Latest news with #MassachusettsInstituteofTechnology
2 days ago
- Politics
Trump claims his uncle taught the Unabomber, offers no evidence to support claim
President Donald Trump raised eyebrows this week when he claimed his uncle, John Trump, taught the Unabomber, Ted Kaczynski. However, there is no evidence that Kaczynski, one of the most infamous domestic terrorists in American history, had ever been in a classroom with the president's uncle despite his boasting. Trump made the claim during one of many off-the-cuff moments Tuesday afternoon at a roundtable with business leaders in Pittsburgh, where he touted tech and brought up his uncle, who taught electrical engineering at the Massachusetts Institute of Technology for 37 years. "Kaczynski was one of his students," Trump claimed. "Do you know who Kaczynski was?" Kaczynski was once one of the most wanted domestic terrorists for his letter bombing campaigns between 1978 and 1995, where he killed three people and wounded 23 others. Kaczynski was a math prodigy and earned degrees at Harvard and the University of Michigan before he became a recluse and went uncaptured for 17 years. He was arrested in 1996 after his brother deduced he was the anonymous Unabomber and contacted the FBI. Kaczynski, who had sent writings expressing anti-technology beliefs before his arrest, pleaded guilty and was sentenced to life in prison. He took his own life in prison in 2023. A spokesperson for MIT told ABC News in a statement Thursday that the school has "no enrollment record or information that Ted Kaczynski ever attended MIT." The spokesperson added that the school creates enrollment records for students who cross enrolled from another school for a MIT program or class. The school said in the past that Kaczynski was accepted to the school in 1958, but he rejected their offer. White House Press Secretary Karoline Leavitt was asked about Trump's claims during a news briefing Thursday, but she did not answer. Instead, she touted John Trump's tenure at MIT and took another question. Trump, however, claimed during the roundtable that his uncle noticed the Unabomber's strange characteristics while at MIT. "There is very little difference between a madman and a genius. But Kaczynski … I said, 'What kind of a student was he, Uncle John? Dr. John Trump, what kind of student?'….He said, 'Seriously? Good.' He would go around correcting everybody.' But it didn't work out too well," Trump claimed. John Trump died in 1985, long before Kaczynski was identified as the Unabomber or in the public discourse.
Mint
3 days ago
- Science
- Mint
Does AI make you stupid?
AS ANYBODY WHO has ever taken a standardised test will know, racing to answer an expansive essay question in 20 minutes or less takes serious brain power. Having unfettered access to artificial intelligence (AI) would certainly lighten the mental load. But as a recent study by researchers at the Massachusetts Institute of Technology (MIT) suggests, that help may come at a cost. Over the course of a series of essay-writing sessions, students working with as well as without ChatGPT were hooked up to electroencephalograms (EEGs) to measure their brain activity as they toiled. Across the board, the AI users exhibited markedly lower neural activity in parts of the brain associated with creative functions and attention. Students who wrote with the chatbot's help also found it much harder to provide an accurate quote from the paper that they had just produced. The findings are part of a growing body of work on the potentially detrimental effects of AI use for creativity and learning. This work points to important questions about whether the impressive short-term gains afforded by generative AI may incur a hidden long-term debt. The MIT study augments the findings of two other high-profile studies on the relationship between AI use and critical thinking. The first, by researchers at Microsoft Research, surveyed 319 knowledge workers who used generative AI at least once a week. The respondents described undertaking more than 900 tasks, from summarising lengthy documents to designing a marketing campaign, with the help of AI. According to participants' self-assessments, only 555 of these tasks required critical thinking, such as having to review an AI output closely before passing it to a client, or revising a prompt after the AI generated an inadequate result on the first go. The rest of the tasks were deemed essentially mindless. Overall, a majority of workers reported needing either less or much less cognitive effort to complete tasks with generative-AI tools such as ChatGPT, Google Gemini or Microsoft's own Copilot AI assistant, compared with doing those tasks without AI. Another study, by Michael Gerlich, a professor at SBS Swiss Business School, asked 666 individuals in Britain how often they used AI and how much they trusted it, before posing them questions based on a widely used critical-thinking assessment. Participants who made more use of AI scored lower across the board. Dr Gerlich says that after the study was published he was contacted by hundreds of high-school and university teachers dealing with growing AI adoption among their students who, he says, 'felt that it addresses exactly what they currently experience". Whether AI will leave people's brains flabby and weak in the long term remains an open question. Researchers for all three studies have stressed that further work is needed to establish a definitive causal link between elevated AI use and weakened brains. In Dr Gerlich's study, for example, it is possible that people with greater critical-thinking prowess are just less likely to lean on AI. The MIT study, meanwhile, had a tiny sample size (54 participants in all) and focused on a single narrow task. Moreover, generative-AI tools explicitly seek to lighten people's mental loads, as many other technologies do. As long ago as the 5th century BC, Socrates was quoted as grumbling that writing is not 'a potion for remembering, but for reminding". Calculators spare cashiers from computing a bill. Navigation apps remove the need for map-reading. And yet few would argue that people are less capable as a result. There is little evidence to suggest that allowing machines to do users' mental bidding alters the brain's inherent capacity for thinking, says Evan Risko, a professor of psychology at the University of Waterloo who, along with a colleague, Sam Gilbert, coined the term 'cognitive offloading" to describe how people shrug off difficult or tedious mental tasks to external aids. The worry is that, as Dr Risko puts it, generative AI allows one to 'offload a much more complex set of processes". Offloading some mental arithmetic, which has only a narrow set of applications, is not the same as offloading a thought process like writing or problem-solving. And once the brain has developed a taste for offloading, it can be a hard habit to kick. The tendency to seek the least effortful way to solve a problem, known as 'cognitive miserliness", could create what Dr Gerlich describes as a feedback loop. As AI-reliant individuals find it harder to think critically, their brains may become more miserly, which will lead to further offloading. One participant in Dr Gerlich's study, a heavy user of generative AI, lamented 'I rely so much on AI that I don't think I'd know how to solve certain problems without it." Many companies are looking forward to the possible productivity gains from greater adoption of ai. But there could be a sting in the tail. 'Long-term critical-thinking decay would likely result in reduced competitiveness," says Barbara Larson, a professor of management at Northeastern University. Prolonged AI use could also make employees less creative. In a study at the University of Toronto, 460 participants were instructed to propose imaginative uses for a series of everyday objects, such as a car tyre or a pair of trousers. Those who had been exposed to ideas generated by AI tended to produce answers deemed less creative and diverse than a control group who worked unaided. When it came to the trousers, for instance, the chatbot proposed stuffing a pair with hay to make half of a scarecrow—in effect suggesting trousers be reused as trousers. An unaided participant, by contrast, proposed sticking nuts in the pockets to make a novelty bird feeder. There are ways to keep the brain fit. Dr Larson suggests that the smartest way to get ahead with AI is to limit its role to that of 'an enthusiastic but somewhat naive assistant". Dr Gerlich recommends that, rather than asking a chatbot to generate the final desired output, one should prompt it at each step on the path to the solution. Instead of asking it 'Where should I go for a sunny holiday?", for instance, one could start by asking where it rains the least, and proceed from there. Members of the Microsoft team have also been testing AI assistants that interrupt users with 'provocations" to prompt deeper thought. In a similar vein, a team from Emory and Stanford Universities have proposed rewiring chatbots to serve as 'thinking assistants" that ask users probing questions, rather than simply providing answers. One imagines that Socrates might heartily approve. Get with the program Such strategies might not be all that useful in practice, even in the unlikely event that model-builders tweaked their interfaces to make chatbots clunkier, or slower. They could even come at a cost. A study by Abilene Christian University in Texas found that AI assistants which repeatedly jumped in with provocations degraded the performance of weaker coders on a simple programming task. Other potential measures to keep people's brains active are more straightforward, if also rather more bossy. Overeager users of generative AI could be required to come up with their own answer to a query, or simply wait a few minutes, before they're allowed to access the AI. Such 'cognitive forcing" may lead users to perform better, according to Zana Buçinca, a researcher at Microsoft who studies these techniques, but will be less popular. 'People do not like to be pushed to engage," she says. Demand for workarounds would therefore probably be high. In a demographically representative survey conducted in 16 countries by Oliver Wyman, a consultancy, 47% of respondents said they would use generative-AI tools even if their employer forbade it. The technology is so young that, for many tasks, the human brain remains the sharpest tool in the toolkit. But in time both the consumers of generative ai and its regulators will have to assess whether its wider benefits outweigh any cognitive costs. If stronger evidence emerges that ai makes people stupid, will they care?
The Independent
4 days ago
- Politics
- The Independent
Did Trump's uncle teach the Unabomber? No. Here's what really happened
At an energy and innovation summit in Pittsburgh on Tuesday, President Donald Trump claimed that his uncle, Dr. John Trump, a professor at the Massachusetts Institute of Technology, had once taught a 'seriously good' student named Theodore 'Ted' Kaczynski. 'He'd go around correcting everybody,' Trump boasted. 'It didn't work out too well for him... but it's interesting in life.' During his winding 30-minute speech at the inaugural 'Energy and Innovation' summit at Carnegie Mellon University, Trump repeated the notion that his uncle taught domestic terrorist and mathematician Kaczynski, aka the Unabomber. After invoking his late paternal uncle, whom he falsely described as MIT 's 'longest-serving professor,' Trump continued his implausible anecdote. 'Kaczynski was one of his students,' he continued. 'Do you know who Kaczynski was? There's very little difference between a madman and a genius.' The crowd showed little reaction to the story, and it was unclear if the president was confusing Kaczynski, who died by suicide in a federal prison in 2023, with someone else. The bizarre claim is not only highly unlikely, it is practically impossible. Kaczynski attacked academics, businessmen, and random civilians with homemade bombs between 1978 and 1995, as part of a campaign aimed at collapsing modern society. He killed three people and injured 23. Before being recognized as the Unabomber, Kaczynski earned an undergraduate degree from Harvard in 1962, having entered at the age of 16, and Master's and Doctoral degrees in mathematics from the University of Michigan by 1967. Kaczynski taught as an assistant professor at the University of California at Berkeley, until 1969, before making a deliberate shift away from academic life and mainstream society, living in a remote cabin near Lincoln, Montana. Despite Trump's statements, Kaczynski never attended MIT. There is no record of him ever visiting or lecturing at the university either. Meanwhile, Prof. Trump, a cancer research pioneer who received the National Medal of Science, taught at MIT for approximately four decades before his death in 1985 at the age of 78. He focused on high-voltage phenomena, electron acceleration, and the interaction of radiation with both living and non-living matter, including the design of X-ray generators for cancer therapy. His expertise has been repeatedly vaunted by his nephew, who on Tuesday described him as a 'smart man.' Unlike Kaczynski, John Trump was not a mathematician; he was a professor of electrical engineering and physics. Even if the renowned physicist did cross paths with the infamous serial killer, he could not have known that Kaczynski was linked to the Unabomber attacks. The alleged conversation would have taken place more than a decade before the FBI identified Kaczynski as the Unabomber in April 1996 after his brother, David Kaczynski, turned him in after reading his manifesto, Industrial Society and Its Future. The manifesto makes no mention of Prof. Trump, MIT, or any figures associated with that institution. His autobiography and prison interviews also contain detailed recollections of his education and professors, with no mention of Trump's uncle or his time at MIT. While MIT geneticist Phillip Sharp received a threatening letter from Kaczynski before his arrest, no one from or affiliated with the technical college was physically attacked or injured.
Scientific American
4 days ago
- Science
- Scientific American
The LIGO Lab Is Pushing the Boundaries of Gravitational-Wave Research
Rachel Feltman: For Scientific American 's Science Quickly, I'm Rachel Feltman. Today we're leaving the podcast studio to take you on a field trip to the LIGO Lab at the Massachusetts Institute of Technology. We're going to chat with Matthew Evans, MIT's MathWorks professor of physics, all about the hunt for gravitational waves. You'll notice that the sound quality isn't up to our usual standard, but that's because we were right there in the lab, surrounded by big, loud science machines. If you want to see all that cool stuff for yourself, head over to our YouTube channel for an extended video version of this episode. On supporting science journalism If you're enjoying this article, consider supporting our award-winning journalism by subscribing. By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today. Here's our conversation with Matt. Thanks so much for joining us. Matt Evans: Thank you for having me. Feltman: So a few years ago we heard a lot about gravitational waves all of a sudden—many of us had not heard of them before that. Evans: Mm-hmm. Feltman: Could you remind us what they are and what happened that was so exciting? Evans: Yeah, so I guess that was almost 10 years ago now, so ... Feltman: Well, that's wild. I don't want to think about that [laughs]. Evans: [Laughs]2016 was when the announcement was made; 2015 was the discovery. And that was the first time that we had detected gravitational waves, despite the fact that we'd been working for many years on the detectors. That was the moment when we were upgrading to the Advanced LIGO detectors, and our first detection of gravitational waves was back in 2015. Feltman: And what is a gravitational wave? Evans: What is a gravitational wave? Well, the, the, like, really concise answer is: it's a ripple in spacetime. And then one could ask, 'Why would we care about a ripple in spacetime? How can we even detect such a thing?' You don't think of your life as going around measuring spacetime. But it turns out that for us that just means th at things move around, and so our detectors are made with big mirrors, which are heavy masses, and when these gravitational waves pass by they move the mirrors in our detectors. So fundamentally, it's a wiggling of, of space, a wiggling of our detector, that we don't explain by anything else going on around. Feltman: And so what is LIGO? How did it make it possible for us to finally detect gravitational waves? Evans: So LIGO is an interferometer. It's based on a concept from, what, the 1800s of interferometry, where you can make a very sensitive measurement of the position of some object by using light waves, and the LIGO gravitational-wave detectors are basically gigantic interferometers. And what we're interfering, in our case, are two laser beams, and they look for a change in the position of the mirrors that are far away from a beam splitter—so far away in this case is two and a half miles, or four kilometers—and a passing gravitational wave will move our mirrors around, and we're looking for that motion. So we start out with a laser, which is at our corner building—it's sort of the, sort of central location of LIGO—and we send that laser down to two buildings that are far away; these are the end stations. They're each two and a half miles away from the corner, and they're L-shaped, like this vacuum system you see behind us. Those two laser beams return back to the central station, and the two laser beams are made of electromagnetic waves, and those waves interfere on a beam splitter when they meet on that mirror. This mirror reflects half of the light in this direction and half of the light in that direction. And depending on the relative phase, or relative timing, of these two waves, the light will either go that way or go this way. And we're just detecting the amount of light that comes out one side of our detector, and that's our interferometer allowing us to measure the distance, but that measurement is on the scale of the wavelength of light, so micron scale. Feltman: And so what are we in front of right now? Evans: Yeah, so this is a prototype here, here at MIT, where we test components before they go to the LIGO observatories, and this is like a little mini LIGO here. So we have a large chamber for putting our isolation systems and our mirrors; that's where we test out the first suspension systems. These tubes [are] where we propagate our laser beams. We have a smaller chamber down there, which you'll see is not very small, but it's for testing the smaller suspension systems where we hang mirrors. Our suspensions and isolation systems are all to keep our mirrors from moving by the ground shaking, essentially, 'cause we want them to be as still as possible so that when they do move we'll know that it's from a gravitational wave and not from a truck or the Red Line or whatever else. Feltman: Yeah, can you give us a sense of how sensitive these instruments need to be to avoid picking up noise and actually find gravitational-wave ripples in spacetime? Evans: Yeah, so the answer is mind-blowingly sensitive, and I'll try to put this in, in scale. So the LIGO detectors should be able to measure a motion of the, the mirrors that are four kilometers away from the central building on a scale of about 1,000th the size of a proton, so this is—10 -18 meters is roughly the, the scale here. And it's beyond microscopic; it's [a] subatomic level of measurement. The only way that we get away with that is [we're] measuring a large surface of the mirror and we're averaging over many, many atoms, and that's how we can measure the average position to a level that's much smaller than the atomic size. Feltman: And the MIT LIGO is not the only LIGO. Can you remind us why that is? Evans: Ah, yeah, so, so first, just to be super clear, this is a place where we prototype stuff ... Feltman: Right, yeah. Evans: We don't detect gravitational waves here. So the same sort of operation is at Caltech; there's the Caltech LIGO Lab. And it's where a lot of the engineering and administrative staff are. They also have a big research staff there. And again, the idea is to build up systems, which then get delivered to the observatories. There are two of those: one is in Washington State, and one is in Louisiana. Feltman: So speaking of prototypes, what has LIGO been up to since that big detection news 10 years ago? Evans: So the big detection happened after we had gotten—some of the things you see here are the prototypes that went in to make Advanced LIGO possible, and that's what made that first detection possible. Since then we've been working on—I think the highlight for MIT is quantum technologies, so we've been working on squeezed light sources. And the idea here is that if we modify the quantum state of our interferometer, we can lower the noise at the readout and detect gravitational waves from more distant sources. Feltman: Cool, and what would that allow us to do? Evans: The farther away you can detect a source, like a binary black hole system coalescing, the more of them you can see. And we have this feature that our detection rate goes with the volume of space we're sensitive to, so if we make the detectors twice as sensitive, they also see twice as far, which gives us eight times larger volume, and we get a lot more events to look at. So right now we're at roughly an event per week, whereas when we first started we were at one event, if you're lucky, in a year. Feltman: And so for, you know, the average person who's maybe interested in space but doesn't know a ton about gravitational waves, why is it important that we look for these events? Evans: So we are detecting, right now, binary systems, and these can be pairs of, of black holes, pairs of neutron stars or a mix-and-match black hole-neutron star system, so a mixed pair. And the interesting thing about these sources is that these are the remnants of big stars ... So large stars that have burned their fuel and collapsed make neutron stars and black holes. And we can detect individual sources from very far away, so 'high redshift' in astro-speak. And with future detectors we'll be able to get really to the edge of the known universe in terms of our ability to detect these sources. These are essentially the stellar graveyard—so the place where big stars go to die. And by detecting these sources, individual sources, we can actually learn about the stellar graveyard and in, in that way about the stars that exist and existed in the universe. Feltman: Very cool. So what's next for LIGO? Evans: So LIGO is working on the next upgrade. We upgrade these detectors regularly; it's really still a new technology—it's only 10 years since the first detection. And we work on making the detectors better as a matter of course. We're always trying to make them better. The next upgrade will be to put in better mirrors. Essentially, again, we're averaging over the surface, over the mirror, to make this measurement. We need a really good surface, and that comes down to the coatings we put on the mirrors, so we're putting in better mirrors with better coatings. That's the next thing. We'll be working on improving our squeezed light source to lower the quantum noise in the detector. So basically incremental improvements to the current detectors. We'll then be working on a relatively large upgrade on a timescale of five years from now and from there incremental upgrades, essentially, for the lifetime of those detectors. And that lifetime is really until we get a next-generation detector going. Feltman: Mm. Evans: And I'm wearing the shirt of Cosmic Explorer here, which is the—our idea for the next generation of detectors. Feltman: Yeah, tell me about Cosmic Explorer. What's gonna be different about those detectors? Evans: Well, over 10 years ago now—and this is in 2014—we realized that we were never gonna be clever enough to really do everything we wanted to do with the current facilities ... Feltman: Mm. Evans: And we were going to have to build bigger detectors at some point. And so over the last—a little more than a decade we've been developing the idea of what these new, bigger detectors would look like, and that's developing this thing called Cosmic Explorer. It's like a supersized LIGO—factor of 10 larger, so 25 miles [about 40 kilometers] on a side. Feltman: Wow. Evans: And as things go roughly a factor of 10 more sensitive. With these detectors we could detect events from throughout the universe. Feltman: Wow, and what's ... Evans: Yeah, wow [laughs]. Feltman: The timeline looking at [laughs]—looking like for that? Evans: At this particular moment in history it's hard to say. Feltman: Sure. Evans: I will go ahead and be optimistic, and I'll say early 2030s we could be building and mid- to late 2030s we could be detecting. And we hope that the LIGO detectors will still be operating and turning out great results into sort of 2040 ... Feltman: Yeah. Evans: So we'd have a, a good handoff to the new detectors as they come online in the late 2030s. Feltman: What's on your wish list for, you know, the kinds of science that might become possible with Cosmic Explorer? Evans: So once we're detecting sources out to high redshift—so we really get a sample of everything that's out there in the universe—we get to learn about how, you know, stars have evolved not just around us, the local universe, but even at the peak of star formation, so z of 2, and then farther out towards the beginnings of star formation, when the first stars were being formed. The heaviest of stars came from those times. So we really get to have a kind of cross section of the evolution of the universe going back in time. And in astronomy there's always this feature that the farther away you look, the farther back in time you're looking. Feltman: Yeah. Evans: So we get to look back towards the beginning of the universe, in some sense, with gravitational waves as we look at these sources that are farther and farther away. With Cosmic Explorer we'll have not just one or two but hundreds of thousands of sources from the distant universe. So it's a really exciting way to explore the universe as a whole by looking at this stellar graveyard. Feltman: And for you personally, you know, what questions really motivate you? Why are you so curious about this? Evans: So my history is instrument science. I've always worked with the lasers and the electronics and the mechanical systems; that's where my love of the thing began. And I see Cosmic Explorer as really an extension of our first attempt. The LIGO detectors are the first attempt—first successful attempt, at least to detect gravitational waves, and Cosmic Explorer is the natural [next] iteration of that, where we get to apply all the lessons we've learned from these detectors to make the next generation, which is a much better detector technologically and, and incorporates now decades' worth of, of learning in—on, on the instrument side ... Feltman: Yeah. Evans: And of course, I'm also excited about the astrophysics we do, but for me the first love of that is really the instrument side. So it's a natural extension of everything we've learned over the last decade. Feltman: Yeah, well, and speaking of, you know, the instrument side, the data, the astrophysics, one of the things that I remember most about that initial gravitational-wave detection were just how many people were involved in the paper tied to the announcement—I think there were more than 1,000 co-authors of, of that paper. How many people are, are working on LIGO, on average? Evans: So it's a very interesting question 'cause if you go to the, the number of people you saw on the author list of that first paper, that's the LIGO Scientific Collaboration ... Feltman: Right. Evans: And also Virgo, so the detector in, in Italy. And you get a, a large group of, of scientists—the whole community, essentially, of gravitational-wave scientists is really a global affair, and we're at something like 2,000 people now in that community, depending on how you draw the, the boundaries. The, the people working on the LIGO detector is a smaller group , maybe about 200 people, and many of those are at MIT or Caltech. So the next cut-down would be: 'How many people are actually at the observatories?' And there you get an even smaller number, maybe 50 at each observatory. Feltman: Mm. Evans: And then you say: 'Who's really, like, in the control room, turning the screws, making it better, doing the instrument science in the observatories?' Oftentimes those are graduate students and postdocs. Feltman: Yeah. Evans: So there you get to an even smaller number—five or 10. And of course, all the rest of the community is necessary for that work to be fruitful, but the number of people who are, are there actually with their hands on the machine is relatively small. And I, I point this out because often people think that the—you know, the graduate students will come in and say, 'What can I ever do that's impactful in such a large organization?' Feltman: Yeah. Evans: Well, the truth is that our students and our postdocs are very impactful, and, and they're the ones who are often the ones there, you know, really with their hands on the machine doing the work. Feltman: That's really cool. So obviously, it's really exciting to think about, you know, detecting more of the kinds of phenomena we've seen, seeing them farther out. Is there also any hope of detecting stuff we've never seen before? Evans: Yeah, so let me first say that I'm super excited about the stuff that we already know exists, and we can calculate rates for them, and for every binary black hole system we detect we find some interesting feature. And as we go from 100 detections to 100,000 detections there'll be really fun corner cases that we get to explore, so there will be new things even in our current population. Of course, we also would love to detect something that we've never seen before, but I have no idea how often they happen out in the universe, right? Maybe these are, you know, some strange kinds of supernova that admit copious gravitational waves or cosmic strings or any number of other things that we have not observed. I don't know what the rate will be, but they're very exciting sources, and we'd love to detect them. Feltman: So for folks who are like, 'I'm down here on Earth; what are these gravitational waves and their detection gonna do for me?' Evans: Mm-hmm. Feltman: Are there any exciting things that we might be able to learn from gravitational waves that'll have applications on Earth, besides just the awesome science we're figuring out? Evans: Yeah, so I'm, I'm sad to say we won't be making your cell phones better anytime soon, and I don't think that we'll be transmitting or receiving gravitational waves from your radio devices or using them for wireless or anything like that. However, first, I would say: learning about the universe is, in and of itself, for me, a great objective, and I think that's true for a lot of people ... Feltman: Sure, yeah. Evans: That learning about the universe is a, is a wonderful thing in its own right. However, we also do look at the, the spin-offs that could come from our technology. And we do work on high-precision lasers; we have helped companies develop higher-precision lasers that we then use, but they're used in other applications. Our squeezed light sources are sort of broadly applicable in quantum information and quantum computing. And so we see these spin-offs as interesting things, which are not our primary objective, but yeah, there are technological spin-offs that come from the development we do to make our detectors better. Feltman: Well, thank you so much for sitting down to chat with us and for showing us around. This has been really cool, and I'm really excited to, you know, see what happens when we can look back to the beginning of the universe. Evans: Thanks for the opportunity to talk about this really exciting science. Feltman: That's all for today's episode, but it doesn't have to be. We've posted an extended version over on our YouTube channel, so take a few minutes to go check that out. We'll be back on Friday with an episode I'm super excited to share with you. It's all about Dungeons and Dragons—and also science, I promise. Science Quickly is produced by me, Rachel Feltman, along with Fonda Mwangi, Kelso Harper, Naeem Amarsy and Jeff DelViscio. This episode was edited by Alex Sugiura. Shayna Posses and Aaron Shattuck fact-check our show. Our theme music was composed by Dominic Smith. Subscribe to Scientific American for more up-to-date and in-depth science news. For Scientific American, this is Rachel Feltman. See you on Friday!
Time of India
4 days ago
- Politics
- Time of India
'He taught the Unabomber': Trump claims his uncle was longest serving MIT professor - is it true?
At a campaign-style event in Pennsylvania, Donald Trump made a characteristically meandering claim that his uncle, the late Dr John Trump, was the longest-serving professor in the history of MIT and had taught one of America's most notorious domestic terrorists, the Unabomber. But while the anecdote grabbed attention, almost none of it appears to be true. 'I have to brag just for a second,' Trump said at the Energy and Innovation event hosted by Senator Dave McCormick. 'Although my uncle was at MIT, one of the great professors, 51 years, whatever. He was longest serving professor in the history of MIT… Kaczynski was one of his students.' Referring to the late Ted Kaczynski, who carried out a 17-year bombing spree that killed three and injured 23, Trump added: 'I said, what kind of a student was he? Uncle John, Dr John Trump? He said seriously good. He said he'd go around correcting everybody. But it didn't work out too well for him.' The problem? Kaczynski never attended MIT, and Trump's uncle was not the longest-serving professor there. A spokesperson for the Massachusetts Institute of Technology had earlier told Newsweek that while Dr John Trump had a long and respected academic career at the university, he was not its longest-serving professor. Based on MIT's records, at least 10 professors have served 53 years or longer. by Taboola by Taboola Sponsored Links Sponsored Links Promoted Links Promoted Links You May Like Villas For Sale in Dubai Might Surprise You Dubai villas | search ads Get Deals Undo Trump's uncle worked at MIT in various capacities from 1933 until his death in 1985, with 37 years as a full professor and later roles as senior lecturer and professor emeritus. There's also no evidence that Kaczynski who completed his undergraduate studies at Harvard and earned graduate degrees in mathematics at the University of Michigan, ever studied at MIT. In fact, by the time Trump's uncle had already been working at MIT for decades, Kaczynski was still a child. The real story of the Unabomber Theodore 'Ted' Kaczynski, later dubbed the 'Unabomber' by the FBI, lived in a remote cabin in Montana where he built homemade bombs and conducted a campaign of terror against scientists and industrial targets between 1978 and 1995. A mathematical prodigy, he had taught briefly at Berkeley before turning against modern society. Kaczynski's identity was revealed after his brother recognised his writing style in a published manifesto. He was arrested in 1996, sentenced to life in prison, and died in 2023 aged 81. Despite Trump's repeated anecdotes, the MIT-Unabomber connection appears entirely imagined.
