Neural networks are models that are inspired by the architecture of the human brain. A full network receives a number of inputs and a number of outputs. Between the inputs and outputs are all the neurons connected in a simpler way compared to the brain neurons. The neurons themselves also differ from each other: In the brain, they are biological, and in the neural network, they are mathematical functions. The mathematical function consists of a weighted sum, a bias, and a nonlinear activation function. This combination allows the network to learn complex and nonlinear relationships between inputs and outputs. The learning process of the neural network requires a large amount of data in order to generalize the model sufficiently. This learning process is done with an algorithm called backpropagation. During this process, the weights and biases of each neuron are updated to minimize a cost function that represents the error between the true output and the predicted output from the neural network. While all of this was originally inspired by the brain's structure and flexible learning abilities, the human brain is much more complex, which allows for deeper context awareness and flexibility. Another key difference is the energy efficiency, where the brain only uses a fraction of the power consumed by neural networks to learn.
Artificial neural networks are a set of nodes connected between them, mimicking a real human brain, where real neurons are connected with other neurons. When you input data to this network, each node will send a “signal” to other nodes it is connected to. These signals are numbers calculated using non-linear functions. The output of the nodes will be determined by parameters called weights, which are tuned during the training phase. Simple neural networks are divided into layers, and signals go through the input layer, hidden layers and finally the output layer. These simple neural networks can be used for linear regression, data classification and time series predictions, for example. These neural networks are the precursors for the big models that are used today, such as transformers or convolutional neural networks.
Imagine your brain as a gigantic network: about 86 billion neurons connected by hundreds of trillions of synapses. Signals travel electrically inside neurons and are passed chemically across synapses. Over time, those connections can strengthen or weaken. Your interaction with the world shapes these connections and influences you and how you think continuously. Large-scale neural networks, such as the one beneath ChatGPT mimic this, by having nodes (neurons) arranged in layers connected by weights (synapses). A weight is simply a number that says how strongly one node influences another. As information passes each layer it adds complexity and extracts features. As an example, I can tell you to think about “apple.” You might picture a red fruit or Apple computers. A botanist might know and think about different apple sorts such as Golden Delicious. These associations come from how your brain has built connections over time. Some concepts are more closely linked than others, apple 🡪 juice might be closer connected than apple 🡪 banana. Even as you read this, new connections are formed – maybe you didn’t know of the apple type Golden Delicious – you do now. Neural networks don’t experience the world directly in the same way, but each time we train them, they adjust their weights, building stronger or weaker connections between nodes, as they receive feedback on whether the output was right, wrong, good or bad. In that sense, both human brains and artificial neural networks “learn” by changing connection strengths. We can’t fully explain why a huge network produces a certain output, because the computation happening between all the weights and nodes in all the layers is extremely complex and spread across so many interacting parts. An interesting side effect of this is transfer. Training a large language model on English can improve performance in other languages like Italian, because the model learns general patterns that can carry over between the two. This has turned out to work across some unrelated disciplines as well. In example, advances in LLMs have been useful in robotics. One important difference: While your brain changes continuously as you live, most deployed base models are snapshots frozen in place. They don’t change their weights when you use it, but rather when they’re re-trained or fine-tuned in model updates.
Neural networks are computer systems that have been developed on the model of the human brain. They consist of many small processing units, so-called "artificial neurons", which are connected to each other. You can imagine it like this: Nerve cells in the brain transmit information - this is how neural networks process data in several steps and learn from experience. They receive inputs, analyze patterns and generate results or decisions from them. The similarity to the human brain lies primarily in the fact that neural networks become better through learning. If a person practices or exercises frequently, his ability to solve tasks improves. In the same way, a neural network adapts its connections when it processes many examples. This allows it to recognize images, understand speech or make predictions, for example. Despite these similarities, however, there are also important differences. The human brain works in a much more complex, creative and conscious way than an artificial neural network. Humans can incorporate feelings, experiences, and social contexts, while AI only works with data and mathematical calculations. For teaching, it is important to convey to students that neural networks are powerful tools, but cannot replace human thinking and a sense of responsibility.
A neural network, as the name implies, is a network of interconnected math combinators, called “neurons”, as they’re inspired on how the human neurons work. Basically receiving chemical or electrical inputs, and activating only under certain conditions.
Neural networks are models in artificial intelligence composed of many interconnected "neurons" that process information through layers of data. They roughly mimic the human brain because, like biological neurons, they use connections and "learning" from examples to recognize patterns and make decisions.
Neural networks are a collection of neural tissues through which information flows from various identification centers.
To put it simply, they work like cells in the human body, communicating with each other. Each researches on its own and passes information to another cell. When many of them have get the same information, it's like a confirmation that this specific information is correct. This prevents the AI from sending wrong information, for example if some cells get wrong information from the internet due to fake news or untrustworthy sources. It's in a way like the blockchain in cryptocurrencies - each transaction needs to be validated by several nodes in order to confirm it and prevent hacking. In relation to the human brain, an example would be if you want to perform a specific action, let's say move your left hand - a lot of brain cells work together to accomplish this so it's done correctly and you actually move your left hand and not the right hand.
