo tokens within a Nexo wallet because that's a requirement for you to receive a cut of the last dividend payment. This math could be completely wrong, but that's why this is an assumption. So we have roughly 360 million Nexo tokens and 20.5 million dollars to share. Each token will roughly get six cents, which in my case will be $84. Yeah, as you can imagine, that's not the reason why I'm excited about the news. I'm excited because my interest that I have earned from my Bitcoin holdings is now generating more interest. So basically the money that I invested created money and now the money makes more money. Compound interest, ladies and gentlemen, this is huge. In my opinion, if you believe that cryptocurrency will be around for a long time, I will now have the opportunity to receive 7, 9 or 12% on my Nexo tokens. If I don't do anything with my coins and I just leave them in there, I will receive 7% per year paid out daily. If I lock them up for three months, I will increase that interest rate to 9% and if I lock it up for an entire year, I will get 12%. One important side note, if you want to take out a loan with Nexo and you lock up those Nexo tokens, you will not be able to use them as collateral. One last note, I understand that 12% on a $3,000 investment doesn't excite anybody on YouTube. But when you look closer, the $3,000 that I generated, I got paid in interest. Besides the fact that I have to pay taxes on the interest, that's 100% passive income and now my passive income generates another $650 a year, completely passive. And I'm talking 650 if nothing changes in my account right now. The beautiful thing is though that I will continue to receive more Nexo tokens on a daily basis, which then increases my interest on the tokens that I got yesterday, tomorrow I'll already get interest on. I'm just really excited about the fact that my most aggressive, most volatile investment, Bitcoin, is now generating a high passive income for me while I'm not even trading it. This is just amazing because now I'm just holding my Bitcoin because I want to hold it for the long run anyways and now it's while I'm holding it, while I'm waiting for Bitcoin to increase in value over the next five years, it's generating income which I can use to reinvest to buy even more. Or I'll just leave it there and have a diversification and not only hold Bitcoin but also hold Nexo. Please, before you invest with Nexo or any other company, do your own research. It's your money and it's your responsibility. Actually, I got one more thing. If you like this video and the information presented, help me out with the YouTube algorithm. Just leave a comment and a like that helps me out a lot. Thank you so much for watching. I'll see you next time.


Okay, this is the bells and whistles, the hair accessories section of the lecture, because there are a lot of other things. We've got our layers of our skin and our tissues, but there are a lot of other things that are associated with skin. The most obvious thing associated with skin is hair. Hair is found in a follicle, and there are many things associated with it. So if you check out this hair follicle, hair is like secreted. It's almost like the hair follicle is like a gland, and it's secreting this thing that then we're like, oh, look how pretty and cool it is, but it's like this weird thing. Associated with hair follicles are a couple of super interesting structures. One of them is this thing. This is a gland, and you'll notice it has an opening right here into the hair follicle itself. That thing right there is a sebaceous gland, and we're going to talk about glands, types of glands in the next section, but the sebaceous gland produces a substance called sebum, which I don't know why, but that sounds super gross to me. Sebum is this kind of oily, maybe it is gross, but it's stuff that lubricates the hair and helps it get out of its follicle. Also associated with hair follicle, this one isn't gross at all. This one might be one of my favorite structures ever. This guy, what does it look like? Does it kind of look like a muscle? It is. It's smooth muscle. So it's a structure made out of smooth muscle tissue, and its name is erector, erector pili muscle. So this is an erector pili muscle, and take a thought about what is an erector pili muscle going to do? Muscle tissue shortens, as we know from the last lecture. What would happen if that tissue shortened? What would happen to my hair follicle? So fun. Erector pili muscle is responsible for goose bumps. Because it's smooth muscle, you can't control it. So even though it would be epically cool to totally be like on command, make yourself have goose bumps, make all your hair stand up straight. Dude, that could be awesome. Yeah, you can't do it. You can think of a stimulus that will cause you to get goose bumps. So it's not entirely difficult, but it's not as easy as just like, dude, go. Yes, those are associated with hair follicles. Check this guy out. We actually have sweat glands associated with skin, which makes complete sense. And we have a couple of flavors of sweat glands, and so I am going to tell you, that choice of words is a little bit unfortunate. One of our types of sweat glands is called a marocrine. Marocrine sweat gland. Marocrine sweat glands, that's a marocrine sweat gland. Marocrine sweat glands are the most common sweat glands. They're found all over your skin, and they produce a really thin, watery sweat. When you work out and you're sweating, and like you're getting all drippy wet, that sweat, that real watery sweat, is marocrine sweat. It's produced by these marocrine sweat glands. So it's super watery, and the purpose really is temperature regulation. Sweat makes water on your skin that evaporates and takes heat from your blood with it when it evaporates, and that's how you end up cooling yourself down with these marocrine sweat glands. Marocrine sweat glands don't stink. However, apocrine sweat glands stink like apes, are stinky. I don't know if apes are really stinky. I hope I didn't offend any apes out there. Apocrine sweat glands produce a protein-rich secretion, and nobody's actually sure what the protein-rich secretion functions in. There's some thought that it might be a critter-to-critter communication, and I will tell you why, because really they are stinky sweat glands, and they're found in stinky spots like your armpits. So you have apocrine sweat glands in your armpits that produce this protein-rich substance that is stinky. So we associate it or we've labeled it as stinky. Some of the stink comes from bacteria that eat the protein and then fart, and when they eat that protein-rich secretion and then pass their little stinky gases, we get stinkier than we otherwise would. Other places that are stinky, anus is filled, covered, embedded with apocrine sweat glands. All your pubic region apocrine sweat glands and your nipples apocrine sweat glands, and that's not all. You thought that, oh God, let's stop talking about apocrine sweat glands, but no, earwax, what? Earwax is produced by a specialized apocrine sweat gland, and that one's kind of like, okay, this one's epic. Breast milk. Breast milk is produced by modified apocrine sweat glands. You're making a protein-rich solution that we decided, dude, feed that to a kid that's hungry, so I don't have to like smash up some bugs or something and give it to them.  It's sweat that your children ingested. If you had me for Bio One, you already know that story because it is so unbelievably cool. Okay, I think that everything, I think I've done everything, like doesn't it feel like I've done everything and more? Okay, glands next.


And she basically is compiling a list of all of the most important things and she's making an e-book out of it that she's gonna offer for free. She is publishing the post today for lung cancer awareness month. And this is kind of a labor of love for her. When our daughter Lauren was diagnosed in December, late December of 2017, Lynn of course looked for all kinds of information about lung cancer. And she found bits and pieces here and there. She studied throughout the whole time that Lauren was sick. So almost six months and she just scoured the interwebs for anything she could find. And she realized that it was kind of, the information was there, but it wasn't altogether in one place and it wasn't easy and it wasn't in layman's terms. And so she decided to, she discovered this pain point. She decided to come up with a solution and she has. And those of you that see her posts and share them on your Facebook page or other social media, that is huge. We honestly truly grateful that you do that. It's incredible. That is how you can help us. Several of you were offering to donate money to us during Lauren's sickness and fortunately we're not wealthy but we're comfortable enough that we didn't need money. However, sharing Lynn's new project, oddly grateful. She has several other things in the works that we're not ready to disclose yet but we're starting out with a freebie. And this is not a loss leader kind of thing. This is just a labor of love for her. So please do like her posts and then share them on your pages. It really means a lot to us. Thank you for all you've done already and all you will do in the future. Please let us know how we can provide value to you.