420 with CNW — AI Could Help Despite Shortage of
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Posted On: 06/15/2021 5:41:27 PM
420 with CNW — AI Could Help Despite Shortage of Cannabis Data
Even though cannabis has been legal for either medical or recreational use in several states for years now, its status as a classified drug at the federal level has been a constant thorn in the state-legal cannabis industry’s side. Not only has this made navigating the business and financial world extremely challenging for cannabis businesses, but it has made any sort of research into cannabis nearly impossible.
With the young cannabis sector growing bigger every year, the lack of scientific data on marijuana poses a major problem, especially in regards to medical cannabis.
Cannabis produces more than 100 compounds called cannabinoids, but most of the existing data focuses on THC and CBD, the most abundant cannabinoids. Although most of the other cannabinoids are present in minute levels, they interact with each other in a phenomenon dubbed the “entourage effect,” affecting how each strain affects people. However, we have barely any data on these other substances of the entourage effect.
Cannabis may still be illegal at the federal level, but stakeholders and medical cannabis patients need this data now. As such, researchers from the University of Colorado Boulder cast their nets out for ways they could find reliable data on how the cannabis plant works. Their study, which was recently published in the journal “Plos One,” suggests that artificial intelligence (“AI”) could help fill the massive knowledge gap plaguing the nascent cannabis industry.
Diana Vergara, an evolutionary biologist at CU Boulder, says that although the industry treats cannabis varieties as if they were standardized, much like Girl Scout Cookies or Gorilla Glue, in actuality the smell, taste and overall effects are never standard. On top of minor cannabinoids, a wide variety of flavonoids and terpenes also impact the taste, aroma and overall effect of a cannabis plant. To get a full analysis of all the chemicals in the cannabis plant, Vergara teamed up with Brian Keagan, an assistant professor in CU Boulder’s Department of Information Science to analyze data from more than 17,600 cannabis flower cultivars.
Most of this data focused on THC and CBD, with only 153 samples containing data on the other seven main cannabinoids. Data on the minor cannabinoids was barely present, with some of them not even being measured. To figure out this missing data, the team employed machine learning, using statistical methods and algorithms to find hidden patterns in the data sets. They found that the assumption that strains with more THC have less CBD, and vice versa, was false, and that strain names are not reliable indicators of the chemical makeup or overall potency.
This is just a start, the researchers note, saying they need widespread industry cooperation if they are to use this technology to fill the sector’s knowledge gaps. Data from more cultivars, however insufficient it is, will help the researchers use AI technology to figure out chemical interactions and how they can be harnessed for human benefit. Among other things, retailers would be able to list full chemical profiles on product labels, and it would lead to a much more reliable strain-naming system.
Cannabis companies such as Hero Technologies Inc. (OTC: HENC), which are looking to genetically engineer cannabis cultivars, could massively benefit from the deployment of AI in their operations.
NOTE TO INVESTORS: The latest news and updates relating to Hero Technologies Inc. (OTC: HENC) are available in the company’s newsroom at https://cnw.fm/HENC
Please see full terms of use and disclaimers on the CannabisNewsWire website applicable to all content provided by CNW420, wherever published or re-published: http://CNW.fm/Disclaimer
Even though cannabis has been legal for either medical or recreational use in several states for years now, its status as a classified drug at the federal level has been a constant thorn in the state-legal cannabis industry’s side. Not only has this made navigating the business and financial world extremely challenging for cannabis businesses, but it has made any sort of research into cannabis nearly impossible.
With the young cannabis sector growing bigger every year, the lack of scientific data on marijuana poses a major problem, especially in regards to medical cannabis.
Cannabis produces more than 100 compounds called cannabinoids, but most of the existing data focuses on THC and CBD, the most abundant cannabinoids. Although most of the other cannabinoids are present in minute levels, they interact with each other in a phenomenon dubbed the “entourage effect,” affecting how each strain affects people. However, we have barely any data on these other substances of the entourage effect.
Cannabis may still be illegal at the federal level, but stakeholders and medical cannabis patients need this data now. As such, researchers from the University of Colorado Boulder cast their nets out for ways they could find reliable data on how the cannabis plant works. Their study, which was recently published in the journal “Plos One,” suggests that artificial intelligence (“AI”) could help fill the massive knowledge gap plaguing the nascent cannabis industry.
Diana Vergara, an evolutionary biologist at CU Boulder, says that although the industry treats cannabis varieties as if they were standardized, much like Girl Scout Cookies or Gorilla Glue, in actuality the smell, taste and overall effects are never standard. On top of minor cannabinoids, a wide variety of flavonoids and terpenes also impact the taste, aroma and overall effect of a cannabis plant. To get a full analysis of all the chemicals in the cannabis plant, Vergara teamed up with Brian Keagan, an assistant professor in CU Boulder’s Department of Information Science to analyze data from more than 17,600 cannabis flower cultivars.
Most of this data focused on THC and CBD, with only 153 samples containing data on the other seven main cannabinoids. Data on the minor cannabinoids was barely present, with some of them not even being measured. To figure out this missing data, the team employed machine learning, using statistical methods and algorithms to find hidden patterns in the data sets. They found that the assumption that strains with more THC have less CBD, and vice versa, was false, and that strain names are not reliable indicators of the chemical makeup or overall potency.
This is just a start, the researchers note, saying they need widespread industry cooperation if they are to use this technology to fill the sector’s knowledge gaps. Data from more cultivars, however insufficient it is, will help the researchers use AI technology to figure out chemical interactions and how they can be harnessed for human benefit. Among other things, retailers would be able to list full chemical profiles on product labels, and it would lead to a much more reliable strain-naming system.
Cannabis companies such as Hero Technologies Inc. (OTC: HENC), which are looking to genetically engineer cannabis cultivars, could massively benefit from the deployment of AI in their operations.
NOTE TO INVESTORS: The latest news and updates relating to Hero Technologies Inc. (OTC: HENC) are available in the company’s newsroom at https://cnw.fm/HENC
Please see full terms of use and disclaimers on the CannabisNewsWire website applicable to all content provided by CNW420, wherever published or re-published: http://CNW.fm/Disclaimer
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