🤖〽️Prompt Engineering: Copilot Unabridged (Part 90: How Many Machines Would It Take to Imitate Human Thinking?)

Prompt Engineering Series

Prompt: "write a post of 600 words on whether two machines can create a symbiosis to imitate thinking?"

Introduction

The human mind is a marvel of biological engineering - an intricate web of neurons, synapses, and chemical signals that gives rise to thought, emotion, creativity, and consciousness. As artificial intelligence advances, a tantalizing question arises: how many machines would it take to replicate the architecture of the human mind and imitate its thinking?

The answer isn’t just a number - it’s a journey through neuroscience, computation, and the philosophy of cognition.

The Complexity of the Human Brain

Let’s start with the basics. The human brain contains approximately:

• 86 billion neurons
• 100 trillion synaptic connections
• Multiple specialized regions for language, memory, emotion, motor control, and abstract reasoning

Each neuron can be thought of as a processing unit, but unlike digital machines, neurons operate in parallel, with analog signals and dynamic plasticity. The brain isn’t just a supercomputer - it’s a self-organizing, adaptive system.

To imitate this architecture, machines would need to replicate not just the number of units, but the interconnectivity, plasticity, and modularity of the brain.

Modular Thinking: One Machine Per Function?

One way to approach this is to break down the brain into functional modules:

• Language processing: Natural language models like GPT
• Visual perception: Convolutional neural networks (CNNs)
• Motor control: Reinforcement learning agents
• Memory: Vector databases or long-term storage systems
• Emotion simulation: Sentiment analysis and affective computing
• Executive function: Decision-making algorithms

Each of these could be represented by a specialized machine. But even then, we’re only scratching the surface. These modules must interact fluidly, contextually, and adaptively - something current AI systems struggle to achieve.

A realistic imitation might require dozens to hundreds of machines, each finely tuned to a cognitive domain and linked through a dynamic communication protocol.

Distributed Cognition: The Power of Many

Instead of one monolithic AI, a distributed system of machines could better mirror the brain’s architecture. Think of it like a digital nervous system:

• Sensory machines gather data from the environment
• Interpretive machines analyze and contextualize it
• Reflective machines simulate introspection and planning
• Creative machines generate novel ideas or solutions

This network would need to operate in real time, with feedback loops, memory integration, and conflict resolution. It’s not just about quantity - it’s about coordination.

Some researchers suggest that to truly imitate human cognition, we’d need a system with millions of interconnected agents, each representing micro-functions of thought. That’s not just a machine - it’s an ecosystem.

Can Machines Match Neural Density?

Even if we match the number of processing units, we face another challenge: neural density. The brain’s neurons are densely packed and highly efficient, using minimal energy to perform complex tasks. Machines, by contrast, are bulky, power-hungry, and often siloed.

To match the brain’s efficiency, we’d need machines that:

• Operate asynchronously
• Learn continuously
• Adapt to new environments
• Communicate seamlessly

This level of sophistication is still aspirational. We’re building impressive models, but they’re nowhere near the elegance of the human brain.

Philosophical Caveats

Even if we build a system with enough machines to imitate thinking, does it think? Imitation is not consciousness. Machines can simulate reasoning, emotion, and creativity - but they don’t experience them.

So while we might need thousands or millions of machines to replicate the architecture, we may never replicate the subjective experience of thought.

Final Thought: A Symphony of Machines

To imitate human thinking, we wouldn’t need one machine - we’d need a symphony. Each machine would play its part, harmonizing with others, adapting to the moment, and evolving over time. It’s not just a technical challenge - it’s a philosophical one.

And perhaps, in building this synthetic mind, we’ll learn more about our own.

Disclaimer: The whole text was generated by Copilot (under Windows 11) at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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🔬Data Science: Multilayer Perceptron (Definitions)

"A neural net composed of three or more slabs (and therefore two or more layers of weighted connection paths); such nets are capable of solving more difficult problems than are single layer nets. They are often trained by backpropagation." (Laurene V Fausett, "Fundamentals of Neural Networks: Architectures, Algorithms, and Applications", 1994)

"A fully connected feedforward NN with at least one hidden layer that is trained using back-propagation algorithmic techniques." (Ioannis Papaioannou et al, "A Survey on Neural Networks in Automated Negotiations", Encyclopedia of Artificial Intelligence, 2009)

"A kind of feed-forward neural network which has at least one hidden layer of neurons." (Fernando Mateo et al, "A 2D Positioning Application in PET Using ANNs", Encyclopedia of Artificial Intelligence, 2009)

"A neural network that has one or more hidden layers, each of which has a linear combination function and executes a nonlinear activation function on the input to that layer." (Robert Nisbet et al, "Handbook of statistical analysis and data mining applications", 2009)

"It has a layered architecture consisting of input, hidden and output layers. Each layer consists of a number of perceptrons." (Siddhartha Bhattacharjee et al, "Quantum Backpropagation Neural Network Approach for Modeling of Phenol Adsorption from Aqueous Solution by Orange Peel Ash", 2013)

"A type of neural network. The MLP is the most common, and arguably the simplest, neural network used for classification." (Meta S Brown, "Data Mining For Dummies", 2014)

"An artificial neural network model with feed forward architecture that maps sets of input data onto a set of desired outputs iteratively, through the process of learning. A MLP consists of an input layer of neurons, one or more hidden layers of neurons and an output layer of neurons, where each layer is fully connected to the next layer." (Eitan Gross, "Stochastic Neural Network Classifiers", 2015) 

"an important class of ANN that typically consists of the input layer, one or more hidden layers of computation nodes, and an output layer. The input signal propagates through the network in a forward direction, on a layer-by-layer basis." (Pablo Escandell-Montero et al,"Artificial Neural Networks in Physical Therapy", 2015)

"Arguably the most popular artificial neural network model. It is usually composed by three or four layers of units. Each unit is fully connected to the units of the previous layer. Learning is customarily performed via the backpropagation rule." (D T Pham & M Castellani, "The Bees Algorithm as a Biologically Inspired Optimisation Method", 2015)

"Is an ANN type that requires a reference to learn patterns. It is trained using (error) back propagation algorithm." (Kandarpa K Sarma, "Learning Aided Digital Image Compression Technique for Medical Application", 2016)

"MLP is a feed forward neural network with one or more layers between input and output layer and are used to solve non-linearly separable problems. MLPs are trained using the back propagation algorithm. MLPs are widely used in pattern classification, recognition, prediction, etc." (Mridusmita Sharma & Kandarpa K Sarma, "Soft-Computational Techniques and Spectro-Temporal Features for Telephonic Speech Recognition: An Overview and Review of Current State of the Art", 2016)

🔬Data Science: Recurrent Neural Network [RNN] (Definitions)

"A neural net with feedback connections, such as a BAM, Hopfield net, Boltzmann machine, or recurrent backpropagation net. In contrast, the signal in a feedforward neural net passes from the input units (through any hidden units) to the output units." (Laurene V Fausett, "Fundamentals of Neural Networks: Architectures, Algorithms, and Applications", 1994)

"A neural network topology where the units are connected so that inputs signals flow back and forth between the neural processing units until the neural network settles down. The outputs are then read from the output units." (Joseph P Bigus, "Data Mining with Neural Networks: Solving Business Problems from Application Development to Decision Support", 1996)

"Networks with feedback connections from neurons in one layer to neurons in a previous layer." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"RNN topology involves backward links from output to the input and hidden layers." (Siddhartha Bhattacharjee et al, "Quantum Backpropagation Neural Network Approach for Modeling of Phenol Adsorption from Aqueous Solution by Orange Peel Ash", 2013)

"Neural network whose feedback connections allow signals to circulate within it." (Terrence J Sejnowski, "The Deep Learning Revolution", 2018)

"An RNN is a special kind of neural network used for modeling sequential data." (Alex Thomas, "Natural Language Processing with Spark NLP", 2020)

"A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior." (Udit Singhania & B. K. Tripathy, "Text-Based Image Retrieval Using Deep Learning", 2021)

"A RNN [Recurrent Neural Network] models sequential interactions through a hidden state, or memory. It can take up to N inputs and produce up to N outputs. For example, an input sequence may be a sentence with the outputs being the part-of-speech tag for each word (N-to-N). An input could be a sentence, and the output a sentiment classification of the sentence (N-to-1). An input could be a single image, and the output could be a sequence of words corresponding to the description of an image (1-to-N). At each time step, an RNN calculates a new hidden state ('memory') based on the current input and the previous hidden state. The 'recurrent' stems from the facts that at each step the same parameters are used and the network performs the same calculations based on different inputs." (Wild ML)

"Recurrent Neural Network (RNN) refers to a type of artificial neural network used to understand sequential information and predict follow-on probabilities. RNNs are widely used in natural language processing, with applications including language modeling and speech recognition." (Accenture)

🔬Data Science: Perceptron (Definitions)

"the term is often used to refer to a single layer pattern classification network with linear threshold units" (Laurene V Fausett, "Fundamentals of Neural Networks: Architectures, Algorithms, and Applications", 1994)

"adaptive element for multilayer feedforward networks introduced by Rosenblatt" (Teuvo Kohonen, "Self-Organizing Maps" 3rd Ed., 2001)

"An early theoretical model of the neuron developed by Rosenblatt (1958) that was the first to incorporate a learning rule. The term is also used as a generic label for all trained feed-forward networks, which is often referred to collectively as multilayer perceptron networks." (David Scarborough & Mark J Somers, "Neural Networks in Organizational Research: Applying Pattern Recognition to the Analysis of Organizational Behavior", 2006)

"A type of binary classifier that maps its inputs (a vector of real type) to an output value (a scalar real type). The perceptron may be considered as the simplest model of feed-forward neural network, as the inputs directly feeding the output units through weighted connections." )Crescenzio Gallo, "Artificial Neural Networks Tutorial", 2015)

"A perceptron is a type of a neural network organized into layers where each layer receives connections from units in the previous layer and feeds its output to the units of the layer that follow." (Ethem Alpaydın, "Machine learning : the new AI", 2016)

"Perceptron is a learning algorithm which is used to learn the decision boundary for linearly separable data." Vandana M Ladwani, "Support Vector Machines and Applications", 2017)

"A simple neural network model consisting of one unit and inputs with variable weights that can be trained to classify inputs into categories." (Terrence J Sejnowski, "The Deep Learning Revolution", 2018)

"The simplest form of artificial neural network, a basic operational unit which employs supervised learning. It is used to classify data into two classes." (Gaetano B Ronsivalle & Arianna Boldi, "Artificial Intelligence Applied: Six Actual Projects in Big Organizations", 2019)

"A perceptron is a single-layer neural network. It includes input values, weights and bias, net sum, and an activation function." (Prisilla Jayanthi & Muralikrishna Iyyanki, "Deep Learning Techniques for Prediction, Detection, and Segmentation of Brain Tumors", 2020)

"The basic unit of a neural network that encodes inputs from neurons of the previous layer using a vector of weights or parameters associated with the connections between perceptrons." Mário P Véstias, "Deep Learning on Edge: Challenges and Trends", 2020)

"these are machine learning algorithms that undertake supervised learning of functions called binary classifiers which decide whether or not an input, usually identified with a vector of numbers, belongs to a particular class." (Hari Kishan Kondaveeti et al, "Deep Learning Applications in Agriculture: The Role of Deep Learning in Smart Agriculture", 2021)