This is a story of an impossible outcome, where AI never worked, nanotechnology never worked, biotechnology only sort-of worked; and yet somehow humanity not only survived, but discovered a way to travel Faster-Than-Light: The past's Future.
It features complex moral dilemmas. It begins with a woman shouting "ALIENS!".
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This post is written as an explanation of a misconception I had with transformer embedding when I was getting started. Thanks to Stephen Fowler for the discussion last August that made me realise the misconception, and others for helping me refine my explanation. Any mistakes are my own. Thanks to feedback by Stephen Fowler and JustisMills on this post.
TL;DR: While the token vectors are stored as n-dimensional vectors, thinking of them as points in vector space can be quite misleading. It is better to think of them as directions on a hypersphere, with a size component.
The I think of distance as the Euclidean distance, with formula:
Thus does not match up with the distance forumla used when calculating logits:
But it does match up with the cosine similarity forumula:
And...
I intend to use my shortform feed for two purposes:
1. To post thoughts that I think are worth sharing that I can then reference in the future in order to explain some belief or opinion I have.
2. To post half-finished thoughts about the math or computer science thing I'm learning at the moment. These might be slightly boring and for that I apologize.
I'm not sure if I can find it easily, but I recall Eliezer pointing out (several years ago) that he thought that Value Identification was the "easy part" of the alignment problem, with the getting it to care part being something like an order of magnitude more difficult. He seemed to think (IIRC) this itself could still be somewhat difficult, as you point out. Additionally, the difficulty was always considered in the context of having an alignable AGI (i.e. something you can point in a specific direction), which GPT-N is not under this paradigm.
So you’ve been reading down a rabbit hole and you have seen something that doesn’t make sense. You feel a quiver in your heart that feels like a cross between excitement and fear. Could all the people who already know stuff be Wrong™. Could eminent researchers in their field be working on a wrong model?
Maybe you have a disease or condition and you followed the directions of the doctors or experts exactly and found results opposite to the intended results. That’s when you started googling. You found an obscure blog written in 2005 by someone with the same problem. You find a few people proposing a different working mechanism and you are rapidly falling down the rabbit hole…
IF and that’s a big bold and underlined “if”, the experts...
The big news this week was that OpenAI is not training GPT-5, and that China’s draft rules look to be crippling restrictions on their ability to develop LLMs. After all that talk of how a pause was impossible and working with China was impossible and all we could do was boldly rush ahead, the biggest American player and biggest foreign rival both decided for their own internal reasons to do something not entirely unlike a pause.
They just went ahead and did it. We kept saying they’d never do it no matter what, and they just… went ahead and did it. At least somewhat.
This is excellent news. I sincerely hope people are updating on the new information, now that they know such things are not only possible but...
The most compelling-to-me argument I've seen in that vein is that human civilization is currently, even without AI, on a trajectory to demand more and more energy, and eventually that will involve doing things on a scale sufficient to significantly change the amount of sunlight that reaches the surface of the Earth.
Humans probably won't do that, because we live here (though even there, emphasis on "probably" -- we're not exactly doing great in terms of handling climate change from accidentally changing the amount of CO2 in the atmosphere, and while that's ...
Some time back, I saw a tweet from somebody that read:
Much of social psychology seems to be premised on the bizarre assumption that what people really care about is not real-world outcomes but the state of their own mind: self-esteem, a positive self-image, dissonance reduction, feelings of control, reducing uncertainty, etc.
I've certainly seen versions of the same myself. Maybe the most poignant example comes from this book review, which suggested that gambling addicts get hooked on a sense of control - even though someone who's hooked on gambling to the point of ruining their life clearly isn't in much control of anything:
...The primary objective that machine gambling addicts have is not to win, but to stay in the zone. The zone is a state that suspends real life, and
This is one of my favorite sequences on this site and I'm quite glad to see a new entry.
Thank you!
How does one gain confidence that the read on their own emotions is an accurate description of the message they're trying to communicate? That is, how can one be more sure that they're actually listening to their emotions and not just assuming?
It can be difficult! Some thoughts:
1) There's a certain difference in what it feels like to intellectualize or guess what your emotions are saying, as opposed to actually listening to them. @pjeby had a nice exercise abo...
Developments in Machine Learning have been happening extraordinarily fast, and as their impacts become increasingly visible, it becomes ever more important to develop a quantitative understanding of these changes. However, relevant data has thus far been scattered across multiple papers, has required expertise to gather accurately, or has been otherwise hard to obtain.
Given this, Epoch is thrilled to announce the launch of our new dashboard, which covers key numbers and figures from our research to help understand the present and future of Machine Learning. This includes:
- Training compute requirements
- Model size, measured by the number of trainable parameters
- The availability and use of data for training
- Trends in hardware efficiency
- Algorithmic improvements for achieving better performance with fewer resources
- The growth of investment in training runs over time
Our dashboard gathers all of this...
Thanks!
Our current best guess is that this includes costs other than the amortized compute of the final training run.
If no extra information surfaces we will add a note clarifying this and/or adjust our estimate.
A while back I read How to Measure Anything and found it fascinating. In my day job, I spend quite a bit of time trying to make sense of the world by looking at dashboards of requests, latencies, error rates, etc. (software systems).
After finishing the book and taking copious notes, I understood that it gave me a prepackaged process that I could apply as-is, but I found it very difficult to adapt to everyday situations. I don't think I picked up a good intuition about stats, in other words.
I'm looking to change that. Specifically, I want to learn to apply stats in these two situations:
- measuring things. Mostly software systems, but open to little experiments. Dan Luu used to measure a lot of fun things.
- understanding how others measure
Being able to accurately assess a paper's claims is, unfortunately, a very high bar. A large proportion of scientists fall short of it. see: [https://statmodeling.stat.columbia.edu/2022/03/05/statistics-is-hard-etc-again/]
Most people with a strong intuition for statistics have taken courses in probability. It is foundational material for the discipline.
If you haven't taken a probability course, and if you're serious about wanting to learn stats well, I would strongly recommend to start there. I think Harvard's intro probability course is good and has...
This is a great compilation of the arguments for why cryonics service providers should offer a brain-only option.
Not mentioned in the article: Note that a brain-only option is also offered by OregonCryo and Cryonics Germany.
Summary of the linked post:
The main advantages are:
- Lower storage cost
- Easier emergency relocation
- Lower "yuck" factor
- Compatible with organ donation
- Compatible with a burial ceremony
- Reduction of fracturing
- Permits emergency fixation (in case of ln2 delivery issues)
Related posts:
- #2: Neurocryopreservation vs whole-body preservation (from the Cryonics Signup Guide)
- Section "Higher packing densities" of The Allocation of Long Term Care Costs at Alcor
How much more advantageous would this be than a "head only" option? To get to the brain, wouldn't you have to cut open the head anyways?
Some have pointed out seemingly large amounts of status-anxiety EAs generally have. My hypothesis about what's going on:
...A cynical interpretation: for most people, altruism is significantly motivated by status-seeking behavior. It should not be all that surprising if most effective altruists are motivated significantly by status in their altruism. So you've collected several hundred people all motivated by status into the same subculture, but status isn't a positive-sum good, so not everyone can get the amount of status they want, and we get the above dyn
I Googled up 'how are tokens embedded' and this post came up third in the results - thanks for the post!