jvanname (OP)
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December 29, 2023, 03:37:14 PM
Last edit: December 29, 2023, 04:20:37 PM by jvanname |
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We have a couple of problems that need to be sorted out.
If you look at artificial intelligence, you would notice that even the experts have a very poor understanding of the inner workings of AI systems. In other words, our current AI systems are uninterpretable. Since our AI systems are uninterpretable, it will be difficult to ensure that we will be able to control these AI systems, predict their behavior, make sure that they do not cause an unexpected disaster. One way of going about solving the interpretability problem will be to use interpretability tools to investigate the inner workings of AI systems. But another way of solving the interpretability problem will be to design the AI systems so that they will be more interpretable. I believe that neural networks are inherently difficult to interpret, so to design more interpretable AI, we will probably need to use AI systems that are not neural networks. I do not believe that neural networks will completely go away, but they need competition. While I have my qualms about neural networks, I do believe that we should still train AI systems using a variant of gradient descent/ascent.
There is another problem that someone should take a look at. We need to create, evaluate, and standardize new block ciphers, hash functions, and CSPRNGs, and we need to continue to analyze the existing cryptographic algorithms. Our block ciphers, hash functions, and CSPRNGs were not designed to run on energy efficient physically reversible hardware (by reversibility, I mean partial reversibility), and since energy efficient reversible computation is the future, we should design these cryptographic algorithms for reversibility. Our current cryptanalysis techniques do not incorporate very much machine learning, so by using machine learning, we can probably substantially improve our cryptanalytic techniques. While NIST's process of standardizing AES and SHA-256 were somewhat decentralized in the sense that these processes accepted input from the entire cryptographic community, as far as I am aware, these standardization processes did not include algorithms that automatically accepted cryptographic functions and returned results about their cryptographic security. We will also need to evaluate cryptographic functions that can be analyzed by these machine learning systems for applications that have not been developed yet. For example, what if we want a block cipher with 128 bit key size and 16 bit message size for some reason? What if we want a block cipher where the key spaces and message spaces are vector spaces over the finite field with 113 elements and where the cipher is composed of polynomial functions? We will need to evaluate that too.
We can make progress in solving the problem of AI interpretability while also analyzing block ciphers at the same time. Block ciphers are more mathematical than your typical machine learning data sets such as feet and toes. And if we are analyzing mathematical data sets such as those produced by block ciphers and other cryptographic algorithms, then since the data being analyzed is more mathematical, one should be able to interpret this data better using mathematical techniques. Not only does cryptography provide more interpretable data for machine learning models, but there is greater motivation for interpreting the AI models that analyze cryptosystems than there is for interpreting other forms of AI. These machine learning models do not necessarily need to consume large amounts of data or analyze overly complicated systems to be helpful. For example, AES has 1 byte long boxes, and machine learning models can easily analyze permutations of bytes. Since AES is also quite mathematical, it will not any problem at all for an AI system to analyze AES and for humans to interpret the results.
I have personally developed my own AI models for analyzing block ciphers, namely LSRDRs (L_{2,d}-spectral radius dimensionality reduction) and their generalizations. These AI models seem quite interpretable compared to neural networks for several reasons. First of all, they are mathematical in the sense that while they are trained using gradient ascent, the attained local maximum is often unique in the sense that if you train the LSRDR twice, you will achieve the exact same local maximum value (I only have a little bit of mathematical theory for why this should be the case, so consider this as an experimental result). LSRDRs also satisfy other interesting mathematical properties that make them quite interpretable. For example, an LSRDR of quaternionic complex matrices is often a collection of quaternionic complex matrices while there is no reason for the LSRDRs to respect the quaternions so much (since quaternionic matrices have even complex dimension, this only works if the dimensions of the reduced matrices are even numbers). I have even obtained a complete interpretation for some LSRDRs of collections of matrices where in each matrix, precisely one entry is non-zero. You cannot get such a complete interpretation of neural networks because a trained neural network will have a lot of noise in the final AI model.
Now, developing cryptographic functions for reversible computation may accelerate the development of reversible hardware. Those primarily interested in AI safety may want to wait as long as possible for the development of energy efficient computational hardware that will enable more advanced AI systems, so they will not want to use AI to develop reversible computation. But those primarily interested in cryptographic advancement should consider the development of more interpretable and safe AI as a pleasant side effect of their cryptography research.
-Joseph Van Name Ph.D.
P.S. Hmm. You all seem to be hating me for developing a cryptocurrency that you all hate, but I am not doing this research for Bitcoin am I? I am instead doing this research for a cryptocurrency that you do not value because you are anti-intellectual, and you do not value research at all. I hope you change your ways.
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