AI automates repetitive learning and discovery through data. However, artificial intelligence is different from hardware-oriented robotic automation. Rather than automating manual tasks, AI performs frequent, high-volume, computerized tasks reliably and effortlessly. For this type of automation, it is still necessary to set up the system and ask the right questions because of human questioning.
AI adds intelligence to existing products. In most cases, AI is not sold as an individual app. Instead, the products you already use will be enhanced with AI capabilities, as Siri has been added as a feature to next-generation Apple products. Automation, speech platforms, bots and smart machines can be combined with large amounts of data to develop many technologies at home and in the workplace, from security intelligence to investment analysis.
AI adapts through progressive learning algorithms to allow data to be programmed. AI finds structures and layouts in the data to gain a skill in the algorithm: the algorithm becomes a classifier or a query. That is, if the algorithm can teach how to play chess, it can teach which product to recommend next online. Models are adapted when new data is given. Back propagation is an AI technique that allows the model to adjust by adding training and data when the first answer is not entirely correct.
AI analyzes more and deeper data using neural networks with many hidden layers. Five years ago, it was almost impossible to set up a fraud detection system with five hidden layers. All this has changed with incredible computing power and big data. You need a lot of data to train deep learning models because they learn directly from the data. The more data you can feed, the more accurate they will be.
AI has achieved incredible accuracy with deep neural networks that were previously impossible. For example, your interactions with Alexa, Google Search and Google Photos are based on deep learning – and they continue to be more accurate as you use them. In the medical field, artificial intelligence techniques such as deep learning, image classification and object recognition can now be used to find cancer in MRIs with the same accuracy as highly trained radiologists.
AI makes the most of data. While the algorithms learn on their own, the data itself can become intellectual property. The answers are in the data; You just need to apply AI to remove them. Since the role of data is now more important than ever, it can create competitive advantage. If you have the best data in a competitive industry, even if everyone uses similar techniques, the one with the best data will win.
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