Why we need to stop confusing human and machine intelligence

We are already accustomed to hearing phrases such as “machine learning” and “artificial intelligence”. We think that someone was able to reproduce a human mind inside a computer. This, of course, not true. But part of the reason why this idea is so common is because the metaphor of human learning and intelligence was very useful to explain machine learning and artificial intelligence. Some researchers of artificial intelligence have maintained close contact with the community of neuroscientists, and the inspiration goes both ways.

However, the metaphor can be a hindrance for people who try to explain machine learning to those who know him less. One of the biggest risks of combining human and machine intelligence is that we begin to transfer too many rights to the machines. But for those of us who work with the software, it is important to remember that an intelligent agent is just a man — a man who builds these systems in the end.

You should spend the key differences between machine and human intelligence. Despite the similarities, of course, looking at the differences, we could better understand how artificial intelligence works and how we build and use it as efficiently as possible.

Neural network

Central to the metaphor that links human and machine learning, this concept of neural networks. The biggest difference between human brain and artificial neural network is scale neural networks of the brain. It is important not just the number of neurons in the brain (which is billions), but a striking number of connections between them.

However, the problem goes deeper than just questions of scale. The human brain is qualitatively different from an artificial neural network on two other important reasons: the connection on which it fed analog, not digital, and neurons themselves are heterogeneous and uneven (unlike artificial neural networks).

That’s why the brain is so complicated. Even the most sophisticated artificial neural network, although it is sometimes difficult to understand the underlying architecture and principles of management. At least, we would like, so we’re striving for.

Even the most complex neural networks with artificial intelligence are designed for a specific purpose and to achieve a certain result. But the human brain does not have the same degree of focus in your project. Yes, he has the principles of self-preservation, etc., but he still requires us to critical thinking and a creative approach to program still does not work.

Beautiful simplicity AI

The irony is that the artificial intelligence system is much simpler than the human brain, allowing the AI to cope with much greater complexity than we can.

Neural network artificial intelligence can store much more information and data than the human brain, mainly because of the type of data stored and processed by a neural network. They are discrete and concrete as the contents of the Excel spreadsheet.

In the human brain data do not have the same properties of discreteness. Therefore, even though artificial neural network can process specific data, it cannot process information in a rich and multi-dimensional manner as the human brain does. This is a key distinction between the designed system and the human brain.

Despite years of research, the human brain remains unclear in many respects. This is due to the fact that analog synaptic connections between neurons almost impenetrable for digital connections in an artificial neural network.

The speed and scale

Consider what this means in practice. The relative simplicity AI allows you to quickly perform complex task very well. The human brain simply cannot process data at this rate, if, for example, translates speech to text, or processing a huge set of cancer reports.

For AI in these contexts it is important that it splits the data and information on the tiny components. For example, he can split the sounds in the phonetic part, which will then be translated into complete sentences, or split images into pieces in order to understand the rules by which the formation of large paintings.

People often do things like this here machine learning resembles a human; and algorithms people are breaking data or information into small pieces to process it.

But there is a reason for this similarity. The process of breaking down developed in each neural network as an engineer-man. Moreover, process design usually is based on the problem background. How artificial intelligence system divides the data set, it is her own way to “understand”. Even when running a very complex algorithm, the parameters of how the study of AI as it splits the data to process is installed from the beginning.

Human intelligence: definition problems

The human intelligence should not be such a set of restrictions that makes us much more effective in solving problems. It is the ability of people to “create” problems allows us to solve them perfectly. In our approach to solving problems there is an element of contextual understanding and decision-making.

The AI could and would be able to unpack problems or find new ways to solve them, but he can’t identify the problems that trying to solve it.

In recent years the subject of attention was the algorithmic insensitivity. More scandals associated with the bias of AI systems. Of course, this is directly related to the prejudices of those who make the algorithms, but the reasons where the algorithms appear these prejudices can only define people.

Human and machine intelligence must complement each other

We must remember that artificial intelligence and machine learning is not just the algorithms that are “out of hand” and out of our control. Created, designed and perpetrated by us. This imposes on us the responsibility for our future — it will be what we make it.

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