Artificial Intelligence is having a transformational impact in the business space and achieving superhuman performance at every level. The spark of the AI ​​revolution is finally blazing and the flood of data is unleashing its full power. The Machine Learning solution is not new. They date back to the 1950s and most algorithmic advances took place between the 1980s and 1990s. So why is it arousing curiosity now and the Harvard Business Review calls the "Data Scientist" the "best job". sexiest of the 21st century”? The reason is that we have finally harnessed vast computing power and huge stores of data (videos, images, audio and text files), allowing neural networks to work better than ever before. Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not Be Banned”? Get an original essay Sophisticated algorithms with astonishing accuracy and broader investments are driving advances in AI. These substantial advances triggered an explosion of technological improvements. As innovations come from multiple directions, many companies and research universities are jumping into the fast-paced world of AI. On the contrary, many companies are also struggling to benefit from crucial analytics, while some have yet to even set foot in the data lake itself. Best-in-class companies are generating significant margin growth by judiciously implementing analytics and artificial intelligence to expand their frontiers in business value creation. Revolutionary Deep LearningDeep learning, an extremely competitive field in artificial intelligence, is now becoming an increasingly crowded battlefield. More recently, a new type of neural network was introduced called Capsules and to train such a network, a “dynamic inter-capsule routing” algorithm was derived. This has exploded the AI ​​community, which is interested in the current workhorse of deep learning: the convolutional neural network (CNN). The learning capability of the capsule approach to achieve peak performance requires only a fraction of the data used by a convolutional neural network. AI machines that beat human experts use techniques ranging from statistical technique - Bayesian inference to deductive reasoning to deep learning. Deep learning excels in problems involving unsupervised learning. The Generative Adversarial Network (GAN) is at the forefront of deep learning research. GAN, a novel unsupervised neural network architecture contains two independent neural networks (discriminator and generator) that operate separately and act as adversaries. They solve problems such as generating images from descriptions, predicting which drug treats a particular disease, and retrieving images containing a given pattern. The openness of the research community is beginning to emerge. Advances in deep learning integrate ideas from statistical learning, reinforcement learning, and numerical optimization. This will be the era where AI is democratized. Merging Deep Learning platforms with Big Data platforms. Big Data has found its equal. Big Data Platforms: Hadoop and Spark remain the backbone of most analytical applications. Now, deep learning workloads coexist with other analytics workloads to leverage real-time data pipeline and monitoring frameworks within.