K
Kathleen Martin
Guest
It’s no secret that AI is changing industries and businesses of all types. Medicine, education, retail, manufacturing, automotive, and many others are impacted by advances in machine intelligence, also known as machine learning, neural network technology, natural language processing, or simply AI.
AI-powered technologies have already been responsible for significant efficiencies and improvements in a wide variety of areas — but this is just the beginning; the AI-wrought changes we’ve seen so far utilize, by many estimates, only a small amount of all data available. It’s safe to say that when we use more data — much of it unstructured — things will really get interesting.
Most AI data-analysis efforts center around text, audio, and videos collected via the web, mostly to provide insights for business, marketing, and customer service, with only a growing minority of organizations now using tools to understand and organize unstructured data from the physical world. But there’s a whole world of unstructured data that could be a boon to many other industries — medicine, agriculture, transportation, construction, to name just a few.
Sensors that are currently in use — and the expected explosive growth of IoT devices — will collect huge amounts of data, much of it unstructured, and much of it in non-text forms. Such data, by definition, is “computer friendly,” but not AI-analysis friendly. While data collected by sensors and machines are easily readable by systems, it cannot provide insights in this “raw” state. In order for AI systems to be able to analyze data and provide those insights, it needs to be deployed in a structure that will enable scientists to mine it for information that will provide the answers they seek. The first step is to apply an initial layer of AI to transform this unstructured data into structured data that can then be exploited by additional types of AI for insights into solutions in a wide variety of areas.
For example, unstructured data will be essential to further the development and use of autonomous vehicles. Utilizing data from cameras and sensors, autonomous vehicles currently do quite well on well-maintained roads with clear markings and signage, where driving is done in a “predictable” manner. A bigger challenge to expanded usage of autonomous vehicles is their performance in non-standard driving situations — where the roads aren’t smooth, neat, straight, or properly signed and marked.
And it’s here that unstructured data could make a difference. By utilizing the data pulled into the system and applying it to the structures that autonomous vehicles can understand, AI systems can enable vehicles to navigate driving those challenging roads just as they would the easy, standardized highways. Given that a massive reconstruction of country roads, urban streets, and long-distance expressways to accommodate autonomous vehicles is unlikely, utilizing unstructured data in this way — converting it to structured data — will be an important component in the growth of autonomous vehicle usage.
Continue reading: https://venturebeat.com/2022/02/20/the-data-that-will-change-the-world-is-scattered-all-around-us/
AI-powered technologies have already been responsible for significant efficiencies and improvements in a wide variety of areas — but this is just the beginning; the AI-wrought changes we’ve seen so far utilize, by many estimates, only a small amount of all data available. It’s safe to say that when we use more data — much of it unstructured — things will really get interesting.
Most AI data-analysis efforts center around text, audio, and videos collected via the web, mostly to provide insights for business, marketing, and customer service, with only a growing minority of organizations now using tools to understand and organize unstructured data from the physical world. But there’s a whole world of unstructured data that could be a boon to many other industries — medicine, agriculture, transportation, construction, to name just a few.
Sensors that are currently in use — and the expected explosive growth of IoT devices — will collect huge amounts of data, much of it unstructured, and much of it in non-text forms. Such data, by definition, is “computer friendly,” but not AI-analysis friendly. While data collected by sensors and machines are easily readable by systems, it cannot provide insights in this “raw” state. In order for AI systems to be able to analyze data and provide those insights, it needs to be deployed in a structure that will enable scientists to mine it for information that will provide the answers they seek. The first step is to apply an initial layer of AI to transform this unstructured data into structured data that can then be exploited by additional types of AI for insights into solutions in a wide variety of areas.
For example, unstructured data will be essential to further the development and use of autonomous vehicles. Utilizing data from cameras and sensors, autonomous vehicles currently do quite well on well-maintained roads with clear markings and signage, where driving is done in a “predictable” manner. A bigger challenge to expanded usage of autonomous vehicles is their performance in non-standard driving situations — where the roads aren’t smooth, neat, straight, or properly signed and marked.
And it’s here that unstructured data could make a difference. By utilizing the data pulled into the system and applying it to the structures that autonomous vehicles can understand, AI systems can enable vehicles to navigate driving those challenging roads just as they would the easy, standardized highways. Given that a massive reconstruction of country roads, urban streets, and long-distance expressways to accommodate autonomous vehicles is unlikely, utilizing unstructured data in this way — converting it to structured data — will be an important component in the growth of autonomous vehicle usage.
Continue reading: https://venturebeat.com/2022/02/20/the-data-that-will-change-the-world-is-scattered-all-around-us/
