The data that will change the world is scattered all around us

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The contribution of this article to Dr. Roman Sandler, CTO and co-founder of Ravin AI,

It’s no secret that AI is changing all kinds of industries and businesses. Advances in machine learning, neural network technology, natural language processing, or machine intelligence, also known as AI, are affecting medicine, education, retail, manufacturing, automotive and many more.

AI-powered technologies are already responsible for significant functionality and improvements in a variety of areas – but this is only the beginning; The AI-generated changes we’ve seen so far use, by many estimates, only a small amount of all available data. It’s safe to say that when we use more data – most of it unorganized – things get really interesting.

Most AI data-analysis efforts are centered around text, audio, and video collected through the web, largely to provide insights for business, marketing, and customer service, with only a growing minority of organizations now using the tools to understand and organize unstructured data. . The physical world. But there is a whole world of unstructured data that can be a boon to many other industries – medicine, agriculture, transportation, construction, just to name a few.

The sensors that are currently in use – and the expected explosive growth of IoT devices – will collect large amounts of data, most of them in non-structured, and most of them in non-text formats. Such data is, by definition, “computer friendly”, but not conducive to AI-analysis. While the data collected by sensors and machines can be easily read by systems, it cannot provide insight into this “raw” state. In order for AI systems to be able to analyze data and provide insights, it needs to be organized into a framework that will enable scientists to obtain the information that will provide the answers they seek. The first step is to apply an initial layer of AI to convert this unstructured data into structured data that can then be used by additional types of AI to gain insights for solutions in different areas.

For example, unstructured data will be required to advance the development and use of autonomous vehicles. Using data from cameras and sensors, autonomous vehicles currently operate very well on well-maintained roads with clear markings and signals, where driving is performed “approximately”. A major challenge for the extended use of autonomous vehicles is their performance in non-standard driving situations – where roads are not smooth, neat, straight or properly signed and marked.

And it is here that unstructured data can make a difference. By using the data drawn into the system and applying it to a structure that autonomous vehicles can understand, AI systems can enable vehicles to navigate those challenging roads as if they were simple, standard highways. Given that mass reconstruction of country roads, urban streets and long-distance expressways to accommodate autonomous vehicles is unlikely, the use of unstructured data in this way – converting it into structured data – will be an important factor in the development of autonomous vehicle use.

With the power of AI on this newly-structured data, not only self-driving cars, but many types of businesses and organizations will have many more resources to work with – which will no longer be their most important and valuable insights. Table

Here are some other ways unstructured data can be used to improve insights:

Farming: Sensors and IoT devices can retrieve data on equipment and on the farm that AI systems can structure for advanced analysis, provide insights that can help farmers grow more crops, harvest at the right time and resource and profit Can maximize. For example, sensors mounted on farm equipment can collect data on sound waves, and analyze them for faults; Temperature and soil readings, when combined with crop images, can gain insights on the ideal growing environment; Analysis of social media posts can provide clues as to which crop has the highest market demand and the highest prices. While some of this data (such as temperature and weather information) is probably already in the structured database, most of it is unlikely – and farmers – and consumers – will benefit from applying AI structuring and analysis to this large amount of data.

Health care: If there should be a model for the potential power of unstructured data, it is healthcare. While much of the data collected by doctors and hospitals is properly coded and labeled for use in structured databases, much more data remains unstructured – and in many cases not even currently recorded.

Sources for unstructured data that healthcare can take advantage of include data from emails, text files, meeting transcripts, videos, photos, videos, chat applications – handwritten notes. Each of these areas can be a great source of insights – from the quality of care to efficiency, whether or not a physician is at risk of making a mistake. Analyzing these data at the patient level can provide insights to healthcare workers about a person’s true situation, emotional or financial issues that may affect their well-being or the overall picture of their health and lifestyle.

In addition to the traditional sources of data listed above, other sources of data including room temperature can be analyzed and the environment can be optimized with data on patient recovery, hospital stay, diet and other factors. Ensure faster recovery and most effective treatment for patients. Here, too, AI systems can be used to create vast resources that can generate life-saving insights for millions of people.

Road and vehicle safety: Vehicles today have dozens of sensors that collect data on everything from speed to atmospheric traffic. Data is uploaded to the computer (onboard or cloud-based) for analysis and quick turnaround, alerting drivers when they get too close to the vehicle in front of them or in dangerous road conditions. But again, data that is not used can be used to make driving safer and more efficient. For example, AI systems can correlate traffic merging data with vehicle collision prevention settings. By using machine learning, systems can provide the highest degree of safety, ensuring that vehicles blend in with incoming road traffic as safely as possible. AI systems that structure and analyze this data can help save lives.

Fleet managers can also benefit from unstructured data. Currently, fleet management systems analyze structured data on speed, driver safety behavior and routes. But unstructured data – properly “treated” with AI structuring and analysis systems – also provide a complete picture of the effect of atmospheric conditions on driver behavior, understanding the relationship between physical road conditions and vehicle depreciation, and how preventive maintenance occurs. Can be used to Combining dozens of pieces of data with vehicle and driver performance can ensure safe operation of vehicles.

AI, even with its currently limited analytical capabilities, has improved lives in a number of ways – but it will have a greater impact in the years to come when it can contain this current unstructured data coming from millions of sensors. By compiling data from real-world reading and recording sensors through AI, difficult real-world problems will benefit from existing AI solutions that are already working really well in the structured world. This will accelerate the number of problems that can be solved by AI solutions that have proven themselves in the traditional AI domains.

Dr. Roman Sandler is the CTO and co-founder of Ravin AI,

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