Berkeley Famous Quotes

We like to believe that we live in a grand age of creative individualism. We look back at the midcentury era in which the Berkeley researchers conducted their creativity studies, and feel superior. Unlike the starched-shirted conformists of the 1950s, we hang posters of Einstein on our walls, his tongue stuck out iconoclastically. We consume indie music and films, and generate our own online content. We “think different” (even if we got the idea from Apple Computer’s famous ad campaign). But the way we organize many of our most important institutions—our schools and our workplaces—tells a very different story.

A well-known study out of UC Berkeley by organizational behavior professor Philip Tetlock found that television pundits—that is, people who earn their livings by holding forth confidently on the basis of limited information—make worse predictions about political and economic trends than they would by random chance. And the very worst prognosticators tend to be the most famous and the most confident—the very ones who would be considered natural leaders in an HBS classroom.

Imagine if we taught baseball the way that we teach science,” says UC Berkeley psychologist Alison Gopnik. “We would tell kids about baseball in the first couple of years. By the time they got to be in junior high, maybe we’d give them a drill where they could throw the ball to second base, over and over and over again. In college, they’d get to reproduce great, famous baseball plays, and then they’d never actually get to play the game until they were in graduate school.” High-quality project-based learning is, essentially, playing ball. There

George Dantzig was a graduate student in math at Berkeley. One day, as usual, he rushed in late to his math class and quickly copied the two homework problems from the blackboard. When he later went to do them, he found them very difficult, and it took him several days of hard work to crack them open and solve them. They turned out not to be homework problems at all. They were two famous math problems that had never been solved.

With the threat of failure looming, students with the growth mindset set instead mobilized their resources for learning. They told us that they, too, sometimes felt overwhelmed, but their response was to dig in and do what it takes. They were like George Danzig. Who? George Danzig was a graduate student in math at Berkeley. One day, as usual, he rushed in late to his math class and quickly copied the two homework problems from the blackboard. When he later went to do them, he found them very difficult, and it took him several days of hard work to crack them open and solve them. They turned out not to be homework problems at all. They were two famous math problems that had never been solved.

If this is true—if solitude is an important key to creativity—then we might all want to develop a taste for it. We’d want to teach our kids to work independently. We’d want to give employees plenty of privacy and autonomy. Yet increasingly we do just the opposite. We like to believe that we live in a grand age of creative individualism. We look back at the midcentury era in which the Berkeley researchers conducted their creativity studies, and feel superior. Unlike the starched-shirted conformists of the 1950s, we hang posters of Einstein on our walls, his tongue stuck out iconoclastically. We consume indie music and films, and generate our own online content. We “think different” (even if we got the idea from Apple Computer’s famous ad campaign). But the way we organize many of our most important institutions—our schools and our workplaces—tells a very different story. It’s the story of a contemporary phenomenon that I call the New Groupthink—a phenomenon that has the potential to stifle productivity at work and to deprive schoolchildren of the skills they’ll need to achieve excellence in an increasingly competitive world. The New Groupthink elevates teamwork above all else. It insists that creativity and intellectual achievement come from a gregarious place. It has many powerful advocates. “Innovation—the heart of the knowledge economy—is fundamentally social,” writes the prominent journalist Malcolm Gladwell. “None of us is as smart as all of us,” declares the organizational consultant Warren Bennis,

It happened because during my first year at Berkeley I arrived late one day at one of [Jerzy] Neyman's classes. On the blackboard there were two problems that I assumed had been assigned for homework. I copied them down. A few days later I apologized to Neyman for taking so long to do the homework — the problems seemed to be a little harder than usual. I asked him if he still wanted it. He told me to throw it on his desk. I did so reluctantly because his desk was covered with such a heap of papers that I feared my homework would be lost there forever. About six weeks later, one Sunday morning about eight o'clock, [my wife] Anne and I were awakened by someone banging on our front door. It was Neyman. He rushed in with papers in hand, all excited: "I've just written an introduction to one of your papers. Read it so I can send it out right away for publication." For a minute I had no idea what he was talking about. To make a long story short, the problems on the blackboard that I had solved thinking they were homework were in fact two famous unsolved problems in statistics. That was the first inkling I had that there was anything special about them. A year later, when I began to worry about a thesis topic, Neyman just shrugged and told me to wrap the two problems in a binder and he would accept them as my thesis. The second of the two problems, however, was not published until after World War II. It happened this way. Around 1950 I received a letter from Abraham Wald enclosing the final galley proofs of a paper of his about to go to press in the Annals of Mathematical Statistics. Someone had just pointed out to him that the main result in his paper was the same as the second "homework" problem solved in my thesis. I wrote back suggesting we publish jointly. He simply inserted my name as coauthor into the galley proof. [interview in the College Mathematics Journal in 1986]

One would expect to find a comparatively high proportion of carbon 13 [the carbon from corn] in the flesh of people whose staple food of choice is corn - Mexicans, most famously. Americans eat much more wheat than corn - 114 pounds of wheat flour per person per year, compared to 11 pounds of corn flour. The Europeans who colonized America regarded themselves as wheat people, in contrast to the native corn people they encountered; wheat in the West has always been considered the most refined, or civilized, grain. If asked to choose, most of us would probably still consider ourselves wheat people, though by now the whole idea of identifying with a plant at all strikes us as a little old-fashioned. Beef people sounds more like it, though nowadays chicken people, which sounds not nearly so good, is probably closer to the truth of the matter. But carbon 13 doesn't lie, and researchers who compared the carbon isotopes in the flesh or hair of Americans to those in the same tissues of Mexicans report that it is now we in the North who are the true people of corn. 'When you look at the isotope ratios,' Todd Dawson, a Berkeley biologist who's done this sort of research, told me, 'we North Americans look like corn chips with legs.' Compared to us, Mexicans today consume a far more varied carbon diet: the animals they eat still eat grass (until recently, Mexicans regarded feeding corn to livestock as a sacrilege); much of their protein comes from legumes; and they still sweeten their beverages with cane sugar. So that's us: processed corn, walking.

The largest, longest study of experts’ economic forecasts was performed by Philip Tetlock, a professor at the Haas Business School of the University of California–Berkeley. He studied 82,000 predictions over 25 years by 300 selected experts. Tetlock concludes that expert predictions barely beat random guesses. Ironically, the more famous the expert, the less accurate his or her predictions tended to be.

How Ma Bell Helped Us Build the Blue Box In 1955, the Bell System Technical Journal published an article entitled “In Band Signal Frequency Signaling” which described the process used for routing telephone calls over trunk lines with the signaling system at the time. It included all the information you’d need to build an interoffice telephone system, but it didn’t include the MF (multifrequency) tones you needed for accessing the system and dialing. But nine years later, in 1964, Bell revealed the other half of the equation, publishing the frequencies used for the digits needed for the actual routing codes. Now, anybody who wanted to get around Ma Bell was set. The formula was there for the taking. All you needed were these two bits of information found in these two articles. If you could build the equipment to emit the frequencies needed, you could make your own free calls, skipping Ma Bell’s billing and monitoring system completely. Famous “phone phreaks” of the early 1970s include Joe Engressia (a.k.a. Joybubbles), who was able to whistle (with his mouth) the high E tone needed to take over the line. John Draper (a.k.a. Captain Crunch) did the same with the free whistle that came inside boxes of Cap’n Crunch. A whole subculture was born. Eventually Steve Jobs (a.k.a. Oaf Tobar) and I (a.k.a. Berkeley Blue) joined the group, making and selling our own versions of the Blue Boxes. We actually made some good money at this.

The insatiable need for more processing power -- ideally, located as close as possible to the user but, at the very least, in nearby indus­trial server farms -- invariably leads to a third option: decentralized computing. With so many powerful and often inactive devices in the homes and hands of consumers, near other homes and hands, it feels inevitable that we'd develop systems to share in their mostly idle pro­cessing power. "Culturally, at least, the idea of collectively shared but privately owned infrastructure is already well understood. Anyone who installs solar panels at their home can sell excess power to their local grid (and, indirectly, to their neighbor). Elon Musk touts a future in which your Tesla earns you rent as a self-driving car when you're not using it yourself -- better than just being parked in your garage for 99% of its life. "As early as the 1990s programs emerged for distributed computing using everyday consumer hardware. One of the most famous exam­ples is the University of California, Berkeley's SETl@HOME, wherein consumers would volunteer use of their home computers to power the search for alien life. Sweeney has highlighted that one of the items on his 'to-do list' for the first-person shooter Unreal Tournament 1, which shipped in 1998, was 'to enable game servers to talk to each other so we can just have an unbounded number of players in a single game session.' Nearly 20 years later, however, Sweeney admitted that goal 'seems to still be on our wish list.' "Although the technology to split GPUs and share non-data cen­ter CPUs is nascent, some believe that blockchains provide both the technological mechanism for decentralized computing as well as its economic model. The idea is that owners of underutilized CPUs and GPUs would be 'paid' in some cryptocurrency for the use of their processing capabilities. There might even be a live auction for access to these resources, either those with 'jobs' bidding for access or those with capacity bidding on jobs. "Could such a marketplace provide some of the massive amounts of processing capacity that will be required by the Metaverse? Imagine, as you navigate immersive spaces, your account continuously bidding out the necessary computing tasks to mobile devices held but unused by people near you, perhaps people walking down the street next to you, to render or animate the experiences you encounter. Later, when you’re not using your own devices, you would be earning tokens as they return the favor. Proponents of this crypto-exchange concept see it as an inevitable feature of all future microchips. Every computer, no matter how small, would be designed to be auctioning off any spare cycles at all times. Billions of dynamically arrayed processors will power the deep compute cycles of event the largest industrial customers and provide the ultimate and infinite computing mesh that enables the Metaverse.

Please don’t, Warren finally says in a voice barely audible. He places an empty purple shell in the bowl between us. What? I say. Don’t introduce yourself, he says. Admit you’re thinking about it. It’s true that my former grad school professor Bob translates the guy at Berkeley, so we connect at some small nexus. Warren and I both pick at our mussels till I say, Why not? It’s something I can tell our grandkids about. I touched the hand that wrote those words. I don’t want to be here for it, Warren says. He raises a finger for the check. Behind his napkin, he says, You don’t have to meet every famous poet.

NAUDSONCE Bishop Berkeley's famous question about the sound of a falling tree may have no standing in Science. But there is a highly interesting question about "sound" that Science needs to consider....