Unlimited learning is great, but unlimited application of that knowledge is potentially dangerous. 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.
As a field of computer science, it is largely responsible for the recent boom in A.I. technology as it has surpassed previous quality records for many intelligence tasks. For context, it played a big part in DeepMind's AlphaGo system that recently defeated the world champion Go player, Lee Sedol. Question: How does a neural network learn?Ī neural network makes predictions based on input. It learns to do this effectively by trial and error. Port forward network utilities 2.0.5 crack trial# 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). For more detail on how this works, see A Neural Network in 11 Lines of PythonĪfter this cycle repeats many millions of times, the network starts figuring things out. The big takeaway here is this error signal. Without being told how well it's predictions are, it cannot learn.
Part 2: What is Homomorphic Encryption?Īs the name suggests, Homomorphic Encryption is a form of encryption. In the asymmetric case, it can take perfectly readable text and turn it into jibberish using a "public key".
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