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Machine learning: how to create an Artificial Intelligence in one infographic - EnjoyDigitAll by BNP Paribas

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Machine learning is what makes artificial intelligence, well, “intelligent” – it makes it able to learn, and not only be a powerful calculator.

It gathers data and has it go through an algorithm that in turn feeds on it to adapt its information managing process and to take decisions. Artificial intelligence is now able to learn and adapt.

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Machine learning: how to create an Artificial Intelligence in one infographic - EnjoyDigitAll by BNP Paribas

  1. 1. by DIFFERENT WAYS OF LEARNING ALGORITHMS & MACHINE LEARNING MACHINE LEARNING IN THE COMPANY The algorithm is fed with “training data”, i.e. data that has been previously labeled by experts. Inferring a function here depends on instructors teaching the machine what type of result it is supposed to come up with. SUPERVISED LEARNING REINFORCEMENT LEARNING Area of machine learning inspired by behavioral psychology, based on the trial and error method. It is concerned with how software agents ought to take actions in an environment so as to maximize some notions of cumulative reward. DEEP LEARNING Is used to learn autonomously from unstructured data, i.e. data that cannot be organized in a table. It is a way for the AI not to be contained to one programmed task. This way of learning comprises and is generated by the four previous techniques. For example, our artificial intelligence now knows if the image it is presented with features a cat or not. We then ask it to learn how to recognize crocodiles. The AI will use its visual recognition techniques, thus training and improving them, and will come back way better at recognizing cats in future images. Are determinists, their operating criteria are explicitly laid out by their operators. It is interesting to note that thanks to deep learning, research has produced unprecedent results, from which experts will undertake new research to understand the new findings. That’s what we call “reverse search”: discoveries that could not have occurred while following the classic path of hypothesis and prospective trials. Almost always requires human interaction, with the risk of introducing incomplete, outdated, or biased databases. Experts have to stay alert and make sure to be inclusive with the data they choose to feed the algorithm with. Lest we forget @TayAndYou, Microsoft’s AI that started to produce hate speeches after being connected to Twitter for less than 24 hours… CLASSIC ALGORITHMS MACHINE LEARNING Are defined as probabilist. If the technology behind them is way more powerful than the one behind classic algorithms, their results are inconsistent and depend on the ever-changing learning database used to teach them. LEARNING ALGORITHMS Machine learning is what makes artificial intelligence, well, “intelligent” – it makes it able to learn, and not only be a powerful calculator. It gathers data and has it go through an algorithm that in turn feeds on it to adapt its information managing process and to take decisions. Artificial intelligence is now able to learn and adapt. 01010101 11100110 10101101 1 0 10 1 1 1 1 0 0 LEARNING MACHINE CAT MOUSE RABBIT CAT UNSUPERVISED LEARNING The data is unlabeled, and it is the algorithm’s task to come up with its own classification system, often using statistics. This way of learning means it is free to evolve towards any type of final state. TRANSFER LEARNING A technique in machine learning where an algorithm learns how to perform one task, and builds on that knowledge when learning a different but related task. This type of learning is often used for visual recognition: for example, we give a thousand images to the AI, labelling them “cat” or “not cat”. It is then the AI that, by confrontation of all the tagged images, comes up with the criteria that enables it to recognize cats in the new images it receives. Designed by by Sources: Les usages de l'intelligence artificielle, Olivier Ezratty • Statistics: Forbes Donner un sens à l'intelligence artificielle pour une stratégie nationale et européenne, Cédric Villani In banks, machine learning has been proved to increase sales of new products by 10% and reduce churn by 20% while augmenting client satisfaction. 1 0 10 1 1 1 1 1 0 0 0 80% Of the data used by companies to take day-to-day decisions is unstructured – hence the necessity to have powerful algorithms to analyze them. 76% Of companies say their turnover has improved after implementing machine learning systems. 40% Of companies already use machine learning to improve sales and marketing performances. FOLLOW US ON TWITTER

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