Brain-Inspired AI to a Symbiosis of Human Intelligence and Artificial Intelligence

Introduction

The goal of neuromorphic engineering is to create a computing architecture that is inspired by the brain as a substitute for the von Neumann processor. For unsupervised learning using flash memory synaptic array and spike-timing-dependent plasticity (STDP) synapse array, a hardware-based SNN architecture has been developed. The statistical theory's probability strategy often matches cognitive computing. It is a technique that understands and analyzes unstructured data by reasoning with goals, learning at scales, interacting naturally with humans (Kang et al., 2019).

A fundamental cognitive process called associative learning connects discrete and frequently different perceptions. Learning paradigms for equivalency that are guided by the auditory and multisensory systems were introduced. Humans can learn through guided associations in three different ways: auditory, visual, and multisensory. The best performance in cognitive learning is elicited by multi-sensory (audiovisual) stimuli. Because multisensory information processing can improve participant performance, the test phase is typically a more challenging cognitive activity than the acquisition phase (Eördegh et al., 2019).

The development of memcomputing and neuromorphic applications can benefit from the usage of phase-change memory devices because of their multi-level storage capability and proven large-scale manufacturing viability. Phase-change materials can reversibly transition from the crystalline to the amorphous phase using electrical pulses. Information is stored using the resistance shift brought on by the structural phase configuration change. Phase-change materials are used in both the writing and retrieval phases of this traditional approach, which is where its primary advantages reside. Although phase-change materials offer excellent phase-transition properties, the method's disadvantage is that their high defect density and extremely disordered nature render them sensitive to highly unfavorable electrical effects (such as noise and drift) (Liu & Zheng, 2017).

A completely new “brain war” combat style can be developed with the aid of military brain science. With the following objectives (Koelmans et al., 2015),

• 1)

  It addresses various brain activity patterns and affecting elements. Understanding the brain means being aware of the risk factors for brain damage;

• 2)

  Preserving the brain means focusing on preventing brain damage;

• 3)

  Monitoring the brain means keeping an eye on how the brain is working using gadgets and technology

• 4)

  Brain damage—encouraging the creation of brain-damaging weapons (such as those that explode, emit light, or use magnetic fields);

• 5)

  Brain interference—causing brain dysfunction or a loss of control;

• 6)

  Brain repair—carrying out the reconstruction of brain functions using cutting-edge technology;

• 7)

  Utilizing a variety of techniques (such as magnetism, sound, and electricity) to increase the brain function of individuals engaged in a certain work;

• 8)

  Mimicking the brain through techniques like brain-inspired robot intelligence;

• 9)

  Arming the brain through an emphasis on brain-machine interfaces (BMIs).

This paper's primary goal is to present emerging brain science and brain-inspired artificial intelligence technologies (such as brain-inspired chips, brain-inspired computing, neuromorphic computing systems, BCI and BMI, brain-inspired robotics, quantum robots, and cyborg); their advancements and trends; and some difficulties in brain-inspired computing and SNNs-based computation (Jin et al., 2018).