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>You can't model and predict the weather just by training on the outputs of the weather system (whether it rained today, whether it was cloudy yesterday, and so

>You can't model and predict the weather just by training on the outputs of the weather system (whether it rained today, whether it was cloudy yesterday, and so on). You have to train on the inputs (air currents, warm fronts, etc.) >You can't model and predict the stock market just by training on the outputs of stock trading decisions (the high today, the low yesterday). You have to train on the inputs (company fundamentals, earnings, market sentiments in the news, etc.) Says who? You can model and predict novel protein sequences by training on....protein sequences. https://www.nature.com/articles/s41587-022-01618-2 https://www.nature.com/articles/s41587-022-01618-2 You don't need to train on the inputs(casual processes) of anything, that's what training is there to figure out.

spookie 3y ago
I'm sorry in advance, but aren't proteins glorified Lego?
- davecap1 3y ago
  There's a lot more to protein sequences than legos. I think the argument is that you don't need to train a model on fundamental organic chemistry/biochemistry, electrostatic protein interaction, hydrogen bonding, hydrophobic interaction, quantum mechanics, etc... in order for it to accurately predict protein sequences.
  - haltist 3y ago
    The data that AlphaFold was trained on included all that information and more. The database they used for training included software simulations (and real world data) that accounted for atomic (quantum) interactions. The 3D structure of proteins includes all the quantum interactions. More generally, AI models (aka very large function graphs) are trained on tuples that represent mappings of inputs to outputs (input -> output). The idea then is that whatever structure exists in those pairs/tuples/mappings is discovered by the training process with the help of gradient descent which tunes the parameters of the model/graph to optimally compress the information contained in the data. This means the model must uncover the quantum effects (or some close proxy of it) and then encode them into the parameters in a way that makes compression/prediction possible [1]. None of this is magic, compressing data requires uncovering structures and symmetries that can be used to reduce the size of the data and it turns out gradient descent with lots of parameters manages to do that for a large class of problems albeit at a very steep computational cost that requires billions of dollars for hardware and software (including nuclear power plants [2]). We are not going to get AGI with this approach but fortunately I know how to make it happen for a mere $80B. 1: https://arxiv.org/abs/2305.15614 https://arxiv.org/abs/2305.15614 2: https://www.cnbc.com/2023/09/25/microsoft-is-hiring-a-nuclear-energy-expert-to-help-power-data-centers.html https://www.cnbc.com/2023/09/25/microsoft-is-hiring-a-nuclea...
bzbz 3y ago
In your example, the amino acids order is sufficient to directly model the result: the sequence of amino acids can directly generate the protein, which is either valid or invalid. All variables are provided within the data. In the original example, we are testing weather using the previous day’s weather. We may be able to model using whatever correlation exists between the data. This is not the same as accurately predicting results, if the real-world weather function is determined by the weather of surrounding locations, time of year, and moon phase. If our model does not have this data, and it is essential to model the result, how can you accurately model? In other words: “Garbage in, garbage out”. Good luck modeling an n-th degree polynomial function, given a fraction of the variables to train on.
- famouswaffles 3y ago
  >All variables are provided within the data. electrostatic protein interaction, hydrophobic interaction, organic chemistry etc all variables are in fact not provided within the data. Protein creation is not just _poof_ proteins. There are steps, interactions and processes. You don't need to supply any of that to get a model accurately predicting proteins. That is the main point here, not that you can predict anything with any data.
- jakderrida 3y ago
  > This is not the same as accurately predicting results, if the real-world weather function is determined by the weather of surrounding locations, time of year, and moon phase. How many have the "human intelligence" to do this? Especially more accurately than a computer (and without using any themselves) training on the same inputs and outputs?
wavemode 3y ago
> You don't need to train on the inputs(casual processes) of anything, that's what training is there to figure out. I mean... this is just obviously false. If the data you're training on isn't causally predictive, you may occasionally find good-enough patterns for a particular use case (i.e. you may occasionally guess better than a coin flip which direction the stock market goes) but you aren't going to accurately model anything, and certainly not well enough to create an AGI that makes intelligent decisions. Words in sentences (and, indeed, proteins in a sequence) are causally predictive of each other - the grammar and semantics of one word tends to dictate what words are likely to surround it. So LLM's are very good at writing, and that is certainly useful! But that's just not the same as human intelligence. When someone makes an AGI out of an LLM then I'll be proven wrong, I suppose. I'm just sharing my personal view on things.
- rafaelero 3y ago
  Then how does ChatGPT end up providing a better/equivalent medical diagnosis than doctors (even though they are the "masters" of the causal pathways)?
- famouswaffles 3y ago
  Being "casually predictive" does not mean you have provided all the variables of your prediction in the data. Protein creation is not just _poof_ new proteins. There are steps and interactions and you don't need to train on all of that. Do you want a list of all the interactions of protein creation we are aware of ? >When someone makes an AGI out of an LLM then I'll be proven wrong, I suppose. I'm just sharing my personal view on things. You're going to have to define AGI first.
  - wavemode 3y ago
    > Being "casually predictive" does not mean you have provided all the variables of your prediction in the data. Not sure where I claimed this. > Protein creation is not just _poof_ new proteins. Not sure where I claimed this either. > There are steps and interactions and you don't need to train on all of that. I agree with this statement as well. Have you read what I wrote? Proteins in chains can indeed be used to predict other proteins in chains, even though you never trained the model on the biological processes of protein generation. Just like words in sentences can be used to predict other words in sentences, even though you never trained the model on the neurological processes of human speech. I'm not disputing any of that. What I'm disputing is that it will eventually become an AGI. > You're going to have to define AGI first. I'm using the definition provided verbatim in the linked article: "We believe superintelligence—AI vastly smarter than humans—could be developed within the next ten years."
lossolo 3y ago
Weather and stock market are both chaotic systems. Increasing evidence suggests that AGI will not be attainable solely using LLMs/transformers/current architecture, as LLMs can't extrapolate beyond the patterns in their training data (according to a paper from DeepMind last month): "Together our results highlight that the impressive ICL abilities of high-capacity sequence models may be more closely tied to the coverage of their pretraining data mixtures than inductive biases that create fundamental generalization capabilities."[1] 1. https://arxiv.org/abs/2311.00871 https://arxiv.org/abs/2311.00871

spookie 3y ago

I'm sorry in advance, but aren't proteins glorified Lego?

davecap1 3y ago
There's a lot more to protein sequences than legos. I think the argument is that you don't need to train a model on fundamental organic chemistry/biochemistry, electrostatic protein interaction, hydrogen bonding, hydrophobic interaction, quantum mechanics, etc... in order for it to accurately predict protein sequences.
- haltist 3y ago
  The data that AlphaFold was trained on included all that information and more. The database they used for training included software simulations (and real world data) that accounted for atomic (quantum) interactions. The 3D structure of proteins includes all the quantum interactions. More generally, AI models (aka very large function graphs) are trained on tuples that represent mappings of inputs to outputs (input -> output). The idea then is that whatever structure exists in those pairs/tuples/mappings is discovered by the training process with the help of gradient descent which tunes the parameters of the model/graph to optimally compress the information contained in the data. This means the model must uncover the quantum effects (or some close proxy of it) and then encode them into the parameters in a way that makes compression/prediction possible [1]. None of this is magic, compressing data requires uncovering structures and symmetries that can be used to reduce the size of the data and it turns out gradient descent with lots of parameters manages to do that for a large class of problems albeit at a very steep computational cost that requires billions of dollars for hardware and software (including nuclear power plants [2]). We are not going to get AGI with this approach but fortunately I know how to make it happen for a mere $80B. 1: https://arxiv.org/abs/2305.15614 https://arxiv.org/abs/2305.15614 2: https://www.cnbc.com/2023/09/25/microsoft-is-hiring-a-nuclear-energy-expert-to-help-power-data-centers.html https://www.cnbc.com/2023/09/25/microsoft-is-hiring-a-nuclea...

bzbz 3y ago

In your example, the amino acids order is sufficient to directly model the result: the sequence of amino acids can directly generate the protein, which is either valid or invalid. All variables are provided within the data. In the original example, we are testing weather using the previous day’s weather. We may be able to model using whatever correlation exists between the data. This is not the same as accurately predicting results, if the real-world weather function is determined by the weather of surrounding locations, time of year, and moon phase. If our model does not have this data, and it is essential to model the result, how can you accurately model? In other words: “Garbage in, garbage out”. Good luck modeling an n-th degree polynomial function, given a fraction of the variables to train on.

famouswaffles 3y ago
>All variables are provided within the data. electrostatic protein interaction, hydrophobic interaction, organic chemistry etc all variables are in fact not provided within the data. Protein creation is not just _poof_ proteins. There are steps, interactions and processes. You don't need to supply any of that to get a model accurately predicting proteins. That is the main point here, not that you can predict anything with any data.
jakderrida 3y ago
> This is not the same as accurately predicting results, if the real-world weather function is determined by the weather of surrounding locations, time of year, and moon phase. How many have the "human intelligence" to do this? Especially more accurately than a computer (and without using any themselves) training on the same inputs and outputs?

wavemode 3y ago

> You don't need to train on the inputs(casual processes) of anything, that's what training is there to figure out. I mean... this is just obviously false. If the data you're training on isn't causally predictive, you may occasionally find good-enough patterns for a particular use case (i.e. you may occasionally guess better than a coin flip which direction the stock market goes) but you aren't going to accurately model anything, and certainly not well enough to create an AGI that makes intelligent decisions. Words in sentences (and, indeed, proteins in a sequence) are causally predictive of each other - the grammar and semantics of one word tends to dictate what words are likely to surround it. So LLM's are very good at writing, and that is certainly useful! But that's just not the same as human intelligence. When someone makes an AGI out of an LLM then I'll be proven wrong, I suppose. I'm just sharing my personal view on things.

rafaelero 3y ago
Then how does ChatGPT end up providing a better/equivalent medical diagnosis than doctors (even though they are the "masters" of the causal pathways)?
famouswaffles 3y ago
Being "casually predictive" does not mean you have provided all the variables of your prediction in the data. Protein creation is not just _poof_ new proteins. There are steps and interactions and you don't need to train on all of that. Do you want a list of all the interactions of protein creation we are aware of ? >When someone makes an AGI out of an LLM then I'll be proven wrong, I suppose. I'm just sharing my personal view on things. You're going to have to define AGI first.
- wavemode 3y ago
  > Being "casually predictive" does not mean you have provided all the variables of your prediction in the data. Not sure where I claimed this. > Protein creation is not just _poof_ new proteins. Not sure where I claimed this either. > There are steps and interactions and you don't need to train on all of that. I agree with this statement as well. Have you read what I wrote? Proteins in chains can indeed be used to predict other proteins in chains, even though you never trained the model on the biological processes of protein generation. Just like words in sentences can be used to predict other words in sentences, even though you never trained the model on the neurological processes of human speech. I'm not disputing any of that. What I'm disputing is that it will eventually become an AGI. > You're going to have to define AGI first. I'm using the definition provided verbatim in the linked article: "We believe superintelligence—AI vastly smarter than humans—could be developed within the next ten years."

lossolo 3y ago

Weather and stock market are both chaotic systems. Increasing evidence suggests that AGI will not be attainable solely using LLMs/transformers/current architecture, as LLMs can't extrapolate beyond the patterns in their training data (according to a paper from DeepMind last month): "Together our results highlight that the impressive ICL abilities of high-capacity sequence models may be more closely tied to the coverage of their pretraining data mixtures than inductive biases that create fundamental generalization capabilities."[1] 1. https://arxiv.org/abs/2311.00871 https://arxiv.org/abs/2311.00871