Joe McKendrick

Edge devices, with their small footprints and low power, are often too constrained to support AI. What’s needed are new approaches to address these issues.

Investment in IoT is set to overtake cloud computing, next-generation security, big data analytics, and other digital transformation technologies in the near future.

The advantage with digital twins is that they not only predict breakdowns in supply chains but also use AI to figure out a workaround.

As enterprises evolve to 5G edge-intensive architectures to deliver and process information, security basics get elevated in importance.

AI has become part of real-life scenarios, including managing national electric grids, supermarket warehousing optimization, drug discovery, and healthcare.

Organizations are turning to conversational AI to meet communication demands, with the adoption of chatbots and conversational interfaces reaching an unprecedented scale.

Most enterprises do not know what to do with IoT technology. And if they do, there is concern over who will be leading these initiatives.

Organizations are using event-driven architectures, Apache Kafka, Apache Pulsar, and other streaming technologies to handle the glut of event messages they must process on a regular basis.

With edge computing, workloads, storage, and networking infrastructures are moved closer to the sensors, actuators, and other IoT devices that generate and use that data.

Untethering compute from the cloud allows the broadening of AI’s reach. And it speeds up response time by reducing the lag caused by communicating with distant servers.

Manufacturing has a very broad set of use cases for artificial intelligence including predictive maintenance, quality control, price forecasting, and more.

Edge computing and 5G in the enterprise will transformation what we know as data centers. They are likely to evolve to be smaller and more geographically dispersed.