sensors as well as AI to track our habits and automatically drive energy saving schedules, in addition to fully connected smart homes and an entire category of Internet of Things (IOT). Similarly with modern cars, they are loaded with the sensors connected with the AI to perform ongoing diagnostics and enable different connected services. Many companies in the MedTech industry have legacy systems that are either becoming obsolete or need to go through a wave AI-supported “smartification” and connectivity enablement in order to meet the expectation of our new IoT reality. On the top of the data pyramid are data that will shape our future more than anything else in the history of mankind (yes, that is a bold statement). These are data that are created by the AI entity itself as a part of a dynamic deep learning iterations (try-and-error) which uses complex reasoning to promote a combination of attributes leading to desired outcome while suppressing those that do not. This progressive improvement through deep learning neural networks is poised to significantly outperform both human learning speed and capacity as well as human reasoning capability. There are tremendous benefits of such AI potential in
that is accessible via the Internet. It has already become a common practice to leverage generative AI for open-source market studies of consumer preferences in electronics, clothing, food and many other consumer- based industries. We are also witnessing the birth of a new digital competency known as prompt engineering that is focusing on text prompts that can get the maximum out of a generative AI model. Now think about the equivalent abundant data that exists in a large corporation including financial data, logistics data, distribution data, customer data and so on. These are typically guarded data accessible only within the company, however, we can still use AI for various analytic purposes. Then there is a powerful category of data associated with any kind of complex systems such as capital equipment and instruments which relays on sensors to extract performance attributes and metrics. For example, Google Nest (formerly Nest Labs) “smartified” the fairly basic instrument that we are all familiar with—the heating thermostat. Now the Nest thermostats include motion and other
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