First, let’s go to Google trends and see the trend for both the terms "Kubernetes" and "Docker Swarm." What do we see? Clearly, we could see that Kubernetes is beating Docker Swarm. But, is that a valid proof to say Kuberneytes is winning? No way.
Of course, the search volume and trend for "Kubernetes" might be higher, but still, this alone doesn’t prove that Docker Swarm is dead.
After a decade of stop-and-go development, Artificial Intelligence has now begun to provide real, tangible value to the business world. McKinsey published an 80-page report titled "Artificial Intelligence: The Next Digital Frontier?" which provides a comprehensive analysis of the value that Artificial Intelligence (AI) creates for businesses.
The report points out that "wide application of Artificial Intelligence technology will bring great returns to businesses." This means that the disruptive nature of AI will continue to become more apparent in the future. Governments, enterprises, and developers should all be clear on this point. Moreover, the report raises some interesting points (all of which we will discuss later in this article):
Established in 2018, the Machine Intelligence Technology Laboratory comprises of a group of outstanding scientists and engineers, with research centers located in Hangzhou, Beijing, Seattle, Silicon Valley, and Singapore. Machine Intelligence Technology Laboratory is Alibaba’s core team responsible for the research and development of artificial intelligence technologies. Relying on Alibaba’s valuable massive data and machine learning/deep learning technologies, the lab has developed image recognition, speech interaction, natural language understanding, intelligent decision-making, and other core artificial intelligence technologies. It fully empowers Alibaba Group’s important businesses such as e-commerce, finance, logistics, social interaction, and entertainment, and also provides outputs to ecosystem partners to jointly build a smart future.
Machine vision, or computer vision, is a popular research topic in artificial intelligence (AI) that has been around for many years. However, machine vision still remains as one of the biggest challenges in AI. In this article, we will explore the use of deep neural networks to address some of the fundamental challenges of computer vision. In particular, we will be looking at applications such as network compression, fine-grained image classification, captioning, texture synthesis, image search, and object tracking.
Even though deep neural networks feature incredible performance, their demands for computing power and storage pose a significant challenge to their deployment in actual application. Research shows that the parameters used in a neural network can be hugely redundant. Therefore, a lot of work is put into increasing accuracy while also decreasing the complexity of the network.
As the Stack Overflow Developer Survey 2018 and GitLab’s 2018 Global Developer are making headlines on the web, developers from around the globe are getting a better view of the various trends and practices in software development. Although these reports are informative, they do not accurately reflect the current state of software development within Mainland China.
In response to this information gap, the Alibaba Cloud Developer Community published its very own annual China Developer Survey Report in December 2017. This study featured over 7,000 developers across China and from various industries answering 118 questions. Through the survey, developers shared their perceptions on several topics, including development environment, development language, programming environment, database, integration management tools, and API capacity preference. They also communicated their understanding and practices in multiple technical fields such as web development, front-end development, cloud computing, big data, artificial intelligence, blockchain, and security.
