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In the asymmetric case, it can take perfectly readable text and turn it into jibberish using a "public key". Part 2: What is Homomorphic Encryption?Īs the name suggests, Homomorphic Encryption is a form of encryption. Without being told how well it's predictions are, it cannot learn. The big takeaway here is this error signal. For more detail on how this works, see A Neural Network in 11 Lines of Python
After this cycle repeats many millions of times, the network starts figuring things out. It begins by making a prediction (which is largely random at first), and then receives an "error signal" indiciating that it predicted too high or too low (usually probabilities).
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It learns to do this effectively by trial and error. Question: How does a neural network learn?Ī neural network makes predictions based on input. For context, it played a big part in DeepMind's AlphaGo system that recently defeated the world champion Go player, Lee Sedol. technology as it has surpassed previous quality records for many intelligence tasks. As a field of computer science, it is largely responsible for the recent boom in A.I. To introduce this idea, I'll quickly describe two very exciting fields of research: Deep Learning and Homomorphic Encryption.ĭeep Learning is a suite of tools for the automation of intelligence, primarily leveraging neural networks.
Unlimited learning is great, but unlimited application of that knowledge is potentially dangerous. technology that can become incredibly smart (smart enough to cure cancer, end world hunger, etc.), but whose intelligence is controlled by a human with a key, such that the application of intelligence is limited.
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In this blogpost, I'd like to give a tutorial on a potential technical solution to this problem with some toy-ish example code to demonstrate the approach. These are pretty bold statements, and I think they reflect the general concern shared between both the scientific community and the world at large. Most recently, Stephen Hawking called for a new world government to govern the abilities that we give to Artificial Intelligence so that it doesn't turn to destroy us. Many people are concerned that superpoweful AI will one day choose to harm humanity.
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I typically tweet out new blogposts when they're complete at Feel free to follow if you'd be interested in reading more in the future and thanks for all the feedback!Įdit: If you're interested in training Encrypted Neural Networks, check out the PySyft Library at OpenMined Superintelligence A human controls the secret key and has the option to either unlock the AI itself (releasing it on the world) or just individual predictions the AI makes (seems safer). If the AI is homomorphically encrypted, then from it's perspective, the entire outside world is also homomorphically encrypted. This creates a valuable power imbalance between a user and a superintelligence.
Secondly, the network can only make encrypted predictions (which presumably have no impact on the outside world because the outside world cannot understand the predictions without a secret key). First, the neural network's intelligence is protected from those who might want to steal it, allowing valuable AIs to be trained in insecure environments without risking theft of their intelligence. The result will be a neural network with two beneficial properties. TLDR: In this blogpost, we're going to train a neural network that is fully encrypted during training (trained on unencrypted data).
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