By now most of us understand that, in our current era, artificial intelligence (AI) and its subset machine learning (ML) have little to do with human intelligence. AI/ML is all about recognizing patterns in data and automating discrete tasks, from algorithms that flag fraudulent financial transactions to chatbots that answer customer questions. And guess what? IT leaders appreciate the enormous potential.
When you say the words “artificial intelligence,” what comes to mind for many is some sort of super intelligent machine life-form we’ve all seen in sci-fi movies, a being that knows more than we do and can do more than we can. Complete awareness and interpretation of its environment, rapid learning and adaptation, and incomprehensibly fast cycling through the orient-observe-decide-act loop are implied AI characteristics in most films.
When AI was first launched, it was perceived as a technology that could revolutionize business models by creating a full understanding of a clients’ profile and their needs. It would be a must-have technology for large businesses to boost their balances and a magic tool for small business owners to remove barriers between their product or service and the final user — using the right data, of course.
Machine Learning – One of the challenges in scaling up meat production are issues of disease for the animals. Take bovine respiratory disease (BRD), for example. This contagious infection is responsible for nearly half of all feedlot deaths for cattle every year in North America. The industry’s costs for managing the disease come close to $1 billion annually.
Artificial Intelligence – “We’ve witnessed ten years of change in a month” is a typical description of how the pandemic is accelerating the use of telemedicine. Before the virus, video appointments made up only 1% of the 350m consultations which Britain’s National Health Service handles each year. Companies like Docly, eConsult and AccuRx are changing that. The latter claims that 90% of primary care clinics in England are now using its video-calling system.
Researchers have used artificial intelligence to accurately predict loneliness in residents at a senior housing community in San Diego. Publishing in the American Journal of Psychiatry, the researchers were able to harness natural language processing (NLP) and machine learning to classify the sentiment and emotions of speech.
Machine learning – In 2018, freight trucks contributed 23% of the greenhouse gas emissions in 2018, and by sector, transportation contributes an overall 28% of those emissions.
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