News Commentary | July 18, 2019
Telit's IIoT platform, mainly dominated by its device and connectivity management features, does not have the requisite native feature to handle communication from devices using diverse protocols. While this is generally an industrywide problem in IIoT deployments, Telit's major competitors like ... Not part of subscription
News Commentary | May 06, 2020
In a collaborative study, researchers at Yale University and the Chinese University of Hong Kong have shown it is possible to use anonymized cell phone data to create a model for predicting the location and speed at which new outbreaks will occur. As the economy slowly reopens in many parts of the ... To read more, click here.
Analyst Insight | May 12, 2020
Many have identified AI and machine learning as potential tools in the fight against COVID‑19. However, as in most discussions involving AI, there's significant hype around the topic, making it difficult for organizations to determine where to focus their efforts. In this insight, we examine various... Not part of subscription
by Cole McCollum
The dataset consisted of an assortment of objects shot at unusual angles and surrounded by clutter. While computer vision performance has increased significantly over the past decade, a top-performing algorithm's accuracy rates fell from a high of 97% on more simple datasets to just 50% to 55% on the new dataset. This study highlights a major challenge for AI, namely, that as the environment becomes more complex and unpredictable, machine learning performance drops off considerably. While clients could wait for the algorithms to become more robust before deploying a solution, a more near-term solution to this challenge is to consider constraining a problem to make it a better fit for today's tools.
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