Generative AI for IoT provides significant value and transformational benefits for adopting businesses. However, generative AI is still an emerging and evolving technology, and its adoption brings a variety of challenges and risks to businesses considering its use.
This article provides business leaders with an overview and understanding of some of the key generative AI for IoT risks and possible mitigation approaches.
The use of generative AI for IoT is poised to revolutionize business operations, automation, and decision-making. By combining structured and unstructured data, generative AI for IoT brings new capabilities and intelligence to enhance processing and analysis of operational data.
This article provides business leaders with an understanding of what generative AI for IoT is, and five opportunities that it provides businesses who adopt it.
Four out of five AI projects fail. One of the top causes of these failures is related to the problems AI is asked to solve. If you fail to specify the problem correctly from the start, you’re setting yourself up for failure before the first algorithm is even written.
This article provides business leaders with best practices and practical guidance on finding and selecting the right problems that lead to successful AI projects.
AI is not one technology, but many different types of artificial intelligence technologies. Each type of AI has different capabilities, strengths, and weaknesses. Applying the wrong type of AI technology to a task can lead to poor results and unacceptable risks.
This article provides business leaders with a working overview of the different types of AI to educate and inform on strategic decision-making around artificial intelligence initiatives.
Most IoT applications are based on a cloud centric architecture. Data collected from sensors and devices are sent to a gateway, which then transfers it to a cloud based IoT platform. For a growing set of IoT applications, including those that are mission critical, latency sensitive, or with high reliability needs, a new architecture is needed. An edge based architecture, with processing performed at the device, or in a gateway near the devices, is now emerging. This article provides an overview of edge or fog computing, and lists some common use cases.
The Internet of Things is set to transform and disrupt what we do and how we do it. But there is a coming revolution – the integration of computer vision, machine intelligence, data analytics, with IoT that promises a whole new set of disruptions.
This post is Part One of a series of briefings on the convergence of computer vision and the Internet of Things (IoT). It discusses what computer vision is, the impact of advanced machine learning algorithms, examples of use cases, and current challenges.
In the digital enterprise, the strategic fusion of IT, operations technology [OT], audio-video [AV] with transformational technologies (Cloud, Internet of Things, AI, analytics, edge processing) leads to richer customer experiences, business acceleration, and operational agility. This fusion leads to new innovation and digital transformation of the organization.
This article highlights the new roles and expectations of IT in an age of digital transformation.