The Intersection of Neuroscience and Artificial General Intelligence

Artificial General Intelligence (AGI) is the ultimate goal of artificial intelligence research, a system that can perform any intellectual task as well as or better than humans. In our quest to unlock AGI, we can draw profound insights from the intricate workings of the human brain. Combining neuroscience with cutting-edge computational techniques lays the foundation for a powerful AGI architecture.

The Limitations of Current AI Architectures

Today’s deep neural networks (DNNs), such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers, are remarkable technological advancements. However, they are merely first approximations in the hardware of the 1970s and 1980s. These models fail to capture the complex mechanisms of human brain functions, such as vision, speech, and motor control, which operate with far greater sophistication.

Taking Inspiration from the Human Brain

Our proposed AI architecture seeks to bridge the gap by utilizing principles derived from brain functions. The brain’s neural structure, particularly the thalamocortical radiations and the hierarchical organization of cortical columns, serves as a model for our AI design. This approach aims to develop a more powerful AGI capable of handling multiple tasks simultaneously.

Data Flow and Hierarchical Processing in the Brain

The Thalamocortical Radiations and Sensory Processing

The thalamocortical radiations are a crucial neural structure that branches out from the thalamus, acting as the primary hub for sensory inputs and outputs. The thalamus receives signals from sensory organs and sends them directly to the cerebral cortex, which processes and interprets this information. This architecture ensures that each cortical column is specialized for a specific type of input or output.

The Cortex as an Autoencoder

The cerebral cortex can be seen as an analogous terminal layer of autoencoders, mapping and processing orthogonal basis vectors representing reality. Each section of the cortex is specialized for a specific function such as visual, auditory, or motor inputs. By organizing these functions hierarchically, the brain efficiently handles complex data and generates appropriate outputs.

Neuro-Inspired Signal Processing in Artificial Intelligence

Our AI architecture includes an artificial ROS-Inhibitory network, which operates similarly to the brain’s inhibitory system. In the brain, the ROS-Inhibitory network creates a sequential output by activating neurons in a linear sequence. In our AI network, a linear series of artificial neurons fires in sequence, generating an excitatory signal. This signal cascades down the hierarchical structure, selectively inhibiting specific branches based on an external time domain control signal.

trained on a set of inputs, our system can generate a sequence of phonemes, words, phrases, and larger groupings, capable of reproducing video from synthetic memories, and directing motion control. This hierarchical processing mimics the brain's ability to handle complex data in a structured and organized manner.

Neuro-Inspired Memory and Language Processing

By training our system on hierarchical signals, we can develop a form of memory that predicts outcomes. This is analogous to a music box where pins represent notes in a sequence. By training on repeated sequences, we can reduce the data needed to represent complex information, just as memory reduces the need to explicitly represent each data point.

Additionally, our AI can generate internal monologues to plan movements and compose high-level commands using language as a code. This hierarchical organization of commands can range from low-level movements like 'flex pinkie finger right hand 10' to high-level commands such as 'walk forward 2 meters turn left and stand on one foot.' This system can be trained to react to sensory inputs and predict future actions, generating intelligent movement based on training and practice.

Conclusion

The intersection of neuroscience and artificial intelligence offers a pathway to achieving AGI. By studying the brain's intricate architecture and processes, we can develop more powerful and efficient AI systems. Our proposed architecture, drawing inspiration from the human brain, aims to create a robust and versatile AGI that can handle speech, vision, and motor control tasks efficiently and effectively.