Featured Author: Kaitlyn Ly
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Chaitanya Gupta, Laiba Farooqi, Sahana Ball
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The Inception of the Nobel Prize
2024 Nobel Laureates of Chemistry, Biology, & Physics
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by Kaitlyn Ly
(Bialkowska, 2016)
Okay so what in the world is that? Do you know?... Yeah, me neither. But, luckily for both you and me, I decided to do some research about this art installation after seeing it plastered in every corner of my recommended page. This piece is titled Can’t Help Myself, and it was created by artists Sun Yuan and Peng Yu for the Guggenheim Museum in 2016 Sun Yuan and Peng Yu are known for their uses of unconventional art mediums to express controversial themes Oftentimes, these themes comment on the state of the world, discussing politics, society, and the habits of the people From this, it’s no surprise that the artists also created another viral piece
This one, albeit slightly more disturbing, continued with Sun and Peng’s habit of unconventional art mediums: Old People’s Home, presents thirteen sculptures, propped up in electric wheelchairs, roaming across a floor. The figures are extremely life-like, with wrinkles and splotches on their skin, as well as dull, aged eyes And yes, I also get slightly terrified whenever one of these sculptures pop up while I scroll through videos of kittens Apparently, you can’t squeal at those cute compilations in peace Nevertheless, the true meanings of these pieces have been debated and discussed throughout many comment sections, and from these comments, I’ve decided to formulate my own amalgamation of meanings and you, the dear reader, shall be subjected to it. Let’s begin!
Firstly, with Can’t Help Myself, I interpret the piece to be a reflection of the human cycle: the whole concept of living for labor and dying from labor. The robot was programmed with thirty-two human-like actions, including the “scratch an itch” and the “bow and shake.” The robot continuously went around trying to collect the red fluid surrounding it, over and over again, until it powered down in 2019 after running out of the hydraulic fluid that energized it But through this, the robot served its intended purpose: to do a job and subsequently die after it wasn’t able to anymore Therefore, in some scope, this piece reflects the human cycle and the purpose of the individual But, beyond this convoluted attack on human nature, Can’t Help Myself works to be rather controversial for more deeper reasons It’s no secret that automation has slowly been overtaking the world in the previous years. Overtime, automation has progressed, and the development of artificial intelligence has assisted in such maturation. The usage of both automation and artificial intelligence has spread to more artistic fields, from illustration to animation, composition to creation.
by Kaitlyn Ly
Because of this, many have expressed their deep concerns and frustrations within this development, and others have argued for the immorality of such uses. Can’t Help Myself can be found as another example of unwelcomed un-human creations, due to its robot figure But, this creation was made by a human, and because of such details, this becomes a much more murky situation, resulting in a more objective question of whether the piece remains ethical or stays more unethical
Next, with Old People’s Home, other than to be slightly daunting and discomforting, I believe the piece’s purpose was to discuss how any individual, no matter the life they lived or the accomplishments they’ve achieved, can end with the same fate. In the piece, the sculptures just slowly stalk across a floor on electric wheelchairs, unmoving, unchanged, and completely idle to any new advancements. This installation therefore appears to comment on how time will always overtake people; an individual with power will always succumb to time’s sword of aging, but I guess that’s what SPF is for. This piece, though less ethically questionable, furthers the specific ideas of the artists, and the thoughts they have consistently chosen to express Their ideas seem to comment on the human condition, comparing humans to machines that have continued on and on, only to have rusted over
Returning back to the artists Sun Yuan and Peng Yu, we now have a slightly better grasp of the messages and themes they wish to express through their form of modern art
However, as most modern art tends to be, some of Sun and Peng’s art have been rather controversial. This controversy was due to the lack of ethics in their Dogs That Cannot Touch Each Other, where pitbulls were tied to treadmills, attempting to attack each other, but being pulled back by their ties.
The piece was supposed to comment on systems of power and control by enacting cruel provisions onto the animals. This, however, creates an entire metaphor of how a system of constant control over another group will always exist, even if you’re attempting to reflect upon the system However, this should not justify the cruelty acted upon the dogs in order to replicate the model This appears to be some gross sense of irony, in that the message is expressed by reenacting the cruelty of the message The authority of morals and ethics should not be whittled down in order to express specific themes, even in modern art and despite the wide range of possibilities with art, there are boundaries that should not be ignored. But nonetheless, despite what I say, these boundaries can and will be ignored for the sake of artistic expression, serving as a firm reminder of the fluidity of art, and the fact that thinking about it too much hurts my brain. Nevertheless, no matter how daunting modern art may be, make sure to check up on new pieces every once in a while, just to gain a new perspective on things. So long, and fun reading!
Alfred Nobel was born in 1833 to a poor Swedish family. His father, an engineer, struggled with multiple business failures until he turned to manufacturing explosives, which eventually brought the family wealth This allowed Nobel to receive an elite education and study abroad. While in the U.S., Nobel worked with engineer John Ericsson, earned a patent for a gas meter, and developed an interest in explosives, particularly nitroglycerin, a highly unstable compound
Returning to Sweden, Nobel focused on making explosives safer. In 1863, he invented the detonator, followed by the blasting cap in 1865, and in 1875, he created gelignite, a more stable explosive His innovations were widely adopted by armament factories, and by his death, over ninety such factories used his technologies. As Nobel gained wealth and fame, he faced criticism for his role in making weapons more deadly. Worsening Nobel’s guilt was the death of his brother, Ludvig, in an explosion at one of his factories. This prompted Nodel to reconsider the consequences of his inventions and, seeking redemption, Nobel decided to use his fortune to fund prizes for those who benefited humanity. This led to the creation of the Nobel Prize, a legacy dedicated to scientific, cultural, and peace efforts, which persists till this day
“Cultureishowbiologyrespondsand makesitsliving conditionsbetter.”-C.J.Cherryh
The human body can be incredibly genetically diverse, thanks to all of the differences arising from gene regulation in our cells. Victor Ambros and Gary Ruvkun are among the few scientists who took the field of genetics to a different level, explaining the unique development and functions of various organisms. But who are these revolutionary scientists? Victor Ambros is an MIT graduate who has been researching genome structure and replication for decades. During his postdoc in H. Robert Horvitz's lab at MIT, he focused on C. elegans, studying the genetic pathways controlling developmental timing in the nematode The many mentors and professional researchers he met along his professional journey guided him to his ambitions. During The Nobel Prize organization's First Reactions interview series, Ambros recalled Edward Gruberg, a fellow postdoc, who he described as "a fantastic mentor that really got me alerted to the fact that you can find out new things doing sort of routine experiments." Gary Ruvkun is also an experienced molecular biologist, as a UC Berkeley graduate and professor of genetics at Harvard Medical School Gary Ruvkun is interested in a plethora of scientific phenomena, from immunity and aging to life on other planets, which contributes to his desire to discover new, life-changing scientific methods Ruvkun studied biophysics, eventually earning his PhD in biophysics from Harvard University, going on to complete his post-doc in H. Robert Horvitz’s lab at MIT. As you may notice, this is the same lab where Ambros pursued his post-doc, which is how they met, starting a strong friendship and exciting journey of scientific advancement Together, they worked to discover the secrets of RNA and genetics in the lab.
Designed By: Ivanka Deshpande
Edited By: Khushee Goel
Victor Ambros and Gary Ruvkun won the 2024 Nobel Prize in Physiology or Medicine for the discovery of microRNAs and their role in post-transcriptional gene regulation
Using C. elegans, Ambros was able to show that the lin-4 gene encoded a small RNA, and Ruvkun demonstrated that this microRNA was complementary to lin-14 mRNA in a binding site that blocked protein synthesis First published in 1993, their discovery revealed a new regulatory pathway at the level of gene expression and showed that microRNAs could control gene expression without making proteins A few years later, Ruvkun discovered a conserved let-7 microRNA across many species, demonstrating the universal importance of microRNA
Their research showed microRNAs control large-scale networks of genes, which affects cellular development, differentiation, and environmental response Misexpression of microRNAs has been known to cause diseases, such as cancer, birth defects, and autoimmunity Their results also found that the mechanisms of microRNAs evolved hundreds of millions of years ago and are essential for normal cell operation.
The findings of Ambros and Ruvkun changed our understanding of gene regulation by proliferating the salience of microRNAs in genetic governance in all multicellular organisms
“Allthatglittersmaynotbegold,but atleastitcontainsfreeelectrons.”
-JohnDesmondBernal
by Sahana Balu
John Michael Jumper is an American chemist and computer scientist, who is celebrated for his groundbreaking work in artificial intelligence and protein structure prediction. Currently he is the director of Google DeepMind, and known as the co-creator of AlphaFold, an AI model that predicts protein structures with high levels of accuracy.
John M. Jumper was born in 1985 in Little Rock, Arkansas and had exhibited a strong academic prowess early on. He earned a Bachelor of Science in physics and mathematics from Vanderbilt University, followed by a Master of Philosophy in theoretical condensed matter physics at the University of Cambridge as a Marshall Scholar He further pursued his passion for theoretical chemistry by obtaining both a Master of Science and a Ph D at the University of Chicago under the guidance of Tobin R Sosnick and Karl Freed
AlphaFold, one of John M Jumper’s accomplishments, employs deep learning to predict the threedimensional structures of proteins based on their amino acid sequences In 2020 AlphaFold established itself as a tool for advancing biological and medical research when it outperformed competing algorithms at the CASP competition The impact of this work has been widely acknowledged, with Jumper being named among Nature's 10 in 2021 and receiving prestigious accolades such as the Breakthrough Prize in Life Sciences (2023) and the Nobel Prize in Chemistry (2024), which he shared with Demis Hassabis.
Jumper's contributions have not only earned him widespread recognition but have also introduced new ideas in computational biology, encouraging a deeper understanding of the fundamental building blocks of life.
John Michael Jumper is an American chemist and computer scientist, who is celebrated for his groundbreaking work in artificial intelligence and protein structure prediction. Currently he is the director of Google DeepMind, and known as the co-creator of AlphaFold, an AI model that predicts protein structures with high levels of accuracy.
John M. Jumper was born in 1985 in Little Rock, Arkansas and had exhibited a strong academic prowess early on. He earned a Bachelor of Science in physics and mathematics from Vanderbilt University, followed by a Master of Philosophy in theoretical condensed matter physics at the University of Cambridge as a Marshall Scholar He further pursued his passion for theoretical chemistry by obtaining both a Master of Science and a Ph D at the University of Chicago under the guidance of Tobin R Sosnick and Karl Freed
AlphaFold, one of John M Jumper’s accomplishments, employs deep learning to predict the threedimensional structures of proteins based on their amino acid sequences In 2020 AlphaFold established itself as a tool for advancing biological and medical research when it outperformed competing algorithms at the CASP competition. The impact of this work has been widely acknowledged, with Jumper being named among Nature's 10 in 2021 and receiving prestigious accolades such as the Breakthrough Prize in Life Sciences (2023) and the Nobel Prize in Chemistry (2024), which he shared with Demis Hassabis.
Jumper's contributions have not only earned him widespread recognition but have also introduced new ideas in computational biology, encouraging a deeper understanding of the fundamental building blocks of life.
“Relativityappliestophysics,not ethics.”
-AlbertEinstein
by Chaitanya Gupta
John Joseph Hopfield, professor emeritus of the acclaimed Princeton University, is an American physicist best known for his work on the development of associative neuronal networks, and of these, the Hopfield network; the latter of which would endow him the Nobel Prize alongside Geoffrey Hinton. Beyond the 2024 award, however, Hopfield has also been awarded major interdisciplinary physics prizes for his work in condensed matter, statistics, and biophysics, such as the Boltzmann and Dirac Medals
Born in Chicago, 1933, to Physicists John Joseph Hopfield (rather, the anglicized J J H; born in Poland as Jan Józef Chmielewski) and Helen Hopfield, John Hopfield’s educational pursuits were well fomented by his family He obtained a doctorate in Physics from Cornell University, from his dissertation on “the quantummechanical theory of the contribution of excitons to the complex dielectric constant of crystals,” under the acclaimed physicist Albert Overhauser.
For the early half of his career, John Hopfield specialized in the structure and function of the hemoglobin molecule, to create a quantitatively accurate model for the process of cooperation (in activating the active site of the enzyme). He would later transition into the theory group of Bell Laboratories on the optical properties of semiconductors with David Gilbert Thomas Later in his career, Hopfield would work on Computation alongside associative neuronal lectures (a combination of other neuronal networks, capable of providing data approximations and learning without the need for retraining) His lectures at Caltech would inspire the university to adopt the Computation and Neural Systems pHD program
Inspired by the spin glass, magnetic states effectively comparable to random, Hopfield developed his namesake network as a form of content-addressable memory (a computer necessary for high-speed calculations and searching). His work would be later optimized by Dmitri Krotov to increase memory. Despite his involvement in the creation of highly advanced neural networks, Hopkins remains adamant in his call on the termination of A.I development; he has cited the uncontrollability of A.I alike to that of nuclear fission.
Geoffrey Hinton, professor emeritus of the acclaimed University of Toronto, is a British-Canadian computer scientist best known for his work on the Boltzmann machine derived from the spinglass models of the Hopfield network; which would endow him the Nobel Prize alongside John Hopfield. Beyond the 2024 award, however, Hinton has also been awarded major computer science prizes for his work such as the Turing Award and James Clerk Maxwell Medal
Born in Wimbledon, 1957, Geoffrey Hinton worked at the University of Sussex and the MRC Applied Psychology Unit; after various difficulties in encountering appropriate funding, Hinton transferred to the University of California, San Diego and Carnegie Mellon University From 1987 onwards, he has been affiliated with the University of Toronto’s computer science department. His 2004 work in the NCAP (Neural Computation and Adaptive Perception, now the Learning in Machines and Brains Unit) would allow him to win the 2018 Turing Award, for his attempt at creating a neural network capable of replicating the human brain.His later 2017 work on capsule neural networks, a neuronal network-system built to simulate biological neural organization (with neuron-like capsules whose outputs are used to communicate with ‘higher’ capsules), would be “finally something to work well,” in his own words His work would be revolutionized by his “Forward-forward” algorithm, an attempt to streamline the process of reaching a given output by only making forward (rather than recursive backward) steps which also negatively assess the environment However, Hinton’s main claim to fame (alongside the reason for his nomination and awarding of the Nobel Prize) would be his usage of the Hopfield network to create a Boltzmann machine. This Boltzmann machine, really capable of simulating the physics of a spin-glass model with an external field to simulate randomness, would be then applied to a series of boolean outputs mimicking neurons.Despite devoting his life’s work to the study of artificial intelligence, and improving neural networks with countless innovations, Hinton has staunchly advocated against the dangers of AI, particularly in spreading misinformation, or forming an extinction-level event; going so far as to publicly resign from Google Brain, his workplace for some 10 years between 2013 and 2023
by Chaitanya Gupta
Geoffrey Hinton and John Hopfield did not cross paths traditionally in their line of work; multiple decades separated their specific contributions, in the same delay between the awarding of the Nobel Prize in Physics to the aforementioned researchers and their innovations Despite this, both scientists made substantial steps necessary to accomplish the means necessary for the creation of advanced neuronal networks, inventions which substantially empower the A I revolution The collaborations between Hinton and Hopfield originated from encountering their respective work rather than mutual collaboration, yet the significance of their creations have fomented an unprecedented period of technological growth. The Nobel Prize in Physics was really an amalgama of various fields, each with substantial contributions; of these, perhaps the key insight was that offered by psychology, and the inherent plasticity of neurons. From scientific theories, the Hebbian hypothesis sprung; neurons that fire together must also wire together. In other words, the creation of an “activity,” that the brain remembers, is merely an interwoven system of neurons that were activated during the respective activity. Perhaps, the insight to mimicking human thinking was to manipulate inputs and outputs, control an activity, to perhaps find the optimal configuration of neuronal wiring (as per its success in achieving a goal)
The key to Human thought is adaptability, spurred by the plasticity of these neuronal connections An artificial intelligence does not pave new paths initially; it learns, and later applies thought, similar to how a baby undergoes development The goal is clear; a human, once capable of driving one specific type of car, can more or less drive any car. If an artificial intelligence understands how to apply thought to one scenario, perhaps it can do the same for another.
Hopfield was inspired by these Hebbian linkages; he was not the first to debut such understandings into a system of neuronal networks. He was, however, the first to apply statistical physics to the equation. He modeled the system to be a spin-glass, a complicated situation “to be solved.” This system, effectively random, composed of independent “nodes,” each a separate neuron These neurons stored data, in the form of patterns These patterns could then be applied to a solution Imagine a landscape of varying topography, a somewhat erratic (and absurdly compact) system of “crests” and “troughs ”
If, then, a ball was to roll down the cliff, what path would most optimally simulate a saved pattern (that is ever-so-slightly differentiated)? This, in analogy, is Hopfield’s network. With saved memory, in the form of neurons, Hopfield’s network attempted to generally apply a memory-space into an output The goal of Hopfield’s network, unlike most other neuronal applications, was to apply Hebbian thinking to a problem of statistical physics, with the latter effectively storing memory for the first
Hinton, who previously involved himself with psychology, was captivated by the application of Hebbian thought into a neuronal network. Yet, he increased the efficiency of Hopfield’s network into (what he called) the Boltzmann Machine. The Boltzmann Machine consists of two node-sets; one “visible,” and the other “hidden.” The visible “nodes” are the outputs, which an outside observer may assess; the hidden “nodes” are the inputs. This system, which Hopfield approximated by a spin-glass system, can be approximated with statistical physics.
A rule consistently runs through the machine, changing specific inputs to monitor a specific change in the collective output. The changing state of each particle cannot be directly measured, as each particle in a hurricane cannot be assessed in a physical model; rather, their collective states are taken into account Although each particle of the hurricane moves about, its properties are consistent Although the inputs are altered, they collectively form the same result Per statistical physics, all possible states are not equally likely; some situations are more prevalent than others With this Boltzmann distribution, dependent on the system’s energy as described by Bolzmann’s equations, the machine’s output can be approximated. Regular computing learns from specific instructions; the A.I built around Hinton’s and Hopfield’s Boltzmann Machine learns from examples, which alter each neuron.
The Boltzmann Machine, derived from decades of scientific advances in innumerable fields, endowed its creators with the 2024 Nobel Prize in Physics. Its applications range from finding substantial data for the Higgs boson to the “household” chat-bots emerging after the pandemic The advances of Hinton and Hopfield have shaped the radical incline of technological growth in the few years they have existed The “history” of these advances is a history in the making, one which will remain crucial to humanity in the millenia arriving
by Arjun Dasgupta
John J Hopfield and Geoffrey Hinton have been awarded the 2024 Nobel Prize in Physics for using physics to train artificial neural networks in machine learning
Hopfield proposed the Hopfield network, which mimics a brain’s memory function using the laws of physics
One such law states that lower energy states are more stable - these lower energy states correspond to the stored images in the Hopfield network. The Hopfield network is composed of nodes (that mimic neurons) that are interconnected. The network adjusts the connections between the nodes with the goal of achieving more stable, lower energy state connections for desired patterns (e.g. stored images). Later on, if the network receives a distorted or incomplete version of the pattern or image, it will be able to recall the original pattern or imagine by minimizing the energy of the received version to match the original.
Hinton elaborated on the underlying concept of the Boltzmann machine, which allows machines to learn patterns based on some principles of statistical physics. The Boltzmann machine helped foster current advances in speech and image recognition and processing Albeit a little unconventional, Hopfield and Hinton won the Nobel Prize for Physics for their incredibly useful application of physics to advance machine learning
The Future of AI: Insights from Geoffrey Hinton. https://www.linkedin.com/pulse/future-ai-insights-from-geoffrey-hinton-igor-vangemert-yrvsf. Accedido 18 de noviembre de 2024.
«At Caltech, John Hopfield Wondered If Computers Could Think; He Just Received a Nobel Prize».
Pasadena Star News, 8 de octubre de 2024, https://www.pasadenastarnews.com/2024/10/08/former-caltech-professor-awardednobel-prize-in-physics/.
«The Nobel Prize in Physics 2024». NobelPrize.Org, https://www.nobelprize.org/prizes/physics/2024/popular-information/. Accedido 18 de noviembre de 2024
Bialkowska, E. (2016). Can't Help Myself. Galleria Continua. Retrieved December 8, 2023, from https://www.galleriacontinua.com/artists/sun-yuan-peng-yu-73
Old people's Home. (2007). Galleria Continua. Retrieved December 8, 2023, from https://www.galleriacontinua.com/artists/sun-yuanpeng-yu-73
The Robert H N Ho Family Foundation Collection (2016) Sun Yuan and Peng Yu | Can't Help Myself | The Guggenheim Museums and Foundation Guggenheim Museum Retrieved December 8, 2023, from https://www guggenheim org/artwork/34812
McCullough, T A (2020, August 16) Sun Yuan and Peng Yu – Can't Help Myself 2016 – Tara A McCullough Art Blogs Retrieved December 8, 2023, from https://blogs ed ac uk/s1709347 art-practice-4-2020-2021yr/2020/08/16/sun-yuan-and-peng-yu-canthelp-myself-2016/
Katz, B , & Brown, T (2017, September 28) What to Know About the Controversy Surrounding the Chinese Art Exhibit Coming to the Guggenheim Smithsonian Magazine Retrieved December 8, 2023, from https://www smithsonianmag com/smart-news/questionsanimal-cruelty-artistic-freedom-swirl-over-guggenheim-exhibit-180965045/
Greenberger, A. (2022, January 13). 'Me Watching Y'all Cry Over a Robot Scooping Red Paint': Sun Yuan and Peng Yu Installation Becomes Bizarre Viral Hit on Social Media. Art News. Retrieved December 8, 2023, from https://www.artnews.com/artnews/news/sun-yuan-peng-yu-cant-help-myself-twitter-tiktok-1234615686/
Lekka, S. (2019, May 12). industrial robot continuously sweeps blood-like fluid in sun yuan + peng yu's 'can't help myself'. Designboom. Retrieved December 8, 2023, from https://www.designboom.com/art/sun-yuan-peng-yu-cant-help-myself-robot-venice-artbiennale-05-12-2019/
“NSF Congratulates Laureates of the 2024 Nobel Prize in Physics.” NSF - National Science Foundation