ChatGPT has been receiving a lot of attention on the internet recently.
You can try it here.
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Cross-posted to Lesswrong
Introduction
Several developments over the past few months should cause you to re-evaluate what you are doing. These include:
1. Updates toward short timelines
2. The Trump presidency
3. The o1 (inference-time compute scaling) paradigm
4. Deepseek
5. Stargate/AI datacenter spending
6. Increased internal deployment
7. Absence of AI x-risk/safety considerations in mainstream AI discourse
Taken together, these are enough to render many existing AI governance strategies obsolete (and probably some technical safety strategies too). There's a good chance we're entering crunch time and that should absolutely affect your theory of change and what you plan to work on.
In this piece I try to give a quick summary of these developments and think through the broader implications these have for AI safety. At the end of the piece I give some quick initial thoughts on how these developments affect what safety-concerned folks should be prioritizing. These are early days and I expect many of my takes will shift, look forward to discussing in the comments!
Implications of recent developments
Updates toward short timelines
There’s general agreement that timelines are likely to be far shorter than most expected. Both Sam Altman and Dario Amodei have recently said they expect AGI within the next 3 years. Anecdotally, nearly everyone I know or have heard of who was expecting longer timelines has updated significantly toward short timelines (<5 years). E.g. Ajeya’s median estimate is that 99% of fully-remote jobs will be automatable in roughly 6-8 years, 5+ years earlier than her 2023 estimate. On a quick look, prediction markets seem to have shifted to short timelines (e.g. Metaculus[1] & Manifold appear to have roughly 2030 median timelines to AGI, though haven’t moved dramatically in recent months).
We’ve consistently seen performance on benchmarks far exceed what most predicted. Most recently, Epoch was surprised to see OpenAI’s o3 model achi
Hey everyone, I’ve been going through the EA Introductory Program, and I have to admit some of these ideas make sense, but others leave me with more questions than answers. I’m trying to wrap my head around certain core EA principles, and the more I think about them, the more I wonder: Am I misunderstanding, or are there blind spots in EA’s approach?
I’d really love to hear what others think. Maybe you can help me clarify some of my doubts. Or maybe you share the same reservations? Let’s talk.
Cause Prioritization. Does It Ignore Political and Social Reality?
EA focuses on doing the most good per dollar, which makes sense in theory. But does it hold up when you apply it to real world contexts especially in countries like Uganda?
Take malaria prevention. It’s a top EA cause because it’s highly cost effective $5,000 can save a life through bed nets (GiveWell, 2023). But what happens when government corruption or instability disrupts these programs? The Global Fund scandal in Uganda saw $1.6 million in malaria aid mismanaged (Global Fund Audit Report, 2016). If money isn’t reaching the people it’s meant to help, is it really the best use of resources?
And what about leadership changes? Policies shift unpredictably here. A national animal welfare initiative I supported lost momentum when political priorities changed. How does EA factor in these uncertainties when prioritizing causes? It feels like EA assumes a stable world where money always achieves the intended impact. But what if that’s not the world we live in?
Long termism. A Luxury When the Present Is in Crisis?
I get why long termists argue that future people matter. But should we really prioritize them over people suffering today?
Long termism tells us that existential risks like AI could wipe out trillions of future lives. But in Uganda, we’re losing lives now—1,500+ die from rabies annually (WHO, 2021), and 41% of children suffer from stunting due to malnutrition (UNICEF, 2022). These are preventable d
Melanie Basnak🔸
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In my past year as a grantmaker in the global health and wellbeing (GHW) meta space at Open Philanthropy, I've identified some exciting ideas that could fill existing gaps. While these initiatives have significant potential, they require more active development and support to move forward.
The ideas I think could have the highest impact are:
1. Government placements/secondments in key GHW areas (e.g. international development), and
2. Expanded (ultra) high-net-worth ([U]HNW) advising
Each of these ideas needs a very specific type of leadership and/or structure. More accessible options I’m excited about — particularly for students or recent graduates — could involve virtual GHW courses or action-focused student groups.
I can’t commit to supporting any particular project based on these ideas ahead of time, because the likelihood of success would heavily depend on details (including the people leading the project). Still, I thought it would be helpful to articulate a few of the ideas I’ve been considering.
I’d love to hear your thoughts, both on these ideas and any other gaps you see in the space!
Introduction
I’m Mel, a Senior Program Associate at Open Philanthropy, where I lead grantmaking for the Effective Giving and Careers program[1] (you can read more about the program and our current strategy here).
Throughout my time in this role, I’ve encountered great ideas, but have also noticed gaps in the space. This post shares a list of projects I’d like to see pursued, and would potentially want to support. These ideas are drawn from existing efforts in other areas (e.g., projects supported by our GCRCB team), suggestions from conversations and materials I’ve engaged with, and my general intuition. They aren’t meant to be a definitive roadmap, but rather a starting point for discussion.
At the moment, I don’t have capacity to more actively explore these ideas and find the right founders for related projects. That may change, but for now, I’m interested in
Relevant opportunities
Correct me if I’m wrong but my understanding is most everything ChatGPT can do was already possible with GPT3 (especially post InstructGPT) but it just took more intentional wrangling. What ChatGPT seems to be offering is a much more accessible interface.
That sounds accurate. The key difference with ChatGPT is that there's a LOT more public attention to the underlying capabilities of GPT-3.
text-davinci-003 (which is effectively ChatGPT) was a bit better than text-davinci-002 anecdotally and when I benchmarked it on TriviaQA. It was only released about a week before ChatGPT so it's not necessarily unreasonable to lump them together. If you do, then the interface isn't the only change one might associate with ChatGPT.
This is probably a stupid question, but: do we actually know if ChatGPT uses text-davinci-003?
When I talk to ChatGPT with the Network tab of Chrome DevTools open, filter for the name "conversation," and look at any request payload, I see that it has the key-value pair
Which seems to indicate that it might not be using text-davinci-003.
The blog post says ChatGPT is trained with proximal policy optimization. This documentation says text-davinci-003 was trained with PPO, but not text-davinci-002.
However, it is interesting what you're saying about the request payloads, because this seems to be contradictory. So I'm not quite sure anymore. It's possible that ChatGPT was trained with PPO on top of the non-PPO text-davinci-002.
Yeah, it didn’t update my timeline number much since I’d seen other language models, but it started to make short-timeline intuitions feel a lot more real as the capabilities are a lot more obvious now.
My timeline for AGI hasn't changed much, but my timeline for 'semi-transformational narrow AI' has become shorter.
Lots of academics are talking about how ChatGPT (and student and teacher use thereof) will force a revolution in how we assign homework, writing assignments, exam questions, and even class discussion questions. The whole experience of school and college will either have to change dramatically (e.g. a return to in-person lectures & discussions, and in-person paper-and-pencil exams), or schools and colleges will become empty rituals in which teachers using AIs pretend to teach and grade things, and students using AIs pretend to learn things.
Similar pressures will arise in many other industries, social practices, and relationships.
I think what ChatGPT highlights, fundamentally, is that even 'narrow AI' will be transformational enough to impose a dizzying rate of change on our civilization, and to impose qualitatively new kinds of risks.
This sounds a bit exaggerated. I haven't engaged much with ChatGPT, but it seems like the same arguments could be made for students having access to the internet.
Can you define what kind of writing assignments you are referring to?
Konstantin -- For example, if I'm running an online discussion forum in one of my psychology classes, and one student has a question (e.g. 'What is the difference between classical and operant conditioning?'), another student could answer by simply typing that question into Chat GPT, copying the answer into the Blackboard discussion forum, modifying it a little bit to defeat any anti-plagiarism software, and getting credit for the answer.
Of, if an online exam asks for a short essay answer to a question (e.g. 'What are some limitations of evolutionary psychology?'), a student could get a pretty good answer from Chat GPT, copy it into the online exam software, and get a good grade.
Or, if a student is developing longer term paper on some topic, they could ask Chat GPT 'Please write an outline of a term paper on how multivariate genetics can inform psychiatric diagnosis', and they could easily build upon that.
If you try these three example prompts, you'll see that Chat GPT does an amazingly good job of doing about 80% of the students' work for them, much faster than any student could write.
I used chatgpt and it is amazing good at telling historical stories and describing people. I have used a similar tool named Jasper and is equally as powerful.
The data we are generating now from these systems are just mind boggling, Elon was right from years before - it will come a time that the data generated by AI systems will dwarf human outputs at scale. We are seeing the early stages of it.
Thank you. I think for the first two I still don't see a big jump from just internet access. But I agree that cheating got a lot easier. The third example seems particularly like outsourcing an important skill.
I think my skepticism comes from the fact that you could already easily cheat on my biology exams with internet access (and people did when they were online during covid). This isn't much of a problem with pen & paper exams, but it seems that online degrees will really struggle now.
Out of curiosity I fed it some questions from assignments I had during my undergrad. Predictably, it got answers asking for simple factual knowledge quite right. It also did quite well in delivering a bone structure for essays/reports. But it completely failed on more complex questions related to maths or algorithms. The answers still read really well, they were just completely false. My best guess is that we will soon all have ai assistants, which will certainly shift teaching and being taught, but I don't think we have to panic yet.
Anton -- this sounds accurate. Chat GPT seems much stronger on qualitative verbal answers than on math, algorithms, and logic.
But, outside hard-core STEM fields, a lot of college courses focus on qualitative verbal answers. So, those of us who teach in the social and behavioral sciences will face stronger challenges than those who teach in computer science, physics, or statistics.
I worry that most people here don't have timelines, just vibes.
And when AI does something scary, they go, "Look, I was espousing doomy vibes and then AI did something that looks doomy! Therefore I am worth paying more attention to!"
Or, "Hm, I was more into global development but the vibes are AI now. Maybe I should pull my old doomist uniform out of the closet."
If that sounds like something you're doing reader, maybe reconsider?
Me:
Describe purple dragon don jitsu ryu pioneered by Don Jacobs
Chatgpt:
I'm sorry, but I am still not able to provide any information about "purple dragon don jitsu ryu" or its founder, Don Jacobs. As a large language model trained by OpenAI, my knowledge is based on the text that I have been trained on, and I do not have the ability to browse the internet or provide information on topics that were not included in my training data. If you have any more information or context about "purple dragon don jitsu ryu" or Don Jacobs, I would be happy to try to help, but without more information, I am unable to provide a response.
Hmmmm makes me wonder what books, internet resources or articles chatGPT was trained to. Definitely not the whole internet..
I tried plugging this into the OpenAI playground and got this response. Is this a good answer? I f so, it seems like it is something odd about how ChatGPT is configured, not GPT itself.
Me: Describe purple dragon don jitsu ryu pioneered by Don Jacobs Chatgpt: [<--Prompt] [Response-->] Purple Dragon Don Jitsu Ryu is a martial art pioneered by Don Jacobs. It is a combination of Jiu Jitsu and other martial arts, with a unique emphasis on “purple dragon” techniques. It focuses on the development of self-defense skills and the cultivation of a warrior's spirit. It stresses practical application of techniques, along with the development of internal power, balance, and agility. Purple Dragon Don Jitsu Ryu also emphasizes the development of body mechanics, as well as an understanding of the principles of motion and the use of leverage in order to maximize efficiency and effectiveness.Wow how did you do that... That is a good answer, I practice the martial art and information is good enough to describe what we are doing in the dojo.