stanford-university-faculty

"Americans have always been especially prone to regard all things as resulting from the free choice of a free will. Probably no people have so little determinism in their philosophy, and as individuals we have regarded our economic status, our matrimonial happiness, and even our eternal salvation as things of our own making. Why should we not then regard our political felicity, likewise, as a virtue which is also virtue's reward?"

"Democracy is clearly most appropriate for countries which enjoy an economic surplus and least appropriate for countries where there is an economic insufficiency."

"Here, for the last time together, appeared a triumvirate of old men, relics of a golden age, who still towered like giants above creatures of a later time: Webster, the kind of senator that Richard Wagner might have created at the height of his powers; Calhoun, the most majestic champion of error since Milton's Satan in Paradise Lost; and Clay, the old Conciliator, who had already saved the Union twice and now came out of retirement to save it with his silver voice and his master touch once again before he died."

"There is substantial evidence, over a wide attitudinal and experimental range, that perceptions, opinions and values are systematically ordered in modern societies....Modern society...is more or less unique in the extent to which it produces standardized contexts of experience."

"We need some citizens committed to exploring and producing knowledge, as well as consuming it, and the outcomes cannot be measured solely in economic terms (nor is the Ph.D. the only path to that end, but it is certainly an important one)."

"The past contains many answers, but until we ask the right questions their meaning eludes us."

"...All historians are nonfiction writers, whether we know it or not."

"My great-grandmother would talk about her uncles who served in the Maine Regiment during the Civil War, my great-grandfather’s work helping to build the Canadian railroad and Halifax harbour, the first time she used a flush toilet, talked on the phone (she still had a party line), road in automobile, and so forth. It made me appreciate how much technology transformed the world within a century."

"...After the more prosperous era of the mid-1990s through around 2008, we seem to have forgotten the truly dreadful market of the 1970s, the awful job market of most of the 1980s, some of the occasional downturns of the 1990s, and the fact that even the best of times has never offered the number of tenure-track jobs equal to the number of Ph.D.’s."

"A friend who did a lot of consulting work when tenure-track jobs were not readily forthcoming once told me that having a Ph.D. means two things: You know a lot about a little, and you know better than most people how to look things up—particularly at a time when there is so much cheap, unreliable, useless information out there."

"...You will be offered a period in your life in which to learn and think, and see where it takes you. That is a rare and valuable thing. We have begun to assess the Ph.D. as if it were an M.B.A. It isn’t."

"Philosophers and jurists who turned natural right into the highest legal authority—often the very writers who feature most prominently in our liberal narratives of reform and progress—were also those most likely to demand the harshest penalties against those who violated it."

"I don’t think it’s a battle that is decisively won and we don’t have to worry about it again, but I think there are enough resources and assets being thrown against this problem that I would like to think it will be a match for the worst and most egregious form of information warfare"

"I think the reason there’s been a decline in elite or expertise has been more of a populist movement. A good amount of the people feels they have been left behind by the world order. I think that would be much more important than technology"

"If you want to build a service agent that does returns, you cannot afford for the returns to be wrong one out of ten times. It has to be 99.9% correct. If you have an agent that comes up with a loan, you cannot have an agent make the wrong recommendation about the loan. These are scenarios where we need to have high consistency."

"Generating thousands or millions of trajectories will help us find the right mechanisms for incorporating feedback and re-training those LLMs [Large Language Models]. There are numerous opportunities for developing new training engines, learning engines, and ultimately creating new benchmarks. We cannot improve what we cannot measure. The benchmarks are critical for assessing our current position."

"This kind of scenario can be implemented by plugging it into the right structure, such as CRM-Arena, where we have started simulating all these different business objects and their dependencies. You start training agents that replicate what a customer will do and what a human agent will do to create a lot of interesting use cases. You can actually cover a lot of ground, which is somewhat difficult to predict beforehand."

"This doesn't cover all of the objects, fields, and complexity of the orgs that use Salesforce. But even within this simplified scenario, we can go towards something that improves performance for those use cases being tested. The next step is to increase the scope. We need to do more work, add more objects, and more complexity. That's what we want to do in the next phase."

"Prompt engineering is a bit of an art. So, there's a lot of work that goes into that, but finding an automatic way to engineer the prompt could be very useful, because it eliminates the need for humans to do the engineering. You have a system that automatically comes up with the right way of engineering the prompt."

"Our goal is to scale it up and make it as close as possible to real-world use cases. I think it's actually the next stage of the arms race for agentic AI. I have seen these other cases of discussion about simulation bubbling up in this area of the enterprise."

"These are very similar to what DeepMind did when they were training AlphaGo. They have instructions on how to play Go, along with some trajectories on how the game should be played. However, to achieve a breakthrough in performance, they had it interact with another AI. Then they began discovering new strategies, architectures, and approaches for winning the games, and eventually ended up with something that actually beat the gold masters at some point. The same concept can be applied here in the realm of enterprise."

"Right now, I don't know, I think I'd try to get involved in the bridge between AI and neurophysiology. Let's take neuroscience in particular because I think there's still a lot of secrets about how the brain works that we don't understand that'll be helpful in engineering. The reason we don't understand them, I think, is we haven't invented the concepts needed to understand them. I have this analogy with computers. If you had a Martian coming down looking at computers, measuring all the currents flowing back and forth from the transistors, no amount of all that measuring, no amount of understanding how a transistor works is going to tell you how say an online banking system works or how an airline reservation system works. You have to have concepts that got invented in computer science to even understand them. You have to have concepts like lists, programs, data structures, compilers and there are probably a whole set of analogous concepts in helping us understand the brain."

"I recorded all of that and I looked at it. I've forgotten some of the questions that were asked. Well, for example, on this Toronto thing. Remember the example in which it was asked, well the clue was World War II aviators or naval battles for which airports were named. And the answer would be Midway and Chicago and O'Hare. And it mentioned something in Toronto, I think, even though it was supposed to be a US city. And I think the people who designed it explained that well, oftentimes the category US city doesn't really mean exactly US city, it's sort of general. And so, Watson didn't count that as heavily as it should have. But, maybe it had some inaccurate common sense that allowed it to answer Toronto. But, anyway it was a failure of some sort of common sense reasoning."

"Well, large corpuses of data are going to be useful in lots of them. I don't know that it'll solve all the problems in AI. I mean, right now we can't do all the things that humans can do. Look around you and you can see. I mean, we have office buildings full of people. And many of them aren't using their hand eye coordination. It isn't mechanical engineering which is a problem. You might ask, why are they there? What are they doing? Well, they're having meetings, they're filling out paperwork, they're doing studies, they're communicating with other humans, they're making plans. And those are things, why can't computers do all that? Why is there anybody in those buildings except the janitors and maybe a few top bosses? Well, and computers would be cheap if we could do it, cheaper than those people. And so, why are they there? Well, because computers can't do it yet. And will lots and lots of data solve that problem? I don't think so. Might help, might be part of the solution, but the reason we don't have what you might call human level intelligence yet is that we just don't have the ideas needed in order to write the programs that would allow us to achieve human level AI. But, we have a lot of smart people and I think we're making some progress."

"So, yes it's important to understand how a neuron works and a lot of neurophysiologists might complain that models of the neurons that the AI people are cooking up aren’t accurate enough. Well, that might be, but no amount of detailed understanding of an individual neuron or even how neurons get interconnected I think will be sufficient to have a good explanation of how it is that we do what we do. We have to have some higher level things and there are people working on that. But, some of it, I think, will get developed by those very people who have one foot in artificial intelligence and one foot in neuroscience to say, "Ah, the analogy to these high level programs is such and such to the way the brain does this." And they will invent concepts that will then help us understand the brain better."

"A machine is not a genie, it does not work by magic, it does not possess a will, and … nothing comes out which has not been put in, barring of course, an infrequent case of malfunctioning. … The “intentions” which the machine seems to manifest are the intentions of the human programmer, as specified in advance, or they are subsidiary intentions derived from these, following rules specified by the programmer. … The machine will not and cannot do any of these things until it has been instructed as to how to proceed. ... To believe otherwise is either to believe in magic or to believe that the existence of man’s will is an illusion and that man’s actions are as mechanical as the machine’s."

"Right, so that’s the thing. Ninety-nine percent of the people who got a one in one of their interviews we didn’t hire. But the rest of them, in order for us to hire them somebody else had to be so passionate that they pounded on the table and said, “I have to hire this person because I see something in him that’s so great, and this guy who thought he was no good is wrong, and I’ve got to stand up for him and put my reputation on the line.”"

"Well, if you had asked me that a few years ago I might have said "No," because I think that whatever the power of computing was at the time it was fully able to handle any of the ideas that we had at the time. I mean, we were idea short, we weren't hardware short mainly. Now, I'm not so sure because the new idea that's come up, the use of lots and lots of data, well, lots and lots of data requires lots and lots of computing. And so, the more computing, the faster it can go the better. I don't know that that's the bottleneck at the moment. After all if you talk to the Watson people which I haven't, but if you did talk to them and you asked them, "Gee, how could Watson have been better? If you had a computer, this IBM 7000 series or whatever it was, if it were 10 times as fast, 100 times as fast, 100 times as much memory would you have done better?" I think they'd still answer "Well, not necessarily. We would need more ideas about how to program all that.""

"It will be a problem in the end, I think, for society what happens, what do we do with all the people that computers replace? And eventually, I mean, right now you need more and more skills in order to have jobs. But, there's this guy Robin Hansen, you know about Robin Hansen? Robin Hansen is an economist and he has got this interesting metaphor of sea level rising. Sea level is what computers can do. And land and the land that's inhabited, the jobs that require humans to do. And sea level's been rising. And on the shore a lot of people are displaced. Well, they've had to move to higher levels. But, to move to higher levels they have to have more training. Now, the fact that sea level's rising itself makes some higher levels. It's a funny thing. At sea level build some mountains so people can climb those mountains, but sea level will keep rising. And the question is will it rise above even those mountains? And so, what do we end up having people do?"

"Well, there's certain jobs that only people can do. You can't have a machine make sweaters made by hand. And there's kinds of things which involve social interaction which only people can do. Some of the social interaction maybe machines can do. But, if you really want a human you got to have a human. And so, I'm not saying that all jobs will be replaced, but we're already seeing a trend of many and I think that trend will continue and you read people who talk about the current slow recovery, the economic situation, and many people say "Well, we've laid off a lot of people because of the recession, but in the meantime we've found out we could do some of those jobs that those people did with machines and by the way, we're not going to hire those people back." And so, I think that's going to be a continuing difficulty."

"I often end up rewriting. Sometimes I do that without ever finding the bug. I get to the point where I can just feel that it’s in this part here. I’m just not very comfortable about this part. It’s a mess. It really shouldn’t be that way. Rather than tweak it a little bit at a time, I’ll just throw away a couple hundred lines of code, rewrite it from scratch, and often then the bug is gone. Sometimes I feel guilty about that. Is that a failure on my part? I didn’t understand what the bug was. I didn’t find the bug. I just dropped a bomb on the house and blew up all the bugs and built a new house. In some sense, the bug eluded me. But if it becomes the right solution, maybe it’s OK. You’ve done it faster than you would have by finding it.”"

"In Lisp, if you want to do aspect-oriented programming, you just do a bunch of macros and you're there. In Java, you have to get Gregor Kiczales to go out and start a new company, taking months and years and try to get that to work. Lisp still has the advantage there, it's just a question of people wanting that."

"No, I had the box set. You had to pull hard, but you could pull one of the box. Now I’m less likely to use any book for reference—I’m just likely to do a search."

"At one point I had it as my monitor stand because it was one of the biggest set of books I had, and it was just the right height. That was nice because it was always there, and I guess then I was more prone to use it as a reference because it was just right in front of me."

"One of the interesting things we found, when trying to predict how well somebody we’ve hired is going to perform when we evaluate them a year or two later, is one of the best indicators of success within the company was getting the worst possible score on one of your interviews. We rank people from one to four, and if you got a one on one of your interviews, that was a really good indicator of success."

"Real cognitive science, however, is necessarily based on experimental investigation of actual humans or animals. We will leave that for other books, as we assume the reader has only a computer for experimentation.”"

"But there was as, you know, from the beginning I was interested also in the aspect of interaction with the environment through understanding of obstacles and the need to avoid collision and the need to create motions around those obstacles. So having resolved the problem in term of reactivity doesn’t mean that we resolved the global problem. Because motion planning requires us to think about a global path. Now, if we think about the computational complexity of this, we realize that this is exponential in the number of degrees of freedom and it is costly to find a path, to plan a path, especially for a robot with many degrees of freedom. I was always interested in finding a way to connect those planning levels, that is the level where we are planning the motion with the levels of execution where those levels are running at very high frequencies. If we think about the control, we need to have a millisecond of control , whereas if we think about a motion planner, well, between the time we find the obstacles and perceive the environment to the time we come up with the new plan, it’s going to take long time. Especially in the ‘80s. What we really tried to do was to create an intermediate level that would allow us to think about this connection between motion planning and control in a way again similar to that of human. The idea that became known as elastic planning is to start with a plan and continuously adjust this plan reactively. Locally by mechanisms really simple as repulsive potential forces, but the trajectory itself is treated as a physical entity with property of elasticity that will make it shorten and adjust to the environment and the changes in the environment. That became a very, very interesting technique that also is being pursued by a number of my current student and previous students like Oliver Brock, who worked on this. Sean Quinlan developed the early version of it, and now a number of new students are working on taking this further, not only to move in the free space, but also to think about the contact space."

"Now, if we are really to think about robots that would interact with human or move in the human environment, we would need to have those robots not fixed but mobile. So we started to look at mobile manipulation, and that brings a lot of interesting issues in the dynamic of macro mini-structures. The properties of dynamics in relation to reduce effective inertias, redundancy, and all kind of things that were amazingly interesting to analyze and work on. As we pursued this work, we discovered that, “Well, there’s more.” When we got to humanoid robotics we have the branching, complex branching structure, with multiple tasks that need to be simultaneously achieved while maintaining constraints, while maintaining balance, given the fact that these robots are under-actuated. It’s amazing. You have one problem after the other. It’s so rich, so exciting. So that was one line of the work that I was really interested in, dynamic control and control structures."

"Right. I mean, the whole idea that really is pursued here is human. I mean, if we think about human, human never go through this exercise of precisely planning every aspect of their motion. We do not compute a full trajectory or a full plan. What we do is like we are sitting here, and after the interview you’re going to go back to the parking. You’re not going to plan all the details of that motion. What you know is you need to go to a door and you know that it’s going to be feasible to go to the door. Probably you have some intermediate landmark along the way. So we plan with this concept of feasible, reachable locations, and along the way we know that we are going to accommodate our motion using our reactive behavior. We are interacting with the world as we are moving. In your car you are driving around other cars and people and bicycles. It wasn’t completely in the plan. But you know that skill is there. Ee make use of skills, and what we do when we are interacting with the physical world, when we are making contact, when we are assembling objects, when we are putting things together. We are also using skills. When we are playing tennis, when we are doing any challenging task, we are using skills. So the idea is let’s then plan – first of all, let’s develop those skills for the robot. That is, skills are a more abstract, a more advanced capability for the robot than the simple control of following a trajectory or reaching a point or just touching a surface. It is a skill to say, “I’m able to accomplish this task, taking into account different things.” By building those skills, the planner can plan using those skills. So the task of planning becomes much less difficult and complex, and in real-time, the skills can perform at much faster servo rate than any dependency, full dependency, on the planner that would be required if we didn’t have the skills. So all the challenge is, “How can we teach these robots those skills?” This is what we are doing. We are making use of a lot of fundamental characteristics of those robots in term of their kinematics, dynamic models, so that we build the first layer and then we abstract that layer to create a behavior that can accomplish a different task in the environment with obstacles, with a object. So we talk about compliant motion behaviors that allow the robot, for instance, to move to a multi-contact with the environment by just detecting and feeling those contacts. You can imagine a task of just putting a lid on a box. This is something really hard, if we want to do it just by controlling the motion and the position, but actually this is something that becomes very easy if we understand compliant motion. So this becomes a skill that then could be used and reused in different situation."

"So arriving to Stanford, it was wonderful. We had the AI lab in Margaret Jacks. In the basement of Margaret Jacks, next to the DEC-20. We had a small room with one table and four robots. The AI lab just moved from the hills down to Stanford to the basement of Margaret Jacks. At that time, there were a number of, I mean, extraordinary people that were involved in the AI lab. The AI lab attracted a lot of talent in robotics over the years. Just behind me you can see the Vic Scheinman robot, the Stanford Scheinman arm robot. There were a number of people who were involved in the ‘70s in the AI lab. In the ‘80s when I arrived, we had John Craig was there. Tom Binford was leading the AI lab. My friend, the very famous researcher in both AI and robotics, Rodney Brooks, was there. In fact, many other people came through as visitors. We had also a very famous designer, Ken Salisbury, who was there. There were a number of people from the field of vision. There was Harlan Baker. I don’t want to go over all the names. I have the list and I have their photos. If you would like, we will look at that later. But it was an amazing dynamic environment that I’ve never experienced in my life. It was just the discovery of that exciting place that made me decide to stay one more year and one more year. Then Stanford just kept me forever, because it is very, very difficult to leave this environment. The research environment, the excitement, the opportunities to take on a new project, is really unique. What is interesting also is the fact that while I was at Stanford, I was in France as well, collaborating with my colleagues. I was in fact involved with other collaborations in Japan. In the year ’84, ’83, and in fact December ’83, I visited Japan for the first time, and I had the great, great opportunity of meeting Professor Inoue, Professor Umetani, Hirose, Professor Nakamura, who was a student at the time. And his advisor, Professor Hanafusa, and Professor Yoshikawa, and also a number of, I mean, just pioneers in robotics. All of that was done during one trip I had to Japan in 1983. Since then I kept in touch with all these different laboratories over the years, and that was really an amazing experience. What I discovered about Stanford and about the AI lab is the fact that what we are doing is so much connected to the world, and that became one of the other dimension for my research."

"So this is the interesting part, is the robots at the time were mostly hydraulic robots. Very large robot, heavy robots. There was a robot from Renault that I think it was called the R80. Huge. Eighty kilograms it can carry. And I really didn’t implement my approach on that robot, but I used this cable-driven robot called the MA23 manipulator that was designed and developed by Jean Vertut. Jean Vertut is probably the father of robotics, robot design, in France. And there is a number of very, very influential people in my early career in robotics, and Jean Vertut was one of them. Jean Vertut developed most of the concept that led to cable-driven actuation. The three-fingered hand Kenneth Salisbury developed was based on a lot of those concepts. Many of the robots that were later developed were based on ideas and concepts that Jean Vertut developed. Jean Vertut passed away very quickly years back, and the whole robotics field missed him. But I had this privilege to know him. And also the privilege of having my paper selected by him, my first paper, to be then – the introduction to me to know Professor Roth, who eventually invited me too to come to Stanford."

"Yeah. It is really, to say, that we get a lot of inspiration from the human. But we are not trying to just record human motion and replay the motion. What we are trying to do is to really to understand what is behind this motion, what is the strategy, and how can we encode that strategy in something that the robot can use as a strategy so we can generalize? But this is, again, part of the whole approach. We are seeking solution that can address a versatile number of problems. We’re not just looking for finding or engineering the solution to a specific problem. Because robotics is about ultimately all kind of task in all kind of environment, and we really need to look at this versatility."

"Well, this is really interesting, an interesting question. Looking back at my Ph.D. where I started, looking at the vision I had in that work, I feel like I’m still working on my Ph.D. That is from the beginning, I took on a challenge, which is to try to understand the robot system with respect to descriptions of the task. That is, to try to not just worry about modeling the physical robot in a convenient space, which is essentially the joint space. I was interested in understanding the characteristics of the robot when it’s interacting at a given point with the environment. What would be the effective masses that I will feel here when I’m going to touch the environment? What is the normal space in which I can exert forces and what would be the corresponding tangent space where I can move? A lot of questions related to the dynamics and the control of robots in task space have not been really fully addressed. There are few people around the world who really look at those problem. Neville Hogan thought about it in the context of analyzing human motion and proposed very interesting models using impedance control. There are a number of other researchers who looked at those problems, but synthesis in task space has not been something that a lot of work was done in, and that was really one of the things that I was committed to pursue and to work on. So there was that work that was taking me from thinking about how, “Can we control just one effector?” Then how can we go from one effector that is not only moving in free space, but obviously interacting with the environment? But then how can we go from one robot end effector to two end effectors? How can we do comparative manipulation when we start to have internal forces? Then how can we have general cooperation between multiple robots? Then how can we make this robot mobile manipulators? Just behind me you can see Romeo and on this side you have Juliet. These are the two platforms that we developed to explore the workspace of a human. That is, we always thought about these manipulators attached to a table and bringing the component to be assembled by those robots."

"So I became involved during these years in one of the early project in robotics called Spartacus. Spartacus is a project to develop a robotic system that is aimed to assist human. And in this project a number of research groups around France, from the South to the North, in were involved in that pioneering project. I think this was probably one of the earliest project in Europe. And it brought a number of people who are still today involved in robotics. So if we go over the group of people who is today involved in robotics we will find many of them who participated in that project. However, my research itself was very closely connected with another laboratory in France. Because of my work was pursued at the school SUPAERO. Next to SUPAERO there is a very important center of robotics, and this is called LAAS, L-A-A-S. And at LAAS, there was a very important project initiated and led by Georges Giralt. And Georges Giralt was in fact one of my other mentors. In fact, he was the director of my post-doctoral study. He was the chair of the committee of my defense, and with Georges we had a long, long years of interaction, sharing a lot of the ideas that in fact became part of my own research or the research that has been pursued over the years at LAAS. Georges Giralt was leading the robotics research effort at LAAS, and one of the researchers there is Mark Runeau. Mark Runeau was one of my very close colleague, because both of us, we were interested in dynamics. And Mark Runeau, together with Wisama Khalil from Montpellier, were also, the three of us, we were working in different aspect of dynamics. In fact, Wisama and Runeau developed software to generate automatically, symbolically, the dynamic equation of robots, that in fact I used myself in developing some of the dynamic models. But my approach to dynamics, as I said, was based on the idea that we need to understand dynamics at the level of the task. Not just at the level of the joints. So I was doing this transformation and I had a great partner with Mark Runeau for discussion. And Mark Runeau was very close to me in Toulouse. And we shared a lot of moment of discussion on dynamics."

"We believe in Creation. We praise the Lord for that faith. But let us avoid either posing creation and evolution as intrinsically antithetical alternatives, the acceptance of one demanding the rejection of the other, or presenting creation as a scientific mechanism alternative to evolution, as though good science must ultimately lead to the verification of fiat creation and a falsification of evolution."

"Evolution is a scientific question on the biological level; it would be unfortunate indeed if a scientific question were permitted to become the crucial point for Christian faith."

"If it is assumed, without due Scriptural support, that the purpose of revelation is to give mankind a source-book of information on all phases of physical, mental, spiritual, sociological, artistic, and scientific life — a source-book which must have meaning for the people to whom it was addressed and to all the generations coming after them in spite of the changes which are continuously occurring — then we have the greatest difficulty in maintaining the doctrine of an inerrant Scripture. If, on this stand, we adopt the position of “arbitrary inerrancy,” we essentially jeopardize the whole truth of Christianity by attempting to balance the great wealth and weight of God’s revelation in Christ upon our ability to show that the words of Scripture can be judged inerrant even when we examine them on the basis of criteria they were not written to satisfy. How much of liberalism and rejection of Biblical revelation has been precipitated as a blind reaction against such a stand!"

"The human senses are tools of science in studying the natural world. If you can see it, hear it, feel, taste, or smell it, then science can’t work with it. This isn’t meant superficially, for scientists have developed a great variety of instruments that extend the capabilities of science far beyond the unaided senses. But even with the most subtle of instruments, the link between instrument and scientist is in the form of a meter needle whose location is seen, a photographic record or computer tape that can be read, or an audible signal that can be heard."

"Since the very beginning of the history of the State, the Catholic Church has been an important factor in the upbuilding of the commonwealth and the welfare and education of the people. The difficulties encountered were not easy to overcome in the midst of an unsettled, careless, and often lawless community."

"Milton] felt you could influence the political sphere. So he wrote a lot about politics--of course--and public policy. Not politics--public policy. But at the same time, I think he believed, or at least it was his public persona, that he was also a scientist. That his scientific work was different. That he could, as one EconTalk guest said: He could put on his science hat, and then take it off and put on his ideology hat. And my claim is that that's a delusion--I don't mean to be critical about Milton, who I'm a big fan of, and incredibly important person in my education and in my life. But I think it is--so I don't know how aware or unaware he was about it. I don't want to say he was deluded. But I think many economists are deluded into thinking, and want to believe that they can wear those two hats separately. And it's very self-serving. We need to confront the fact that it's very much in our interest to pretend to the world that we have a scientific aspect to our work that is free of values. And I think that's--I think it's fundamentally deceptive, and dishonest."