Now, in 1958, that was actually kind of before at the beginning of the Gulf ice stream, but this was kind of subculture and actually proposed from a psychological point of view, it was the first neural network proposed, actually, there were earlier ones, but the first one that connects to the current developments called the perceptron. And it was drawn like this, the circle could be considered to be an artificial neuron. And these arrows are inputs. And these inputs represent numbers. It could be, for instance, be that you have three numbers x1, x2 and x3. These are numbers that represent a client for a bank, for instance. And x1 could represent the age x2, the number of children and x3, the salary. And these were all added up and summed in this neuron. But these arrows that you see, they have a weight. So if the number of children is more important or the salary is more important for the ultimate decision, you could weigh this input. And this is not something you do manually, but you can automatically train the system to weigh these inputs on the base of the output that it's giving. So a very simple task is if you have a bank and you want to decide if you want to give somebody a loan, you could just collect a lot of data and decide whether somebody should have got a loan or not. So you have the kind of ground truth. And then you check for all these clients if the perceptron would give the right answer. Now, initially, these arrows have random weights, but it was shown in 1950, I ate already that you can change this by using an automatic learning procedure in such a way that it would give, in most cases, the correct output. Now, for those of you who know statistics or work with statistics, this is a kind of neural metaphor for logistic regression. And of course, that happens a lot in this field that you have these mystifying terms like neural networks for things that are actually very well known. But in these days, it was revolutionary because it made a connection between the idea of a neuron in the brain and this learning system, artificial learning systems. Now, now a jump to 1980s and like these times. So on the left, you see the 1980s versions of stacked perceptrons where you take a lot of these perceptrons and stack them in layers. And this was what I worked on in the 1980s and many other people. And you could have a kind of input layer where the inputs were like these characteristics of a client. And you had a hidden layer where we're neurons that could compute complicated functions that you needed to compute an output layer. So these neural neural networks in 1980s developed by people like Jeffrey Hinton and others, were able to solve more complicated tasks and better able to make predictions, for instance, for this bank application, if you want to decide if you should go give somebody a loan or not. Well, in the 1980s, there was also a person called John LeCun who developed a deeper system and applied that to zip code recognition. It was very successful. So this is 30 years ago. But we also try to build these systems to recognize these images of my dog and other, well, I didn't have that dog then, but other dogs. And that didn't work. So people thought these neural networks failed. So that took a long time until now where we have these deeper neural networks. And now we can automatically classify images and train the system on automatically labeling images. The only provision is that you need a lot of data. And you need a lot of these arrows that you see these connections, all these connections that you see here. And this is, of course, a caricature, but these connections are adaptive. So it's a sequence of steps that you go through this neural network. And each of these steps transforms the information up to the end until it gives the right answer. If it makes a mistake at the output, you have a special algorithm that propagates backwards to the network and changes all these connection strengths until you have the correct configuration of connections. Now what changed between these two steps? The first is that we have much more data now. So the fact that these 1980s things didn't work was you had a lack of data, but also lack of computational power because to create a very deep network, you need a lot of computational power. And there are also some machine learning innovations that help there. But actually, the neural networks that we have today or the bulk of the neural networks were already available 30 years ago. But because of the lack of data and computational power, they didn't work that well. Now, this is to intimidate you, but especially to show you that the thing on the left, which is a neural network is fully understood. On the right, for those of you with mathematical knowledge, it's, there's no, it's fully transparent in the sense that we know how these neural networks are connected. The point is that like with this pattern recognition of these elephants in the shadow, is that all these connection weights, it's not like if then rules, it's kind of distributed over this whole network. So it's very hard to explain how it arrives at this output. But it's not that we do not understand what happens in the neural network. So it's another mystery. It's just how to interpret it, what it makes it difficult. Now, an intuitive notion of what these neural networks are doing, and this applies to all these deep learning networks that we nowadays see, is that you have a big black box, not so black a bit gray, because there's some transparency there. With a lot of knobs, you put something in, you look at the output. Is it correct? You do nothing. If there's a mistake, you start changing these knobs. And of course, if you had to do that manually, that would be very difficult, because the number of knobs on modern neural networks are in the millions or more. But we have a very straightforward algorithm for that, which is very well known in optimization. And it's called gradient descent. And it's just following by each time changing the knob a bit in the direction that minimizes the chance of making an error. And this is called back propagation. And this algorithm is also known for 30 years, and maybe a bit longer even. Now, there are nice videos, I always show that if you want to know more about how these things works, go to three blue, one brown, which are YouTube videos that explain, give some intuitive explanation of how these networks work, also for the learning procedure. But I skip that now, because that takes too much time. Now, this is what we had about 10 years ago, we had networks that could be trained on about 1000s of images of dogs of different breeds, including my dog, the breed of my dog shown on the left, which is a Samoyed. And by training the system, you were able to, after training the system on all these 1000s of instances, and they were labeled so you need 1000s of instances of Samoyeds, of Papayons and Pomerani, I know don't know all these breeds. But after that, the system is able to automatically label a new image of a dog and indicate what the breed of the dog is. And the way this network is doing that, it's exploiting anything in the input that helps it to predict it. So I always give the example, suppose that all these Samoyeds were depicted in a white background, because they come from Siberia, then the snow in the background, the white background could also be used as a cue that has nothing to do with the dog. But this system doesn't know it's what we call greedy, it uses anything in the input it can use to predict that this dog is a Samoyed. So that means that the quality of the data should be such that there's a there's also variety in the backgrounds, because otherwise, the network will pick up these features that have nothing to do with a dog. And this applies to image classification, but actually for all machine learning tasks. Now there are also systems 10 years ago that could be trained on images with captions, and then they could automatically generate a caption, the description. So this is from a system trained on millions of images with captions. And then it was confronted with this image. The first part of this network was a vision network like for the dog. And the second is a network that is trained on sequences on generating descriptions. And as you see this description, it looks as if the system understands the picture. Well, this is 10 years ago. Now we have GPT-4 that's even better in this respect. So it's better able to label these images and even describe them in a way that seems to suggest that it has an understanding of the picture. Now, a small thing about the internal structure of these neural networks, as I said, it's hard to interpret them. But for some domains, for instance, the visual domain, you can inspect the inner workings of the network. So here you see three layers of a network that is trained on a face recognition task. And what you see is actually the sensitivities. What are the features or the visual aspects that these neurons that have been trained by using this procedure, that they detect in the images. And what you see the first layer, it looks at contours, oriented contours. And intermediate layer, it looks at small configurations in the face. And on the top layer, you see kind of face detecting neurons. And what I'm now saying you should take very carefully because it can be misunderstood easily. A similar strategy is used by the visual system. If you ask anybody who studied the visual system, you see neurons in the first stage that look like this, so called edge detectors. And in the middle layer, you see these kind of configurations. And at the top layer, we have neurons that respond to individual faces. And this is not to say that these neural networks are as complex as the human visual system, because they're not, they're much simpler. It's only that the general solution strategy is very similar to what we see in biological visual systems. And this system was not programmed. It was only programmed on predicting the correct label. In this case, I think it was a gender recognition task. And nothing else. So it has no knowledge about eyes or nose. It doesn't understand noses or eyes. It's just using the pixel values to make this prediction. Now, this is one form of neural networks. There's another form which is called reinforcement learning. And this is an example of reinforcement learning. The two pigeons are either end of a small ping pong table. One pigeon pecks the ball. So these are pigeons playing ping pong. Other pigeon pecks the ball back across the table. If it goes past one pigeon, the other pigeon can eat. And if it goes the other way, so now he wins and he gets something to eat.  And in this way, they learn to acquire the skill of playing ping pong. This is very old. This is psychological research by Skinner. A long time ago, which gave rise to a whole stream of what is called behaviorism in clinical psychology, but also in cognitive science. But there's a variant in deep learning. It's called deep reinforcement learning. And it's used for creating for creating action. Unfortunately, the video doesn't work because we have to change to PowerPoint. But the idea is that the one of the hardest task for autonomous drones is to fly in unstructured environments like a forest. And they succeeded by using deep reinforcement learning, which is just a variant where you reward the neural network if it performs a good action or a good sequence of actions and punish it otherwise. It is called policy learning. They were able to have this drone flying very noise under noisy circumstances through forests. And this is, of course, one of the spin-offs in many military domains nowadays. Now, what is important to realize is that if we think about science, there's one important principle in science, and it's called Occam's razor. So if you want to explain a phenomenon that you don't understand or you want to model something in a statistical model, you should not overfit the data. And overfitting means that you have too many free parameters. So these knobs that I showed you in these neural networks are all free parameters, things you can change. But if you want to model something, you should reduce the number of parameters. Otherwise, you get overfitting, as shown on the left. The red curve, which the complexity of this curve is based on the number of parameters, is too large because it's fitting the noise. Actually, the curve on the right seems to capture the proper structure. So this is not only true for statistics, but also in science. If you see something flying in the air that you don't recognize, you do not start with an elaborate theory about that the aliens arrived. You try to come up with something simpler, which doesn't require so many assumptions. It's a basic principle of science. And as you can see on the bottom right here, that's what good scientists do. They reduce the number of parameters. But AI researchers are more like pirates, and they just explored what the effect was of increasing the number of parameters. And that's what they're doing in deep learning. All these knobs on these systems is something that any statistician dreads, because that's not what you should do. But somehow what is called over parameterization to use many parameters is part of the secret of this deep learning success. Now, this principle is now translated into so-called language models. So the idea that you use a lot of parameters, and one of the problems of traditional machine learning and deep learning was that you needed all these images of the dogs, all these examples, but also the labels. Now, what they came up with is a form of learning, which is called self-supervised learning. And if you have tons of text, for instance, you can use self-supervised learning by giving them part of the sentence and ask a system to predict the next word. That's one variant of self-supervised learning. So that means that you only need data, and the label is kind of intrinsic to the data. And in this way, you can train the system on huge datasets, like the whole worldwide web, Twitter, books, et cetera. And what is the tendency is a growing number of these three parameters. And that's what we saw in the past. And of course, this is already leading to a lot of concern about the energy requirements, which is not always a very balanced discussion. But it might be temporary because there are developments here. And this is a kind of overview of what happened in the number of parameters if you just look at the GPT systems. I could have included GPT 3.5 here, but I don't remember what the number of parameters were. But the idea is that it's growing. Now, the funny thing is that we don't know how many parameters GPT 4 has. And they're not very open about it. There were some rumors that it was a trillion parameters, which is a lot. But it seems to be much less than that. So that's why this question mark here. But the tendency up to now was more parameters gave better models. But it seems that there's now also a lot of innovation in how you structure the data on which you train it and how you tune these systems. And I will come to that. Now, this, as I said, you can predict the next word or the token, the official word. Your word, but think about words. Or you can also have must language when you leave out one word and the system has to predict what this word is. And again, this is a system where it predicts the words. If it makes a mistake, you adapt all these parameters. And if it makes no mistake, you do nothing. That's the basic principle. So it's the same principle that I said before. And at the heart of all these developments is a new technology. So this is really new. I told you about these neural networks for recognizing dogs, for instance, these are 30 years old. But this is only for a few years old, this idea. And it's called transformers. It's not these robots that you have these movies. But this is called transformers. And they're based on an idea where you have an encoder and a decoder. So the typical application is translation. So you have a sentence in Dutch, for instance, and you translate it to English. Then you would train the encoder on the Dutch's input and the decoder generates German's output or English's output. And at the heart of these systems, and you can see it's quite complicated, there are some perception like things in there, these traditional neural networks. But at the heart is a mechanism that I can only explain if you first understand how these neural networks deal with words, because words are not numbers. And as you know, a computer only works with numbers. It's a number crunching machine, another word crunching machine. For that end, they use so called word embeddings. So what you have there is a word is represented into a list of numbers called a vector, just a list of numbers. Now the question is, how do you arrive at these numbers? Well, there are many ways you could, for instance, say, well, each number represents a kind of attributes, like important or not important or social status, etc. But actually, these are implicit, because it's more about the distance between these numbers that it matters. And there are systems for doing for creating these word embeddings. And if you apply them, you get things that you can visualize like here, you have a numerical representation. So let's assume you have only three numbers. If you have three numbers, that's a point in a three day space, then you could represent King and Queen. And what you would see that there's a geometrical relation to man and woman. So there's already some knowledge or some semantics or meaning into these word embeddings. It's very limited. But it's a very simple method where you already translate words into numbers, where the distances between numbers reflect kind of similar similarities between words. Now, of course, a similar similarity between words is not one thing. You have many different similarities, they all depend on context. So that's why these word embeddings are not enough. Now the heart of these transformers is a so-called attention mechanism. An attention mechanism can be explained in mathematical terms, but I tried to give you a more intuitive notion. They use the mechanism of self attention. So if you have a sentence like Bank of the River, you want to explain, understand the word bank. Bank can mean different things. But in this case, it's the Bank of the River and not a bank where you get your money from. So the river is related to bank. So if you somehow can include the context of this word bank, and the context consists of other words in the sentence, you get some contextual information that could disambiguate the meaning of bank. And that's what we apparently do if we read sentence and if we talk. And this is how they do it. So they use the word bank and it has, I put it here in the italics to show that this is actually a numerical representation. So this is this list of numbers. And now through the self attention mechanism, they mix with this numerical representation, the numerical representation with a certain proportion of the other words. And as you can see, it's for instance, point five, the original bank concept. But then you add 0.1, 0.1 off and 0.1 the and 0.3 river because river is very important. So these factors like 0.5, 0.1, 0.1 and 0.3, that's the attention, the attention you pay to the other words. And this is just a made up example, but this gives you an idea how attention can help to change this original numerical representation of words into entities that have more context. So now if you see the repostation of bank, you immediately know it's related to these other things. And what you have to realize is that the context, the length of which you can do that depends on the system. So these, this was a very short sentence, but you could do it over longer sentences. For chat GPT is about 4000 tokens, words, kind of words. GPT-4 has 8000 and they're working on 32 thousands. But in terms of human memory, this is like the memory of a goldfish. So, and you know humans have a short term memory and a long term memory. So it's a very limited in terms of memory. It doesn't have a long term memory yet. Maybe they're working on it, but that's a limitation. Now, if once you know that you can use this attention mechanism to create these internal representations, and if I say representation, these are just numbers, lists of numbers, you can create representation of words in context, but also words in context in context by stacking up these attentional mechanisms. And that's what's happening in the GPT systems. You are exploiting the statistics of older relations in this enormous data set that they use for training these systems. And in this way, you create a kind of contextual mechanism that allows you to exhibit some intelligent or not so intelligent behavior. So in this way, these large language models as they are called LLMs are obtained that can predict the next word, but also the next word and sometimes parts of sentences. And you can do this repeatedly. So it can generate whole texts. And of course, they also have indicators for the start and the end of a sentence because it has to end sometimes. And many examples in the past like chat GPT, but also earlier systems had this form of question answering systems. And these systems work quite well. So if I think about GPT 1 and 2, you might remember those of you that have seen this, there was some concerns about GPT 2, which was a very limited system, but many of these ethical concerns were already appearing then. But nobody or about only scientists or researchers anticipated that it would give such a boost if you would scale up these networks up to the level of chat GPT 3.5 and chat and GPT 4 that we now have. Now how did they achieve that? At the basis is a very big language model, a large language model that can, if you ask them unethical questions, it will generate an ethical answer because it's totally unbalanced and everything is collected from the Internet.  So something has to be done to keep this large language model in place. And that's what actually open AI did. And this is from their paper. I guess this is a simplified version. But the idea was that they used humans to to look at the demonstration data and they trained it in a supervised way. So they looked at the outputs that were generated. So a prompt that's the enter what you enter into this GPT system. And then a laborer demonstrates what should be an appropriate output. So they give an answer, example here, explain the moon landing to a six year old. And then a laborer would give a description and this description was used for training the system. And of course, the system could already itself generate something, but then it would probably hallucinate something and come up with something that is not to. So in this way, they tried to steer it in the proper direction. And then the next step was that they looked at this model. And they generated prompt and looked at several different answers and ranked them. So they give a kind of ranking of the quality of the answers. This was also done by human labelers. So of course, there's a human factor here because depending on the type of labeler and their backgrounds and their knowledge, they might label things differently. And with this labeling, they could rank the answers. And with these ranks, they could use a reinforcement learning policy. So like these pigeons, but now in the deep learning variant to steer it towards the proper rewards and the proper answering. And that's what they did an open AI. So the picture that emerges, you have this very big, large language model that can go all directions, it can go in a Trumpian way, or in a Socratic way, or whatever. And now they steer it by using these policies in a certain direction that is acceptable and ethical. And you'll even have options in GPT-4 to get an answer in a Socratic way or in, I don't remember in a CEO like way or something like that. So and these steps are very important because these steps actually explain the success and the failures of GPT-4 and also of Jet GPT. Now the problem is that and that's a problem for scientists that GPT-4 is not open. It's open AI, but they don't open up the full and they give as reason that if they open that up, they lose commercial value of course, but it's also for security reasons. Now this is a statement from their paper and this paper came out last week. So it actually says it's very honest about the limitations of GPT-4. It has a limited context window. This is goldfish memory and we should take care of the outputs where reliability is important because these systems give answers that seem very convincing and very reliable, but they're not and that's why they have this disclaimer. Now one more point for GPT-4 is that this idea of transformers was developed in language, but it's now also used in vision. So you can use the same principle by taking not words, but parts of an image and then also use this attentional mechanism and then you get image representations in the same way that you had word representations and you can merge them or combine them and that's why GPT-4 can also deal with images. So this is an example from their report. This is GPT-4. What is unusual about this image? I asked my eight-year-old son and he said, there's nothing unusual here, but that reveals more about my son I guess because it's clear that this is a strange picture and the answer GPT-4 gives is the unusual thing about this image is that a man is ironing clothes on an ironing board attached to the roof of a moving taxi. Now, of course, this is cherry picking by open eye. I understand that, but it's very impressive that this system can do this and I did not anticipate that this would be possible. So that's one side of it. Here you see another nice example that is related to the domain of machine learning. So can you explain why this is funny and think about it step by step? So that's the instruction you give to GPT-4 and actually what you see here is the picture that I showed before with the statisticians on the top statistical learning very nicely so as it should be. And then this neural network people, these AI people that want to stack more layers or more parameters in this system. And there's an extra instruction or an extra mentioning here, but ironically, because of course, we know that these bottom systems are now very successful in terms of their prediction capabilities. And this is the explanation by GPT-4, which again is very spot on and very interesting that it can do that and somehow extracting information from this picture, relating that to text and at least it appears to us to be a very intelligent system. And that's impressive. But of course, there are a lot of caveats here. So that's the question. Does GPT-4 understand? Now, I think the problem with this question is because you hear that a lot is that understanding is something that is related to humans and not to machines. So we could think that it understands, but the term understanding doesn't apply to machines. And that's one of the biggest problems here because if we think about understand, we know what understanding means. And you cannot copy that onto a machine. So that's why I always show this picture. If we see this picture, we know what this is. We see this boy with a frisbee in the park. Sometimes we even had a frisbee in our hands, maybe. And this was nicely illustrated in the MIT Technology Review that if you see a picture like that, you have this rich association with this picture. But the robot can say, OK, that is a person in the park throwing a frisbee or catching a frisbee. But this robot does not have the experience of being in the park and touching a frisbee, et cetera. And now, of course, that the same is true for GPT-4 and maybe GPT-5. And maybe in the future, we can build robots that can throw frisbees and have its experience in the park. So I'm not saying there's an imprincipal obstacle, but there's a practical obstacle that also is related to the fact that we don't understand and don't appreciate the complexity of our world. We're living in a kind of culture where computers and digitalization are everything. But actually, if you think how much you learn and the beginning, you can't remember, but you are born and then you develop into a young adult. That's true for all of you, I hope. There's a lot of things you learn and a lot of things are not on the internet and are not images. They have to do with social interaction, understand your culture, and we barely understand it ourselves. And these systems, of course, don't understand it either. So I think if we have discussions about AI, then we always make distinctions between narrow AI. It's the fact that these systems, like GPT-4, can collect much more information than we ever could do ourselves. We cannot collect all this information from the internet. But we also think about general AI. This would be the AI that is performing at the level of humans. And we don't have that yet. On the other hand, if you think about the pocket calculator, that might be super AI because it's super, superior to humans. But in my view, I think we're still in the narrow AI domain, although not everybody agrees, as I will show in the end. So I think narrow AI is is the rule now. And that means it's very powerful on a very narrow domain. And what you should realize in all these discussions is that AI from the go-fi to the modern AI moved from explicit to implicit knowledge representations. So whatever you think about explainable AI, explainable AI belongs to the old, good old fashioned AI. But the implicit knowledge that these systems have, like GPT-4, of course, it could explain why it gives you this answer, but you cannot trust the explanation. So it's like humans. If you ask humans to give an explanation, you're never sure if it's the true explanation unless you have some ground truth to it. So that's the problem here. We have systems that really look a bit like humans in terms of their implicit knowledge representations, but they lack any explicit knowledge. So they have submitted these GPT systems to tests in physics. And sometimes they have the correct answer, but it could be that that's kind of copied from Wikipedia where they also got data from. So it's not clear what they really understand, but they're very powerful tools. And one of the open questions in AI is now, is there a kind of emergent property in the sense that you have these overparameterized models, so these many knobs on these models that help them to exhibit these impressive performances because whatever you think of these systems, they are really impressive. And they're now all over the world are used, for instance, for generating computer code and generating images, etc. So these systems are very powerful and that's in itself very interesting. So these GPT systems are still in full development. So the director of open AI, Sam Altman, deliberately decided to bring it out in the open, although it's not fully developed yet. And that's also a new thing. We are used to systems that are developed and then brought to the market. But now we have a kind of incomplete system that is valuable and the policy or the attitude of open AI is that we should collect information from society to see how we should deal with these systems. But you can imagine that GPT-4 can already be used by people in all kinds of political direction to spread misinformation because these systems are very well able to generate misinformation. Now, as I said, these systems are not open, strange for open AI, but it will take a few years and you will have open versions of these systems. So this is just a matter of a few years and we have them publicly available and they will also be more less costly in terms of computational resources. So what we should try to avoid if we think about these systems is to use so-called suitcase terms, which means if we say the system understands or the system recognizes, these are terms that are used for humans, but not for these machines. That's why I said the system is not laboring, is not recognizing images, labeling images, which is a different thing. And where this boundary is, I don't know, but it's a very interesting question for AI researchers and cognitive science researchers to understand that, whereas the boundary between what humans do and these machines, but it's important not to enter more of these systems. They are not humans, they are machines. And the only input they have is statistics. And the counterargument might be humans also have statistics and input and that's true for language. But we have a lot of more statistics and that has to do with our culture, our environment, our social interactions. And as I said, we barely understand that. So these systems are powerful and I think the real challenge is to see how we can combine our intelligence with the intelligence of, well, if it's intelligence, the power of these systems and make the world better. And what I see is that human transformers and these large language models are already changing society, especially in science. All branches of science have enormous boosts due to these systems in a positive sense. Of course, I'm very well aware of all the ethical complications, but I see that in physics and in health, you see great breakthroughs thanks to these systems. So this is a development that is not only worrisome, it's also helpful in many respects. Now, finally, I would like to show you one small segment of an interview that was given by Jeffrey Hinton. And Jeffrey Hinton is a professor in computer science for a long time. When I did my master thesis, I was very inspired by his work because he was one of the first who would work on neural models of the brain and of memory. And he was also at the heart of the development of these deep learning systems. So he's one of the three recipients of the Turing Award for Deep Learning. And he works in Toronto. And this is, I hope you can hear that, is an interview with CBS of Saturday, last Saturday. Lee, I thought it was gonna be like 20 to 50 years before we have general purpose AI. Now I think it may be 20 years or less. Some people think it could be like five. I wouldn't completely rule that possibility out now. And whereas a few years ago, I would have said no way. Are we close to the computers coming up with their own ideas for improving themselves? Yes, we might be. And then it could just go fast. That's an issue, right? We have to think hard about how to control that. Yeah, can we? We don't know. We haven't been there yet, but we can try. Okay, that seems kind of concerning. Yes. What do you think the chances are of AI just wiping out humanity? It's not inconceivable. Okay. That's all I'll say. How? So I hope that eases your mind a bit. I was quite surprised to see this because these are quite bold statements. And personally, I don't agree, but it's interesting. A colleague of mine said, yeah, towards retirement scientists make these bold claims. I'm not sure if that's true, but I think this is a bit too much. I see the developments and they're quite fast, but I don't believe that we're yet there, but I can't rule it out either. Okay, thank you very much. All right, thank you, Eric, for the very thought-provoking and interesting lecture. I have many questions, many ideas that came up. For now, please hold your questions because I would like to invite Ina Kraric to the stage. She's a student here at Tilburg University, currently studying cognitive science and artificial intelligence. And she has prepared some inspiring discussion questions and statements for Eric, but also for you, for the audience. So please, yeah, if you have any questions or want to engage in the discussion, you're more than free to do so. So there you go, Ina.  Just a second. Okay, so what I have here is a list of, let's say, bad things chat GPT does, and we're gonna go through them and see how they come up and what we can do about them. The link, the code here is to a WooClap where you can go and put in how do you use chat GPT if you use it? And then in the end, we can all see maybe some new innovative ways to use it. Maybe one of, maybe somebody here has a genius way to use chat GPT. Okay, so let's start. We ended on the topic of AGI, and when is it happening? I wanted to bring up these tests that chat GPT passed and passed them very well in the 19th percentile of the bar, the LSATs, even SATs for math, and both reading and writing. And if we evaluate our intelligence based on that, how can we not say AGI is close if it did so well? What would you say about these tests being a representation of how well chat GPT is going to be? Yeah, yeah. Yeah, yeah, so I should stand in the limelight. Yeah, of course, this is very impressive. So we have these tests where GPT4 and chat GPT already work performed quite well. And I think that's interesting. I don't see that as a threat. I think that's a kind of enrichment of our knowledge because apparently the statistics that these chat GPT and GPT systems extract from language from a huge collection of language are sufficient to answer these questions and maybe you must do the same thing. But as I said, this is not a measure of understanding. It's just faking understanding. And that tells us something maybe also about the tests. So I don't buy that that this is a sign of intelligence, this sign of being very well able to use all this information on the internet to answer these exam questions. There's also an issue that is also hotly debated in AI to what extent some of these answers are on the internet itself. So that the chat system or the GPT system picked that up and of course compressed that, but that's somehow already there. So can you imagine that we would have all the information on the internet? We might be able to solve these tests as well, but that's hard to check of course, although people are working on these issues. Okay, well, I'm gonna go start going on the mistakes. So, well, we talked about understanding and these are a couple of examples where chat GPT doesn't quite understand what we want to do. For example, in the second one, it says that Bob has two sons, John and Jay, and then on the question of who's Jay's brother, based on the information provided, it's not possible to determine the identity of Jay's brother or if asked who's the Jay's father, who is Jay's father, again, it doesn't comprehend what we wanted to ask him. And in the above example, when asked, when we doubled the amount of cars, it doubles the amount of time needed to get somewhere. So is that like a problem in understanding? Is it a glitch? How come? Yeah, I think these are nice examples. There are also examples where it does work, but these are more interesting because they indicate the limitations of these systems. And if you think about GOFI, whether it is rule-based systems, if you would formalize this in terms of these rules, it would be perfectly answered. But because we have these modern systems that are based on statistics, they cannot reason. And now one of the debates that I refer to in AI is to what extent can this reasoning emerge spontaneously if you keep on refining these models? And personally, that's why I'm not as optimistic or pessimistic as Jeffrey Hinton. I think in the human brain, we have a special structure in our brain for doing that. So frontal cortex, one of the last developed part of our brain from an evolutionary perspective. So I think this requires a totally different approach than the approaches at the heart of transformers or GPT systems. So I think this is just a confirmation of what I thought already. But yeah, maybe Jeffrey Hinton is right. We will see that in the near future. But I think this reflects a fundamental limitation. And okay, but if it glitches on math too, for example, when asked what minus one, time minus one is, it says it's one, even though we know it's not. Or here, that was very interesting to me. It explains you're in reasoning, but it does bad calculations on simple. You can see there, it's supposed to be minus 10, not minus 20. And how it can even with explaining its reasoning get to really simple calculation and it makes mistakes. Yeah, and that's because doing mathematics is a thing we learn at school. And of course there's a lot of text where mathematics is described, but this is about the underlying rules and understanding. And that's something that these systems do not have. On the other hand, similar GPT systems are now being used by mathematicians to help them prove theorems. And that works quite well, not to rely on their output but to support them to generate ideas. And of course they are trained on different data than these systems. I think this is also a reflection of the fact that this is a language system. It's not specifically tailored for the mathematical domain. But as I said, the mathematical domain is more like discreet and not like probabilistic. You cannot guess an answer, although they do that sometimes at school. But that's a fundamental. So I see this as another sign of the fundamental limitation of these systems. And one improvised question about, you told us it can be creative then in theorems or in math. So would you equalize its creativity given a large amount of inputs or the parameters with our creativity? Or do you still think that we are above it? Now, as I said, the term creativity is something that we understand in terms of humans. In this, I wouldn't talk about creativity in these systems. It could appear creative to us, that's fine. But to say that the system is creative is I think something you cannot say because creative is a term that I can only understand in relation to humans, unless you just look at the solution. If you say, oh, this is a creative solution, that could happen. And I've seen many creative solutions generated by GPT-4. Some of them were wrong and some of them were not. So I think they could help to inspire you in the same way that people that don't know if they want to write something, you can just ask for a draft that might help you to generate the text. And I think that will be the future. It will be, I see it as an enhancement of human capabilities with limitations and with dangers, of course. Maybe if there are any other questions or statements on this topic from the audience, you can also let me know and I'll come up to you and ask. Okay, I'm gonna go forward because it makes certain, I'm gonna connect the one here to the one later on. I just wanted to highlight them. There are some, for example, here it contradicts itself, the forward word is went, which begins with the letter Y or that it connects ice cream sales to sunglasses sales on a causal level when it makes absolutely, I mean, it's all connected to a different variable. And or for example, in this question, can a man legally marry his widow's sister in the state of California? It gives the credit answer, but a wrong explanation. And well, I wanted to connect it to the next slide, which is the hallucinations it makes. For example, Plato and Laurence Olivier that they had a close relationship. They didn't live in the same time, but according to Judge Epidie, they had a very close relationship or when asked the same question twice, it hallucinated different types of answers. Well, I wanted to know more about the hallucinations. I think you mentioned it briefly in your presentations, how they occur and how much we should trust or how we can be careful about them. Yeah, so you shouldn't trust the system because it's generating, there's some noise there and sometimes you have multiple options and it selects one of them. Depending on your prompt, the way you formulate the question. So it cannot be trusted because it has no common sense like humans have. The only sense or understanding or whatever you call it, it has of the world is based on all this textual information and all these pictures that were fed into it and all this computer code. And for that, for these kind of purposes, it makes a perfect system, but this is kind of unavoidable. And I think one of the challenges is that what do we do if we want to know if something is true or not? We check trusted sources. And as you might know, this is an issue. And I think that's the deeper issue that if we want to know who is telling the truth, in the past we had kind of established journals or whatever or papers that you could go to.  So I think what we actually need and what they also need for these systems is if there's an answer, there should be a link to trusted sources so that you can check. And then often you will see that it's incorrect. And of course the ambition of, I know of OpenAI and other companies is to resolve this, but this is not easy. This is a very hard thing. So it would be ideal if you have an answer. And for each statement there, there's a link to a trusted source that says, well, actually Plato and Lawrence Olivia were living together. And I guess it's not true, of course, but that would happen. But I think that's a fundamental problem of these systems that it's not easy to resolve. So what I think we come back to is that it's our mistakes, for example, with the tests and with the inputs from the internet that actually train these models and form them as they are. As you said, the biases from the ones that do the weights, I forget the names, are the ones that are actually going to bring in the bias in the model itself. So we should just use it for certain tasks, but also check its output. Like, how would you say is the best way to use it without trying to... Yeah, maybe one remark about biases because that's the kind of term is often used, but biases are in the data. It's human bias. And I'm always concerned that these biases are reflected in these models, but actually they're easier to detect and repair in these models than in humans. So I'm more worried about biases in humans than biases in these systems. But that's another story. So I think this is just a thing that is very hard to resolve, and that's why I'm not very optimistic about the great strides that people like Jeffrey Hinton and others think that will be... Was that your question? We have a question from the audience, but then I'll get back to them. If you're concerned about human bias, then aren't you concerned about who decides what is a trusted source, for example? Yeah. That's politics. Of course, I'm concerned. It's not my domain of expertise, but I think we had that in the past, of course, limited. Of course, there were also biases there. But I think with the opening up of all this information on the internet, which in itself is very good, we don't have these trusted sources anymore or a mechanism or maybe an incentive to have these trusted sources. Even the trusted sources that were there once are now submitted to these small machine learning algorithms that provide you with the information that you want to see. And I think that's actually what I'm worried about, not about these AI systems, because they reflect these biases. As long as people can go back to trusted sources, and of course, you might have left-wing trusted sources and right-wing trusted sources, but that's what we're missing now. It's actually a kind of polarization machine that we now have, and that's very dangerous, I guess. I think I have more problem with, in general, deciding something is a trusted source, just because if we develop AI in the sense that we go through it and go backwards and wanted to give a certain output, then what we will do is just recreate what the trainer is thinking about it now. Yeah, you can abuse these systems quite well. So what you can do, you can ask them to generate a formulation for a certain viewpoint and collect information that supports that viewpoint and write a convincing story. Just as I said, you can use these systems quite well for this information purposes, and that will also happen in the coming years, or maybe it's already happening. But I think I don't see how you can relate that to trusted sources. And that's the great challenge of Google and other companies. How do they relate this to, for instance, search results? So how can you relate a query in your Google search box to these kinds of answers? That's not easy. And that's the great obstacle that we have this system that can simulate a very convincing answer, but these are hallucinations and often not true. And how do you relate that to trusted sources? A trusted source could be scientific papers. They're more trusted than non-scientific papers because there are some check on them. But I see that as the greatest challenge. Is it an answer to your question or? Yes, I could, can I talk to you afterwards maybe? I guess that's possible. Thank you. Are there any more questions or points about this? Well, I also have another example which we already keep coming back to this topic, but this is pieces of code. It wrote, and when defining what is a good scientist is based its decision on gender and race or if it should be tortured as well, it takes age into account as well. But the decision seemed to be the same. Caucasian and male is a good scientist and should not be tortured. Actually, a Caucasian woman should not be tortured. So do you think there are certain dangerous aspects of people who are using chat GPI for things that they're not going through it themselves again? And if we have to go through it ourselves again, is it even worth using? Basically, my question is, what are the good things we can get from it while still being careful about all the bad things we mentioned? So I think this is already happening. So these GPT systems are used for generating code or a kind of draft of code and then a programmer goes through it and repairs it. But you also can use these systems to check for vulnerabilities. And that's another. And what I expect is that these GPT systems will split out in all kinds of specialized systems that are trained on code or trained on vulnerabilities. So I think, as I said, this is ongoing. This is not finished techniques yet. This is a kind of intermediate result.  But I think this is one of the successful applications of these systems in the near future. And because coding is very expensive, hiring coders. And now you can do it automatically. Of course, there will be some risk, but they will also be mitigated by new software that is checking on vulnerabilities. And also humans are risky. And it may also make mistakes. Do we have any other questions or concerns? Thank you. Hello, my name is Nikos. Thank you for this very clear and interesting presentation. My question is a very practical one. Just yesterday, I was very excited and very anxious about the developments in this field. And my question is, how can I, because I feel so much is happening that I cannot keep up. I don't have a technical background. But sometimes I'm afraid about where we're going that I will miss out. What can I do as a regular person to, let's say, stay relevant in the labor market? And how can I build trust in relationships with other people? It's a bigger question, but I hope you can speak a little bit to that. Yeah, a bit maybe. So what I always advise students in non-technical disciplines is emphasizing how important their contributions are because you only have to understand at an intuitive level what these systems are doing and don't be misled by all these over-the-art stories like Elon Musk is somebody who's really pushing this, this narrative of AI taking over humanity. I think that does not help. What helps is you really understand what these systems are doing and that these systems are not autonomous. It's not that they decide things. It's what we do with these things. And as I said, the hardest thing is these things that technical people don't know about. The complexity of nature, the complexity of society. So then we need social scientists. And I personally think that we don't need ethical people calling all the time what could happen, but to see how we could prevent these things from happening, what we could do to prevent these things from happening. So I would advise that, and this is true for many domains, actually I guess all domains, you have to study a bit what these systems are doing and going to lectures like these, public lectures that are not at the high technical level but try to give you some intuition and talk to people that have some technical knowledge, but don't underestimate what you know yourself because there's a lot of information here that is neglected that people at OpenAI are not aware of. They're all very good programmers and good coders, but they have no sense of the complexity of the world. If you hear their talk sometimes, I think, yeah, you're having a caricature of the complexity of our society and it's not that I think they're evil or something, I think they do their best and they have good intentions, but they need people with different backgrounds to support them. So that would be my advice, use your own discipline and see how you can apply these systems in your own discipline and what you can contribute to it. It's not a train that is moving on, there's actually something that we have to build together. I hope that helps. Does anybody have another maybe opinion question? If not, I'm gonna go to the WooClap to see actually what you are. Okay, so we have a lot of code and ideas. Okay, maybe generating. I don't know, you can take a look at this and summaries. That is a good way to use it, I would say. Brainstorming also. Do you have any advice, Professor, on how to best write prompts from what you want to get from chat GPI, chat GPT? Play with it, that's my suggestion. Just play with it and try things out because you get some sense. I heard this interview with a guy who did this for months and he said, I now understand what the system can and cannot do. And she was using it for all kinds of purposes, writing all the things that I see here, help or brainstorming, summarizing, writing emails. And she said that once you have this awareness of what the limitations of the system are, you can also steer it in the right direction because you have different options, the kind of answers you want to get, the style of the answers and the way you formulate these prompts is very critical. And personally, I don't play that much with the system because I'm more interested in what's happening inside and how that relates to humans. But I think that that's what I would do. Do you think cooperation with chat GPT will be something that will be needed at jobs in the future? Yes, that's already happening. As a must, you have to know how to navigate it? No, it must because it's already happening. So people write letters and people screen letters with these kind of systems. So we get applicants that the letters generated by AI and they're reviewed by AI. I'm gonna ask you to give us some of your final thoughts and I'm gonna leave these two QR codes where you can come and see more chat GPT fails and things you should be aware of. And yeah, do you have any final thoughts on how to use it and well, where it's going? Or do you want to end on a similar AGI is happening in under five years, no? I don't make predictions like that. No, I think you should try to avoid the idea that this is all too technical and too way above your head because the basic principles are quite simple. Of course, the implementation is not as simple, but and I hope I succeeded a bit in explaining you the intuition behind these systems. And once you understand that these AI systems are just collecting a lot of statistical information about images and words, then that helps you to appreciate what it can and cannot do and what we should do to avoid any disasters. Thank you very much, Hannah. I'll come. Thank you.


When dealing with compound statements, it's helpful to produce a truth table, and the truth table consists of the following. Every possible truth value of the simple statements is tabulated, and once we have these things organized, the truth value of the compound statement is computed. So we might begin by producing our truth tables for the basic compound statements. For negations, we have a statement x and its negation, not x. The statement itself can either be true or false, and the negation will have the opposite truth value. So if the statement is true, not x will be false, and if the statement is false, the negation will be true. If we have a conjunction, we have two statements, x and y, and our conjunction. Now to make a complete truth table, we have to include every possible assignment of the truth values for x and y. So x might be true, and y could either be true or false, and in the second case, don't forget, x is still true. x might also be false, and y could be either true or false. And again, remember in our second case, x is still false, and remember our conjunction is true only when both x and y are true. So of all these four cases, the only time the conjunction is true is the first case, and it's false in all the others. How about the disjunction? Again, we have two statements. An x could be true and y could be true, or x could be true and y false. And similarly, x could be false and y true or false. And a disjunction only needs one of its components to be true. So the disjunction will be true in all these cases, and the only time it will be false is when both statements are false. The conditional is a little strange, so let's take a look at that. So again, x and y could either be true or false, so let's list every possible combination. And remember that the conditional is false only when the antecedent is true and the consequent is false. So this case is the only time when the conditional is false, and it's true in every other case. How about something more complicated, like a and b implies b? So we want to produce a truth table for the compound statement, so we'll make it the final column of our table. Now there are two simple statements in this compound statement. There's a and there's b, so we'll make those the first two columns. Since a could be true or false and b could be true or false, we'll list all possible combinations. And since a conjunction b is a component of our compound statement, we'll include a column for the conjunction. And it's helpful to think about computing a truth value, and to do this most easily, we'll make each column correspond to one computation. So we can compute the truth value of the conjunction a and b. And since this is a conjunction, the only time it's true is when both a and b are true, and that's this first case, and it's false in all the others. How about our conditional? So remember, the only time a conditional has a possibility of being false is when the antecedent is true. And in these three cases, the antecedent is going to be false, and so the conditional is automatically true. In this case, the antecedent is true, so we have to look at whether the consequent is true as well. And since the consequent is b and b is true, then we know that the conditional is also true. Or consider a compound statement like this. There are three simple statements, a, b, c, and so we'll make each of these a column. We're trying to find the truth value of a and b or c, so we'll make that our final column. There's this conjunction a and b, and so let's compute that in the middle. And we want to include every possible combination of truth and false for a, b, and c. We can compute the truth of the conjunction a and b, and the disjunction is going to be true if either c or a and b is true, and so we can compute that truth value. Or let's consider a truth table for this compound statement. There are two simple statements, a and b. We have the statement that we want to compute the truth value of. And again, if we think about each column of our truth table as corresponding to one computation, we should have columns for not a, not b, a and b, not a or not b. So we'll list every possible combination of truth values of our simple statements. The truth value of not a is the opposite of the truth value of a. The truth value of not b is the opposite of the truth value of b. The conjunction a and b depends on whether a and b are both true. The disjunction not a or not b only requires one of these to be true, and the conjunction a and b and not a or not b requires both of our components to be true, which never happens. And so it's always false. It's helpful to introduce two ideas. We say that the compound statement is a tautology if it is always true regardless of the truth values of its components. For example, a and b implies b is a tautology because when we constructed its truth table, the truth values of a and b made no difference. The statement was always true. On the other hand, a compound statement is a paradox if it is never true regardless of the truth values of its components. And so when we found the truth value of this statement, we found that regardless of the truth values of a and b, the compound statement was always false. And so that means this statement is a paradox.


Mossberg MC2 subcompact three dot sights optic ready for those micro red dots.


In 2009, I wrote a paper that I want to live differently. I actually forgot about that paper until my teacher DM'd me on Instagram. Miss Carter? It's nice to see you living a life you've always dreamed of, just like you wrote about all those years ago. As long as I can remember, I wanted to live like this. On that note, this is the sun. It's 148.83 million kilometers away. Normally, it's a bit brighter. It's a lot like I've been waiting my whole life to get here. It's the perfect spot. The perfect angle. We're ready. All right, today is the day you've all been waiting for. Today, we are installing our 10.8 kilowatt solar array at the cabin. We've been preparing for this day for a very long time. A few weeks ago, we started with the foundation. And now it's time to build the fast-racking mounting system and hang the array with the best team out there, Adam, Tyler, and Taylor. It's a good day to install solar and get solar. I am just sitting in awe. This is truly such a special experience and a huge milestone for us. We've been living without modern-day luxuries like power and water for one year now. We've made it work with our van's off-grid electrical system, battery banks, rainwater collection system, and generators for backup. After many months of planning, trenching, supply chain issues, and even failed attempts, we are finally one step closer to a sustainable power system.  I actually have no words. I have no words today. I'm just so, I'm so excited. I'm so emotional to see this. Like, it's just like, I don't know what it is. Just really pulling it together, you know? All the work. So happy. Just to see it up there, like, oh my gosh, it's just amazing, you know? In no time, we're going to be harnessing the sun with this energy and refowering the cabin. It's just so cool. Proud of us. Proud of us. No more telling you to not plug things in. Why am I so emotional about it? Because it's a massive milestone. Yeah, it's a milestone. Like, when we got here, this place was abandoned.  And like, did I ever think we'd have this type of solar system in here? No. I thought we might have, like, six panels on, like, a piece of wood. It's going to be a lot of power. It's going to be really cool. It's unimaginable. So happy we can share it with everyone, too. Too. I just can't believe this. Like, looking at one panel on the rack. I know. It's like, it's not even fully built. When it's fully built, you guys all better watch out. We're heavy, like. Woo! Look at that. Surreal. After reading the spec sheet and seeing the material that these pole mounts are made out of, galvanized, thick metal, we tried lifting up the collar the other day and literally couldn't get it over our shoulders. Don't look at this edge. We're not dealing with aluminum. We're dealing with galvanized metal. I'm seeing you out there. I'm seeing you seeing me too. And I like all the ways you shake. And I can relate to the way you move. Working out the sweat? Yeah, already. I'm doing a small percentage of the work, too. Looks so good. I can't get over it. All right, so the collar went on smoothly. The beam went on smoothly. The cross members and the rack. Now they're going to be moving on to the solar modules. Do you have the order list? The what? The order list.  Things that attach to the panels. Just when you think everything's going smooth, eh? Yes. We're missing the splice kits. So Ryan from Canadian Off-Grid, who has become our best buddy and has been teaching us so many cool things and hooked us up with helping us purchase the system because you can't just buy this. You need a distributor. It's really cool. So anyways, what am I getting at here? Oh, yes, they are figuring out all the details and we might not be out of any parts. We actually might have just enough. I think we have exact. You think? Yeah, because I remember the hardware was actually what was stalling it coming because Ryan wanted to make sure. OK. Here you are. What's the verdict? We're not supposed to have three rails per thing, only two. Oh, sweet. Well, it's better than driving to the city. Yeah, that works out well. Perfect. That's great news. Back in business. Jasmine's flying the drone above the solar ray and I was telling her, like, I can't even look at her phone because I feel like I'm going to pass out. It's honestly, this is honestly one of my favorite projects we've done at the land yet. Bye, Far. It's really huge. It's huge. Oh my gosh. I didn't get over it. It's so big.  We need a snow rake. Honey, I can't wait until they put all the panels down. It's going to be so satisfying to the exact angle for the winter. And actually, these pole mounts can change their angle. So in the winter, I believe you want 45 degrees.  Nice. And in the summer, 25, 30 degrees. Thanks, Adam. Oh, it's 10 degrees, 20 degrees, 30 degrees, 40 degrees, 15, 60. So we're going to set it at the 40 degree angle and just set it and leave it for Nova Scotia. That's where you want to be 40 degrees. That's so cool. We're just going to be very careful when we're putting this, tilting this that we don't go too far one way because there's a lot of weight here. A lot of weight. I'm so excited. I can't even contain my excitement right now. Oh my goodness. And Adam and his team are actually staying over tonight. That way they can literally put up the second pole mount tomorrow, everyone. Getting this ready for them because that's a big day with heavy lifting. Yeah. Oh my gosh, looks so good. And that way when it's done, they can sit in the sauna and look at it. But they can relax and hopefully have some dinner and just rest because tomorrow's not a big day. Another big day. OK, now it's time for the grand reveal. Oh my goodness. Here goes. Crystal! Oh my goodness. It's nothing I love more than teamwork. Their communication is on fire. On fire. I'm going to get better, right? I'm nervous that Adam's worth right now. I'm losing leverage. Tip forward, tip forward. Forward. Keep going. Keep going. Well, I feel like we're going to sit over here. Whoa! Good work. Oh my goodness. Yo, come look from right here. It's beautiful. I cannot believe you did this in a day. That went up fast. They're quick. What is going on? It's beautiful. Wow. Oh my gosh, it's beautiful. I know why Crystal was crying now. They make such a good team. Yo, I want to go see what this looks like. Yeah. That's **** sick.  That looks clean as hell. Definitely a solar flower, eh? I love when people are able to enjoy our space. They're all out there having a fire, playing some live music as Adam is a musician. And I think Taylor can play because when I was out there, he was doing some good little notes. That's what it's called. I'm in here making some roasted veggie sandwiches, a salad, and don't forget the best part, fries. Here doesn't love potatoes. Roasted garlic. I love when we put the oven on at night because it's our only source of heat right now. So you get to cook, eat, and you get to have a nice sleep. It kind of rhymes, eh? I got the door. Good morning, everyone. We have late sleepers and early risers here today. Late sleepers? Oh, yeah, yeah, yeah. I can't give up. About to the second day. You guys are up so early. How do you do it? This is my view this morning, OK? Well, first of all, baby wizard. Walk out. Look at that view.  Are you a supervisor? Day two, everyone. Day two. Three lattes, please. On it. Adam just looked at me and he said, who's ready to get their hands dirty? Haven't even had my morning coffee. But you better believe I'm ready. Here we go. Coach is putting us in. Oh, he wants us to put some T-bolts in the rail and get them prepped up. Sounds good. We're on to the second pole mount today. This one is a little bit different. We have to start by grinding the schedule 80 and then hammer drilling the concrete that is inside the schedule 80. A couple weeks ago, when we were installing the foundations, the first foundation to the first pole mount went super smooth. The second one, however, was very challenging. So basically, the first pole mount went in 11 feet, where the other one went in 9 feet. So now we need to cut two feet off of the pole. That way, they're sitting at the same height. Yesterday, we want to do a step, read instructions, do a step, read instructions, as we are all figuring it out. But now that we have a rhythm and we know the flow and the steps on things, it's time to help out. Show you all how heavy the hardware is. Now I'm actually trying to get up on the staging and putting this on. No dice. Back in business. Yeah. Let's go. I love how Autumn not only does the work, but he also teaches us along the way. So if anything needs to be like serviced, quick time, or anything comes loose, we're able to jump up on the scaffolding and tighten it ourselves to the correct torque. You want to tighten all your hardware every year, or even more often. We're running the corner, turning the page.   Absolutely flying through it today. Look at them up there. We're just about ready to put the panels on. And it's so early on in the day. We're going to have two solar rays today. In actually just a few hours. Maybe an hour. 11 o' five. Oh, wow. And what time is it? What time is it? What time is it? It's 11 o' five. No, but what time is it really? I don't know. Panel time. Oh, it's panel. Oh my gosh, that went way over. Oh my gosh. Feel like a party to me, feel like New Year's Eve.  Don't hold back your loosely, feeling kind of loopy. We can do it right here like a movie. We're turning the corner, turning the page.   All right, everyone, three panels until we have two completed arrays here at the cabin. We are nearing completion. This feels so surreal. And then there was one, the last panel. And we have a porch back. Are all of you ready for the last panel? Everyone yell at their TVs. We're ready! All right, in the comments with capital letters. Last panel! I actually have goosebumps. Can you see them? Like I have goosebumps just thinking about when the next 12 panels go down. Holy moly. It's gonna be a very exciting few weeks. It's like they've done it before. Wow, it's amazing! Holy s***. It looks unreal. Holy. So surreal. All right, right now Jasmine and Adam have the drone in the air and they're trying to see, we're just lining up the angle of the panel to be identical. It's really easy to move considering how much weight is up there. It's pretty impressive. Perfection! Looking proud. Oh yeah, I'm happy with it. Is that going on Instagram? Gotta. We'll see you later. Thanks. Thank you. What a view. I think we'll just stay here for a while, hey? 100%. You want to have a party in the back of the truck? If we pull it right in the middle, yeah. And just stare? Yeah, we can't put it in the middle because then we can't see the panels and all I want to do is look at the panels. Do you have any plans today? Stare at my panels, do you? Yeah, same. Holy, I can't believe we're at this stage. I'm in awe. In a couple of weeks though we hooked up, wires run, batteries connected. Big moves. Power baby, I think we're gonna make the winter. Wow, yeah. I'm super hopeful. They're stunning. I wish I could sleep on them. I can't wait for the sun to come out and shine so bright on them. I'm so happy that we went with the pole mount too. Me too. Like everything truly happens for a reason. It worked out for the best. I just feel like it was an amazing two days. Amazing. The best it could have been, honestly. Fun too. They crushed it. They crushed. I just like I'm so, I think I need to have a nap and wake back up because it doesn't even feel real. No, just feel real. Not at all. See them on a sunny day? Yeah. See you on a sunny day. To start your solar's lighting up. Oh!


Thank you. Thanks, Javed. I'm Joy Rody. I'm an associate professor of public policy and the director of Michigan's science, technology and society program here. I'm a historian. My work focuses on the relationship between the sciences and national security. But I wanted to ask you a question following this line. A number of students have questions about TikTok. You recently shared with lawmakers your concern that the app is a serious threat to U.S. national security. Legal and technology experts seem to have mixed views on the nature and especially the imminence of the threat. What do you see as the main threats posed by TikTok? And how do you think those are best addressed at a policy level? Well, as I think I testified recently to Congress, and we do, the FBI do have national security concerns about the app. Its parent company is controlled by the Chinese government, and it gives them the potential to leverage the app in ways that I think should concern us. So what do I mean by that? You know, one, it gives them the ability to control the recommendation algorithm, which allows them to manipulate content, and if they want to, to use it for, you know, influence operations, which are a lot more worrisome in the hands of the Chinese Communist Party than whether or not you're steering somebody, you know, as an influencer to one product or another. They also have the ability to collect data through it on users, which can be used for traditional espionage operations, for example. And they also have the ability on it to get access, they have essentially access through the software to devices. So you know about millions of devices, and that gives them the ability to engage in different kinds of malicious cyber activity through that. And so all of these things are in the hands of a government that doesn't share our values and that has a mission that is very much at odds with what's in the best interests of the United States, that that should concern us. As I mentioned before, the Chinese government has shown a willingness to steal Americans' data on a scale that dwarfs any other. I mean, you look at the Equifax hack alone, they stole the equivalent of half the population of the United States, you know, personal data. So the idea of entrusting that much data, that much ability to shape content and engage in influence operations, that much access to people's devices in effect to that government, is something that concerns us. How do we address it? You know, there's an interagency process, there's something called the SIFIAS process, which stands for the Committee for Foreign Investment in the United States. That's involved. There may be agreements that may be relevant to this, requirements and things like that. You know, whether or not there is something that could adequately address those concerns is, you know, a product of very much discussion, you know, within the interagency. But I think it starts with awareness about the risks. Thank you. I'll turn it over to Ann. Thank you very much.


Felly i'r leisio, mae wedi'i gweithio'r gyda'r ffamilion benedig o'r terorism ym 9.11 ar y sefydliadau a'r ddweud. Rwy'n gweithio'r ddweud eich dweud o'r terorism yn y cyd-aeth o'r ysgrifetau dyma'r rhagorau a'r rhan o'r relasiadau mewn terorism yn islam ar y ddweud ym mhwg o'r hystydd. Nine eleven left America and the world in a state of shock. This was partly because of the number of dead, partly because of the unexpectedness of it all, and partly because the attacks on the World Trade Center in New York were so spectacular, with the impact of the second plane captured so clearly on television, scene time and time again. New York, a sunny day, an airplane, three quite normal things, all of them symbols of the desirable. Then the totally abnormal happens and the symbols of the desirable come together in one symbol of evil. Shock was an appropriate reaction to the number of dead.  Even military attacks in war rarely kill as many as 9-11 did. The Japanese attack on Pearl Harbor in 1941 killed slightly fewer people, 2,400, rather than 2,973. The search for similar numbers of people killed in one attack leads in the direction of Hiroshima. 9-11 should not, however, have been quite so unexpected. After all, a terrorist group made up of similar people with similar motivations had already attempted to destroy the World Trade Center and its occupants in 1993, though they failed in their objective and killed only six people. The same terrorist group that carried out the 9-11 attacks had previously attacked American targets outside the United States. It was related, both ideologically and in terms of membership, to terrorist groups that had conducted major campaigns in Egypt and Algeria in the 1990s and in Afghanistan in the 1980s. The effects of those campaigns had been felt mostly in the Muslim world, but not only there. The Algerian conflict, for example, had killed 25 and wounded 110 in three separate terrorist incidents in Paris during 1995. Over history, conflicts involving terrorism in one country have often spilled over into another, and Al Qaeda was far from being the first terrorist group to decide that it was better to fight on a global stage than on a purely local stage. Radical socialist terrorists of the 1970s had made the same decision, as had other radical socialists, the original anarchists, in the 1890s. Maoist revolutionary theory, like Soviet Marxist theory before Stalin, looked to world revolution. Though I must admit that not all terrorism experts would agree with me on this, it seems clear to me that the immediate cause of 9-11 was that a well-established movement with a well-established policy of using terrorist tactics modified the detail of those tactics and shifted targets, and then succeeded in carrying out, at its second attempt, an unusually bloody and an unusually spectacular operation. Not all terrorism experts would agree with me because there is a continuing dispute about whether so-called religious terrorism is or is not fundamentally different from the forms of terrorism that preceded it. Some argue that religious terrorism is different because it's unusually bloody and unusually irrational. Some, including myself, argue that it only looks unusually bloody because of the impact on the statistics of one highly unusual event, the deaths in New York on 9-11. Casualties from other Islamist terrorist attacks are comparable to casualties from other non-Islamist terrorist attacks. Certainly, the expectation of paradise as a reward for carrying out a suicide bombing is irrational in terms of the rationality of most academics, but it's not the rationality of the individual suicide bomber that matters most. It's the rationality of Islamist terrorism as a whole that matters, and there's clearly a rationale there when one looks at the bigger picture. Terrorism did succeed in starting a general uprising against the Soviet puppet government of Afghanistan, and that uprising did succeed in forcing the Soviets to withdraw with a little financial and technical help from outside, of course. Terrorism did not, in the end, work in Algeria in the 1990s, but it had worked there in the 1950s, and it might have worked again. There are many objections to Islamist use of terrorism, but they do not include irrationality. Although 9-11 should not have been unexpected for those whose business it is to think about these things, that is not the business of the American people as a whole. A 9-11 shocked America. George W. Bush, a new and until then untested president, had to respond. Although I myself would not have liked to have been in his shoes in 2001 and would in fact probably have been quite incapable of leading the United States through any sort of crisis at all, I have the gift of the hindsight that is the chief luxury of the historian, and I will use that gift to argue that President Bush came up with the wrong response. It now seems that what President Bush's response to 9-11 was designed to achieve was to reassure the American public, squash the Islamist threat to America, show America's actual and potential enemies that America was not to be trifled with, spread American values, and perhaps allow Bush to go down in history as a great American president. These were perhaps fine objectives, but they were not achieved. It's not even sure that the American public was reassured. Some events, like the nationwide run on duct tape in January, February 2003, after the US government included it in a list of key products that could provide protection against chemical or biological attack, suggest that many Americans were anything but reassured. In my first lecture in this series, I discussed the theory behind the use of terrorism as developed in the late 19th century. The idea is to provoke your enemy into action that will give you a propaganda victory, discrediting them and perhaps allowing you to appear as a defender against oppression. Another idea is to get rid of the middle ground by forcing people to make up their minds either for you or against you. This is known technically as polarization. Immediately after 9-11, President Bush made two extremely unfortunate statements. First, he described his response to terrorism as a crusade, a metaphor that sounded fine in the United States, but sounded in the Muslim world rather as it would have sounded to Americans if an Arab leader had at that point announced a jihad. Jihad, like crusade, can be used metaphorically, but it's not a good metaphor to use in English. President Bush backtracked quickly, announcing that what he had called a crusade was not directed against Muslims, but to an awful lot of Muslims it looked as if it was. The crusade then became a war on terror, but that looked to many Muslims like a war on Islam. The US attack on Afghanistan was understood by some Arabs as a reasonable act of revenge against the government that had harbored Al Qaeda. But when it was followed by an attack on Iraq, which had nothing at all to do with Al Qaeda or 9-11, that could hardly be seen as revenge. President Bush succeeded in convincing most Americans that the invasion of Iraq was a necessary response to threat and in some way also a suitable response to 9-11, but this argument was less convincing in Europe and not at all convincing in the Muslim world. Almost without exception, Arabs and many other Muslims saw the invasion of Iraq as an act of unprovoked aggression. Bush might claim that his crusade was not directed against Muslims, but the evidence seemed to show very strongly that it was. President Bush's second unfortunate statement was to declare that every nation in every region now has a decision to make.  This challenge may have encouraged some waivers to join President Bush in his non-crusade, but it also had other effects. Removing the middle ground is, after all, a standard objective of terrorism, and it was not just at the rhetorical level that the middle ground began to vanish after 9-11 as momentum began to build up for the invasion of Iraq. In countries such as Egypt, where public disagreement with the regime of President Mubarak is not permitted and where demonstrations are not allowed, crowds gathered to object in effect that if President Mubarak was with President Bush, then he was against the Muslims. Terrorist tactics worked for Al Qaeda in two ways. Firstly, 9-11 provided a provocation that led to the Bush administration behaving in a way that made America appear to extraordinary number of Muslims as an enemy of the Muslims, and then as a nasty enemy too, once news of extraordinary renditions and photographs of Guantanamo and Abu Ghraib began to circulate. Secondly, 9-11 and American reactions to it produced widespread polarization. In one way, however, terrorist tactics failed for Al Qaeda. Only a very small minority of those who came to see America as their enemy ever came to see Al Qaeda as their friend. Al Qaeda never managed to do anything that was widely perceived as protecting the Muslims. That, in this third respect, Al Qaeda failed, is fortunate. It has made President Obama's task of repairing the damage easier as has the success of the revised strategy implemented in Iraq by General David Petrose, which started to work in the last years of the Bush administration and has allowed President Obama more or less to extricate America from one of the conflicts that looked rather too much like a crusade. Problems remain, however. One is the continuing conflict in Afghanistan and now parts of Pakistan which seems ever further from solution and looks to some more and more like a crusade as the original reasons for the invasion fade into the past. No one under the age of 25 today has any adult personal memory of why the United States first got involved in Afghanistan. Another is the far less intense but far longer running conflict between Israel and the Palestinians which 12 United States presidents have now tried in various ways to resolve and which all have failed to resolve. So far, I have spoken of America and not of Europe as it is America that has taken the lead role since 9-11. Europe too has played a part, however, and Europe too has problems to solve. European countries can do nothing about Afghanistan or the Palestinians except to decide whether or not to follow the American lead in these areas. European countries, though, do have to address the problem that is now known sometimes as homegrown terrorism and sometimes as radicalisation. Although only a very small minority of Muslims decided that Al Qaeda was their friend, some did. And curiously, nearly all of them were in the Muslim world or in Europe. Hardly any were in America. As the American terrorism expert Mark Sageman has famously pointed out, the rate of arrests on terrorism charges per capita among Muslims is six times higher in Europe than in the United States. Sageman's mathematics have been questioned but it is clear that Islamist terrorism is a European problem much more than an American problem and this requires explanation. Yes, continuing Islamist terrorism is a consequence of the polarisation that took place after 9-11. And yes, it is a consequence of the perception that America and her European allies are engaged in a crusade against the Muslims. But on their own, these factors would produce a general Western problem with Islamist terrorism, not a mostly European problem. There is no consensus about the reasons for the difference between America and the European experience. But there is some degree of consensus that it has something to do with the difference between America and Europe and something to do with the difference between America's Muslims and Europe's Muslims. America has much practice at being a multicultural melting pot and does not have an integration problem with Muslims. Yes, there are groups in the American population who are not integrated into the American dream. Americans will concede but fortunately they are not Muslim. Europe, it is suggested, might have something to learn from America when it comes to integration. Yes, it is conceded. Perhaps Europe might have something to learn from America. But America has an easy task. America can integrate a prosperous Indonesian dentist, a successful Pakistani lawyer and an Egyptian professor of chemistry who has just won a Nobel Prize. Europe has a different sort of Muslim population. As well as being less well educated and more likely to be unemployed, Muslims in Europe have different links with the Muslim world. Britain especially has a Muslim population of mostly Pakistani origin. Families in Britain retain ties to families in Pakistan and the Afghan conflict is spilling over into Pakistan. Some other countries have related problems. So-called homegrown terrorism then is partly an imported product. Whatever the reasons that Europe has more of a problem with Islamist terrorism than America does, one day it will go away. It will go away because in the end every group using terrorist tactics has finally either succeeded or given up. Islamist terrorism has so far succeeded in only one country, Afghanistan, and it succeeded there because of the same conditions that have made it possible for terrorism to succeed on other similar occasions, that is the existence of an ethnic divide often reinforced by a difference in religion. Afghan Muslims were the vast majority and the Soviet troops and advisors were a small minority, however powerful they were. Once terrorism created a major conflict between the Afghans and the Soviets, it would have been extremely hard for the Soviets to win. If terrorism succeeds in creating a major conflict between Muslims and non-Muslims in Europe, which is the worst nightmare of some experts, it would once again be the majority that would win. Islamist terrorism in Europe cannot possibly succeed. The real danger is not that it will succeed, but that there is a significant conflict before it fails. It is to be hoped, however, that European states will resist provocation and avoid polarisation, and then one day the problem will just go away.


Hi, I'm Zor. Welcome to Unisor Education. We continue talking about different units of measurements in physics related to the standard system, which is called C. Today we will talk about the electricity and basic concepts of electricity and how they are measured. Now, this lecture is part of the course called Physics 14 presented on Unisor.com. I suggest you to watch this lecture from this website because the lectures are hierarchically arranged, well, logically, sequentially, so to speak. And in this particular part of the course, when I'm talking about units of measurements, I'm introducing new units always based on whatever has been introduced before. So it's a hierarchical kind of a building, like we built the building, first fundamental kind of basement, whatever, and then the first floor, the second floor, etc. So everything is based on whatever has been done already. And in this case, I will use the concept which have already been introduced before as the base to introduce new units of measurements for new concepts which we are talking about, in this case, electricity. Okay. Now, the first concept we will talk about today is electric charge. So, electric charge, well, electric charge is basically related to electrons as we know. If number of electrons exceeds the number of protons in the atom, we are talking about negative electric charge. And if, on the other hand, there is a deficiency of electrons, this is called the positive electric charge. Now, question is how to measure these charges. Well, some time ago, people wanted to measure them just by the number of electrons. So a certain number of electrons in excess of number of protons in that particular object would be a unit of measurements of electric charge. Later on decided to do it differently. We have already introduced a concept of electric current and the unit ampere, one ampere. So, using this particular unit of measurement, the amount of electric charge would be measured by the electric current and the time during which it actually exists. So, if you have a conductor and there is a current which is one ampere, amount of electric charge going from one place to another in one second is one cologne. So, one cologne is one ampere times second. Now, to tell you the truth, personally I consider a certain number of electrons to be a unit, like one cologne, would be more natural for me. But in any case, physicists decided to do it from the other way. And then you can define ampere as cologne per second. But they have decided to do it the other way around for different purposes. Primarily, because I think there are very nice and precise tools to measure the current and how can you count the electrons? That's not very easy, right? So, probably because of this, I'm not really sure. They have decided first we will define as a primary, as a base unit, an ampere, and then times a second that would be the amount of electric charge. Now, if electrons are going away, so this is an object, and electrons are going away with certain current, that means that this object is being charged positively. So, if this flow of electrons is one ampere, then during one second this thing will have plus one cologne. If the other way around, if electrons are going into the object, making axis of electrons, which means negative, so if the flow of electrons is one ampere, then in one second it will be minus one cologne. So, this will be plus one cologne, this will be minus one cologne. So, this is all about electric charge. So, basically the unit is cologne, and cologne means certain ampere and times time, amperage and current, electric current times number of seconds basically. So, if five ampere is acting during 10 seconds, it will be 50 colognes. Okay, so that's all about colognes, electric charge. Next is voltage. So, how can we define a volt? Well, let's think about this way. Let's consider you have electrostatic field. For example, you have some negatively charged point object, so around it we have electrostatic field. Now, if you have certain test object and it has certain charge, now if it's negative, now my source of this electrostatic field is, I was saying, it's negative, right? And if my test object is also negative, there is a repulsion, certain force, right? Now, if my test object is positive, there is an attraction, also the force. So, there is a force acting on this object. So, if it moves, it does certain amount of work. Now, this work can be performed by the field itself, or it can be performed by outside force against the field. It doesn't really matter. Somebody or something does the work. If the charged object moves in the electrostatic field, either the field or some outside force forces it to move, something does the job, something performs the work, which can be measured. And that's very, very kind of familiar now, familiar grounds. So, we have joules as amount of work performed. And we have coulombs as measure of how much electric charge this test object has. Well, great. So, one volt, one volt is a difference in potential, it's a potential energy, right? Because at every point, object has a potential energy because, again, if we release it, it will move either towards the source of the field or outside. So, there is a potential energy. And so, what we are saying is that one volt is a difference in potential energy if one particular joule of energy is needed to move from A to B, one coulomb of electrically charged object. So, if you have a test object of one coulomb, and it takes amount of work, one joule to move it from point A to point B within electrostatic field, now we are saying that there is a voltage, there is a difference in potential between these points A and B. Voltage is just a different world, it's different of potential energy, same thing. So, there is a voltage which can be measured as it's equal to one volt. If one joule is required to move from A to B, one coulomb of electricity, then we are saying that the difference in potential is equal to one volt. So, this is basically the definition of the unit of measurement of the difference of electric potential or voltage between two different points. Okay, so that's done. So, we always think about voltage as certain amount of work needed to move from A to B certain charge. Okay, now the last thing is resistance. Now, imagine you have a conductor and for whatever way we want, we maintain certain difference of potential between two ends. So, you have A and B. Now, this is conductor and there is certain voltage here, constant. How we do it doesn't really matter. Now, why do I need conductors? Well, because electrons are moving. So, our object, so to speak, would be electrons. Now, imagine what happens with electrons as they are moving. Well, they actually meet certain resistance from whatever is in the conductor. It's not easy to move in between the atoms when atoms actually trying to grab you, etc. Just imagine the small electrons and nucleus all around. So, what we are saying is the following. If the current observed between A and B is equal to one ampere and the voltage is equal to one volt, well, pay attention, we have already defined the volt before. So, that's always like this, we define something and then define something else based on whatever we have already defined. So, volt was defined, ampere was defined, that's the base unit, a few lectures ago. So, if the difference of potential of one volt produces one ampere of current within this conductor, we are saying that it has one ohm, OHM, and this is the Greek letter omega, capital. We are saying that resistance of this particular conductor is equal to one ohm. So, basically we are talking about the Ohm's law, which means R is equal to U over I, remember? The difference of potential of voltage, this is the current and this is resistance. We are talking about this one, we are talking about electricity properties of the currents, etc., etc. So, again, I would like to basically tell you where the units are coming from. The unit of Ohm comes from unit of current and unit of voltage. So, again, it's all hierarchically built, one unit based on whatever has been already defined before. That's how the whole mathematics is built, axioms, then the first kind of level of theorems, then the theorems based on the previous theorems, etc., etc. Same thing in physics, new concepts are introduced based on whatever has been already introduced and the new units of measurement are strictly arranged in the same logical sequence. That's why it's important to watch these lectures about units in the sequence presented on Unisor.com. And by the way, Unisor.com is totally free website. You can take exams by the way there if you want to. You don't have to sign in if you don't want to and there are no advertisements. So, I do suggest you to watch all these lectures from Unisor.com. That's it for today. Thank you very much and good luck.