Since the start of 2016, advances in artificial intelligence (AI) have been documented and analyzed in publications as varied as The Economist, Rolling Stone, Time, and Fortune under catchy titles like "The Artificial Intelligence Revolution". and “AI: the march of machines”. Beyond science fiction scenarios where intelligent machines turn against their creators, artificial intelligence is making great strides and making it possible to design and build increasingly impressive systems. Some people, including Elon Musk, founder of Tesla and SpaceX, for example, believe that driverless cars will be perfect within two years.
2Above all, artificial intelligence is a scientific discipline that combines computer science, mathematics, engineering, and statistics. It is a field of research to which an academic community has devoted itself for more than 50 years. This up-to-date science has seen its performance grow prodigiously, largely thanks to big data and the computing power of modern computers.
- Learning Techniques
Over the past decade, artificial intelligence has been advancing by leaps and bounds largely thanks to machine learning and more specifically deep learning techniques.
Machine learning algorithms “learn” by extracting operational knowledge from data (so it is inductive learning). For example, such algorithms can extract the preferences of a company's customers from their past transactions. We then use these algorithms to generalize the results to new similar situations (i.e., in the previous example, to predict the future preferences of the same customers). Compared to classical statistics, which is primarily interested in understanding data and in particular their causal relationships – that is to say, in demonstrating what causes what – machine learning focuses on the quality predictions, to the possible detriment of the interpretability of the estimated parameters.
Deep learning is inspired by the structure of the human brain and, like it, uses neural networks. Neurons are organized into several layers that use all the information from the previous layer, hence the notion of depth. These networks are obviously only a very simplified model of our brain, but they have the particularity of being able to use their layers to represent the knowledge acquired at different degrees of abstraction. For example, from transactional data, a neural network can represent, with its first layers, the characteristics of the items that customers like to buy. These characteristics are combined by the following layers to represent groups of items often purchased together. The final layers combine these groups to represent each customer's buyer profile.
- The Contribution Of Big Data
By definition, machine learning algorithms largely depend on the quality and quantity of data available for each task to be performed (e.g. the number of transactions per customer and their granularity, or the size of database pictures). The greater the knowledge required to perform a task, the greater the amount of data required. On the other hand, the more abundant the data, the more it is possible to extract complex knowledge leading to more precise and more efficient algorithms.
Of all the machine learning techniques, deep learning is currently the best at extracting complex knowledge from big data. Big data and the growth of computing capacities are therefore essential to recent breakthroughs in artificial intelligence.
- The Impact of AI on business
Many large companies in the technology sector are already benefiting from recent advances in AI. Google CEO Sundar Pichai promises to use machine learning systematically across its product line. Through start-up acquisitions and targeted recruiting, these large companies, including Google, Facebook, Amazon, Microsoft, Twitter, and Nuance, are developing their research and design potential for algorithms and artificial intelligence systems. Anyone who interacts with the technologies of these companies is therefore already benefiting from some of the advances attributable to artificial intelligence. In addition, the transfer of innovation into industrial production can be rapid: it took only two years for the most recent phones to be equipped with speech recognition algorithms.
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Artificial intelligence algorithms are not reserved for large companies. A large number of small companies offer flagship products based on artificial intelligence. This was not the case just a few years ago when AI was often a secondary tool used to improve an existing product.
In general, the collection and storage of data are tasks within the reach of companies of all sizes working in various fields. A number of recent advances in artificial intelligence offer the possibility of applying existing algorithms to this big data to extract relevant information. It is also possible that recent methods improve the performance of predictive algorithms of a previous generation.
- Automation Of Decision Making
Artificial intelligence techniques allow us to automate the extraction of knowledge for predictive purposes. Second, these predictions can be used to make better decisions. For example, based on predicted customer preferences, one can determine the order size for each available item and the personalized offer for each customer. There are also algorithms that automate predictions and decisions. Driverless cars are a good example of this kind of system. This type of automation is increasingly studied in academia.
In short, if the past is a guarantee of the future, multiple industrial applications should soon see the light of day.
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