AI is having a transformative impact in the business space and achieving superhuman performance across the board. The spark of the AI ​​revolution is finally dazzling, and a wave of data is unleashing its power. The machine learning solution is not new. They date back to the 1950s, and most algorithmic breakthroughs occurred between the 1980s and 1990s. So why does it now spark curiosity and Harvard Business Review calls “data scientist” “the sexiest job of the 21st century”? The reason is that we have finally harnessed vast computational power and huge stores of data (videos, images, audio and text files) that ultimately make neural networks more performant than ever. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an Original Essay Sophisticated algorithms with astonishing accuracy and broader investments are driving advances in artificial intelligence. Substantial advances have triggered an explosion of technological improvements. As innovations come from multiple directions, many companies and research universities are entering the world of artificial intelligence. Conversely, there are also many companies struggling to take advantage of crucial analytics, while some have yet to even set their feet in the data lake itself. World-class companies are achieving significant margin growth by wisely implementing analytics and AI to expand their frontier of business value creation. Revolutionary deep learningDeep learning, a highly competitive arena in the field of artificial intelligence, is becoming an increasingly crowded battlefield today. More recently, a new type of neural network called Capsule has been introduced, and a “dynamic inter-capsule routing” algorithm has been derived to train such a network. This has exploded the AI ​​community, which is busy with today's deep learning workhorse: the convolutional neural network (CNN). The capsule approach's learning ability to achieve state-of-the-art performance requires only a fraction of the data used by a convolutional neural network. The AI ​​machines that are beating human experts use techniques ranging from statistical - Bayesian inference to deductive reasoning to deep learning. Deep learning excels at problems involving unsupervised learning. The Generative Adversarial Network (GAN) is the cutting edge of deep learning research. GAN, a new unsupervised neural network architecture contains two independent neural networks (discriminator and generator) that work separately and act as adversaries. They solve problems such as generating images from descriptions, predicting which drug treats a particular disease, and retrieving images that contain a certain pattern. The openness of the research community is starting to emerge. Deep learning discoveries incorporate ideas from statistical learning, reinforcement learning, and numerical optimization. This will be the era in which artificial intelligence is democratized. Fusion of Deep Learning platforms with Big Data platforms. Big data has found its match. Big Data Platforms: Hadoop and Spark remain the backbone for most analytics applications. Now, deep learning workloads coexist with other analytics workloads to leverage the real-time data pipeline and monitoring frameworks within the platform. Tensorflow and Spark are integrated to improve pipelines.