According to Reuters, the city's economics and information technology bureau in China's capital, Beijing, will assist major firms in developing artificial intelligence (AI) models capable of challenging the top AI ChatGPT. Beijing indicated that they will assist major corporations in investing in the development of an open source framework and accelerating the delivery of essential data. They also stated that as of October last year, Beijing was home to 1,048 core AI businesses, accounting for 29% of the country's total, and that they will investigate methods to foster talent and do research in areas such as ethical governance. The current AI race against large US corporations is gaining traction in China. While people in the country are unable to register OpenAI accounts to use the AI chatbot, companies are scrambling to incorporate the technology into their businesses, and Chinese tech behemoths such as Baidu and Alibaba Group are preparing to develop competing services.

No-code artificial intelligence (AI) tool sets out to demystify and democratize AI by providing non-technical users with code-free environments for building AI models. No-code tools use techniques like intuitive interfaces, templates, and drag-and-drop editors to build AI for tasks like image recognition, object detection, data classification, and predictive analytics. Here, we look at some of the no-code products currently available-- from free computer vision tools for home users to enterprise-level platforms. An uncluttered interface offers three categories of project to pick from: image, audio, or pose (body positions). Training data--image files and one-second audio clips--can be uploaded or captured via a Webcam or mic.

All the sessions from Transform 2021 are available on-demand now. Spell today unveiled an operations platform that provides the tooling needed to train AI models based on deep learning algorithms. The platforms currently employed to train AI models are optimized for machine learning algorithms. AI models based on deep learning algorithms require their own deep learning operations (DLOps) platform, Spell head of marketing Tim Negris told VentureBeat. The Spell platform automates the entire deep learning workflow using tools the company developed in the course of helping organizations build and train AI models for computer vision and speech recognition applications that require deep learning algorithms.

AI continues making headlines in the data science community, and predictive models are front and center in engineering applications such as autonomous driving and equipment monitoring. Introducing AI models into engineering systems can be challenging, however, especially when predictions must be reported in near real-time on data from multiple sensors. Many data scientists have implemented machine or deep learning algorithms on static data or in batch, but what considerations must you make when building models for a streaming environment? In this post, we will discuss these considerations. If streaming movies or music comes to mind, you've got the right idea! Data is incoming continuously, but instead of simply watching, actions must be taken based on the information.

We hear about AI everywhere. Machine learning models are now incorporated into several applications, such as medical devices and automated vehicles. These systems include many sensors, streaming data from hardware. The model is applied to the data in the stream and predictions are sent to a dashboard, database, or another device (repeatedly!). Data prep and model development challenges are exacerbated with such high-frequency, time-series data.

IBM this week revealed it has added a drift detection capability to the Watson OpenScale platform to govern artificial intelligence (AI) models that will become a foundational piece of IBM's approach to defining best DevOps practices for building AI-infused applications. Rohan Vaidyanathan, program director for IBM Watson OpenScale, said one of the biggest AI issues organizations face today is determining when to update or replace an AI model. Announced at the IBM Data and AI Forum event, the drift detection software added to Watson OpenScale provides a continuous monitoring capability that detects how far an AI model has moved from its original parameters, Vaidyanathan said. Drift in AI models usually occurs over time, especially as use cases change in ways that are unexpected. Once that drift is detected, organizations eventually want to either retrain that AI model or replace it with a new one.