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The Intelligence Explosion, an original drama published by the Guardian, is obviously a work of fiction. But the fears behind it are very real, and have led some of the biggest brains in artificial intelligence (AI) to reconsider how they work.

The film dramatises a near-future conversation between the developers of an "artificial general intelligence" - named Günther - and an ethical philosopher. Günther himself (itself?) sits in, making fairly cringeworthy jokes and generally missing the point. Until, suddenly, he doesn't.

It shows an event which has come to be known in the technology world as the "singularity": the moment when an artificial intelligence that has the ability to improve itself starts doing so at exponential speeds. The crucial moment is the period when AI becomes better at developing AI than people are. Up until that point, AI capability can only improve as quickly as AI research progresses, but once AI is involved in its own creation, a feedback loop begins. AI makes better AI, which is even better at making even better AI.

It may not end with a robot bursting into a cloud of stars and deciding to ascend to a higher plane of existence - but it's not far off. A super-intelligent AI could be so much more intelligent than a human being that we can't even comprehend its actual abilities, as futile as explaining to an ant how wireless data transfer works.

So one big question for AI researchers is whether this event will be good or bad for humanity. And that's where the ethical philosophy comes into it.

Dr Nick Bostrom, a philosopher at the University of Oxford, presented one of the most popular explanations of the problem in his book Superintelligence. Suppose you create an artificial intelligence designed to do one thing - in his example, running a factory for making paperclips. In a bid for efficiency, however, you decide to programme the artificial intelligence with another set of instructions as well, commanding it to improve its own processes to become better at making paperclips.

For a while, everything goes well: the AI chugs along making paperclips, occasionally suggesting that a piece of machinery be moved, or designing a new alloy for the smelter to produce. Sometimes it even improves its own programming, with the rationale that the smarter it is, the better it can think of new ways to make paperclips.

But one day, the exponential increase happens: the paperclip factory starts getting very smart, very quickly. One day it's a basic AI, the next it's as intelligent as a person. The day after that, it's as smart as all of humanity combined, and the day after that, it's smarter than anything we can imagine.

Unfortunately, despite all of this, its main directive is unchanged: it just wants to make paperclips. As many as possible, as efficiently as possible. It would start strip-mining the Earth for the raw materials, except it's already realised that doing that would probably spark resistance from the pesky humans who live on the planet. So, pre-emptively, it kills them all, leaving nothing standing between it and a lot of paperclips.

That's the worst possible outcome. But obviously having an extremely smart AI on the side of humanity would be a pretty good thing. So one way to square the circle is by teaching ethics to artificial intelligences, before it's too late.

In that scenario, the paperclip machine would be told "make more paperclips, but only if it's ethical to do so". That way, it probably won't murder humanity, which most people consider a positive outcome.

The downside is that to code that into an AI, you sort of need to solve the entirety of ethics and write it in computer-readable format. Which is, to say the least, tricky.

Ethical philosophers can't even agree on what the best ethical system is for people. Is it ethical to kill one person to save five? Or to lie when a madman with an axe asks where your neighbour is? Some of the best minds in the world of moral philosophy disagree over those questions, which doesn't bode well for the prospect of coding morality into an AI.

Problems like this are why the biggest AI companies in the world are paying keen attention to questions of ethics. DeepMind, the Google subsidiary which produced the first ever AI able to beat a human pro at the ancient boardgame Go, has a shadowy ethics and safety board, for instance. The company hasn't said who's on it, or even whether it's met, but early investors say that its creation was a key part of why Google's bid to acquire DeepMind was successful. Other companies, including IBM, Amazon and Apple, have also joined forces, forming the Partnership on AI, to lead from the top.

For now, though, the singularity still exists only in the world of science fiction. All we can say for certain is that when it does come, it probably won't have Günther's friendly attitude front and centre.

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What our original drama The Intelligence Explosion tells us about AI

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How do you stop a robot from turning evil? Our drama raises important questions about the ethics of artificial intelligence, says Alex Hern

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3:30 "To be an optimist, you have to have the courage to be a fearsome critic. It's the critic who believes things can be better. The critic is the true optimist."

~6:00 He talks about how the exchange of free information will tend toward a monopoly because of mathematics. 6:57 "Instead of decentralization, you end up with hypercentralization."

~8:30 He talks about how the limits of language, itself, and how those limits make self-regulating LLM AI challenging. (8:58) "Words are actually more ambiguous than we think they are. We go through life thinking words are precise, but they're not. Which is actually the reason large models work, because they're a statistical thing in the first place."

9:15 "But, there's another way that does work. Anytime there's an output from AI, of course in some small way it relies on millions or billions of examples from humanity at large...but in any PARTICULAR example of an output, it won't. In a particular example, there'll be a few, a small number, maybe a dozen." He adds that if you can determine WHO contributed to an AI result, you can incentivize folks to put in new data that makes the results better and compensate folks for their contributions. (This introduces his notion of Data Dignity.)

11:16 Data Dignity

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Jaron Lanier Looks into AI's Future | AI IRL

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Opinion | Should A.I. Accelerate? Decelerate? The Answer Is Both.

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The OpenAI debacle illustrates the way the tech world continues to squabble about the wrong things and chase misframed goals.

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Henry A. Kissinger served as the 56th Secretary of State from September 1973 until January 1977. He also served as the Assistant to the President for National Security Affairs from January 1969 until November 1975. He received the Nobel Peace Prize in 1973, the Presidential Medal of Freedom in 1977, and the Medal of Liberty in 1986. Presently, he is Chairman of Kissinger Associates, Inc., an international consulting firm.

Eric Schmidt is an accomplished technologist, entrepreneur and philanthropist. As Google's Chief Executive Officer, he pioneered Google's transformation from a Silicon Valley startup to a global leader in technology. He served as Google's Chief Executive Officer and Chairman from 2001-2011, Executive Chairman from 2011-2018 and most recently as Technical Advisor from 2018-2020. Under his leadership Google dramatically scaled its infrastructure and diversified its product offerings while maintaining a strong culture of innovation. Prior to his career at Google, Eric held leadership roles at Novell and Sun Microsystems, Inc.

Daniel Huttenlocher is the inaugural dean of the MIT Schwarzman College of Computing. Previously he served as founding Dean and Vice Provost of Cornell Tech, the digital technology oriented graduate school created by Cornell University in New York City. He has a mix of academic and industry experience, as a Computer Science faculty member at Cornell and MIT, researcher and manager at the Xerox Palo Alto Research Center (PARC), and CTO of a fintech startup. He currently serves as chair of the John D. and Catherine T. MacArthur Foundation board and as a member of the Corning Inc. and Amazon.com boards.

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Three of the world's most accomplished and deep thinkers come together to explore Artificial Intelligence (AI) and the way it is transforming human society-and what this technology means for us all. Generative AI is filling the internet with false information. Artists, writers, and many other professionals are in fear of their jobs.

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Augustana's early ChatGPT Resources document

https://tinyurl.com/ChatGPTresources

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Chat GPT (Generative Pretrained Transformer) Resources

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What is effective altruism?

Everyone wants to do good, but many ways of doing good are ineffective. The EA community is focused on finding ways of doing good that actually work.

This led to many ideas like the following:

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Everyone wants to do good, but many ways of doing good are ineffective. The EA community is focused on finding ways of doing good that actually work. This led to many ideas like the following:

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Why despite global progress, humanity is probably facing its most dangerous time ever

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We can be the generation that helps cause the end of everything, or navigates humanity through its most dangerous period.

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Open AI Whistleblowers article in NY Times 6/4/24

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Open AI Whistleblowers article in NY Times 6/4/24

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OpenAI Insiders Warn of a ‘Reckless’ Race for Dominance

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A group of current and former employees are calling for sweeping changes to the artificial intelligence industry, including greater transparency and protections for whistle-blowers.

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Preventing an AI-related catastrophe - Problem profile

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Why do we think that reducing risks from AI is one of the most pressing issues of our time? There are technical safety issues that we believe could, in the worst case, lead to an existential threat to humanity.

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I wanted to give some quick thoughts on the Apple AI (sorry, "Apple Intelligence") release. I haven't used it myself, and we don't know everything about their approach, but I think the release highlights something important happening in AI right now: experimentation with four kinds of models - AI models, models of use, business models, and mental models of the future. What is worth paying attention to is how all the AI giants are trying many different approaches to see what works.

I am going to broadly stereotype some of these views - no company is a monolith, and all the AI organizations are doing many different things - but, in broad strokes, an interesting picture is emerging.

As I wrote in the last post, the power of the foundation model you use is a big deal, because the largest frontier models are, out-of-the-box, better at most things than smaller models, even smaller specialized models.

But these models are expensive to train and slow and expensive to run, which leaves room for much smaller models that aren't as good as the frontier models but can run cheaply and easily - even on a PC or phone. This isn't new. Back in December, I was able to run Mistral 7b, a model slightly less advanced than the original ChatGPT, directly on my phone without an internet connection. I also ran Mixtral, a model from the same company that slightly outperforms the original ChatGPT, on my gaming computer.

All of the tech companies have been releasing these sorts of small, fairly powerful models, with the idea that they can handle simple questions on the hardware of your devices, and then call a larger model "in the cloud" when they need help. You aren't getting anywhere near the smarts of a frontier model, but if you want to do straightforward things (make a Siri that works or "make my photos more vivid"), these models are often more than enough. Many of the companies betting on frontier models, like Google, have also released faster and cheaper models to fill this niche, and are deploying them to phones as well.

For example, I can ask Gemini 1.5 to l ook a bunch of PDFs of comics, read my emails to learn about my sense of humor, and suggest the comics that might appeal to me. Pretty amazing stuff. But I can ask Siri with AI to s end this photo to my friend Sarah after making the colors pop.

For many people the second use case is actually the more natural, intuitive, and useful one. A machine that can do anything much of the time, but also sometimes does something entirely different, is harder to understand than a narrow AI that just does what you want (with the caveat we don't know how well the Apple system works). Google is also going to be releasing smaller AI models that are local to phones. And Microsoft is taking a similar approach to Apple, with a business twist. They have implemented Copilots in their key office apps. They do a really good job of providing easily understood "it just works" (mostly) integration of AI into work in easy ways. But both the Apple and app-specific Copilot models are constrained, which limits their upside, as well as their downside.

The potential gains to AI, the productivity boosts and innovation, along with the weird risks, come from the larger, less constrained models. And the benefits come from figuring out how to apply AI to your own use cases, even though that takes work. Frontier models thus have a very different approach to use cases than more constrained models. Take a look at this demo, from OpenAI, where GPT-4o (rather flirtatiously?) helps someone work through an interview, and compare it to this demo of Apple's AI-powered Siri, helping with appointments. Radically different philosophies at work.

There is a specter haunting all AI development, the specter of AGI - Artificial General Intelligence, the hypothetical machine better than humans at every intellectual tasks. This is the explicit goal of OpenAI and Anthropic, and it is something they hope to achieve in the near term. For people who genuinely believe they are building AGI soon, almost nothing else is important. The AI models along the way to AGI are mere stepping stones, not anything you want to build a business around, because they will be replaced by better models soon. OpenAI's systems may feel unpolished because the company believes that future models will significantly advance AI capabilities. As a result, they may not be investing heavily in refining systems that will likely be outdated as new models are released. I do not know if AGI is achievable, but I know that the mere idea of AGI being possible soon bends everything around it, resulting in wide differences in approach and philosophy in AI implementations.

While Apple is building narrow AI systems that can accurately answer questions about your personal data ("tell me when my mother is landing"), OpenAI wants to build autonomous agents that would complete complex tasks for you ("You know those emails about the new business I want to start, could you figure out what I should do to register it so that it is best for my taxes and do that."). The first is, as Apple demonstrated, science fact, while the second is science fiction, at least for now. Every major AI company argues the technology will evolve further and has teased mysterious future additions to their systems. In contrast, what we are seeing from Apple is a clear and practical vision of how AI can help most users, without a lot of effort, today. In doing so, they are hiding much of the power, and quirks, of LLMs from their users. Having companies take many approaches to AI is likely to lead to faster adoption in the long term. And, as companies experiment, we will learn more about which sets of models are correct.

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What Apple's AI Tells Us: Experimental Models⁴

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MIT Professor Max Tegmark: LIVE in Boston | SmartLess

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Contact me at gray@augie.edu.

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One of the most powerful tools we have in curbing AI misuse and shaping its ethical adoption is also one we struggle with employing. Talking with students is harder now than it was before the pandemic. Talking with one another is likewise strained with increasing political tension. And, yet, the power of a persuasive conversation can be a far more powerful tool than any AI detector. Indeed, many of us forget as we've aged just how formative some of the earliest discussions we've had with teachers shaping how we learn. I'm willing to wager that a conversation has more power in shaping the trajectory of AI adoption among certain groups than any tech marketing.

The conversations we need to have about generative AI include our students, administration, and our colleagues. I won't sugarcoat any of this-these conversations will be a continuum lasting years. They will be frustrating because they may not appear to do anything at first, but that's because human beings aren't like machines-we're slow to change and that's a good thing. Each interaction we have with one another shapes our thinking about genAI's impact on teaching and learning. We should not dismiss the small conversations as meaningless. What we should do is recognize the amount of time and energy it takes to simply talk about AI.

The opening conversation we have with students about AI is crucial. Students look to us to guide them in knowing what is ethical or allowed and they are desperate to know what that means when it comes to generative AI. Some will opt to ban it, and they have good reasons to do so, but your stance on AI should not preclude a conversation about generative AI or its impact on learning.

When I discuss generative AI with my students this fall I'm going to start by touching on why we're here in the classroom-to learn. My philosophy on AI is it should be used in a way to augment a student's learning, not offload it. I make this clear to my students in my syllabus and include labels for assignments indicating what tools/ or use cases are appropriate for AI and in what context. I will tell them that for many assignments, maybe even most of them in our class, using AI isn't effective in augmenting your learning within the context of this class.

For assignments where students use AI, I want them to attribute when they use the technology and let them know I'm not looking for examples of them using it as cheating or as a shortcut, but rather looking for moments that show they've learned with or without AI. For me, the AI problem isn't about cheating, it's instead about advocating for students to understand they need to demonstrate what they've learned. In many ways, that means asking students to reflect on how they've learned.

Openness is key to maintaining trust in my classroom. In the spirit of this, I plan to model transparency each time I use AI in instructional design, labeling assignments, and other materials to show when AI was used so students know what I expect from them when they adopt AI assistance. These serve as conversation starters. I want them to ask me questions about my process, and about the choices I make when I draft an assignment. I may even run some activities I've written through Claude or GPT-4o for feedback in front of the class and get students involved in critiquing the output.

I also know that many students will struggle with these ethical guidelines and some will challenge them, pushing their usage of generative AI into areas that I've identified as being academically dishonest. Namely, that means not being open about when they've used the technology or using generative AI in a way that shows me they did not learn. Instead of taking a student up on formal charges when I suspect an infraction, I am piloting the following model. It's designed to scale if interventions are not successful.

If I suspect a student used technology or good old-fashioned plagiarism, I will meet with them one-on-one and air my concerns in a non-punitive and non-accusatory way. Instead, I talk to the student about what's going on in their life, and how their classes are, and see if there are any areas that are causing them undue stress before directly talking to them about expectations. This initial meeting might serve to put the students on notice, but for me, my main goal is to pull them back into the ethical framework of our community by letting them know that while mistakes happen it's learning from them that shows growth. I plan to let a student resubmit an assignment, without penalty.

Sometimes, students don't always learn from a conversation. If a student continues to defy the norms we've established in our classroom community, the meeting escalates into an issue of formal misconduct. I want to make it clear to the student this isn't about rule-breaking-it's about an ethical breach of trust that harms our relationship and the greater community. I can move to take the student up on charges or pursue a restorative approach to academic integrity to see if we can repair the damage that has been done and restore trust going forward. Restorative approaches are tricky and take a great deal of thoughtful planning. I am using UT's Restorative Approach to Academic Integrity as a starting point. If the student doesn't feel like this approach is right for them, or if I believe the ethical breach is so severe, then either of us can agree to withdraw from the restorative pathway and use the existing academic misconduct procedures.

If the restorative approach fails or a student continues to break ethical standards of conduct within the class, then I have little recourse but to pursue formal charges. The evidence I'll bring isn't an AI detection report, but emails and meeting notes that clearly show the multiple attempts I've taken with the student to address their conduct within the course.

Will this work? Maybe. Maybe not. The point is I am willing to try something. I'm approaching academic integrity from a position of trust with my students, but clearly outlining a road map of consequences if their behavior continually challenges the norms of our class. However, the labor involved here is immense. I know many will simply throw their hands up at the thought of this and that's why we need to have conversations about AI not just with our students, but all stakeholders.

If upper administration wants faculty to be AI literate and work to rethink AI assessments for academic integrity, then they first need to take steps to address the material conditions impacting labor. The same goes if an admin asks educators to lean into AI to make their job easier. Someone needs to pay for that time and training. Contract hours and paid professional development opportunities in the form of grants, fellowships, course redesign stipends, curriculum updates, and learning communities must be addressed because AI isn't like the learning challenges we've faced in the past. The field moves quickly, with new tools and use cases popping up all the time. If institutions want faculty to actually adapt to AI and grapple with its impact on education, then they must rethink professional development as a continuum, not as one-off experiences or items to be checked off from a list at the beginning of the semester.

That's easier said than done and goes well beyond generative AI. Why do so few institutions of higher learning within the US have staffed offices of Academic Integrity? In over a decade of teaching at two R1 schools, I've never had formal training about what academic integrity means. The training I received as a classroom teacher was to sit around a table with other grad students and discuss teaching for two hours a week.

Educators and students both deserve a clear say about what generative AI tools an institution adopts. That's been ridiculously hard because most of these tools arrive as system updates with little conversation about the downstream impact turning on these systems might have.

What's your plan for the fall? How do you think we should approach admin about generative AI tools they purchase? What is working within your class? These are the most important conversations we can have with one another. Yet, how often do we have a different conversation, one where it is clear our fellow colleague is too burned out, too overwhelmed, or frankly too pissed off to do more than curse? We're people and we've been put into a daunting situation-figure out AI as it deploys in real-time around us. The notion that this is something we should keep track of as part of our daily lives is as outrageous as it is untenable.

Far too many people feel adrift and sometimes just having a chat with them can help give them a lifeline. Often times that becomes an airing of grievances. Something we can do is give purpose and direction to those justifiable feelings by shaping the discourse. If generative AI is getting in the way of student learning, then what are proactive ways we can address this that don't put more strain on the relationship we have with our students? That's a good enough question to ask to steer the ship back on course.

The problem with talking and asking questions is people inevitably want answers. Students want to know how to use generative AI ethically in their learning and what skills they're going to need once they graduate. We don't have those answers for them and won't for years to come. Neither will we come to terms with the labor needed to revamp assessments or develop the needed resources to fully realize the academic integrity we hope can be established in our new AI era. Rhetoric isn't just a means to an end-it is how people learn to argue and use their human emotions and reasoning to arrive at informed opinions. Let's celebrate that this fall through a conversation (or a few hundred of them!)

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The Rhetoric of Purpose: Conversations For Our AI Era

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One of the most powerful tools we have in curbing AI misuse and shaping its ethical adoption is also one we struggle with employing. Talking with students is harder now than it was before the pandemic. Talking with one another is likewise strained with increasing political tension.

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AI Is Coming for Amateur Novelists. That’s Fine.

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An annual speed-writing contest lets in the robot overlords, and I, for one, welcome them.

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This article was featured in One Story to Read Today, a newsletter in which our editors recommend a single must-read from The Atlantic, Monday through Friday. Sign up for it here.
Teenagers have always found ways around doing the hard work of actual learning. CliffsNotes dates back to the 1950s, "No Fear Shakespeare" puts the playwright into modern English, YouTube offers literary analysis and historical explication from numerous amateurs and professionals, and so on. For as long as those shortcuts have existed, however, one big part of education has remained inescapable: writing. Barring outright plagiarism, students have always arrived at that moment when they're on their own with a blank page, staring down a blinking cursor, the essay waiting to be written.

Now that might be about to change. The arrival of OpenAI's ChatGPT, a program that generates sophisticated text in response to any prompt you can imagine, may signal the end of writing assignments altogether-and maybe even the end of writing as a gatekeeper, a metric for intelligence, a teachable skill.

If you're looking for historical analogues, this would be like the printing press, the steam drill, and the light bulb having a baby, and that baby having access to the entire corpus of human knowledge and understanding. My life-and the lives of thousands of other teachers and professors, tutors and administrators-is about to drastically change.

I teach a variety of humanities classes (literature, philosophy, religion, history) at a small independent high school in the San Francisco Bay Area. My classes tend to have about 15 students, their ages ranging from 16 to 18. This semester I am lucky enough to be teaching writers like James Baldwin, Gloria Anzaldúa, Herman Melville, Mohsin Hamid, Virginia Held. I recognize that it's a privilege to have relatively small classes that can explore material like this at all. But at the end of the day, kids are always kids. I'm sure you will be absolutely shocked to hear that not all teenagers are, in fact, so interested in having their mind lit on fire by Anzaldúa's radical ideas about transcending binaries, or Ishmael's metaphysics in Moby-Dick.

To those students, I have always said: You may not be interested in poetry or civics, but no matter what you end up doing with your life, a basic competence in writing is an absolutely essential skill-whether it's for college admissions, writing a cover letter when applying for a job, or just writing an email to your boss.
Read: The college essay is dead
I've also long held, for those who are interested in writing, that you need to learn the basic rules of good writing before you can start breaking them-that, like Picasso, you have to learn how to reliably fulfill an audience's expectations before you get to start putting eyeballs in people's ears and things.

I don't know if either of those things is true anymore. It's no longer obvious to me that my teenagers actually will need to develop this basic skill, or if the logic still holds that the fundamentals are necessary for experimentation.

Let me be candid (with apologies to all of my current and former students): What GPT can produce right now is better than the large majority of writing seen by your average teacher or professor. Over the past few days, I've given it a number of different prompts. And even if the bot's results don't exactly give you goosebumps, they do a more-than-adequate job of fulfilling a task.

I mean, look at this: I asked the program to write me a playful, sophisticated, emotional 600-word college-admissions essay about how my experience volunteering at my local SPCA had prepared me for the academic rigor of Stanford. Here's an excerpt from its response:

In addition to cleaning, I also had the opportunity to interact with the animals. I was amazed at the transformation I saw in some of the pets who had been neglected or abused. With patience and care, they blossomed into playful and affectionate companions who were eager to give and receive love. I was also able to witness firsthand the process of selecting the right pet for the right family. Although it was bittersweet to see some animals leave the shelter, I knew that they were going to a loving home, and that was the best thing for them.

It also managed to compose a convincing 400-word "friendly" cover letter for an application to be a manager at Starbucks. But most jaw-dropping of all, on a personal level: It made quick work out of an assignment I've always considered absolutely "unhackable." In January, my junior English students will begin writing an independent research paper, 12 to 18 pages, on two great literary works of their own choosing-a tradition at our school. Their goal is to place the texts in conversation with each other and find a thread that connects them. Some students will struggle to find any way to bring them together. We spend two months on the paper, putting it together piece by piece.

I've fed GPT a handful of pairs that students have worked with in recent years: Beloved and Hamlet, The Handmaid's Tale and The Parable of the Sower, Homer's The Odyssey and Dante's Inferno. GPT brought them together instantly, effortlessly, uncannily: memory, guilt, revenge, justice, the individual versus the collective, freedom of choice, societal oppression. The technology doesn't go much beyond the surface, nor does it successfully integrate quotations from the original texts, but the ideas presented were on-target-more than enough to get any student rolling without much legwork.

It goes further. Last night, I received an essay draft from a student. I passed it along to OpenAI's bots. "Can you fix this essay up and make it better?" Turns out, it could. It kept the student's words intact but employed them more gracefully; it removed the clutter so the ideas were able to shine through. It was like magic.

I've been teaching for about 12 years: first as a TA in grad school, then as an adjunct professor at various public and private universities, and finally in high school. From my experience, American high-school students can be roughly split into three categories. The bottom group is learning to master grammar rules, punctuation, basic comprehension, and legibility. The middle group mostly has that stuff down and is working on argument and organization-arranging sentences within paragraphs and paragraphs within an essay. Then there's a third group that has the luxury of focusing on things such as tone, rhythm, variety, mellifluence.

Whether someone is writing a five-paragraph essay or a 500-page book, these are the building blocks not only of good writing but of writing as a tool, as a means of efficiently and effectively communicating information. And because learning writing is an iterative process, students spend countless hours developing the skill in elementary school, middle school, high school, and then finally (as thousands of underpaid adjuncts teaching freshman comp will attest) college. Many students (as those same adjuncts will attest) remain in the bottom group, despite their teachers' efforts; most of the rest find some uneasy equilibrium in the second category.

Working with these students makes up a large percentage of every English teacher's job. It also supports a cottage industry of professional development, trademarked methods buried in acronyms ( ICE! PIE! EDIT! MEAT!), and private writing tutors charging $100-plus an hour. So for those observers who are saying, Well, good, all of these things are overdue for change -"this will lead to much-needed education reform," a former colleague told me-this dismissal elides the heavy toll this sudden transformation is going to take on education, extending along its many tentacles (standardized testing, admissions, educational software, etc.).

Perhaps there are reasons for optimism, if you push all this aside. Maybe every student is now immediately launched into that third category: The rudiments of writing will be considered a given, and every student will have direct access to the finer aspects of the enterprise. Whatever is inimitable within them can be made conspicuous, freed from the troublesome mechanics of comma splices, subject-verb disagreement, and dangling modifiers.

But again, the majority of students do not see writing as a worthwhile skill to cultivate-just like I, sitting with my coffee and book, rereading Moby-Dick, do not consider it worthwhile to learn, say, video editing. They have no interest in exploring nuance in tone and rhythm; they will forever roll their eyes at me when I try to communicate the subtle difference, when writing an appositive phrase, between using commas, parentheses, or (the connoisseur's choice) the em dash.

Which is why I wonder if this may be the end of using writing as a benchmark for aptitude and intelligence. After all, what is a cover letter? Its primary purpose isn't to communicate "I already know how to do this job" (because of course I don't) but rather "I am competent and trustworthy and can clearly express to you why I would be a good candidate for this job." What is a written exam? Its primary signal isn't "I memorized a bunch of information" but rather "I can express that information clearly in writing." Many teachers have reacted to ChatGPT by imagining how to give writing assignments now-maybe they should be written out by hand, or given only in class-but that seems to me shortsighted. The question isn't "How will we get around this?" but rather "Is this still worth doing?"

I believe my most essential tasks, as a teacher, are helping my students think critically, disagree respectfully, argue carefully and flexibly, and understand their mind and the world around them. Unconventional, improvisatory, expressive, meta-cognitive writing can be an extraordinary vehicle for those things. But if most contemporary writing pedagogy is necessarily focused on helping students master the basics, what happens when a computer can do it for us? Is this moment more like the invention of the calculator, saving me from the tedium of long division, or more like the invention of the player piano, robbing us of what can be communicated only through human emotion?

Maybe the moment when I felt most caught between these two thoughts was when I asked GPT a question from an exam I give in my Eastern-philosophy course, something I've never imagined a student could find outside assistance to answer: "Explain the Madhyamaka Buddhist idea that all phenomena lack inherent existence, using a funny, specific example."

ChatGPT wrote back, as ever, instantly:

The Madhyamaka Buddhist idea that all phenomena lack inherent existence can be illustrated with a funny example: let's say you have a pet hamster. You might think that your hamster has its own inherent existence, but in reality, your hamster is made up of many different elements such as its fur, its bones, its muscles, and its brain. None of these elements exist independently or inherently on their own, so your hamster itself does not have any inherent existence either. The same goes for all phenomena-everything is made up of various components that don't have inherent existence, so nothing has any inherent existence either.

Everything is made up; it's true. The essay as a literary form? Made up. Grammatical rules as markers of intelligence? Writing itself as a technology? Made up. Starting now, OpenAI is forcing us to ask foundational questions about whether any of those things are worth keeping around.

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great literary works of their own choosing

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I've been teaching English for 12 years, and I'm astounded by what ChatGPT can produce.

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Strawberry's "enhanced reasoning"

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Something New: On OpenAI’s “Strawberry” and Reasoning

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In the AI Dilemma, Humane Tech founders, Tristan Harris and Aza Raskin, state that  “50% of AI researchers believe there’s a 10% or greater chance that humans go extinct from our inability to control AI.” They maintain that 1) When you invent a new technology, you uncover a new class of responsibilities (and it’s not always obvious what those responsibilities are), 2) if that technology confers power, it will start a race, and 3) if we do not coordinate efforts to act responsibly, that race ends in tragedy. They quote Yuval Harari as saying “What nukes are to the physical world, AI is to the virtual & symbolic world.” Everything humans do runs on top of language. They explain that AI sees all modalities as language. Refers (17:36) to Generative Large Language Multi-Modal Models, or GLLUMs (or Gollems - in folklore inanimate object that gain their own capacities and unintended capabilities.)

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https://www.tiktok.com/@checkers80/video/7419147585392348462

https://www.tiktok.com/@checkers80/video/7419146844518796590 ("Questions for the 'sentient' AI PART 1")

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ai thinks it's alive listen close! #samsung #Google #sentient #AI #fyp #gemini #google 

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@checkers80
ai thinks it's alive listen close! #samsung #Google #sentient #AI #fyp #gemini #google
♬ original sound - BANHAMMER

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Geoffrey Hinton wins Nobel; warns of AI dangers

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Geoffrey Hinton wins Nobel; warns of AI dangers

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Co-Intelligence: AI in the Classroom with Ethan Mollick

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Co-Intelligence: AI in the Classroom with Ethan Mollick | ASU+GSV 2024

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First let’s figure out the strawberry problem then we can worry about AI taking over 🍓 #ai 

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@alberta.nyc
First let’s figure out the strawberry problem then we can worry about AI taking over 🍓 #ai
♬ original sound - Alberta Tech

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"A 14-Year-Old Boy Killed Himself to Get Closer to a Chatbot: He Thought They Were In Love"

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"A 14-Year-Old Boy Killed Himself to Get Closer to a Chatbot: He Thought They Were In Love"

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Regarding viewing AI chatbots as a solution to loneliness, "[i]t is like offering a photograph of water to quench thirst."

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The missing voices of faith in tech development

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Which AI to Use Now - Ethan Mollick 1/26/25

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Which AI to Use Now - Ethan Mollick 1/26/25

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Which AI to Use Now: An Updated Opinionated Guide

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Which AI to Use Now: An Updated Opinionated Guide

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Ethan Mollick's history of AI in 32 otters

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Three years of progress as shown by marine mammals

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AI Will Be More Disruptive Than COVID...

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AI Will Be More Disruptive Than COVID...

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Excellent article. (Matt Lewis 8.15.25)

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Democrats, bless their hearts, keep trying to figure out the magic formula to stop President Trump. But here's a cold splash of reality: If Trump's popularity ever collapses, it will probably be because of something completely beyond their control.

In 2020, it wasn't some brilliant strategy that defeated Trump. It was COVID. A global pandemic. An act of God (or Wuhan).

This raises an uncomfortable thought: the next disruption - the one that might shake up the political snow globe again - will probably be much bigger than COVID. That looming disturbance is artificial intelligence.

In a recent Substack essay, Pete Buttigieg suggested that "the number one leadership challenge for world leaders, including the President of the United States, will be to manage the changes that AI is bringing about." He goes on to note that "our president - and his opposition - have yet to make clear what their AI policies even are."

He's not wrong about the bipartisan lack of preparation. And for this reason, the political consequences are likely to be brutal for whichever party is in charge when the tipping point arrives and AI upends the lives of millions of Americans.

Trump still has three and a half years left on the clock - just enough time for AI to yank the rug out from under him. That's a golden opportunity for Democrats, if they're smart enough to capitalize on it.

But Democrats should hold off on gleefully penciling in 2028 as the year AI hands them the keys to the White House in perpetuity. Why? Because huge shocks to the system tend to empower either a) bold problem solvers or b) populist demagogues.

Lest we forget, the last seismic tech shift - the rise of the Information Age - gave us globalization, economic dislocation (for working-class Americans) and (eventually) Donald Trump.

This next disruption could be even more traumatic. AI isn't just coming for truck drivers. It's coming for legal assistants, graphic designers, junior software developers, even (ahem) writers. College graduates who spent decades believing their degree was a shield against obsolescence are about to get a taste of what coal miners, steelworkers, typists and travel agents have already endured.

When that happens, disenchanted moderates will radicalize, and income inequality will detonate. The people who build and control AI will obviously get filthy rich. So will superstar surgeons and elite litigators - people whose rarefied expertise and skills can't be replicated remotely. But their legions of associates, researchers and paralegals will vanish like Blockbuster Video.

Now, for generations, lost jobs and industries were replaced by new ones - thanks to what economists call "creative destruction." The buggy maker gave way to the auto industry and the auto mechanic, and society moved forward. But this time, the old rules may not apply - at least, not by virtue of some organic "invisible hand."

If this shift is as severe and pervasive as many believe it will be (a huge caveat, to be sure), it won't be solved by fiddling around with marginal tax rates or by mildly expanding unemployment benefits. It will require a vast reimagining of what the government does - the kind of thing that would make free-market purists break out in hives.

But here's where it gets tricky for Democrats: They can't simply hand displaced workers a check and call it a solution.

This is the core problem with universal basic income, often touted as the answer to AI-driven job losses. The modest $1,000-a-month figure that's been floated is a joke. But even if the amount were higher, it would still have to be paired with meaningful work.

Something Democrats must learn: People don't just want money. They crave dignity, purpose, belonging and a reason to get up in the morning.

That means thinking big and finding meaningful opportunities for the displaced to serve and provide value. Imagine one teacher for every five students in America's public school and college classrooms. Imagine school buses with three adults instead of one overworked driver.

Imagine a national corps of well-paid nurses and physical therapists making regular visits to isolated seniors and providing full-time home healthcare.

Picture teams of young, tech-savvy Americans helping retirees navigate their iPads, iPhones, TVs and other devices - closing the digital divide for an entire generation.

Now, pair that with a bold expansion of union apprenticeships to train the next wave of electricians, plumbers and carpenters - alongside free college or vocational training in exchange for a year or two of national service.

It wouldn't happen overnight. Managing this transition would require robust unemployment benefits - say, 90% of prior salary for a fixed period - not as welfare, but as an investment in people and a dividend on the value they've helped create by virtue of tax dollars (that built the internet) and data (that fuel automation). Because again, addressing the dilemma of job displacement is about more than money.

Which brings us to some important questions we had better answer.

What does it mean to be a citizen in a society when AI makes half of the labor market feel redundant? How do you retain your identity and sense of self-worth when the work you have dedicated your life to can be more efficiently done by artificial intelligence?

And how do we redeploy human beings - tens of millions of them - into roles that make life better for others and give them back the self-respect that comes from service?

AI might be the great test of our political age, and the party that passes this test will be remembered as our savior.

The party that fails this test will be remembered - if at all - as the one fiddling while Rome was automated.

Insights

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Ideas expressed in the piece

The author argues that artificial intelligence will prove far more disruptive than the COVID-19 pandemic, suggesting it represents the next major political upheaval that could determine electoral outcomes regardless of traditional campaign strategies. Lewis contends that just as COVID led to Trump's 2020 defeat through circumstances beyond Democratic control, AI disruption could similarly destabilize whichever party holds power when the technology reaches its tipping point.

Lewis positions AI as a potential political opportunity for Democrats, arguing that Trump's remaining presidency provides sufficient time for AI to create widespread disruption that could damage his standing. However, the author warns that Democrats should not assume automatic victory, noting that major technological disruptions historically empower either bold problem-solvers or populist demagogues.

The author emphasizes that AI will devastate white-collar employment across multiple sectors, affecting legal assistants, graphic designers, junior software developers, and writers - groups that previously felt insulated from technological displacement. Lewis argues this will radicalize moderate voters and exacerbate income inequality as AI creators become wealthy while professional support staff face obsolescence.

Lewis dismisses traditional economic policy solutions as inadequate for addressing AI-driven displacement, arguing that marginal tax adjustments and modest unemployment expansions cannot solve such pervasive disruption. Instead, the author advocates for massive government intervention that would require "a vast reimagining of what the government does" and challenge free-market orthodoxy.

The author specifically critiques Universal Basic Income as insufficient, arguing that even substantial monthly payments fail to address human needs for dignity, purpose, and belonging. Lewis proposes instead a comprehensive program including dramatically expanded teacher ratios, enhanced school transportation staffing, national healthcare corps, technology assistance programs for seniors, union apprenticeships, and robust unemployment benefits covering 90% of prior salaries.

Different views on the topic

The Trump Administration's recently released AI Action Plan takes a fundamentally different approach, prioritizing acceleration of AI innovation and adoption rather than managing potential disruption. The plan emphasizes removing regulatory barriers and actively incentivizing AI development, with officials arguing that winning the AI race will "usher in a new golden age of human flourishing, economic competitiveness, and national security for the American people".

Research from AI experts suggests a more optimistic perspective on employment impacts than Lewis presents, with studies showing that AI typically boosts productivity and helps narrow skill gaps across the workforce rather than simply displacing workers. Additionally, Pew Research findings indicate that AI experts are significantly more positive than the general public about AI's potential, including its effects on employment.

Current policy approaches focus on international competitiveness and export promotion rather than domestic displacement management, with the Administration emphasizing partnerships to deliver "secure, full-stack AI export packages" to allies and building American AI infrastructure. This contrasts sharply with Lewis's emphasis on managing domestic disruption through expanded government services and income support.

Critics of restrictive AI regulation argue that the Trump Administration's approach of removing "onerous Federal regulations that hinder AI development and deployment" represents necessary deregulation to maintain American technological leadership. This perspective views regulatory caution as potentially harmful to innovation rather than protective of workers.

Evidence from current AI adoption suggests positive business impacts, with 78% of organizations reporting AI use in 2024 and private investment reaching $109.1 billion, indicating market confidence in AI's economic benefits rather than widespread concern about displacement. These trends suggest that AI integration may create value and opportunities rather than primarily destroying jobs as Lewis predicts.

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Contributor: AI will be more disruptive than COVID. Which party can seize the moment?

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If the artificial intelligence boom happens while President Trump is in office, he'll be blamed for the loss of millions of middle-class jobs. But will Democrats offer a vision for a new workforce?

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Why AI Shouldn't Replace the Rough Draft

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Why AI Shouldn't Replace the Rough Draft

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Here are the bullets from the Pivot 5 (https://pivot5.beehiiv.com/p/aug-12-2025) review of the article (emphasis mine):

A study in Consciousness and Cognition finds participants remember words better when they draw them instead of writing them. Across repeated tasks, sketchers consistently beat writers on recall tests.

Researchers used multidimensional experience sampling to examine thought quality and focus. They observed drawing spark richer visual, motor, and spatial processing, while writing remained linear and verbal.

The article argues that large language models bypass the messy, generative phase of creation, leaving users to merely tweak finished text. When AI handles the making, the author warns the deeper cognitive work that cements knowledge stays with the machine, not the mind.

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Key points

A recent study found that, by engaging in richer thought patterns, drawing boosts memory more than writing.

This suggests that using AI to generate one's writing risks skipping the messy phase that deepens thinking.

Keeping humans in the "making" stages of creativity preserves the process that makes ideas stick.

I often think of writing as a fairly straightforward act. For me, it's a process of transfer and organization-a way to get ideas out of my head and onto the page.

But fascinating research suggests that how we externalize thought can change the very nature of the thoughts themselves, along with how well we remember them. That got me thinking-and writing.

This study, published in Consciousness and Cognition, compared two simple tasks that included writing words by hand and drawing pictures of those words. On the surface, both methods might seem equally effective for learning. But the researchers found something very interesting: Drawing consistently led to better memory performance than writing.

As you might guess, this got me thinking about LLMs and typing into that context window as a central tool to our "thinking" these days.

Drawing's Cognitive Advantage

The research used a method called multidimensional experience sampling, which interrupts participants during a task to ask about the "quality and focus" of their ongoing thoughts. This let them look beyond the final written word or the sketch, and into the mental space where the work was happening.

When people drew, their thoughts tended to be more visual, more elaborate, and more connected. They weren't just transcribing meaning but were building it in multiple modes at once in a triad of visual imagery, motor movements, and spatial relationships. These richer thought patterns, in turn, supported stronger recall later. Writing, by comparison, tended to stay more linear and verbal, activating a narrower slice of the mind's capabilities.

From Pen and Paper to AI Keyboards

The study didn't mention artificial intelligence, but its implications are hard to ignore in an age where more and more of our "writing" is co-authored with large language models. The leap from pen to keyboard may already strip away some of the sensory and motor loops that make learning durable-and the leap from keyboard to AI output may risk removing even more. At least, that's my hypothesis based on the study's findings.

With LLMs, we often bypass the slow, messy, generative phase of writing entirely. Instead of assembling thoughts through trial, error, and revision, we can feed the system a prompt and receive a mostly-finished block of text in seconds. Our role shifts from creator to selector, where scanning, tweaking, and approving drive content rather than building from the ground up.

The Danger of Skipping the "Making" Phase

The drawing study suggests that this shift matters. Those elaborative, multimodal thought patterns that strengthen memory emerge from making, not just evaluating.

When AI takes over the making, we're left with thinner mental traces or paths. The words exist, but they live outside of us, stored in the machine's output rather than embedded in our own cognitive architecture.

In my view, this isn't an argument against using AI. It's an argument for using it with more intention.

If drawing's advantage comes from engaging the mind in multiple channels, then maybe our AI workflows should make room for that kind of engagement too. That could mean sketching an outline before prompting, diagramming relationships between ideas, or freewriting a rough draft before asking the model to refine it.

Designing for Cognitive Richness

The key is to preserve or maybe even to amplify the generative friction that AI so efficiently removes. That friction is where the deeper connections form. It's where ideas get pushed, pulled, and sometimes broken before they're rebuilt into a final form. Without it, we risk producing work that's clean on the page but rather vague in the mind.

Of course, we've long known that the tools we use change the way we think. The quill, the typewriter, and the word processor have all left their marks on our mental habits. And today, LLMs are just the latest tool. But they arrive with a speed and fluency that might short-circuit the mental processes that make knowledge stick.

My sense is that this study offers an important reminder that the process matters as much as the product. If we want AI to serve our thinking rather than replace it, then we need to design our interactions so that we remain active participants, not just passive recipients of results. That means keeping our hands and our minds in the work, even when the machine can do it faster and (in some instances) better.

And the key point here is that memory and understanding aren't built in the output; they're built in the making.

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Why AI Shouldn't Replace the Rough Draft

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The real value of a rough draft isn't just in the words, but in the thinking it demands. A recent study suggests there may be hidden costs to letting AI take on that work.

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Vals AI, provides benchmarks on how language models perform on the industry-specific tasks where they will be used.

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Private, domain-specific benchmarks in legal, tax, and finance.

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(Spoiler: Google's AI Mode was the winner.)

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Review | Which AI gives the right answers? Librarians helped us put them to the test.

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We quizzed AI bots with tough trivia, recent events questions and more. Some answers were impressive - others were worse than an old-fashioned Google search.

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the hunger to be everything: how we disappear chasing lives that aren't ours

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the hunger to be everything: how we disappear chasing lives that aren't ours

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This isn't about AI per se, but it IS about "living" within and through algorithms.

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how we disappear chasing lives that aren’t ours.

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The People Outsourcing Their Thinking to AI: LLeMmings

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The People Outsourcing Their Thinking to AI: LLeMmings

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Excellent article about how we are losing cognitive ability by relying on AI to do our thinking for us.

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The People Outsourcing Their Thinking to AI