7.5.1SNARKs and multi-party computation
7.6State-of-the-Art: Zether for Account-Based Blockchains
7.6.1Bulletproofs: Confidential transactions for UTXO chains
7.6.2Zether: Confidential transactions for account chains
7.6.3Confidential smart contract transactions
7.6.4IPFS interactive proof-of-time and proof-of-space
Chapter 8Post-quantum Cryptography and Quantum Proofs
8.1.1Proof technology: The math behind STARKs
8.1.2Probabilistically checkable proofs
8.1.3PCPs of proximity and IOPs: Making PCPs more efficient
8.1.4IOPs: Multi-round probabilistically checkable proofs
8.1.5Holographic proofs and error-correcting codes
8.3Post-quantum Cryptography: Lattices and Hash Functions
8.3.1Lattice-based cryptography
8.3.3Lattice-based cryptography and zero-knowledge proofs
8.3.4Lattice-based cryptography and blockchains
8.3.5Hash function-based cryptography
8.4.1Non-interactive and interactive proofs
8.4.2Conclusion on quantum proofs
8.5Post-quantum Random Oracle Model
8.6Quantum Cryptography Futures
8.6.1Non-Euclidean lattice-based cryptosystems
Part 3 Machine Learning and Artificial Intelligence
Chapter 9Classical Machine Learning
9.1Machine Learning and Deep Learning Neural Networks
9.1.1Why is deep learning called “deep”?
9.1.2Why is deep learning called “learning”?
9.1.3Big data is not smart data
9.1.4Types of deep learning networks
9.2Perceptron Processing Units
9.2.1Jaw line or square of color is a relevant feature?
9.3Technical Principles of Deep Learning Networks
9.3.1Logistic regression: s-curve functions
9.3.2Modular processing network node structure
9.3.3Optimization: Backpropagation and gradient descent
9.4.2Spin glass: Dark knowledge and adversarial networks
9.4.3Software: Nonlinear dimensionality reduction
9.4.4Software: Loss optimization and activation functions
9.4.5Hardware: Network structure and autonomous networks
9.5.1Object recognition (IDtech) (Deep learning 1.0)
9.5.2Pattern recognition (Deep learning 2.0)
9.5.3Forecasting, prediction, simulation (Deep learning 3.0)
Chapter 10Quantum Machine Learning
10.1Machine Learning, Information Geometry, and Geometric Deep Learning
10.1.1Machine learning as an n-dimensional computation graph
10.1.2Information geometry: Geometry as a selectable parameter