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How has Artificial Intelligence progressed? Is it the technology of the future?

2024-09-09 11:13:13Lifestyle SHKRUAR NGA REDAKSIA VOX

How has Artificial Intelligence progressed? Is it the technology of the future?

How Artificial Intelligence became from a lost hope to the most promising technology in the world, writes The Economist

During the summer of 1956, a small but famous group of scholars gathered at Dartmouth College in New Hampshire.

It included Claude Shannon, the creator of information theory, and Herb Simon, the only person to have won the Nobel Memorial Prize in Economic Sciences from the Royal Swedish Academy of Sciences, and the Turing Award from the Society for Computing Machinery.

They had been invited by a young researcher, John McCarthy, who wanted to talk about "how they could make machines use language, form abstractions and concepts" and "solve different kinds of problems like humans."

This was the first academic meeting devoted to what McCarthy called "artificial intelligence". For the next 60 years, in this field, advances would be made that were not on par with the ambitions of researchers.

The meeting at Dartmouth College did not mark the beginning of scientific research into machines that could think like humans. Alan Turing, after whom the Turing Award is named, was skeptical of the idea; so did John von Neumann. Until 1956, there were a number of approaches to this issue.

Historians think that one of the reasons why McCarthy coined the term "artificial intelligence" for his project was that the name was all-encompassing, leaving open the question of what the best approach might be.

Some researchers liked systems that combined facts and axioms about the world, such as those in geometry and symbolic logic, in order to derive the right answers.

Others preferred building systems where the probability of something depended on the updated probabilities of many other things.

In the following decades, there were many intellectual debates and arguments on this topic, but by the 1980s, a general agreement was reached on the way to go: "expert systems" that used symbolic logic to capture and apply the most advanced knowledge. human good.

The Japanese government in particular financially supported such systems, paying for the equipment they might need.

But for the most part, these systems couldn't handle the messiness of the real world. By the late 1980s, the reputation of artificial intelligence had declined. Scholars began to avoid the term.

How today's boom was born

Today's boom was born precisely from those persistent few who were left. After a better understanding of how brain cells worked in the 1940s, scientists began to wonder if machines could be wired in the same way as neurons.

In a human brain, neurons are connected in a way that allows activity in one neuron to stimulate or suppress activity in another.

This makes a neuron dependent on what other neurons connected to it are doing.

In the first attempt to recreate this model in the laboratory (by Marvin Minsky, a meeting participant at Dartmouth College), devices were used to mimic neural networks. Since then, layers of interconnected neurons have been simulated in software.

These artificial neural networks are not programmed using clear rules. They actually "learn" by being exposed to many examples.

During this training, the strength of connections between neurons is adjusted from time to time so that a given input of data produces an appropriate output.

Minsky himself abandoned the idea, but others took it forward. In the early 1990s, neural networks were trained to help with sorting by recognizing handwritten numbers.

The researchers thought that adding more layers of neurons could allow for more sophisticated achievements. But the downside was that the systems ran much slower.

A new type of computer hardware provided a way out of the problem. Its potential was clearly demonstrated in 2009, when researchers at Stanford University increased by 70 times the speed at which an artificial neural network could operate, using a gaming computer in their dorm room.

This was possible because, like the "central processing unit" (CPU) found in all computers, this network also had a "graphics processing unit" (GPU) to create a game world on the screen. And the GPU was designed in a suitable way to allow the implementation of the neural network code.

Coupling this hardware with more efficient training algorithms meant that networks with millions of connections could be trained in a reasonable amount of time.

Also neural networks could handle larger inputs and more importantly, they could be given more layers. These "deeper" networks turned out to be much more capable.


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