We all experience that artificial intelligence will transform our society; however, we do not know how or to what extent.
Two things are certain:
Ray Kurzweil believes that by 2035, the human brain will be equalled and overtaken by computing power.
As scientists do not yet really know how the human brain works, I am not going to get into a broad definition of what artificial intelligence is or is not.
To put it simply, it means being able to process unstructured data. That is 80 to 90% of the available data: image, email, chat, phone, scan, news,…
Until recently, we didn’t know how to process it. Today, this is possible thanks to the technological revolution in deep learning.
What we call AI at Kili is the ability to process this data with the help of the machine.
Let us look at the automobile, the oil and piston industry, just 10 years ago. It is becoming one of the most data-generating industries. Thank you Tesla & Google… General Motors, Renault still sell cars. But they will offer a mobility service… or lose control of their value chain by leaving the place to Uber.
Let us look at health, in France alone, the number of deaths linked to medical errors is estimated at several tens of thousands per year (even if doctors cannot agree on the figures). Tomorrow, radiologists, oncologists, will all be assisted in their diagnosis, in their prescriptions, to focus on what makes their value: discernment. Artificial intelligence is already beginning to save lives because it is more suitable than man for these complex and very vertical tasks.
And on a more cross-functional subject, it transforms operating methods and is a source of competitiveness. It reduces the difficulty of simple tasks with low added value in back-offices, for example, and it makes the most of the complementarity between man and machine: the machine excels on repetitive tasks with limited scope, where man gets tired quickly, and it revalues expertise and human discernment, where the machine remains stupid.
To make AI you need 3 components: algorithms, computing power, data.
In short, the key to industrializing AI is data and labelled data.
Today, 80% of projects fail or remain in the state of POCs, largely due to the lack of image, text and audio learning data, in sufficient quality and quantity. Few companies have already integrated that it is necessary to create datasets to deal with topics. And not choose the subjects for which the data is available. And when this is understood, the annotated data remains one of the main bottlenecks to the deployment of machine learning. Since the annotation task is mainly manual, it can be very expensive and time-consuming.
Kili allows you to