introduction

So far we have seen two booms of AI and it is said that we are experiencing the third one. It’s the deep learning who is driving this boom.
Thanks to deep learning, the accuracy of information extracted from output data.
However, what are the differences from conventional machine learning? This time, I will explain about deep learning.

1-1.Outline of machine learning

• Machine learning, a technology which reproduces human ability to learn using computer, is one of the research about AI. In machine learning, AI program learns rules or knowledge based on sample data. Here, learning means “dividing”. It divides objects, like drinks or foods, right or wrong answers. AI itself learns by improving its accuracy and percentage of questions answered correctly.

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1-2.Outline of deep learning

Deep learning is, in a broad sense, one of the machine learning technologies using multilayer neural networks.

I already explained that learning means dividing and there are many ways of dividing. One of the typical ways of dividing is neural networks that I mentioned above. Neural networks are composed of three different layers like human brains; input layer, hidden layer and output layer. We change learning processes from the input layer to the output layer by weighting and then coordinate them so that they output optimum value.

It was difficult to make the neural networks more multi-layered because if we dig into more than three layers, the results of learning would be worse. That is why they were composed of three layers. But recently, it became possible to learn by digging into multiple hidden layers. This is the deep learning, the high-visibility technology which outputs more accurate values than neural networks with larger number of learning processes.

1-3.Confusing “machine learning” and “deep learning”

The key difference between machine learning and deep learning is “feature value”.
Feature value, variable used for an input of machine learning, represents features of object quantitatively. It isbuilt into computer by human because in machine learning, it is difficult to create the feature value by itself.

For example, when you want to know the type of baggage, you would set the feature value like weight, size and whether it is breakable or not. If you put irrelevant feature, computer cannot find correct type. Computer is inferior to human being in finding out appropriate feature.

However, in deep learning, computer can learn and find feature value by itself. We human being no longer have to show feature value and computer can do much more thing.

2-1.Explanation of mechanism of deep learning

Briefly, in deep learning, computer digs into many layers of information.

As I explained in 1-2, these layers are composed of input layer, hidden layer and output layer. In deep learning, computer can drill down into many hidden layers to find hidden feature value.

In the second hidden layer, there are combined feature values which are found in the first hidden layer. Thus, the deepercomputer drills down, the more high-quality feature value could be found which brings more accurate output value.

3-1.The trigger of getting attention

Deep learning grabbed its first attention in 2012 at worldwide competition of visual recognition “ILSVRC (Imagenet Large Scale Visual Recognition Challenge)”. In this competition, the Super Vision of Toronto University, who participated for the first time, made a clean sweep of AIs of other research institutes such as University of Tokyo and University of Oxford.

• Institutes competed in percentages of correct answers of their computers who recognize visuals. Super Vision got its error rate of 15 to 16 % while other institutes’ AIs were battling around 26%. This extraordinary result sent a shock wave on the industry because they were spending one whole year to reduce 1 % of error rate at that moment.

  The cause of victory was the deep learning, one of machine learning method which professor of University of Toronto, Geoffrey Hinton, developed with his group. This victory consolidated his talent and he became the focus of attention, although he already had started his research on deep learning in 2006.

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3-2.Background of progressing practical use of deep learning

With this competition as a trigger, deep learning started to yield practical applications. After the next year of the competition, major companies such as Google and facebook started to focus on AI research.
Google accepted Professor Hinton in its research team and facebook established three AI institutes in the world.
These move raised a profile of the application of AI and deep learning, which prosecuted their practical use.

3-3.Present situation of practical use

Normally, it takes years when new technologies are put to practical use after they make headlines around the world. Now it’s been four years after deep learning grabbed a great attention and it is put to practical use in many ways.

4-1.Conclusion

After the competition in 2012 where Prof. Hinton announced Super Vision, deep learning is sharply raising a profile and major companies of internet industry like Google have started investing in it.
It is said that deep learning, a technique which can extract feature value from input data, is a great breakthrough in AI and will be put to practical use in various other industries.