Artificial intelligence (AI) has taken the consumer world by storm, and hence, it is no surprise to see innovators in every other industry follow the suit. This week’s AI guide discusses its developments in Journalism, Healthcare and Agriculture.
According to Francesco Marconi, a Journalism Professor at Columbia University, the future of journalism and its survival lies in artificial intelligence. According to him, the world of journalism is not entirely keeping up with the evolution of newer technologies. He strongly feels that newsrooms need to take advantage of what AI can offer and come up with a new model that can assist in the betterment of Journalism.
It is essential to note that artificial intelligence is not there to replace journalists or eliminate jobs. In fact, at the moment, AI-powered robots are performing basic writing tasks that include two to six paragraphs on sports scores, quarterly earnings reports, election results in Switzerland and Olympic results too! These robots are also capable of analyzing large databases that can send journalists at Bloomberg News an alert as soon as there is a trend or an anomaly. AI can also help save reporters a lot of time by transcribing audio and video interviews — AFP has managed to create a tool for it. The same is true for all major reports that rely on vast databases.
The general goal of AI is to augment the ability of humans to provide and elevate the standard of healthcare rather than replace the caretakers themselves. Here is how AI can help overcome age-old healthcare conundrums and assist in improving access, outcomes, and efficiency to the extent that it will be a necessity to re-evaluate practice guidelines and standards of care. To that end, there are 3 categories of AI solutions that support three distinct needs:
- Process Automation: Process Automation AI is found to be the easiest way to implement while bringing quick and high returns on investment. Example- AdaptDx Pro guided by Theia (in eye care), an AI-driven onboard technician that coaches patients through definitive diagnostic tests for age-related macular degeneration (AMD).
- Cognitive Insight: Cognitive Insight AI has led to the development of a software program designed specially to perform screening for diabetic retinopathy. The technology known as IDx-DR, underwent the FDA’s Automatic Class III or De Novo premarket pathway – achieving a breakthrough device designation.
- Cognitive Engagement: One of the most common applications of AI-based cognitive engagement is the use of chatbots that rely on customer input to perform tasks like answering FAQs. Ideally, a future technology may be able to combine elements from these categories of AI into a comprehensive solution that adheres to modern healthcare challenges.
The growth of AI in the agriculture market has been propelled by the increasing implementation of data generation through sensors and aerial images for crops, deep learning methods to increase crop productivity, and adoption of modern agricultural techniques. Considering that the high cost of gathering precise field data restrains market growth, countries like China, Brazil, and India have taken initiatives to provide an opportunity for AI in the agricultural market.
Furthermore, various Machine learning-enabled solutions have also been significantly adopted by agricultural organisations worldwide to enhance farm productivity and gain an edge in business operations. In fact, the application of machine learning agricultural practices is expected to rise exponentially in the coming years. As per reports, the AIML in the agricultural market is projected to grow at a CAGR of 25.5% between 2020 and 2026.
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