What is it ?
Machine Learning in simple word is when the machines begin to learn like humans and not only execute the instructions contained in the algorithms present in their processors.
It is therefore the automatic learning of any (re) programmable system, which presupposes that the notion of algorithms does not disappear but that other elements, in this case the data, are added to the algorithms. The goal is to allow the systems to learn and evolve towards performances not only of calculations but also related to actual thoughts. In fact, at this level, the systems know how to adapt the results according to available algorithms and all the data which is available and has been acquired, interpreted.
It is also really important to state the difference between data mining and machine learning. In fact, data mining is a discipline already known and which consists of reprocessing the already known data to leave properties and precisions still unknown. On the other hand, thanks to the ‘known data’ Machine Learning tries to teach systems to predict what the result could be obtained from the data still unknown.
Enough with theory, let’s see how Machine Learning can affect businesses:
According to a recent study published by the consulting firm Accenture, French companies are receiving and suffering two to three effective cyberattacks per month. Given the increasing amount of data, identifying illegal activities among a stream of legitimate actions is becoming increasingly complex. Manual scanning of such a large amount of data is no longer possible.
Nowadays, there are two ways of detecting cyberattacks and intrusions which, both, have their own limits: the human and the machine. In addition to the constantly increasing data volume, the typology of attacks is becoming ever more diverse.
The AI is a revolution in cybersecurity mostly because it takes a similar approach to the human one. Indeed, the AI is looking for an abnormal fact and the differentiation between a legitimate action and an attack is therefore based on the experience of what it has already seen in the past. It is precisely on this principle that an anomaly detection operated by artificial intelligence is based: with time, it will learn (via machine learning) what is the usual behaviour and qualify it as normal. Any other behaviour will then be considered as suspect.
Machine learning gradually tends to revolutionize the medical world as well.
Researchers from Stanford University created a new algorithm with a database of nearly 130,000 clinical images of skin lesions, representing more than 2,000 different diseases. The machine learning massively ingested the huge quantity of information and then processed them by algorithms. And it allowed to classify the lesions into three categories: non-cancerous, benign cancer and malignant cancer. The test has shown impressive results with a number of 69.4% accuracy for the machine against 65.8% for dermatologists.
Machine Learning in the marketing:
In marketing, the identification of combinations of factors is crucial because it has a real impact on sales.
We can take the example of the retail industry and more specifically the clothing’s industry. In certain periods of the year, clothing sales are determined and set up in advance. In fact, winter is associated with warm clothing (hats, coats, sweaters), while summer comes with light clothing (shorts, skirts, sandals, short sleeves). It can be biased when the seasons are not following the usual cycle with cold temperatures in winter and warm ones in summer. Significant variations are increasingly taking place today because of the climate change and the global warming. Machine Learning will, then, allow retailers to provide clothes that people are the most likely to buy in the event of severe temperature fluctuations. Machine Learning will take into account the weather and each customer will have items personally suggested according to many factors and temperatures will be one of them.
For example :
I am working as an Inside Sales at ContentSquare and I am helping online businesses to change their culture by empowering all digital teams to understand the impact and effectiveness of every piece of content and user experience they create.
As a guide within the ContentSquare solution (a user experience and optimisation platform), ARTI, the Artificial Intelligence, is present in all the analysis modules.
This machine learning has a simple objective: contribute to the optimization of the conversion of e-commerce websites. Thus, ARTI suggests operational recommendations to accompany, in a daily routine, digital teams in optimizing the user experience for their Internet customers.
The three key points to remember is that ARTI helps to identify the pages of the e-commerce site and the areas that need to be worked on and optimized. Mr Robot also transmits methodological expertise, giving teams advice on how to analyze a given page or area. And last but not least, ARTI acts as a guide within the modules of the solution. The autonomous access to the data makes it possible to offer the teams a better knowledge of the users who are always more demanding and in search of personal relations with the brands. This constitutes a real vector of change: the more competent in behavioural analysis, the latter can finally give meaning to their actions by placing the users at the heart of the reflection.
The main idea with machine learning at ContentSquare would be to go from a SaaS model to a Site as a Service. Meaning that, thanks to AI and Machine Learning, it will be possible to propose a different user experience and interface for each one.