Brain Inspired Artificial Intelligence (BIAI): Everything you need to know

The Artificial Intelligence (AI) industry has seen exceptional success since the inception of Neural Networks. Designed after the Human Brain, Neural Networks are the fundamental element supporting modern AI systems. The research involved in modern AI involves creating and implementing algorithms aiming to resemble the neural processes of the Human Brain. Their fundamental goal is to create systems similar to the Human Brain in the manner in which they act and learn. In this blog, we will learn about Brain Inspired Artificial Intelligence, the principles surrounding its evolution, and how it contributes to the future of AI. 

Table of contents 

1) Brain-Inspired Artificial Intelligence (BIAI)- an overview  

2) The history of Brain Inspired Artificial Intelligence (BIAI) 

3) Understanding how Brain Inspired Artificial Intelligence (BIAI) works 

4) Principles behind Brain Inspired Artificial Intelligence (BIAI) 

5) Challenges in implementing Brain Inspired Artificial Intelligence (BIAI) 

6) Conclusion 

Brain-Inspired Artificial Intelligence (BIAI)- an overview 

Brain-Inspired Artificial Intelligence (BIAI) is a field of Artificial Intelligence (AI) that seeks to mimic and replicate the cognitive and computational processes observed in the human Brain. It involves developing algorithms, models, and systems inspired by the Brain's Neural Networks and mechanisms to enable machines to perform tasks such as learning, reasoning, problem-solving, and pattern recognition in a manner reminiscent of human intelligence. BIAI aims to create AI systems that exhibit qualities like adaptability, self-learning, and robustness, drawing insights from neuroscience to advance the capabilities of Artificial Intelligence. 

History of Brain Inspired Artificial Intelligence (BIAI) 

The drive to create intelligent machines was largely inspired by Norbert Wiener, a Professor at MIT who possessed extensive knowledge of various fields, including Mathematics, Neurophysiology, Medicine, and Physics. Wiener believed that the most significant opportunities in science came from exploring what he called "Boundary Regions." These areas of study do not solely belong to a particular discipline but rather involve a combination of disciplines, such as the field of Medical Engineering, which brings together Medicine and Engineering. 

In 1934, Weiner and a few other academics held monthly discussions to review papers related to boundary region science. During these sessions and through his research, Weiner became familiar with the latest developments in biological nervous systems and pioneering work on electronic computers. Naturally inclined towards blending these two fields, he forged a relationship between Neuroscience and Computer Science, which eventually became the foundation for creating Artificial Intelligence (AI) as we know it today. 

After the end of World War II, Norbert Wiener began developing theories about the intelligence of humans and machines. This led to a new field of study, which he named Cybernetics. Wiener's work in Cybernetics successfully stimulated discussions among scientists about the possibility of merging Biology with Engineering. One of the scientists influenced by Wiener's ideas was a neurophysiologist, Warren McCulloch, attending a scientific conference in New York. During this conference, McCulloch stumbled upon papers written by his colleagues on biological feedback mechanisms. 



In collaboration with Walter Pitts, McCulloch proposed a theory about how the Brain works the following year. Their conclusion was based on McCulloch’s research on the likelihood of neurons processing binary numbers (computers communicate via binary numbers). This theory helped to establish the widely accepted perception that computers and Brains operate similarly. It is the foundation of the initial model of an artificial neural network named the McCulloch-Pitts Neuron (MCP). 

The MCP acted as the basis for the development of the first-ever neural network, which was named the perceptron. The Psychologist Frank Rosenblatt was responsible for creating the Perceptron. He was inspired by the synapses in the Brain and thought that if He drew inspiration from the Brain's synapses, proposing that digital computers could replicate the Brain's information processing through Neural Networks. 

Prioritise Personal Development to enhance skills that are effective across many fields by signing up for our Personal Development Training now! 

Understanding how Brain Inspired Artificial Intelligence (BIAI) works 

Now that we have covered briefly the history of Brain-Inspired Artificial Intelligence, let us understand how the Brain works before we proceed to understand the relationship between the Brain and AI. 

How does the Brain work? 

The Human Brain processes thoughts using neurons. A neuron comprises three main sections: the Dendrite, Axon, and Soma. The Dendrite receives signals from other neurons while the Soma processes the information received from the Dendrite. Finally, the Axon transfers the processed information to the next Dendrite in the sequence. 

When you see a car approaching you, your eyes instantly send electrical signals to your Brain via the optical nerve. The Brain then forms a chain of neurons to interpret and make sense of the incoming signal, allowing you to perceive and react to the situation. This process provides insight into how the Brain processes and understands information. 



In order for information to be passed on from Axons to Dendrites, a connection is required, which is called a Synapse. This process continues until the Brain receives an optimal response to the signal that is sent to it, which is referred to as a Sapiotemporal Synaptic Input in scientific terminology. Once the Brain has found this optimal response, it sends signals to the necessary effectors, such as the legs. For instance, if there is an oncoming car, the Brain sends a signal to the legs to run away from it. 

Learn the role of Career Development in the workplace by signing up for our Career Development Masterclass now! 

The relationship between the Brain and AI 

The relationship between the Brain and AI is such that they benefit each other. The Brain is the primary source of inspiration behind the design of AI systems, and the progress in the field of AI has resulted in a better understanding of the Brain and how it works. 

The Brain and AI have a mutual exchange of knowledge and ideas. There are multiple examples to confirm the complementary nature of this relationship; let us go over some aspects to understand it better: 

a) Neural Networks: The creation of Neural Networks is the most significant contribution made by the Human Brain in the field of AI. To understand it simply, Neural Networks are computational models that resemble the function and structure of biological neurons. The structure of Neural Networks and their learning algorithms are primarily inspired by the manner in which the neurons in the Brain interact and adapt. 

b) Brain simulations: The implementation of Brain simulations in order to understand and study how the Brain interacts with the physical world is possible with the help of AI systems. For example, researchers have used Machine Learning (ML) methods to mimic the activity of the biological neurons involved in visual processing. As a result, researchers have been provided with insights into how the Brain understands visual information.  

c) Insights into the Brain: Researchers have started using Machine Learning (ML) methods to analyse and gain insights from fMRI scans and Brain data. These insights have helped researchers identify patterns and relationships which would remain undiscovered if AI was not involved. These insights help in understanding internal cognitive function, memory, and decision-making. They are also used to help in the treatment of Brain-related illnesses such as Alzheimer's. 

Principles behind Brain Inspired Artificial Intelligence (BIAI) 

Let us look at principles which help AI resemble the manner in which the Human Brain functions. These principles have helped AI researchers in creating powerful and intelligent systems that are capable of performing complex tasks.

 
 

Neural Networks 

As we mentioned earlier, the creation of Neural Networks is the most significant contribution made by the Human Brain in the field of AI. Neural Networks are computational models that resemble the function and structure of biological neurons. Neural Networks constitute multiple layers of interconnected nodes, called artificial neurons, that help process and transmit information. This resembles how biological Neural Networks function using dendrites, somas, and axons. 

Distributed representations 

Distribution representations are a method of encoding concepts or ideas within a Neural Network as a pattern along multiple nodes. Let us consider an example to understand this concept. The concept of smoking could be encoded in a Neural Network with the help of certain nodes. So, if a network comes across an image of a person smoking, it then refers to those particular nodes to make sense of the image. This is just a simple example to explain the concept; the actual implementation is far more complex. 

Distribution representation assists AI systems in remembering complex concepts or relationships in the same manner that the Brain remembers and identifies complex stimuli. 

Recurrent feedback 

Recurrent feedback is a technique utilised to train AI models in which the output from a Neural Network is returned as an input, enabling the network to integrate its output as additional data input in training. This is similar to how the Brain utilises feedback loops to adapt its models based on previous memory. 

Parallel processing 

Parallel processing is a method in which complex computational tasks are broken down into smaller tasks in order to process the smaller tasks on another processor to improve the speed. This method enables AI systems to process large data input faster, similar to how the Brain is able to achieve and implement different tasks at the same time, also referred to as multitasking. 

Attention mechanisms 

This method enables AI models to focus on specific sections of input data. It is usually implemented in areas such as Natural Language Processing (NLP) that contain complex data. The Brain's ability to concentrate on specific parts of a large, complex environment inspires the attention mechanisms method. This is similar to how a person can concentrate and engage in a conversation in a distracting environment. 

Reinforcement Learning 

Reinforcement Learning is a method utilised to train AI systems similar to how humans learn skills through trial and error. This method involves an AI model subjected to a reward or punishment established by its actions. This allows the AI systems to learn from their mistakes and improve efficiency for future actions. This method is commonly used in creating games. 

Unsupervised Learning 

The Human Brain receives a new sequence of data (also known as a data stream) in the form of sounds, visual cues, sensory feelings to the skin, and so on. The Human Brain has to make sense of all these different data streams to form a logical understanding of how these instances or events affect its physical state. 

Let us consider a scenario to explain the method mentioned above. When a person feels water drops on his/her skin, when a person hears the sound of water droplets falling on rooftops when a person feels the clothes becoming wet and heavy due to water droplets. It is based on these data streams that a person concludes that it is raining. 

After coming to the senses that it is raining, the person then recalls if he/she has carried an umbrella. If yes, then it's fine or else, the person estimates the distance from their current location to their home. If the home is nearby, then it's fine or else, the person tries to analyse the intensity of the rain. If the intensity of the rain is light, then the person can decide to go home, or else the person looks for a shelter nearby to shield from the rain. 

The ability to make sense of multiple data points (visual, sound, feeling, distance, etc.) is a method implemented in AI systems in the form of Unsupervised Learning. In AI training, AI systems are taught to make sense of unstructured, raw data without labelling (Without establishing any logic to the data). 

Understand what is active listening by signing up for our Active Listening Skills Training now! 

Challenges in implementing Brain Inspired Artificial Intelligence (BIAI) 

We have learned about the approach that goes behind the research in building Brain Inspired Artificial Intelligence (BIAI), along with its core principles. Now let us look at certain challenges that are inherent in building AI systems resembling the Brain. 


 

Complexity 

Complexity is a significant challenge in implementing Brain Inspired Artificial Intelligence (BIAI). The fundamental idea behind this approach is to model the Human Brain and create AI systems based on such model. But the Human Brain is a fundamentally complex system comprising over 100 billion neurons and about 600 trillion synaptic connections. These synapses are constantly interacting actively and in unpredictable ways. 

Data requirements 

Data requirements also pose a challenge for training large AI models. For instance, Open AI's Chat GPT is currently the most advanced text-based AI model. It was trained using 570GB of data using multiple sources of text. In the same manner, obtaining acceptable results in Building Brain Inspired AI systems requires vast amounts of data, especially to perform auditory and visual tasks. 

Implementing this also requires creating data collection pipelines. For example, Tesla, the electric car maker, has 780 million miles of driving data, adding a million miles every 10 hours. 

Energy efficiency 

Executing energy efficiency that of a Human Brain in building Brain Inspired AI is a huge challenge. For instance, the Human Brain consumes about 20 watts of energy. In comparison, Tesla's autopilot chip consumes about 2500 watts per second. Even while training an AI model, the size of ChatGPT takes around 7.5 megawatt-hours (MWh) of energy.   

Lack of insight 

The Human Brain, after which the AI systems are supposed to be created, is essentially a black box. The fundamental reason to state this comes from the fact that it is not easy to understand the inner workings of the Brain. There is a lack of information as to how the Human Brain processes thoughts. 

Over the years, multiple research has provided sufficient information regarding the biological structure of the Human Brain. However, there is a lack of insight into the functional qualities of the Brain. Aspects like how thoughts are formed, why Deja vu occurs, and so on. This becomes a hurdle in Building Brain Inspired AI accurately. 

Interdisciplinary requirements 

Building Brain Inspired AI systems is a complex process since it involves professionals from various disciplines. The knowledge required to execute this process involves experts from fields like Computer Science, Neuroscience, Engineering, Philosophy, and Psychology. 

Gathering experts from different fields is a challenge in itself, and this becomes financially expensive, too. Other aspects come into the picture, such as conflict of interest, ego clashes, and so on. Creating a process to accommodate experts from various fields to function harmoniously is a tough job. 


 

Conclusion 

Building Brain Inspired AI systems clearly has a vast scope. Being at the early stages of implementing such systems has shown significant and rewarding results and indicates the possibility that AI systems can help technology and mankind itself. But the pursuit of creating such systems also presents itself with challenges that we covered in this blog. Constant efforts towards overcoming these challenges will provide much-awaited results.  

Learn how emotional intelligence is relevant in organisations by signing up for our Emotional Intelligence Training now!