Yeah, this is the kind of thing that Eliezer Yudkowsky is kind of right about. You have a draw a clear line about what advice you are going to be taking from any kind of AI whatsoever, otherwise it's really confusing to people why this time was fine because the AI is not "general" but in some unspecified future time it's not okay. Especially if you want to tell people that some future IA will kill everyone with synthetic biology.
> Yeah, this is the kind of thing that Eliezer Yudkowsky is kind of right about.
I find it very hard to take him seriously or assign him any credibility with how he acted regarding the whole "Roko's basilisk" thing, not to mention some of his views just seem plain nuts to me.
I agree, but that does not mean everything he says is wrong. I think the public having clear expectations of AIs and how they interact with them is important in general.
I have no fear of mRNA vaccines nor LLM "AI"'s but I agree with your point that large numbers of people will strongly react to the notion of "AI created gene altering vaccines" etc.
The future will clearly be a dystopian hellscape populated by people looking as if drawn by DALL-E. /s
I don’t specifically know about the US experience, but mass covid vaccination didn’t appear to be all that easy to achieve where I live. It required an overwhelming government information campaign, removal of the ability to engage in many activities if unvaccinated, multiple “lockdowns” and the threat of mandating compliance. I almost wonder that anyone managed to get through all of that when refusing the vaccine.
I'd say more than 81% generally comply with the law. Given the extreme measures employed to "get" people to take it, that might be a closer comparison than whatever it is you're imagining comparing this to.
you can get 81% of the population to do a lot of things, when you lock them in their homes, forbid them to see their family or get a coffee or go to work unless they do the thing you want them to do.
(i am fully vaccinated, but i wouldn't consider what happened successful in any way)
> - 1 in 8 who initially accepted them rejected the second; 5 in 8 who initially accepted rejected the third
Isn't that somewhat expected? I mean, I got covid when I had the vaccine and it was OK (nearly no symptoms), so I (perhaps naively) concluded "ok, this is probably nothing". Then I happened to get it again and again, longer after I had the vaccine so indirectly I was passively and gradually testing how I would react to the virus without the vaccine. In the end I said ok, this is fine, I probably don't have to be vaccinated again.
Expected by whom? Most doctors probably expected that, but the people setting COVID policy appeared to believe that after being vaccinated people wouldn't get or transmit COVID and therefore coercive tactics could be used to force people to get jabbed. Turns out in hindsight they were both authoritarian and spreading medical misinformation. And have probably undermined trust in vaccines and the medical establishment for a good decade.
The issue here isn't that somehow this is surprising news, the issue here is that there was a general suspension of a lot of basic human rights for, in hindsight, poor reasons and insufficient evidence.
This post conflates a lot of errors. But specifically, I remember the uk chief medical officer being fairly open that the effect of vaccines was stochastic and would only reduce spread on the average, and this was uncertain. The emphasis at the time was very much that vaccines would prevent serious disease and death. Specifically, they wanted to keep people out of hospitals to avoid them being overwhelmed. There was much better evidence for that, and in retrospect vaccination was successful in that.
Here is a montage of various vaccine "leaders" making statements ranging from the vaccines completely stopping the spread, to you being unable to catch COVID with the vaccine. [1] Some people, sometimes, made accurate statements. But by and large misinformation and hyperbole were the rule rather than the exception.
Accepting and approaching this is very important, because while some may simply want to forget many of the things that happened, others never will. And so it will only further galvanize the differences and divides between people. What should be done instead is to consider why such statements were not only made, and not only left unchallenged, but in most cases - actively encouraged. And what we could do to help have a better outcome next time.
There's relatively little doubt that the past years have shattered confidence in many US institutions (public and private...), and that's not really good for anybody. Repairing that damage can only happen once the past is reconciled in a way that everybody can feel generally positive about.
CMO liked to say that up until UK data started showing the vaccinated getting sick (and therefore spreading it) more often than the unvaccinated. That data got worse with time until the vaccinated had a >3x case rate.
2022 meanwhile had record-breaking case levels. Vaccines didn't reduce the spread even on average. But the UK still treated the unvaccinated horribly, justified by these lies about spread.
The correct response to all this is to realize that governments employ a lot of people who are fanatical about vaccines and will lie to any extent necessary to get people to take them.
This is an optimization "AI", like a computer chess program, right? It's not determining in advance which kinds of structures are going to be most stable and generating them, it's working toward pre-determined structural optima?
AI is usually a marketing term. They used machine learning to learn the topology of a prediction space related to desirable properties of mRNA vaccines.
I could be wrong, but that makes it sound algorithmically similar to AlphaFold 1 where they used ML to learn properties of the folded protein and then optimized the final structure to fit those properties
The scientific article seems to be open access [1].
Before people draw links to recent large language model breakthroughs: Although they do use techniques from computational linguistics, there are no neural networks involved. This is more like old-school AI.
They have essentially a giant optimization problem, and they (approximately) model it as a lattice parsing problem, with a stochastic context-free grammar. They can solve that to optimality in O(n^3), which is too slow for some applications. So they propose a O(n) heuristic (hence no optimality guarantees, but the model was approximate to begin with anyways, and the heuristic is a lot faster), which is the reason for the name of their code: "LinearDesign".
"The lattice parsing problem refers to the task of parsing a word lattice, which is a graph structure that represents multiple possible sequences of words that could have generated a given speech signal [1]. The word lattice is a weighted directed acyclic graph, where each node represents a word hypothesis and each edge represents a transition between two words. The weights on the edges represent the likelihood of the transition. The goal of lattice parsing is to find the most likely sequence of words that generated the speech signal, given the word lattice [1]. Lattice parsing is a challenging problem because the word lattice can be very large and contain many alternative paths, making it difficult to find the most likely path efficiently [1]. Several techniques have been proposed to address this problem, including bi-directional LR parsing from an anchor word, augmented chart data structure, and attention shifting for parsing speech [2][3][4]."
(full disclosure this might not be correct, I tried this with an LLM approach we're beta testing at my job called scite Assistant that answers with real references - no hallucinations, just curious how the response is against someone that knows the field a bit more..!)
Not sure why this would matter, generally medication is incredibly expensive because of the research that needs to go into it not scaling well to the amount of people that might need it for rare disease. If drug discovery costs go down to almost nothing, and trialling is greatly assisted with AI driven protein folding, it would almost become trivial to cure most diseases.
Where are you seeing this? Everything I saw is that they are eligible for copyright, and the copyright is owned by the person who ran the tool and selected the output.
The news was dumb media bait where people tried to claim the AI itself owned the copyright which makes no sense since AI is not a legal entity able to own things.
Judge declared it about a month ago. Positon was the human edited parts were but machine generated parts were not. Sorry don’t have the reference at hand.
Seems almost impossible to enforce. Say I generate an image, then go in photoshop and play with the levels/colors. Every single pixel was modified by me. And the only way you could use it without infringing on my copyright is to work out what the original image was, which is impossible.
You wouldn’t copyright an RNA sequence like this. While it is novel, I’m not sure you’d be able to get a copyright on it, as it’s a set sequence.
Instead, you’d patent it. And I’m not aware that that question has yet been asked. Especially when computationally driven drug design has a rich history, I’d expect for AI generated work to be patentable.
This linear-time approximation algorithm for mRNA design (LinearDesign) was inspired a lot from their previous work on a linear-time RNA folding algorithm (LinearFold).
Of course it is. It's artificial and makes intelligent decisions. So is a pile of if sentences if they encode logical reasoning that applies some knowledge.
If one had a database of all answers to every question in the universe, making AGI would be as simple as fetch call.
Hopefully these ones will be tested longer than the last batch of mRNA designs, so that risks like the doubling of retinal vascular occlusion risk are identified before they go into production: https://www.nature.com/articles/s41541-023-00661-7
Statistically, based on the paper you linked longer testing to pick up this side effect would have been the wrong decision. It seems to me that longer testing would have caused more deaths than would have prevented RVO. From your paper:
Based on the official COVID-19 death reports, it is estimated that vaccinations have prevented 14.4 million excess COVID-19 deaths worldwide between December 2020 and December 202139. Thus, vaccination is the most effective method for preventing the spread of SARS-CoV-2.
The number of reported ophthalmic complications has remained low, and vaccine-related retinal vascular occlusion is very rare, although the number of COVID-19 vaccinations is enormous.
That paragraph is a logical fallacy. To compute a cost/benefit analysis for something you have to include all the costs, not just one. There are many papers like that one covering many different side effects.
Note that there was an attempted discussion of that paper on HN but it was immediately flagged to death. It always happens for every such paper. People still aren't ready to do a full and honest accounting. Probably they never will be.
I want to comment on this because you are phrasing it as if people who got the vaccine are in denial and not wanting to talk about it. Topics like this are hard to discuss because they are immediately polarizing and everyone who has a strong opinion but very little understanding of the science feels the to get their quip in about how right they are and how wrong the other people are. You can especially since it in threads relating celebrity deaths with comments like "well we know what REALLY killed them wink wink"
People flag conspiratorial antivax comments, news at 11.
Personally, I already spent two years dealing with physical threats and violence from antimask/antivax crazies, I'm not about to give them the time of day when they crawl out of the woodwork on the internet.
Like everyone else, you're not reading what you don't want to see.
I said people flag stories that are research papers. As in, published by doctors. Not comments, not conspiracies, just collected data. Doesn't matter. People (like you?) flag them anyway to try and keep everyone else at the same intellectual darkness.
Dr Oz and Jordan Peterson are doctors too, but that doesn't mean I'm not going to flag their quackery and words-shaped toxic waste every time I see it. The ability to receive a doctorate and publish a paper does not preclude a person from being an acidic blockhead or from receiving a thusly befitting amount of attention.
63 comments
[ 3.2 ms ] story [ 131 ms ] threadI find it very hard to take him seriously or assign him any credibility with how he acted regarding the whole "Roko's basilisk" thing, not to mention some of his views just seem plain nuts to me.
I mean I agree having rules and expectations is important though.
On the other hand I wonder how enforceable it will be since people can generate models in secret in their homes.
Yudkowsky may have talked about artificial consciousnesses at various points, but that's not even close to a critical issue for why he's a doomer.
The TLDR quote I have of his fear is: “The AI does not hate you, nor does it love you, but you are made out of atoms which it can use for something else.” - https://www.goodreads.com/quotes/499238-the-ai-does-not-hate...
The future will clearly be a dystopian hellscape populated by people looking as if drawn by DALL-E. /s
People at some point stop believing the bullshitting.
1. https://usafacts.org/visualizations/covid-vaccine-tracker-st...
- 1 in 5 people refused them, entirely.
- 1 in 8 who initially accepted them rejected the second; 5 in 8 who initially accepted rejected the third.
- In a three round series, approximately 2 in 3 overall rejected them before the full course.
I agree people eventually stop accepting bullshit — but we may disagree on what they’re rejecting.
you can get 81% of the population to do a lot of things, when you lock them in their homes, forbid them to see their family or get a coffee or go to work unless they do the thing you want them to do.
(i am fully vaccinated, but i wouldn't consider what happened successful in any way)
Isn't that somewhat expected? I mean, I got covid when I had the vaccine and it was OK (nearly no symptoms), so I (perhaps naively) concluded "ok, this is probably nothing". Then I happened to get it again and again, longer after I had the vaccine so indirectly I was passively and gradually testing how I would react to the virus without the vaccine. In the end I said ok, this is fine, I probably don't have to be vaccinated again.
The issue here isn't that somehow this is surprising news, the issue here is that there was a general suspension of a lot of basic human rights for, in hindsight, poor reasons and insufficient evidence.
Accepting and approaching this is very important, because while some may simply want to forget many of the things that happened, others never will. And so it will only further galvanize the differences and divides between people. What should be done instead is to consider why such statements were not only made, and not only left unchallenged, but in most cases - actively encouraged. And what we could do to help have a better outcome next time.
There's relatively little doubt that the past years have shattered confidence in many US institutions (public and private...), and that's not really good for anybody. Repairing that damage can only happen once the past is reconciled in a way that everybody can feel generally positive about.
[1] - https://twitter.com/ITGuy1959/status/1581034815700488192
2022 meanwhile had record-breaking case levels. Vaccines didn't reduce the spread even on average. But the UK still treated the unvaccinated horribly, justified by these lies about spread.
The correct response to all this is to realize that governments employ a lot of people who are fanatical about vaccines and will lie to any extent necessary to get people to take them.
Before people draw links to recent large language model breakthroughs: Although they do use techniques from computational linguistics, there are no neural networks involved. This is more like old-school AI.
They have essentially a giant optimization problem, and they (approximately) model it as a lattice parsing problem, with a stochastic context-free grammar. They can solve that to optimality in O(n^3), which is too slow for some applications. So they propose a O(n) heuristic (hence no optimality guarantees, but the model was approximate to begin with anyways, and the heuristic is a lot faster), which is the reason for the name of their code: "LinearDesign".
[1] https://www.nature.com/articles/s41586-023-06127-z
1. https://doi.org/10.21437/interspeech.2016-1583
2. https://doi.org/10.3115/997939.997950
3. https://dl.acm.org/doi/10.3115/991146.991188
4. https://doi.org/10.21236/ada105028
----
(full disclosure this might not be correct, I tried this with an LLM approach we're beta testing at my job called scite Assistant that answers with real references - no hallucinations, just curious how the response is against someone that knows the field a bit more..!)
The news was dumb media bait where people tried to claim the AI itself owned the copyright which makes no sense since AI is not a legal entity able to own things.
Instead, you’d patent it. And I’m not aware that that question has yet been asked. Especially when computationally driven drug design has a rich history, I’d expect for AI generated work to be patentable.
We re-wrote their C implementation[0] of LinearFold in Go and added comments to explain how the algorithm worked: https://github.com/allyourbasepair/rbscalculator/blob/main/l...
[0] - https://github.com/LinearFold/LinearFold
If one had a database of all answers to every question in the universe, making AGI would be as simple as fetch call.
Based on the official COVID-19 death reports, it is estimated that vaccinations have prevented 14.4 million excess COVID-19 deaths worldwide between December 2020 and December 202139. Thus, vaccination is the most effective method for preventing the spread of SARS-CoV-2.
The number of reported ophthalmic complications has remained low, and vaccine-related retinal vascular occlusion is very rare, although the number of COVID-19 vaccinations is enormous.
Note that there was an attempted discussion of that paper on HN but it was immediately flagged to death. It always happens for every such paper. People still aren't ready to do a full and honest accounting. Probably they never will be.
Personally, I already spent two years dealing with physical threats and violence from antimask/antivax crazies, I'm not about to give them the time of day when they crawl out of the woodwork on the internet.
I said people flag stories that are research papers. As in, published by doctors. Not comments, not conspiracies, just collected data. Doesn't matter. People (like you?) flag them anyway to try and keep everyone else at the same intellectual darkness.