Columbia University and the New York City Department of Health and Mental Hygiene (DOHMH) have developed a machine learning computer system that uses keywords found on Yelp reviews to identify foodborne illnesses and outbreaks. Findings are published in the Journal of the American Medical Informatics Association.
Ginni Rometty, CEO of IBM, creators of the Watson AI system, spoke of AI and individual interactivity, and the fear individuals have of their positions being rendered obsolete. AI can sort potatoes into those set for French fry production, or those better suited to crisp or potato wedge products, meaning less waste. AI technology is being developed that could render fast food burger cooks obsolete through new and innovative cooking methods, entirely automated. Artificial intelligence (AI) and its impact on business was a key talking point at this year's World Economic Forum in Davos, Switzerland (Davos 2017). Speaking at an AI panel at Davos 2017, Microsoft CEO Satya Nadella discussed how simple it was to eliminate human input altogether: "its augmentation or replacement, that's a design choice.
Sadilek, Adam (University of Rochester) | Kautz, Henry (University of Rochester) | DiPrete, Lauren (Southern Nevada Health District) | Labus, Brian (Southern Nevada Health District, Las Vegas, Nevada) | Portman, Eric (University of Rochester) | Teitel, Jack (University of Rochester) | Silenzio, Vincent (University of Nevada Las Vegas,)
CDC has even identified food safety as one of seven "winnable battles"; however, progress to date has been limited. We show that adaptive inspection process is 64 percent more effective at identifying problematic venues than the current state of the art. If fully deployed, our approach could prevent over 9,000 cases of foodborne illness and 557 hospitalizations annually in Las Vegas alone. Additionally, adaptive inspections result in unexpected benefits, including the identification of venues lacking permits, contagious kitchen staff, and fewer customer complaints filed with the Las Vegas health department.
Food poisoning can strike anywhere hygiene standards are lax, but researchers have developed a new app that uses machine learning to help minimize the number of people affected. One out of every six U.S. residents gets food poisoning each year, and when they do, many of them write about it on Twitter. That's where nEmesis comes in. Developed by computer-science researchers from the University of Rochester, the software uses natural language processing and artificial intelligence to identify food poisoning-related tweets, connect them to restaurants using geotagging and identify likely hot spots. The researchers developed their app by analyzing almost 4 million tweets generated by people in the New York City metropolitan area in late 2012 and early 2013.
Using 106 restaurants as a sample size, these results show that restaurants with higher restaurant grades (administered by the NYC DOH) tend to have higher restaurant ratings. Using the same sample size of 106 restaurants, these results show that restaurants with a no words related to foodborne illnesses in their comments tend to have higher average restaurant ratings as compared to restaurants that have 1 or more words related to foodborne illnesses in their comments. Using 106 restaurants as a sample size, these results show that restaurants with higher restaurant grades (administered by the NYC DOH) tend to have higher restaurant ratings. Using the same sample size of 106 restaurants, these results show that restaurants with a no words related to foodborne illnesses in their comments tend to have higher average restaurant ratings as compared to restaurants that have 1 or more words related to foodborne illnesses in their comments.
Machine learning can predict strains of bacteria likely to cause food poisoning outbreaks, research has found. The study – which focused on harmful strains of E. coli bacteria – could help public health officials to target interventions and reduce risk to human health. The team trained the software on DNA sequences from strains isolated from cattle herds and human infections in the UK and the US. The study highlights the potential of machine learning approaches for identifying these strains early and prevent outbreaks of this infectious disease.