Artificial intelligence (AI) and machine learning (ML)
Artificial intelligence (AI) and machine learning (ML)
Machine Learning: The study of computer algorithms that improve automatically through experience and by the use of data.
Artificial intelligence (AI) and machine learning (ML) are not new concepts, but they are becoming more accessible and powerful thanks to the availability of data, cloud computing, and open-source frameworks. AI and ML can enhance software development in many ways, such as automating tasks, improving quality, generating code, and creating new applications. As a developer, you need to understand the basics of AI and ML, and how to use them in your projects. You also need to keep up with the ethical and social implications of these technologies, and how to ensure fairness, privacy, and security.
If you are interested in using Apache Spark (Big Data Processing Frameworks) for machine learning tasks, having a background in machine learning algorithms and data science concepts can be beneficial. Spark MLlib provides scalable machine learning libraries that can be leveraged for building models.
Some history
Jensen Huang (CEO of Nvidea) has made a pattern of positioning Nvidia in front of every big tech trend. In 2012 a small group of researchers released a groundbreaking image recognition system, called AlexNet, that used GPUS, instead of CPUS, to crunch its code and launched a new era of deep learning. Huang promptly directed the company to chase AI full-steam. When, in 2017, Google released the novel neural network architecture known as a transformer - the T in ChatGPT - and ignited the current AI gold rush, Nvidia was in a perfect position to start selling its AI-focused GPUs to hungry tech companies.