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All 24 Russell Group universities have reviewed their academic conduct policies and guidance
UK universities have drawn up a set of guiding principles to ensure that students and staff are AI literate, as the sector struggles to adapt teaching and assessment methods to deal with the growing use of generative artificial intelligence.
Vice-chancellors at the 24 Russell Group research-intensive universities have signed up to the code. They say this will help universities to capitalise on the opportunities of AI while simultaneously protecting academic rigour and integrity in higher education.
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Harvard students and supporters marched through Harvard Square during a rally on Saturday to oppose the Supreme Court’s ruling against affirmation action.
Enlarge / A myriad of ways one might react to Gfycat's closure, trending on Gfycat itself at the moment. (credit: Gfycat)
The Internet continues to get a bit more fragmented and less accessible every week. Within the past seven days, Reddit finished its purge of third-party clients, Twitter required accounts to view tweets (temporarily or not), and Google News started pulling news articles from its Canadian results.
Now there's one more to add: Gfycat, a place where users uploaded, created, and distributed GIFs of all sorts, is shutting down as of September 1, according to a message on its homepage.
Users of the Snap-owned service are asked to "Please save or delete your Gfycat content." "After September 1, 2023, all Gfycat content and data will be deleted from gfycat.com."
On July 1, laws requiring adult websites to verify user ages took effect in Mississippi and Virginia, despite efforts by Pornhub to push back against the legislation. Those efforts include Pornhub blocking access to users in these states and rallying users to help persuade lawmakers that requiring ID to access adult content will only create more harm for users in their states.
Pornhub posted a long statement on Twitter, explaining that the company thinks US officials acting to prevent children from accessing adult content is "great." However, "the way many elected officials have chosen to implement these laws is haphazard and dangerous."
Pornhub isn't the only one protesting these laws. Last month, the Free Speech Coalition (FSC) sued Louisiana over its age-verification law, with FSC Executive Director Alison Boden alleging that these kinds of laws now passed in seven states are unconstitutional.
Enlarge / This is Apple’s Vision Pro headset. It looks a bit like a particularly bulky pair of ski goggles, with the materials and design language of Apple’s AirPods Max headphones. (credit: Samuel Axon)
Apple has been forced to make drastic cuts to production forecasts for the mixed-reality Vision Pro headset, unveiled last month after seven years in development and hailed as its most significant product launch since the iPhone.
The complexity of the headset design and difficulties in production are behind the scaling back of targets, while plans for a more affordable version of the device have had to be pushed back, according to multiple people with direct knowledge of the manufacturing process.
Apple has already flagged that the $3,500 “spatial computing” headset device will not go on sale until “early next year,” a lengthy gap from its June 5 launch. Analysts have interpreted this as being more to do with supply chain problems than allowing developers time to create apps for the Vision Pro.
This was originally published at The Rumpus on April 24, 2017.
By mid-morning, it was so hot her breath felt as if it were being drawn back into her. She took the tin washbasin out to the front yard, filled it with cold water, and shampooed her hair. If she turned her head, she could watch her reflection in the kitchen window as she leaned over the tub. Her hips seemed so wide in that position, tapering down from the wraparound skirt to legs that were girl-like. She watched her hair turn from yellow to brown with the wetness.
Around noon, with her hair now sticking to the back of her neck with perspiration, she heard the screen door slam once, then again. It was odd for him to come home in the middle of the day.
She went to the kitchen but he was already gone. This was the way he did things. She looked at the kitchen table for a box, some sign of the gift she was sure he would sneak in and leave her just as he had every anniversary. She heard his truck backing down the dirt drive. There was no chance she’d catch up with him.
This time of day, the sun came in through the slatted windows and settled on the yellow linoleum in stripes. Now she saw it. There lay her gift, basking in the sunlight. A gray-green lizard the size of a shoe. It stood so still she thought it was fake. A joke he had played on her, like the time he told her he was fixing the kitchen faucet and put a gag faucet where the real one had been. She remembered how she ducked and screamed, thinking she would be splashed with water when the new faucet came off in her hands.
But this was not plastic. He had tied a long piece of thick string from one of the lizard’s ankles to the kitchen table. Around the neck was a thin yellow crinkly ribbon that she had seen him pull out of the junk drawer the day before. She had suspected it was to wrap her gift. The ribbon was tied sideways around the animal’s neck in a bow. The lizard squinted as it turned its head slowly to look around the room. Its bulgy, liquid eyes scared her. She moved and the thin plates of skin on its back stood up. Now it turned its head swiftly and the scales rippled as if it were shivering.
She heard herself sigh, rubbed her hands on her skirt, and walked toward the white pine cupboards, making a full circle around the lizard’s body. It watched her. She found an aluminum pie pan under the sink and grabbed the pitcher of cold water from the refrigerator. She put the pan on the floor, poured the water in, and inched it over to the animal with a broom, backing away quickly and waiting to see if it would drink. The lizard sat on its squat legs and narrowed its lids into slits like cat’s-eye marbles. It appeared to be asleep.
Throughout the day, she kept going to the kitchen to check on it, afraid it might get loose in the house. In the late afternoon, she stood a distance away and threw a leaf of Bibb lettuce by the pie pan. She didn’t want anything to do with it, but she didn’t want it to starve. The creature, startled, was set into motion, skittering back and forth, first in one direction, then another, yanking itself back again and again by the string. For a while, she took a seat across from it, leaning forward. I’m sorry, I’m sorry, she said.
She finished cleaning the house and had no choice now but to come back to the kitchen. She had to clear out everything to wash the floor, which meant moving the tables and chairs and putting it somewhere. Outside was where she wanted it. She could tell him it escaped, ran away. But that wouldn’t be honest and if they had promised each other anything when they married, it was honesty. Letting his gift run away, or rather, pushing his gift out the door, wouldn’t be a white lie. It would be flat-out deception.
She moved the chairs into the hallway and tried to untie the string, cursing him for making a knot she couldn’t undo. She went to the junk drawer, took out the scissors and, grasping the string, clipped it quickly and led the lizard toward the kitchen door, then the porch, like a dog on a leash. When she opened the screen door, the lizard tried to run back inside, as if it were afraid of the outdoors. She pulled it along, but it planted all four paws firmly on the floor. Its nails made a pitiful sound on the linoleum, then became stuck on the doorjamb. She gave a tug and over it rolled, like a child’s toy truck. Another tug, and it was up again and furious and ran towards her. It followed her the whole length of the porch until she scooted over the banister and tied it to one of the posts. She walked around to the back of the house and let herself in.
What a gift, she thought. Her present for him was wrapped and put away in a bedroom drawer days before he suggested they skip gifts this year. She had bought him a new jacket and white shirt. She undid the ribbon to look at them, then replaced the clothes and surrounded them with tissue paper. They looked so nice she took the shirt out again and held it up to her cheek. It felt so crisp and cool.
When the day had cooled, she bathed and changed into a fresh cotton dress and lifted her hair away from her neck to pin it up.
*
“What’s it doing out there?” he said when he came home. “Don’t you like it?”
On the table, she had put a candle and the gift box in navy blue paper and the good dishes, but he didn’t look at those.
“What’s it doing?” she said absently, for she had taken him to mean that the thing was doing something interesting or different and that she should go and look.
The lizard stood very still, as if it might be dead. The bow was gone.
“Why’d you put it out there?” he said.
“Because it belongs out there,” she said as she closed the screen door.
From the heat, his black hair had separated into individual strands, making him look older and scraggly.
“You didn’t like it,” he said and began to follow her around the kitchen.
She retrieved his favorite pasta dish from the oven and the salad from the refrigerator and he followed right behind. Their bodies made a shadow on the yellow floor that looked like the silhouette of two shy, hesitant boxers in a ring.
“Oh, I like it,” she said. She was intent on getting the dinner ready and didn’t look at him. “I like it just fine. You didn’t pay any money for it, did you?”
His face looked tight.
She motioned toward the window with her cooking mitt. “It’s just that there’s a million of them out there, and it’s a shame to throw away good money after one.”
“I bought it, all right? Cheap. From a guy at work. I thought you’d like it. I thought you’d think it was funny.”
“I do think it’s funny. I laughed.”
“It’s really neat,” he said, trying to convince her. “It looks prehistoric or something.”
She made him sit through dinner before opening his package.
She expected him to say, I thought we agreed, but he didn’t. Instead, he looked eager, put his glass down, and said, “Well, let’s see what this is.”
He seemed stunned for a moment when he saw the clothes and then whistled low as he lifted them out of the box. He felt the material, ran his fingers down the length of the lapel, and smiled at her. “This is a good one. But what‘s it for? God knows there’s nowhere around here to wear this.” And then he laughed and said, eyes crinkling, “What have you got up your sleeve? I think you must be up to something, baby doll.”
“They’re interview clothes. You’ll need something nice to interview in if you try to get transferred back home or if you go to another company. Isn’t that why we came here? So you’d have a better job after this one? The next step up, you said.”
He went back to examining the jacket, rose half out of his chair and sat down again.
“Isn’t it?” she repeated and motioned with the back of her hand to the open bedroom door. “Try it on.”
He was standing now. He had the jacket on and went to the mirror, looking at himself this way and that, sizing up every angle.
“I told you,” he said. “I’ve got to put in a couple of years first before I’d even try to move on. You don’t just go looking for another job when you’ve hardly been here. You have to pay your dues.” He ran his hand through his hair. “I was hoping that once you were here for a while, you’d like it.”
“What’s there to like?” she said. She began biting some ragged skin on her bottom lip. She fingered the rim of her glass. She knew her voice sounded bitter but she didn’t care. “You told me about the place. Patience, you said. You’d have to be brain-dead to have this much patience. To want to live here. You’d have to be a fool.”
He stepped in front of her. “I’m a fool then,” he said, sticking his hands in his pockets.
“You’re a fast learner. Everyone has always told you that. You’ll find another job. You don’t have to stay at that place.”
“You don’t want me to blow what I have, do you? If they get wind of me applying other places it won’t look good. And if I go in there now and ask the boss for a transfer back to where I came from, they’d die laughing. There are other guys, ahead of me, willing to pay their dues.”
She thought of those other men and what they and their wives must be like to be so patient, so accepting. She found herself wondering, for the first time since they had been together, what other kinds of men she could have married. Maybe I should have waited, she thought. And then she thought, I’ve heard about this. This is how things change.
“You act as if I don’t know what I’m talking about,” he said. “They said I’d have to wait two years for a transfer. At least two years.”
“Oh, great,” she said, fingering the glass again. “I’ll be dead in two years in a place like this.”
He smiled at her. “There she is. My melodramatic sweetheart.”
He removed his jacket and draped it neatly over his chair. He stepped behind her and put his arms around her.
“Look,” he said. “Baby doll. This is nothing. We’ll laugh about this later. It’ll be a story. Like a joke about how many miles we walked to school when we were kids.”
She looked through the window to where there was a thin stream of orange light across the horizon and nothing more. Some people might think the sight was beautiful. To her it had become barren.
“Let’s eat,” she said. “It’s getting cold.”
And in the end, after they had finished dinner and lain together and after she waited for the movements of his body to cause hers to shiver, she turned on her side and closed her eyes. He put his hand on her hip and said in a whisper, “Baby doll? You still awake?”
She was in the lazy space between wakefulness and sleep and, so, didn’t answer. She thought she heard the animal stumbling off the porch, down the steps, and into the night, finally free.
Before she dreamed, an image came to her of the liquid eyes. As she began to fall asleep, her body jerked, quick and hard. She felt as if she were jumping straight up into darkness.
***
Rumpus original art by Aubrey Nolan.
Pay cuts are just one factor: working conditions are also getting worse and that’s bad for both staff and students
Since late April, staff at 145 UK universities have been refusing to mark students’ work. The marking and assessment boycott is the most recent action by the University and College Union (UCU), which represents academics and other university staff. With graduation ceremonies now upon us, the boycott is causing significant havoc. Just how significant is a matter of some dispute. But what is indisputable is that many students have had their marks delayed, and some will be unable to graduate as normal this summer.
Industrial action by (mainly) academic staff is always a hard sell. Lecturers are seen as relatively privileged people. The students being hit by their latest action have already had their studies disrupted by a pandemic and a series of strikes. Seen this way, the current marking boycott can look like a selfish step too far.
Lorna Finlayson is a philosophy lecturer at the University of Essex
Continue reading...The last post on Lefebvre’s banned books was one I wrote a few days ago but hadn’t quite finished. I’m posting it now and expect it will be the last substantive post for some time on Progressive Geographies.
I am currently in hospital undergoing some tests and awaiting surgery. The condition is serious but treatable, and I am expected to make a good, though slow recovery.
I’ll hopefully be back before too long. Many thanks for reading this site and hopefully the archive and resources remain useful.
stuartelden
“Proof differs from analysis. Proof establishes that something happened. Analysis shows why it happened. Proof is a mode of argument that is, by definition, complete; but the price of its completeness is that proof is always formal. Only what is already contained in the beginning is proven at the end. In analysis, however, there are Read More
The post “Proof differs from analysis. Proof establishes that something happened. Analysis shows why it…” appeared first on Philosophy News.
Group issues 12-point plan calling for stricter immigration rules for care workers, students and refugees
Rishi Sunak is facing demands from “red wall” Conservative MPs to slash the number of overseas care workers, foreign students and refugees allowed into the UK in time for the next election.
The MPs from the 2017 and 2019 intake, who call themselves the New Conservatives, have issued a 12-point plan to cut net migration to Britain from 606,000 to 226,000 before the end of 2024.
A cap of 20,000 on the number of refugees accepted for resettlement in the UK.
Caps on future humanitarian schemes such as the Ukraine, Afghanistan and Hong Kong schemes should the predicted 168,000 reductions not be realised.
Implementation of the provisions of the illegal migration bill, which it is claimed would lead to a reduction of at least 35,000 from LTIM.
A raise in the minimum combined income threshold to £26,200 for sponsoring a spouse and raising the minimum language requirement to B1 (intermediate level). This should lead to an estimated 20,000 reduction in LTIM, the MPs claim.
Making the migration advisory committee report on the effect of migration on housing and public services, not just the jobs market, by putting future demand on a par with labour requirements in all studies.
A 5% cap on the amount of social housing that councils can give to non-UK nationals.
Raising the immigration health surcharge to £2,700 per person a year.
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It was just published yesterday, HERE. The issue consists of interviews about Heidegger with a number of people, myself included.
doctorzamalek
Camp counselors leading a group of youth campers on a wilderness excursion had to fight off a bobcat attack on Selden Neck Island in Connecticut on Friday. The New York Times writes that three adults were hospitalized and the animal was killed. — Read the rest
Every person starts as just one fertilized egg. By adulthood, that single cell has turned into roughly 37 trillion cells, many of which keep dividing to create the same amount of fresh human cells every few months.
But those cells have a formidable challenge. The average dividing cell must copy—perfectly—3.2 billion base pairs of DNA, about once every 24 hours. The cell’s replication machinery does an amazing job of this, copying genetic material at a lickety-split pace of some 50 base pairs per second.
Still, that’s much too slow to duplicate the entirety of the human genome. If the cell’s copying machinery started at the tip of each of the 46 chromosomes at the same time, it would finish the longest chromosome—No. 1, at 249 million base pairs—in about two months.
“The way cells get around this, of course, is that they start replication in multiple spots,” says James Berger, a structural biologist at the Johns Hopkins University School of Medicine in Baltimore, who co-authored an article on DNA replication in eukaryotes in the 2021 Annual Review of Biochemistry. Yeast cells have hundreds of potential replication origins, as they’re called, and animals like mice and people have tens of thousands of them, sprinkled throughout their genomes.
“But that poses its own challenge,” says Berger, “which is, how do you know where to start, and how do you time everything?” Without precision control, some DNA might get copied twice, causing cellular pandemonium.
Keeping tight reins on the kickoff of DNA replication is particularly important to avoid that pandemonium. Today, researchers are making steps toward a full understanding of the molecular checks and balances that have evolved in order to ensure that each origin initiates DNA copying once and only once, to produce precisely one complete new genome.
Bad things can happen if replication doesn’t start correctly. For DNA to be copied, the DNA double helix must open up, and the resulting single strands—each of which serves as a template for building a new, second strand—are vulnerable to breakage. Or the process can get stuck. “You really want to resolve replication quickly,” says John Diffley, a biochemist at the Francis Crick Institute in London. Problems during DNA replication can cause the genome to become disorganized, which is often a key step on the route to cancer.
Some genetic diseases, too, result from problems with DNA replication. For example, Meier-Gorlin syndrome, which involves short stature, small ears, and small or no kneecaps, is caused by mutations in several genes that help to kick off the DNA replication process.
It takes a tightly coordinated dance involving dozens of proteins for the DNA-copying machinery to start replication at the right point in the cell’s life cycle. Researchers have a pretty good idea of which proteins do what, because they’ve managed to make DNA replication happen in cell-free biological mixtures in the lab. They’ve mimicked the first crucial steps in initiation of replication using proteins from yeast—the same kind used to make bread and beer—and they’ve mimicked much of the entire replication process using human versions of replication proteins, too.
The cell controls the start of DNA replication in a two-step process. The whole goal of the process is to control the actions of a crucial enzyme—called a helicase—that unwinds the DNA double helix in preparation for copying it. In the first step, inactive helicases are loaded onto the DNA at the origins, where replication starts. During the second step, the helicases are activated, to unwind the DNA.
Kicking off the process is a cluster of six proteins that sit down at the origins. Called ORC, this cluster is shaped like a double-layer ring with a handy notch that allows it to slide onto the DNA strands, Berger’s team has found.
In baker’s yeast, which is a favorite for scientists studying DNA replication, these start sites are easy to spot: They have a specific, 11- to 17-letter core DNA sequence, rich in adenine and thymine chemical bases. Scientists have watched as ORC grabs onto the DNA and then slides along, scanning for the origin sequence until it finds the right spot.
But in humans and other complex life forms, the start sites aren’t so clearly demarcated, and it’s not quite clear what makes the ORC settle down and grab on, says Alessandro Costa, a structural biologist at the Crick Institute who, with Diffley, wrote about DNA replication initiation in the 2022 Annual Review of Biochemistry. Replication seems more likely to start in places where the genome—normally tightly spooled around proteins called histones—has loosened up.
The initiation of DNA replication starts at the tail end of the previous cell division and continues through the cell cycle phase known as G1. DNA synthesis happens during the S phase. Levels of a protein called CDK are critical to ensuring that DNA is replicated once and only once. When CDK levels are low, helicases can jump onto the DNA and start to unwind it. But repeat binding does not happen because CDK levels rise, and this blocks the helicase from binding again. (credit: Knowable Magazine)
Once ORC has settled onto the DNA, it attracts a second protein complex: one that includes the helicase that will eventually unwind the DNA. Costa and colleagues used electron microscopy to work out how ORC lures in first one helicase, and then another. The helicases are also ring-shaped, and each one opens up to wrap around the double-stranded DNA. Then the two helicases close up again, facing toward each other on the DNA strands, like two beads on a string.
At first, they just sit there, like cars with no gas in the tank. They haven’t been activated yet, and for now the cell goes about its usual business.
Things kick into high gear when a crucial molecule called CDK waves the green flag, jump-starting chemical steps that lure in even more proteins. One of them is DNA polymerase—what Costa calls the “typewriter” that will build new DNA strands—which hitches onto each helicase. Others activate the helicases, which can now burn energy to chug along the DNA.
As this occurs, the helicases change shape, pushing on one DNA strand and pulling on the other. This creates strain on the weak hydrogen bonds that normally hold the two strands together by the bases—the As, Cs, Ts and Gs that make up the rungs of the DNA ladder. The two strands get ripped apart. Costa and colleagues have observed how the two helicases untwist the DNA between them, and they’ve seen how the helicases keep the unbound bases stable and out of the way.
Left: Several genes involved in the initiation of DNA replication (horizontal axis) are amplified—that is, mistakenly copied in extra numbers — to varying degrees (vertical axis) in different cancers. Right: On chromosome 8, a cluster of three genes—shown in green text—are frequently amplified together in certain cancers. (credit: Knowable Magazine)
At first, both helicases are wrapped around both strands of DNA, and they can’t get very far like this, because they are facing each other and will just run into each other. But next, they each undergo a change in position, spitting one DNA strand or the other out of the ring. Now separated, they can jostle past each other, and replication proceeds apace.
Each helicase motors along its single strand, in the opposite direction from the other. They leave the origin behind and yank apart those hydrogen-bonded base pairs as they travel. The DNA polymerase is right behind, copying the DNA letters as they’re freed from their partners.
CDK’s second job is to stop any more helicases from hopping on the origins. Thus, there is one start of replication per origin, ensuring proper copying of the genome—although copying doesn’t begin at the same time at each site. The whole process of DNA replication, in human cells, takes about eight hours.
There is still plenty to be worked out. For one thing, the DNA that’s being copied is not a naked double helix. It’s wrapped around histones and attached to lots of other proteins that are busy turning genes on or off or making RNA copies of the genes. How do those jostling proteins affect each other and avoid getting in each other’s way?
Beyond this fascinating, fundamental biology—a remarkable process essential for all life on Earth—there are implications for diseases like cancer. Scientists already know that faulty replication can destabilize DNA, and an unstable genome that’s prone to mutation may be an early hallmark of cancer development. And they are further investigating links between replication proteins and cancer.
“I think that there are opportunities for therapeutic interventions for these systems,” says Berger, “once we have enough insights about how they work and what they look like.”
Amber Dance, a science writer in the Los Angeles area, also likes to break large tasks into smaller segments: It took her five days to complete the steps to draft this article. This article originally appeared in Knowable Magazine, an independent journalistic endeavor from Annual Reviews. Sign up for the newsletter.
Written by Neil Levy
Large language models look set to transform every aspect of life over the coming decades. Some of these changes will be dramatic. I’m pretty unconcerned by the apocalyptic scenarios that preoccupy some people, but much more worried about the elimination of jobs (interestingly, the jobs that seem likeliest to be eliminated are those that require the most training: we may see a reversal of status and economic position between baristas and bureaucrats, bricklayers and barristers). Here, though, I’m going to look at much less dramatic, and very much near term, effects that LLMs might have on academic writing. I’m going to focus on the kind of writing I do in philosophy; LLMs will have different impacts on different disciplines.
A number of academics, writing in academic journals and on Twitter, have suggested that LLMs could be used to streamline the writing process. As they envisage it, LLMs could take on the burden of writing literature reviews and overviews, leaving the human free to undertake the more creative work involving the generation and testing of hypotheses (here, too, though, the LLM might have a role: it could generate candidate hypotheses for the human to choose between and refine, for example).
As a proponent of what we might call extended cognition, the general idea is one to which I’m sympathetic. The extended mind hypothesis is a metaphysical claim: on this hypothesis, mind can extend beyond the skull and into the artifacts that enable certain kinds of thinking (my smartphone might partially constitute my mind, when its reminders, navigational capacities, search functions, and so on, are sufficiently integrated into my cognitive activities). The extended cognition hypothesis is agnostic about metaphysics: it simply emphasises the degree to which our thought is offloaded onto the world, including artifacts. New technologies enable new kinds of thinking, and this has always been true. As Richard Feynman said, notes on paper aren’t merely a record of thinking, “not really. It’s working. You have to work on paper, and this is paper.”
Extending cognition through new technologies opens cognitive horizons that are otherwise inaccessible to us. Supercomputers that perform millions of operations per second allow us to analyse data and perform mathematical calculations that were utterly closed to previous generations. But in opening up new horizons, new ways of extending thought can make others less accessible and have unwanted impacts on our native cognition. In The Phaedo, Plato expressed the fear that writing would undermine our capacity to remember things. He may have been right about its effects on our memory, but that’s more than compensated for by our increased capacity to record things externally. There are no guarantees, however, that changes will always be for the better.
The idea of a division of labor between the relatively routine and the creative imagined above, with the LLM taking on the first and the human (alone or in collaboration with the LLM) the second, is not unattractive. It can be tiresome to review a literature one already knows well. Sometimes, I find myself in the position of having to rewrite pretty much the same points I’ve made in a previous paper in an introductory section. It’s only norms against self-plagiarism that prevent me from cutting and pasting from the older paper to the newer one. Allowing the LLM to do the work of rephrasing is a tempting option. We might think that whatever other costs and benefits they have, getting them to do what we the drudge work is surely an unalloyed benefit.
Perhaps – perhaps – it’s a benefit overall, but it’s not an unalloyed benefit. While we may approach a paper with a hypothesis in mind, and think of the introductory sections as merely sketching out the terrain, the relationship between that sketch and the meat of the paper is not always so straightforward. Sometimes, in rephrasing and summarizing ideas that I thought I already knew well, I discover relations between them I hadn’t noticed, or a lack of clarity that hadn’t struck me before. These realisations may lead to the reframing of the initial hypothesis, or the generation of a new hypothesis, or simply greater clarity than I had previously. What I took to be mere drudge work can’t be easily isolated from the more creative side of thought and writing.
More generally, the drudge work lays down the bedrock for creative activity. If I had never attempted to review and synthesise the work that appears in the review section of a paper, I wouldn’t know it well enough to be able to generate some of the hypotheses I go on to explore. That drudge work is an essential developmental stage. It’s also a developmental stage for a set of skills at navigating a terrain. This is a generalizable skill, one we can apply in future to different material and different debates. It may be that those who have already developed such skills – those who became academically mature before the advent of LLMs – can outsource drudge work at a smaller cost than those who have not yet developed this set of skills. Perhaps doing the task for oneself, boring though it may be, is necessary for a while, before we throw away the ladder we’ve climbed.
I’ve got no doubt that LLMs can and will be incorporated into academic writing, in ways and with effects we’re only beginning to imagine. Externalizing thought is extremely productive: it’s always been productive to write down your thoughts, because externalizing them allows us to reconfigure them, and to see connections that we mightn’t otherwise have noticed. The more complex the material, the greater the need to externalize. LLMs allow for a near instantaneous kind of externalization: we might regenerate multiple versions of a thought we’ve written once, and the permutations might allow us to see new connections. LLMs can also be used to generate new candidate hypotheses, to identify gaps in the literature, to synthesise and visualise data, and who yet knows what else? Perhaps the day will come – perhaps it will even be soon – when AI replaces the human researcher altogether. For now, it’s a powerful tool, perhaps even a partner, in the research process.
Some of those who have worried about the singularity – the postulated moment when AI design takes off, with ever more intelligent AIs designing even more intelligent AIs, leaving us humans in their dust – have proposed we might prevent human obsolescence by merging with the machines, perhaps even uploading our minds to artificial neural networks. I don’t know whether the singularity or human obsolescence are real threats, and I’m very sceptical about mind uploading. Whatever the prospects might be for mind uploading, right now we can integrate AIs into our thinking. We may not stay relevant for ever, and we may never merge with the machines, but right now they’re powerful tools for extending our cognition. They might homogenize prose and lead to a loss of creativity, or they might lead to an explosion of new approaches and ideas. They’re certain to have unanticipated costs, but the benefits will probably be much greater.
Inevitably, I ran this blogpost through an AI tool – the free version of Quillbot. It identified one or two typos, which of course I corrected. It also made a number of stylistic suggestions. I accepted almost none of them, but several led me to think I ought to rephrase the passage. Perhaps that’s not a model for how AI might be useful for writing right now.
In a conclusion to one of the most consequential Supreme Court sessions in many years, the Court released an opinion today on the Biden administration’s proposed plan to forgive up to $20,000 in federal student loan debt per borrower. After dismissing one case due to lack of standing from the plaintiffs, the Court voted 6-3 to block forgiveness in the second case (giving standing based on the servicer MOHELA).
This decision will have major implications for higher education policy. Here are the things that I will be looking for in the coming months and years:
Restarting student loan repayment was already going to be a nightmare, and this creates additional challenges. The first challenge is the sheer number of borrowers re-entering repayment. Roughly 43 million Americans have federal student debt, and the Biden administration estimated that about 20 million would have their loans completely forgiven by their proposal. I have little confidence that the Department of Education, student loan servicers, and colleges can smoothly handle 23 million borrowers that would have remained, let alone 43 million. Federal Student Aid badly needed additional resources to manage a return to repayment, but Republicans were only willing to provide the funds if it came with a rider blocking its use on debt relief. Since both parties agreed on no riders in last year’s omnibus spending bill, no additional funding was provided.
In an overlooked item due to yesterday’s important decision on college admissions, the Department of Education released information about how they plan to manage the return to repayment. ED plans to give a 90-day grace period for missed payments and is considering future grace periods. Needless to say, Republicans are not happy and may go to court to stop grace periods based on the agreement in this summer’s debt ceiling legislation.
How many borrowers are willing to start making payments? There is going to be a group of people who are livid about having to resume payments after not getting the loan forgiveness they were expecting. I am expecting a substantial group of borrowers to not make any payments until they get to the brink of default—which could take a while. These borrowers may still hold out hope for another forgiveness effort (more on that in the next section) and they may not proactively reach out to servicers to update their information if they have moved since March 2020. A particularly interesting group is the 20 million students who would have received complete forgiveness, as the frustration factor is likely higher among this group than among students who knew they would still have a balance remaining under this plan.
As a note, with income-driven repayment, students at least in theory should be able to start making some payments. But adding an expense back to the monthly budget is painful and income-driven repayment is still complicated to navigate. So there will be challenges even among people who are not as upset about this decision.
How will Democrats respond? The progressive wing of the Democratic Party has been pressuring the Biden administration to forgive all student debt and immediately pivot to using the Higher Education Act instead of the HEROES Act. That is likely not happening given today’s court decision. But a few moderate Democrats voted in favor of a Republican-led resolution disapproving of debt forgiveness and ending the repayment pause. The Biden administration will point to its expanded income-driven repayment plan, which could also face legal challenges in light of this decision. Free college and debt forgiveness were key issues in the 2020 Democratic presidential primary, and they will continue to be key issues in contested Democratic primaries for the next several years.
How will Republicans respond? By the time you read this, there will be plenty of press releases from Republican politicians celebrating the discussion. But there are still concerns about a future administration trying another avenue to forgiveness, particularly through income-driven repayment. There are some thoughtful efforts among Republicans to maintain income-driven repayment while reversing most of the Biden administration’s proposed changes. But Republicans are also seeking to limit borrowing for graduate students, which is something that I have been expecting for years.
This week’s Supreme Court decisions are likely to influence the direction of American higher education for years to come, and some of the influences are not going to be immediately obvious. But the items discussed above are going to play an outsized role in policy discussions for a good while.
rkelchen
By Austin Lam.
With recent news of a major private donation to transform addictions care in Vancouver, British Columbia, I have reflected on my experiences as a resident physician taking care of patients with substance use disorders.
Drawing from her personal exposure to the devastating effects of addiction through the death of her brother, Jill Diamond reflected on the existing gaps in care and the importance of a seamless continuum of care. She rightly pointed out the need for inpatient recovery-focused beds. This has clicked with a reflection that I have held for this past year: where are our publicly-covered inpatient addiction beds in our hospitals?
Beyond opportunistic interventions, we need systematic interventions. There is a glaring incommensurability between opportunities for intervention and actual intervention needs in substance use disorders in inpatient hospital settings. In the current landscape of opportunistic addiction treatment, a patient is admitted under a Most Responsible Physician (MRP) service, e.g., Internal Medicine, Psychiatry, Surgery, etc. However, we do not have focused inpatient beds with the express purpose to optimize addiction treatment and to arrange appropriate community follow-up.
In the current landscape, addiction treatment teams ‘jump’ on the opportunity to help when requested by the aforementioned MRP services. The teams can then assist with managing substance withdrawal, offer medication therapies, engage in motivational interviewing, and provide connections to psychosocial services. But the addiction teams do not have beds themselves. The length of the patient’s hospital stay is contingent on their ‘primary’ reason(s) for admission or other medical/psychiatric/surgical issue(s) that necessitate continued inpatient treatment — and these may well be the consequence of addiction, such as cellulitis from intravenous drug use. However, substance use disorder is not by itself a reason for inpatient treatment optimization (though collegial work environments offer the opportunity for addiction teams to advocate for longer inpatient stay to optimize treatment, e.g., opioid agonist therapy).
Are substance use disorders not disorders that merit treatment in and of themselves in inpatient hospital settings?
Hence, we are left with a hodgepodge landscape of care, contingent on the collegiality amongst addiction and MRP teams as opposed to a systematic continuum of care that may allow patients who have been medically/psychiatrically/surgically stabilized to then benefit from longer inpatient stay under the care of a specialized addiction team who can focus on optimizing addiction treatment and to engage the patient and their support system (e.g. family/friends) in their recovery journey.
The task before us is not only to meet patients ‘where they are at’, but also to show patients ‘where they can be’ by discovering anew their self-understanding of life goals and larger sense of meaning/purpose. The risk of merely meeting people ‘where they are at’ is that it can objectify people as static entities. We are not static. We are dynamic beings, perpetually engaged in understanding ourselves and our world in the act of living. There is a risk of indulging in the bigotry of low expectations if we view patients as static at their ‘baseline’.
Beyond the laudable and crucial aim of reducing harms, we must encourage and promote flourishing in patients’ lives. Aristotle spoke of eudaimonia; Maslow wrote of self-actualization; and Frankl wrote of the importance of meaning. All these ideas tie to the fact that there are things that make life purposeful. We share common aims as human beings geared towards the discovery of meaning. John Finnis elaborated on the basic goods of human life: life, knowledge, play, aesthetic experience, sociability of friendship, practical reasonableness, spirituality/metaphysical orientation, and partnership. These are the goods that we must promote in conjunction with reducing harms.
Accordingly, the types of inpatient beds we need are not the ones we currently have accessible. Importantly, many patients with substance use disorders do not want addiction treatment or are ambivalent. For the latter, we can lower the barrier to recovery by offering longer inpatient stays with the aim of addiction treatment optimization, continued psychosocial engagement, and arrangement of appropriate follow-up care. We can not only meet the patient ‘where they are at’ but also actively foster recovery: we can identify and work towards the patients’ goals and importantly, support the patient in aligning their goals with what it means to flourish.
Rudolf Virchow, pioneer of social medicine, is quoted as having said: “Medicine is a social science and politics is nothing else but medicine on a large scale … the politician, the practical anthropologist, must find the means for their actual solution”. However, rather than just ‘the politician’, we — as citizens in the public realm — hold the influence to push for change. Our collective society must have the will to invest in inpatient addictions beds as part of a systematic continuum of care. We must strengthen participatory democracy. As John Ralston Saul pointed out, against the specialized nature of non-governmental organizations, “self-interest or business cannot lead in a decent society. Society must lead them”.
Author: Austin Lam
Affiliation: Department of Psychiatry, University of British Columbia
The views expressed in this publication are those of the author. They do not purport to reflect the opinions or views of the UBC Department of Psychiatry.
Competing interests: None declared
Social media account of post author: @austinaldenlam
The post Beds, beds, beds — We need more addictions beds appeared first on Journal of Medical Ethics blog.
Last month on the blog, I floated the idea of thinking about generative AI tools like ChatGPT, Bing, and Bard as technologies for learning. I drew an analogy to the digital camera I used as I learned photography. As I was developing a conceptual understanding of photography, I was also learning how to use the tool in my hands, with all its knobs and buttons, and these two kinds of learning informed and supported each other. Could we use generative AI tools in a similar fashion, teaching students to use the tools well as part of learning other skills, like writing and researching? If so, it might help to know what kind of tools we’re dealing with when we talk about ChatGPT and other AI text generation tools.
For instance, I saw this post by Simon Harris go around Mastodon a couple of weeks ago:
15yo: I use ChatGPT quite a bit.
Me: Oh?
15yo: Today, I asked it to give me the last digit of Pi.
Me:
15yo: …exactly
Me:
15yo: Apparently, it’s 4
This is funny because ChatGPT is so, so wrong about pi. As an irrational number, pi has a nonterminating decimal representation; there’s no last digit of pi. The joke, however, relies on an assumption that ChatGPT is supposed to be some kind of answer machine, a tool for answering questions correctly. However, ChatGPT is famously not that. It’s well known for “hallucinating” facts and inventing citations. There are, in fact, a lot of educators who are worried that students will rely too much on ChatGPT and similar tools because these tools produce text that is factually incorrect. For example, check out this YouTube video of a chemistry instructor evaluating the accuracy of ChatGPT’s answers to various organic chemistry questions. Spoiler: It didn’t do that great.
So if these tools aren’t answer machines, what are they? I would argue that ChatGPT and similar tools are actually wordsmiths. They are powered by large language models that mix words together to create fluent prose. Ask it to draft an essay for you on a given topic, and it will put words together to do just that. Ask it to revise that draft based on your feedback, and it will do that, too. See, for instance, how Ethan Mollick used Bing to help him craft the final paragraph of his April 26th newsletter. Ask it to summarize a long text, and, because it’s good with words, you’ll get a pretty decent summary. It’s a wordsmith that actually doesn’t know what it’s writing but still produces intelligible texts.
This is, in fact, the other reason that educators are worried about AI text generators, because they can quickly produce acceptable responses to pretty standard essay question prompts, and educators are worried that students will use these tools to cheat, whether the essay question is of the “writing as assessment,” “writing to learn,” or “learn to write” variety. Because ChatGPT and its peers are good with words.
I’m struck by how the initial reporting and framing of these tools affect the ways we think about them. While AI text generators were seen as answer machines (albeit flawed ones), AI image generators have been seen as “smiths” of a different kind, working with pixels to create images. While a tool like ChatGPT making stuff up is seen as a problem, if I ask Midjourney or DALL-E (a couple of popular image generators) to give me a scene of robots playing basketball, no one worries about the result not being real-world realistic.
If AI text generators are tools for learning, as I argued on the blog last month, then I think it’s helpful to think of them as wordsmithing tools. Does a student struggle to come up with counter arguments for their thesis? Ask an AI tool to generate a few. Does a student freeze up when confronted with a blank page? Ask an AI tool to write a first draft. Does a student find it hard to adopt the right voice for their writing? Ask an AI tool to rewrite a draft essay in different voices. These are all wordsmithing tasks that ChatGPT and its ilk can handle pretty well.
Please note that I’m not suggesting we shouldn’t help students learn to develop counter arguments or to get past writer’s block or to figure out their writing voice. I am suggesting, however, that AI tools might be used to help students learn these skills, just as my digital camera helped me learn photography. Cynthia Alby uses the metaphor of learning to ride a bicycle with training wheels to talk about how AI tools might provide a kind of scaffolding for students learning writing skills. When I heard that metaphor, I was reminded of a 2012 blog post by Mark Sample, in which he talks about balance bikes as a different way to teach kids to ride bicycles. Learning with training wheels means getting help with balancing while learning to pedal, while learning with a balance bike means not worrying about pedaling while learning to balance, which is apparently easier for most kids. We might need to think carefully about where our students need the kind of support that AI tools can provide, and the answer might vary by student.
I’ve been using the training wheels and balance bike metaphors in my recent talks on teaching with AI, and I was all ready to bust out the “AI as wordsmith” argument above at a talk I gave earlier this week. But then I signed up for ChatGPT Pro to see what ChatGPT’s plugins are all about. Back in March, OpenAI, the company behind ChatGPT, announced that the tool would have plugins that allowed it to communicate (in a fashion) with other web tools and websites. It’s taken me three months, but I finally started playing around with these plugins. I quickly realized that some of the advice I had given faculty earlier in the year about designing “ChatGPT-proof” assignments hadn’t been accurate for a while!
For instance, if I ask the free version of ChatGPT, which uses GPT-3.5, to tell me about the May 2023 Supreme Court decision that dealt with Andy Warhol’s art, here’s the response I get:
I apologize for the confusion, but as of my knowledge cutoff in September 2021, I’m not aware of any specific Supreme Court decision in May 2023 related to Andy Warhol’s art. It’s possible that such a decision may have occurred after my knowledge cutoff or it may be a hypothetical scenario that hasn’t actually taken place. I recommend checking reliable news sources or legal references to get the most accurate and up-to-date information on any recent Supreme Court decisions regarding Andy Warhol’s art.
If I think about ChatGPT as an answer machine, I’m not getting the answer I want, but at least the tool is telling me what it doesn’t know. That’s an improvement over how the chatbot presented information at the start of 2023. But watch what happens when I use the same prompt in ChatGPT Pro with the VoxScript plugin enabled:
In May 2023, the U.S. Supreme Court ruled against the Andy Warhol Foundation in a copyright infringement case. The case was centered around a series of silk screen images that Warhol created based on a photograph of the late musician Prince, taken by photographer Lynn Goldsmith in 1981.
The court ruled in a 7-2 vote that Warhol infringed on Goldsmith’s copyright. The case hinged on whether Warhol’s images of Prince transformed Goldsmith’s photograph to a great enough degree to stave off claims of copyright infringement and therefore be considered as fair use under copyright law.
Goldsmith had sued the Andy Warhol Foundation for the Visual Arts (AWF) for copyright infringement after the foundation licensed an image of Warhol’s titled “Orange Prince” based on Goldsmith’s image of the pop artist to Condé Nast in 2016 for use in its publication Vanity Fair…
It goes on for another couple of paragraphs, and it gives me the news source it used to generate this summary of the case, a May 18th NPR report. This version of ChatGPT is indeed acting like an answering machine!
Quick sidebar: It is also now possible to share one’s ChatGPT correspondences with others. A few months ago, you needed some third-party tool to do so, but now ChatGPT has a share button for every chat. Thus the argument that there’s no “paper trail” for how one uses ChatGPT is no longer true.
I used ChatGPT for the above examples, but please remember that ChatGPT isn’t the only game in town. I asked Bing about the Andy Warhol case, and I got a similar summary, this time with three different news sources cited. Google Bard gave me a similar summary and added some text about how fair use works in the United States. Its sources, however, were meeting notes from a 2004 Congressional subcommittee hearing and a Quizlet created for maybe a law course? Your mileage may vary with these tools.
So are these tools now, in fact, answer machines? I’m going to argue that while they can be used in that way, a better way to think of them is as interfaces to other systems. Just as Siri or Alexa act as a voice-based interface to the apps on your digital devices, ChatGPT and Bing and such are text-based interfaces to, for instance, all the transcripts attached to all the YouTube videos. These tools are still wordsmiths, but wordsmiths used as interfaces.
To use a left field example, the fast food chain Wendy’s recently announced that it was working on an AI chatbot to take drive-thru orders. That’s AI-as-interface, with the chatbot, which is good with words, taking spoken orders for a cheeseburger and a Frosty and turning that into an order in the restaurant’s food production system. For a more academic example, consider Elicit, an AI research assistant that can help you find research in its corpus of 115 million academic papers that might address your research question. Ask it a question like “How should I form groups of students when using group work in the college classroom?” and (because it’s good with words) it will return a table of potentially relevant papers, summarized and tagged by intervention, sample size, and outcomes.
AI as answer machine, AI as wordsmith, AI as interface… I’m not sure where we’ll land with how we think about these tools, but I believe these framings are helpful in assessing what the tools can and cannot do and what roles they might play as learning technologies.
One more caveat: Most of what I’ve presented here is focused squarely on AI text generation tools powered by large language models. There are lots of other kinds of AI tools, like Cornell University’s Merlin app, which can identify birds by their songs, or that AI-powered agent that excelled at the strategic negotiation game Diplomacy, that don’t fall into any of the categories I’ve described here.
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