neural networks are the latest incarnation of artificial intelligence, which is using computers to do cognitive work (physical or mental) that usually requires a human. Deep learning neural networks are mechanistic systems that “learn” by modeling high-level abstractions in data and cycling through trial-and-error guesses with feedback to establish a system that can make accurate predictions about new data. Machine learning systems are IDtech (identification technology), which conveys the ability to recognize objects (physical or digital), by analogy to FinTech, RegTech, TradeTech, and HealthTech as standard technologies that digitize, standardize, and automate their respective domains. Objects include patterns, structures, and other topological features that are within the scope of geometrical deep learning.
The premise of deep learning is that reality comprises patterns, which are detectable through data science methods. Deep learning is notable as a smart network technology that replaces hard-coded software with a capacity, in the form of a learning network that is trained to perform an activity. Whereas initially software meant fixed programs running in closed domains (Software 1.0), software is starting to mean programs that dynamically engage with reality in a scope which is not fully prespecified at the outset (Software 2.0).
2.3.2.3Internet of data structures (Web 3.0)
Web 3.0 means adding more functionality, collaboration, and trust to the internet (Web 1.0 was the read web, Web 2.0 the read/write web, and Web 3.0 the read/write/trust web). The idea is to install trust mechanisms such as privacy and verifiability from the beginning, directly into the software. Blockchains are in the lead of incorporating such PrivacyTech and ProofTech, and the functionality could spread to other domains.
Web 3.0 further connotes the idea of an internet of data structures. There are many different internet-based data structures such as block-chains, software codebases (Github), and various other content libraries. There may be a URL path to each content element. By using a Merkle tree structure tool, internet-based content trees become available to be called in other applications as distributed authenticated hash-linked data structures. A hash code (or simply hash) is the fixed-length output (often 32 bytes (64 characters) in blockchain protocols) of a hash function which is used to map data of arbitrary size onto data of a fixed size. A Merkle tree or hash tree is a tree in which every leaf node is labeled with the hash of a data block, and every non-leaf node is labeled with the cryptographic hash of the labels of its child nodes. Hash trees are widely used for the secure and efficient verification of the contents of large data structures.
Distributed authenticated hash-linked data structures can be deployed with a project from Protocol Labs called InterPlanetary Linked Data (IPLD). An earlier project InterPlanetary File System (IPFS) is a content-addressable file system (an improvement to file name-addressable systems which can result in errors when information paths no longer exist). IPLD is a data model for the content-addressable web in which all hash-linked data structures are treated as subsets of a unified information space, integrating all data models that link data with hashes as instances of IPLD. The web effectively becomes a Merkle forest of Merkle trees that can all be linked with interoperability through multi-hash protocols (connecting different hashing structures and content trees). These kinds of innovations are emblematic of infrastructural upgrades to the internet that facilitate privacy and security as important properties of smart network technologies.
2.3.3 Smart Networks 3.0: Quantum smart networks
In the farther future, quantum smart networks could comprise a next-generation of smart networks, Smart Networks 3.0 (Table 2.3). The first quantum smart network application is the quantum internet, which is already in the early stages of development for quantum key distribution and secure end-to-end communications. Quantum blockchains are a possibility, with quantum key distribution and a more substantial implementation of blockchain protocols in quantum information systems, possibly using new concepts such as proof-of-entanglement, holographic consensus, and quantum channels as the analog of payment channels. Quantum machine learning is already progressing (through quantum annealing optimization, quantum simulation, and geometric deep learning). Finally, there could be quantum brain–computer interfaces (for example, using interference-based amplitudes as a firing threshold mechanism).
Table 2.3. Quantum smart networks (Future-class Smart Networks 3.0).
| Quantum internet (Q-internet) | Quantum blockchains (QBC) |
| Quantum machine learning networks (QML) | Quantum brain–computer interfaces (QBCI) |
2.3.4 Smart network convergence
2.3.4.1Autonomous vehicle networks and robotic swarms
Many smart networks have fleet-many items that are autonomously coordinated. This includes unmanned vehicles (aerial, underwater, and space-based), autonomous-driving vehicles (automobiles, commercial trucks, and small transportation pods), drones, robotic swarms, and industrial robots. (Robotic swarms are multiple robots that are coordinated as one system.) Autonomous vehicle networks and robotic swarms must coordinate group behavior between themselves such as flocking, foraging, and navigation, in order to carry out tasks.
Complexity theory is used to study autonomous vehicle networks because they can exhibit undesirable emergent behavior such as thrashing, resource-starving, and phase change (Singh et al., 2017). Constraining flocking to optimized formations is a research focus for unmanned aerial vehicles, particularly those with autonomous strike capability (Vasarhelyi et al., 2018). Optimal control theory has been proposed as one basis for the development of risk management standards in self-controlling software (Kokar et al., 1999).
Applications for smart network fleets of self-coordinating machines arise in an increasing range of industrial and military use cases related to targeted material delivery, precision agriculture, and space-based and underwater exploration. Autonomous driving is a smart network technology with considerable interest. Some of the topics in contemporary research focus on collision avoidance (Woerner et al., 2019) and defining a set of international standards for the levels of driving automation (SAE, 2018).
There is a convergence story in that autonomous vehicle networks and robotic swarms are using other smart network technologies such as block-chain, machine learning, and IoT sensors. Blockchains offer a variety of functionality to autonomous vehicle networks including automated record-keeping, liability-tracking, compliance-monitoring, privacy, secure communications, and self-coordination. Notably, blockchain consensus algorithms are a method by which a group of agents can reach agreement on a particular state of affairs. Consensus is a generic technology that could be used for the self-coordination of any multi-agent system, not necessarily restricted to the context of mining and transaction confirmation. Hence, the blockchain-based self-governance of robotic swarm systems has been proposed (Ferrer, 2017).
The blockchain record-logging functionality together with smart contracts could be used to automatically file insurance claims when needed, and also register discovery claims (claiming rights over discovered objects) in new venues such as undersea and space-based exploration. Smart city IoT sensor networks could be used in conjunction with block-chains and robotic swarms for consumers and businesses to request robotic swarm-as-a-service functionality to send out a swarm to conduct a sensing project. Robotic sensor swarms could survey the aftermath of accidents to summon emergency medical and police services, be engaged to provide security, and scan pipelines and other infrastructure for routine maintenance. The IoT robotic swarm model is in some sense a realization of the science-fiction idea of fleets of entrepreneur-owned mobile video cams for hire (Brin, 2002), as a sort of next-generation citizen journalism.
Verifiable computing and zero-knowledge proofs enable a new level of smart network self-coordination and control. Advanced applications, particularly for military use, could include scenarios in which agents can cooperatively work towards a solution while having minimal information. For example, the mission information could be stored in a Merkle tree such that swarm operators can see the general blueprint of the mission