This lecture number eight is a difficult one. Not, not a surprise. Thinking is not easy to understand. Technology is not easy to understand. So when you try to analyze the impact of one on the other. Not a surprise, it's difficult. So probably, it's a good moment to make a recap. The whole, the whole book is based on one question, how do we think? So let's summarize what we saw together. So, and to make very long story short, the world in front of you, simplification within you. With the two directions. From the world we make simplification and we come with theories, ideas, hypothesis, concepts, et cetera. And out of those hypothesis we can deduce lots of things changed the world. Now when you look at this, this is not new for you, we, I think we saw that during lecture in 2. But when you look at this, and you think in terms of technology. Of technology. In the end, what is changed? Probably on your side, very little. I'm not sure we think very differently compared to our parents or grandparents. But in front of you, of course, it has changed a lot. But. We still face big issue. Remember. Look at this painting. Probably it's connected to, what you saw 15 minutes ago. You, find again the and others. You can, organize the 15 I show in different way. You can have the. Discover, right? On one side in inventors on the other side. You can have people with a concept, a theory, like in play, in France. Others who really conceptualize on a very different way than we in France. So, it's a good way to see how it happened. Whatever happened, big data is a big thing. But remember what I said, said fir, five minutes ago. Big data isn't the good way to name what's happening. I don't think so. It's a bit like low cost. When you have the low cost airline, it's not a good denomination, because it looks like. Simply the cost is a bit low. Absolutely not. Low cost airline is a new way to think about airlines and planes and travel. Exactly the same. Big data is not the right way to call what's happening now. We should find something new and maybe it's a good exercise but big data is really big. We go through a thresh, a threshold, really. Just an anecdote, but to me it's really interesting because I study mathematics. A teacher of mathematics 200 years ago, just to teach at the university, was supposed to know the entire world of mathematics. Today, when I talk to teacher, they say 2, 3, 4% doesn't, doesn't matter. So you can, a teacher is more or less the same. So it looks like the volume has exploded. And of course, it's not, you cannot do today the way you use 200 years ago. You have to find new ways to teach mathematics. It's exactly the same here. You have to find new way to think about the what's happening. When you look at technology in fact, it has an impact on the most phenomenal, philosophical. Issues, concepts. Do we still needs concepts with technology? The answer is, yes, because a machine cannot conceptualize. Do we still have to know causes? Maybe less than before. And with so correlation can help a bit. But not only concept and causes, like identity. The identity, one of the most fundamental concept in, in philosophy. What's the identity today with the, you have, like, the av avatar and things like that, so what's the identity? What's friendship? What is friendship? I don't know but when you look at what is happening on Facebook, friendship is not what it used to do to be Always has like this, this way to word between look at the world. What's happening, and also, at the same time, looking backwards. Let's talk about a knowledge. Knowledge. Where and how can we build a knowledge? The very first person to answer the question it was 500 years ago. It's Bacon, philosopher Bacon. And in fact he said, you have three ways to build knowledge. And Bacon is important for us because he's really the first philosopher who tried who decided to apply philosophy to science, industry, and well eventually business. But he looked for applied philosophy, that's why Bacon is so important. And when Bacon was asked about the foundations of knowledge. How can we build knowledge? He had this answer, it's a beautiful metaphor, he said, finally, you have like three types of approaches. You can have and I'll show you immediately the first one. It's like an ant. It's on the right side of the drawing, it's the teacher, the scientists, who collect facts and data, but he doesn't have big ideas. I don't think we should be ants. Bacon said there is another type of teacher, the spider and spider teachers are teachers who believe they can create knowledge just out of their minds. Just, they, we call that sometime rationalist. And the first one, the ants, called empiricists. But it's not too important. So I don't think we should be spider as well, because a spider. Daum doesn't build his knowledge on reality. And according to Bacon, there is a third, a third type, a third profile, the bee, the bee. The bee is interesting and I'm convinced we should become bees. Moving from one side to the other. And on one hand, going everywhere trying to collect things. But then on the other side, building honey and think like theory. It's a beautiful metaphor. It's a beautiful, and what's interesting, it is still valid. Whatever happens on big data internet, et cetera, doesn't change the metaphor, the metaphor. We still need bee. And part of the bee cannot be on the machine. It's human by definition, like, like, you and me. So just to close this eighth lecture, I would say this. A bulldozer has no idea, a coffee machine has no idea, a gear box has no idea. Let's tell this way, internet, big data, don't have any idea either.
