Your wifi Router helps AI read your mind and map out the humans in that house for pinpoint attack with 5G

Your WiFi Router helps AI read your mind and maps out the humans in your home for pinpoint attack by 5G

Wednesday, August 16, 2023

How your WiFi router helps AI read your mind…. (side note: You wait until they start talking about sleep interrogation technology which they’ve had for a very long time

We can now decode dreams and recreate images of faces people have seen, and everyone from Facebook to Elon Musk wants a piece of this mind reading reality

Airport Scanners Show something People’s Arms And Brains!!

AI Routers & Network Mind: A Hybrid Machine 

Q-routing (DRQ-routing), which uses information gained through backward and forward exploration to

accelerate the convergence speed. In [11], [12], Reinforcement Learning (RL) was successfully applied

in wireless sensor network routing, where the sensors and sink nodes could self-adapt to the network

environment. However, in a multiagent system, single-agent RL suffers from severe non-convergence.

Instead, applying multiagent RL to improve the cooperation among network nodes is more feasible, and

there have been a series of works on ML-driven routing based on multiagent RL.

2.1.2 Multiagent Reinforcement Learning

In [13], [14], Stone et al. proposed the Team-Partitioned Opaque-Transition RL (TPOT-RL) routing algo-

rithm, which allows a team of network nodes working together toward a global goal to learn how to

perform a collaborative task. In [15], Wolpert et al. designed a sparse reinforcement learning algorithm

named the Collective Intelligence (COIN) algorithm, in which a global function is applied to modify the

behavior of each network agent. In contrast, the author of [16] proposed a Collaborative RL (CRL)-based

routing algorithm with no single global state. The CRL approach was also successfully applied for delay-

tolerant network routing in [17]. However, in an inherently distributed system, state synchronization

among all routers is extremely difficult, especially with increasing network size, speed, and load. With

the development of SDN technology, centralized AI-driven routing strategies have received considerable

attention.

2.2 Centralized Routing

In [18], Stampa et al. proposed a deep RL (DRL) algorithm for optimizing routing in a centralized

knowledge plane. Benefiting from the global control perspective, the experimental results showed very

promising performance. In [19], Lin et al. applied the SARSA algorithm to achieve QoS-aware adaptive

routing in multilayer hierarchical software-defined networks. For each flow, the controller updated the

optimal routing strategy based on the QoS requirements and issued the forwarding table to each node

along the forwarding path. In [20], Wang et al. proposed a RL-based routing algorithm for Wireless Sensor

Networks (WSNs) named AdaR. In AdaR, Least-Squares Policy Iteration (LSPI) is implemented to achieve

the correct tradeoff among multiple optimization goals, such as the routing path length, load balance, and

retransmission rate. However, the overhead incurred for centralized AI control is high.

3 AI-DRIVEN NET WORK ROUTING

In this section, we first propose a three-layer logical functionality architecture for AI-driven networking.

Then, we discuss the problem of how far away the intelligent control plane can be located from the

Mind-reading AI turns thoughts into words using a brain implant