Tag: #aiarts04

INTRO

When searching Neural Network on the internet, we have got 2 results – ANN (Artificial Neural Network) and BNN (Biological Neural Network). The latter one is the natural cells and connections in lots of creatures as well as human. They are made by (so called) gods’ hands. In this biological neural network, multiple neurons convey informations with bioelectricity under the assistant of some biochemical medium. In hundreds years ago, human has been dreaming about becoming the cepheid of intelligence. They invented machines to release human from labor. And they wanted more — they wanted machines to be smart enough in order to take care of almost everything human needs to do. Just like an ancient Chinese story — 女娲抟土造人 (Nvwa created human according to her own body). Human designed Artificial Neural Network according to Biological Neural Network — to make machines think.

SIMILARITIES

Since ANN is built as the structure of BNN, it basically has similar look as BNN. They are both the composition of neurons (BNN is consist of neurons as cells well ANN is consist of neurons as nodes and edges). Multiple neurons switch informations by electric power to send or receive variables from their ancestors and pass it to their descendants. 

(cited from: https://blog.knoldus.com/first-interaction-artificial-neural-network/)

In the picture above, the left side shows a biological neuron, and the right side shows the connections of multiple nodes(neurons) in ANN (we also call this fully-connected layer in this case). Each node stores a very simple and single-dimensional data xi, and outputs the result from an also very simple equation like yi+1=wi*xi+bi. In this scenario, each node (neuron) only does very small part of the job, but when we connect billions of nodes together, it becomes a super powerful self-fitting function after back propagations or reward functions, which help creatures/machines to make logical decisions.

DIFFERENCE

The scale of network is a huge difference. In human nervous system, there are over 100 billion neurons and over 7,000 synaptic connections within for each node (Cited: https://aiimpacts.org/scale-of-the-human-brain/). However, machines only owns few thousands or millions of computational nodes, let alone the limited connections between them. The storage and computational speed of human neural network is much more stronger than that of machines. Furthermore, all human do to create ANN nowadays is to make math formulas to make ANN acts locally. In another word, normal machine learning networks are always right — it is a huge different from human. They are taught by human to be good, but are not eligible to recover from failures. We learn from some essays that proves some network to be self-recoverable and self-extendible. But it should take time.

For all those who’re newly exposed to Machine Learning, it’s quite common to run into the a simple but puzzling question: why is this thing called a Neural Network, and what does it have to do with the brain?

Despite a rather opposite first impression, after building a more composed knowledge set on this matter, I’ve come to believe that the relationship between Neural Network and the prototype of its name — the human brain, is circumscribed down to a figurative and superficial level; in other words, other than a certain level of resemblance ( graphically and cognitively ) between a Neural Network graph and the brain’s neurons, these two entities can’t be compared or related to each other on any deeper level.

*Due to research topic choices, the Neural Network discussed here is limited to Convolutional Neural Networks, and will be referred to as CNN.

Continue reading “Reflection on Neural Network & the Brain | aiarts.week04”

With the development of Artificial intelligence, more and more attention has been drawn to the relationship between neural networks and human brains. What are the similarities? Why do we call it neural networks? Can computers actually “think” like human beings?

As we all know, there are a lot of things that a computer can do better than human beings such as calculating the square root of a number or browsing from the websites. At the same time, there are also things that human brains are better at: such as imagination and inspiration. Therefore, combining both of the strengths of computers and human brains, scientists have invented neural networks to simulate human brains and to help the machine behave more like us. Therefore, in my opinion, neural networks is a similar structure to human brain neurons for processing information and making decisions.

One of the reasons why we want machines to reason more like human beings is that we are developing technology for better life quality. Thus, we need to utilize the power of technology to think in the position of human beings and to understand human behaviors better. Second of all, human beings have the ability to learn and gain knowledge from previous experiences and we want computers to also have this ability so that they can have self-learning experiences at a high speed than us.

 In human being’s neural networks, there are three key parts which are the dendrites (the input mechanism), the Soma (the calculation mechanism) and the axon (the output mechanism). Similarly, there is an equivalent structure in computer neural networks: Incoming connections, the linear calculation and the activation functions and the output connections. (see pictures below)

 

According to Yariv Aden, “Plasticity — one of the unique characteristics of the brain, and the key feature that enables learning and memory is its plasticity — ability to morph and change. New synaptic connections are made, old ones go away, and existing connections become stronger or weaker, based on experience. Plasticity even plays a role in the single neuron — impacting its electromagnetic behavior, and its tendency to trigger a spike in reaction to certain inputs.” This is also the key point for training computer neural networks.

However, although neural networks are inspired by human brains, the ML implementation of these concepts has diverged significantly from how the brain works. 

First of all, neural networks’ complexity is much lower than human brains. This does not just mean the number of neurons but about the internal complexity of the single neuron. 

Second of all , power consumption. The brain is an extremely efficient computing machine, consuming on the order of 10 Watts. This is about one third the power consumption of a single CPU. (Adan)

In a word, although neural networks are inspired by human brains and they are indeed a lot of similarities between two, there are still some key differences: human brains are far more complex and cost-efficient than neural networks.

Source:

Do neural networks really work like neurons?

https://medium.com/swlh/do-neural-networks-really-work-like-neurons-667859dbfb4f

What are Artifical Neural Networks?

https://www.forbes.com/sites/bernardmarr/2018/09/24/what-are-artificial-neural-networks-a-simple-explanation-for-absolutely-anyone/

I think that there is no doubt that artificial intelligence neural networks are, at the very least, inspired by the brain/neuron structure. Even without reading any McCulloch and Pitts, I think it makes sense if we assume that 1) we cannot create anything that is completely new, but we can build off of previous discoveries and knowledges, and 2) developers over the years did not have any other established system of learning on which to base their work, aside from biological neural networks (human or otherwise). But, as a lot of classmates have pointed out, the key word is “inspired”; this does not imply that the structures are exactly the same, and it does not imply that the processes of learning are the same either.

The structures of these neural networks are similar, but different. After receiving an input, the processing of biological and artificial structures (in cell bodies and hidden layers, respectively) vary; this is in part due to the fact that most artificial neural networks are built in a way that accomplishes a single task, whereas biological can learn entirely new tasks. In that same vein, the ways AI learn and the ways humans learn are also different. Actually, while we can categorize different ways of AI learning, is it possible to do this for human learning? Is it supervised or unsupervised? While I was researching I also came across the terms inductive, deductive, transductive learning, but I still don’t know nearly enough about any of these or even enough about neuroscience to try to speculate on how they tie into human learning. One thing that’s clear, though, is that humans learn constantly and continuously; even if AI learned through a similar method, I don’t know if it would be possible for it to function with the same degree of complexity as a human, and I don’t think that outcome would be very predictable or controllable.

Ultimately, I think that artificial neural networks are definitely inspired by biological neural networks. However, the networks themselves, as well as the way AI and humans learn, can be vastly different. I think this conversation is interesting since other questions inevitably come out of it—will we ever be able to construct an ANN so that AI can learn like a human? Is this even the goal?