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This blog will start with some background on how AI models first started to resemble the human brain and conclude with a discussion of the difficulties researchers still have replicating the human brain.
An extensive explanation of each section's expectations may be found below.
Although this subject is naturally large, we will keep it interesting by being concise and straightforward. Subtopics with more complex sub branches will be treated as stand-alone articles.
Now, let's get started.
How Do AI Systems Relate To And Function From The Human Brain?
We've discussed how scientists first tried to simulate the human brain in AI models. Let's examine the brain's functioning and clarify how artificial intelligence (AI) systems interact.
Understanding The Brain: A Simplified Overview Of Its Functionality
The human brain primarily uses neurons to process thoughts. Three fundamental components make up a neuron: the soma, axon, and dendrite.
Receiving signals from other neurons is the responsibility of the dendrite. The soma processes information received from the dendrite. Then, it is transferred to the subsequent dendrite in the sequence via the axon.
Imagine you notice an approaching car to understand how the brain works. Via the optic nerve, your eyes instantly transmit electrical signals to your brain.
Subsequently, the brain organizes a network of neurons to interpret the received signal.
For the soma to process the signal, the first neuron in the chain gathers it through its dendrites. Following the completion of its function, the soma transmits a signal to the axon, which subsequently transmits it to the dendrite of the subsequent neuron in the chain.
A synapse is the connection formed during information transfer between axons and dendrites. Therefore, the whole process keeps on until the brain discovers a Spatiotemporal Synaptic Input—scientific jargon for the brain to continue to process information until it determines how best to respond to a signal that is provided to it.
The brain then instructs your legs to flee from the approaching car by sending signals to the appropriate effectors, such as your legs.
The Synergy Between The Brain And AI Systems
The brain and artificial intelligence have a mutually beneficial interaction. Brain inspired AI system design and advancements help us better understand the brain and its functioning.
In terms of the brain and artificial intelligence, there is a reciprocal flow of information and concepts. Numerous instances support the notion that this interaction is positively symbiotic:
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Neural Networks: The development of neural networks is arguably the most significant contribution the human brain has given to artificial intelligence.
Neural Networks are computational models that imitate the structure and functionality of real neurons.
The interactions and adaptations of brain neurons heavily influence neural network architecture and learning algorithms.
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Brain Simulations: AI systems have been used to research how the human brain interacts with the outside environment and to replicate the human brain.
For instance, scientists can mimic the activity of biological neurons involved in visual processing using machine learning techniques.
The outcome sheds light on how the brain processes visual information.
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Insights into the brain: Scientists have started using machine learning algorithms to analyze and draw conclusions from fMRI images and brain data.
These revelations help to reveal relationships and patterns that might have stayed concealed otherwise.
These discoveries can aid in our understanding of memory, decision-making, and internal cognitive processes.
Additionally, they support the treatment of diseases of the brain like Alzheimer's.
Read more: 4 Types of AI — How Much Will They Transform Our World?
Core Principles Behind The Brain-inspired Approach To AI
We'll talk about a few ideas here that assist AI in mimicking the human brain's operations. These ideas have aided academics studying artificial intelligence in building more potent, intelligent systems that can handle challenging jobs.
Neural Networks
As was previously mentioned, neural networks have undoubtedly had the most influence on artificial intelligence and have drawn the greatest inspiration from the human brain.
Neural Networks are computational models that imitate the structure and functionality of real neurons. Artificial neurons, interconnected layers of nodes that help with information processing and transmission, make up the networks.
This is comparable to the functions of organic brain networks' axons, somas, and dendrites. Like how the brain learns from prior experiences, neural networks are designed to accomplish the same.
Distributed Representations
In a neural network, distributed representations record thoughts or ideas as a pattern over multiple network nodes to generate a pattern.
For instance, a particular set of nodes in a neural network could be used to encode (represent) the idea of smoking. Thus, a network employs those chosen nodes to interpret images of smokers when it encounters them (it gets much more complicated than that, but let's keep things simple for now).
The brain recognises and recalls complicated stimuli, and this technique helps AI systems do the same. It helps them remember complex concepts or relationships between concepts.
Recurrent Feedback
This is a method for training AI models in which a neural network's output is returned as input, enabling the network to incorporate it as additional training data.
This is comparable to how the brain uses feedback loops to modify its model in response to past events.
Parallel Processing
Parallel processing is done by splitting large computational jobs into smaller pieces and trying to process those smaller pieces on a different processor to increase speed.
By using this method, AI systems can analyze more incoming data more quickly—much like the human brain can multitask or handle multiple jobs simultaneously.
Attention Mechanisms
This method allows AI models to concentrate on particular sections of the incoming data. It is frequently used in fields like natural language processing, where complicated and time-consuming data is present.
It draws inspiration from the brain's capacity to focus on only specific aspects of a highly distracting environment, such as your capacity to focus on and participate in one conversation among a flurry of others.
Reinforcement Learning
Reinforcement learning is a method used in AI system training. It was influenced by how people pick up abilities by trial and error.
It involves an AI agent being rewarded or punished according to its behaviour. This allows the agent (typically applied in game development) to learn from its errors and become more proficient in subsequent actions.
Unsupervised Learning
New information streams are continuously sent to the brain through sounds, images, tactile sensations, and more.
It must try to make sense of everything and develop a logical and cohesive theory explaining how all these seemingly unrelated events affect its physical state. Consider this analogy: you instantly know it's raining when you feel a drop of water on your skin, hear it falling swiftly from rooftops, and feel your garments becoming heavier.
Afterwards, you go through your memory bank to see if you brought an umbrella. If so, you're good to go, if not, find out how far it is to your house from where you are right now.
You attempt to estimate how hard the rain will get, but you're okay if it's not close. You should seek cover if the drizzle turns into a more brutal downpour rather than trying to make your way back to your house.
Artificial intelligence employs a method known as Unsupervised Learning to make meaning of seemingly unrelated data points (water, sound, feeling, and distance).
Through this kind of AI training, systems are trained to interpret unstructured, raw input without explicit labeling (after all, nobody tells you it's raining when it's raining, does it?).
Challenges In Building Brain-Inspired AI Systems
You have seen how scientists drew inspiration for artificial intelligence systems from the human brain thus far.
We've also discussed the fundamental ideas of brain-inspired AI and how the brain connects to AI. In this part, we will discuss some of the conceptual and technological difficulties in developing brain-inspired AI systems.
Complexity
This challenge is quite intimidating. The brain is modeled, and AI systems are constructed based on that model, according to the brain-inspired approach to AI.
However, the human brain is an innately complicated system with 100 billion neurons and over 600 trillion synaptic connections (each neuron having 10,000 synaptic connections with other neurons on average). These synapses are constantly engaging in unanticipated and dynamic ways. It is a challenge in and of itself to build AI systems that strive to match, if not surpass, that complexity, this requires similarly sophisticated statistical models.
Data Requirements For Training Large Models
The state-of-the-art text-based AI model, Open AI's GPT 4, currently requires 47 GigaBytes of data. By contrast, the training set of its predecessor, GPT3, included 17 gigabytes of data, which is around three orders of magnitude less.
Just consider how much GPT 5 will be practiced.
Data requirements for brain-inspired AI models are substantial, particularly for tasks involving auditory and visual processing.
This underscores the necessity for establishing robust data collection pipelines to ensure the competence of these AI systems. For example, Tesla's data-collecting pipeline adds one million miles of driving data every ten hours to its existing 780 million miles of driving data.
Energy Efficiency
It is tough to create brain-inspired AI systems that mimic the brain's energy efficiency. The average human brain uses about 20 watts of electricity.
In contrast, the Tesla Autopilot, which runs on dedicated CPUs, uses roughly 2,500 watts per second and requires 7.5 megawatt hours (MWh) to train an AI model equivalent to ChatGPT.
The Explainability Problem
Creating user-trustworthy, brain-inspired AI systems is essential to advancing and widespread AI applications, but herein lies the issue.
AI systems mimic the brain's functioning, which is essentially an opaque structure. The brain's inner workings are difficult to comprehend, partly due to the lack of knowledge regarding the brain's thought-processing mechanisms.
While there is a wealth of study on the biological makeup of the human brain, there is a shortage of actual data regarding the brain's functional properties, such as how memory is generated and how déjà vu strikes.
This causes issues when developing AI systems that are inspired by the brain.
The Interdisciplinary Requirements
Building AI systems that are inspired by the human brain involves the expertise of specialists in a variety of disciplines, including computer science, neuroscience, engineering, philosophy, and psychology.
However, this comes with foundational and logistical difficulties because hiring expertise from other industries is costly.
Getting an engineer to care about the psychological implications of what they're producing is challenging. Still, there's also the issue of colliding egos and knowledge conflict.
Conclusion
Though creating AI systems using an approach inspired by brain structure is made simpler with such services, complications remain.
Modeling human minds remains challenging. Data requirements remain substantial. Energy efficiency discrepancies exist. Explainability issues remain perplexing issues to address as well.
However, optimism increases with each problem that is tackled, suggesting companies looking for significant progress with AI development should seek AI system development services that would assist them in navigating past current roadblocks within this domain and eventually get beyond them altogether.
