A brief history of AI: how we got here; where we're going

History Of AI News

A brief history of AI: how we got here; where we're going
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To understand the current generation of AI tools and where they might lead, it is helpful to understand how we got here.

current generation of AI tools and where they might lead, it is helpful to understand how we got here.

From those early beginnings, a branch of AI that became known as expert systems was developed from the 1960swith the opening sentence: “I propose to consider the question, ‘Can machines think?’.” He goes on to propose something called the imitation game, now commonly called the Turing test, in which a machine is considered intelligent if it cannot be distinguished from a human in a blind conversation.

The MLP is an arrangement of typically three or four layers of simple simulated neurons, where each layer is fully interconnected with the next. The learning algorithm for the MLP was a breakthrough. It enabled the first practical tool that could learn from a set of examples and then generalise so that it could classify previously unseen input data .

Both the MLP and the CNN were discriminative models, meaning that they could make a decision, typically classifying their inputs to produce an interpretation, diagnosis, prediction or recommendation. Meanwhile, other neural network models were being developed that were generative, meaning that they could create something new, after being trained on large numbers of prior examples.

As well as producing an impressive generative capability, the vast training set has meant that such networks are no longer limited to specialised narrow domains like their predecessors, but they are now generalised to cover any topic.The capabilities of LLMs have led to dire predictions of AI taking over the world. Such scaremongering is unjustified.

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