Artificial intelligence does not have to threaten humans; it can also work with us to solve big problems. Are you still feeling a little wary of algorithms? We rounded up a slew of stories from the past year that demonstrate the many ways in which this technology can have a positive impact. This year AI revealed its prowess as a powerful tool to help prevent climate change from wreaking irreversible damage to the planet, something that requires more than one solution. Researchers have been using AI to visualize the future effects of floods and wildfires, improve climate decision-making, monitor forests and share data. Other climate projects powered by artificial intelligence have included building digital twins of the planet to test out the impact of different warming-mitigation policies and mapping thinning sea ice.

IN RECENT YEARS, underground chemists have increasingly made small chemical tweaks on known recreational drugs to skirt laws, creating novel designer versions. Instead of cannabis, for instance, these chemists could offer up XLR-11, or instead of PCP they might have 3-MeO-PCE. Novel designer drugs, also called research chemicals or legal highs, still produce physiological and psychological effects, though experts say that they can come with a slew of risks. Synthetic opioids such as fentanyl, for instance, are increasingly cited among the number of opioid-related deaths in the United States, which reached more than 75,000 this year. According to the Centers for Disease Control and Prevention, synthetic cannabinoids can cause heart attacks, kidney failure, and, in some cases, death.

In recent years, underground chemists have increasingly made small chemical tweaks on known recreational drugs to skirt laws, creating novel designer versions. Instead of cannabis, for instance, these chemists could offer up XLR-11, or instead of PCP they might have 3-MeO-PCE. Novel designer drugs, also called research chemicals or legal highs, still produce physiological and psychological effects, though experts say that they can come with a slew of risks. Synthetic opioids such as fentanyl, for instance, are increasingly cited among the number of opioid-related deaths in the United States, which reached more than 75,000 this year. According to the Centers for Disease Control and Prevention, synthetic cannabinoids can cause heart attacks, kidney failure, and, in some cases, death.

Want to seem like the smartest member of your family these holidays? Why not brag about your vast knowledge of AI? It was a big year for artificial intelligence, so here's a round-up of Cosmos' AI favourites from 2021. A team of researchers from the University of Glasgow, UK, used machine learning algorithms to find future zoonotic (originating in animals) virus threats. According to the researchers, a major stumbling block for understanding zoonotic disease has been that scientists tend to prioritise well-known zoonotic virus families based on their common features.

Law enforcement agencies are in a race to identify and regulate new versions of dangerous psychoactive drugs such as bath salts and synthetic opioids, even as clandestine chemists work to synthesize and distribute new molecules with the same psychoactive effects as classical drugs of abuse. Identifying these so-called "legal highs" within seized pills or powders can take months, during which time thousands of people may have already used a new designer drug. But new research is already helping law enforcement agencies around the world to cut identification time down from months to days, crucial in the race to identify and regulate new versions of dangerous psychoactive drugs. "The vast majority of these designer drugs have never been tested in humans and are completely unregulated. They are a major public health concern to emergency departments across the world," says UBC medical student Dr. Michael Skinnider, who completed the research as a doctoral student at UBC's Michael Smith Laboratories.

New drugs are created all the time. And many are extremely dangerous. This is why researchers trained computers to predict what designer drugs will emerge onto the scene before they hit the market, according to a recent study published in the journal Nature Machine Intelligence. With highly-addictive drugs flooding regions throughout the U.S., this program could save countless lives. But it could also unlock an entire "dark matter" world of unknown psychoactive possibilities.

An AI tool can quickly suggest possible candidates for the chemical structures of psychoactive "designer drugs" from a simple analysis. "Our method could cut down the time required to identify a new designer drug from weeks or months to just hours," says Michael Skinnider at the University of British Columbia in Canada. Skinnider and his colleagues created a machine learning tool called DarkNPS by training it with chemical structures of around 1700 known designer drugs, collected from forensic labs around the world. The training set included tandem mass spectrometry results for each drug, which is a common technique that provides information on the mass of a molecule and the elements it contains. This allowed the AI to identify patterns between tandem mass spectrometry data and chemical structures.

An AI tool can quickly suggest possible candidates for the chemical structures of psychoactive "designer drugs" from a simple analysis. The tool could fast-track the development of lab tests which screen the use of drugs that have similar effects to substances such as cocaine and heroin, but have been designed to evade detection. "Our method could cut down the time required to identify a new designer drug from weeks or months to just hours," says Michael Skinnider at the University of British Columbia in Vancouver. Skinnider and his colleagues created a machine learning tool called DarkNPS by training it with chemical structures of around 1700 known designer drugs, collected from forensic labs around the world. The training set included tandem mass spectrometry results for each drug, which is a common technique that provides information on the mass of a molecule and the elements it contains.

A combined team of researchers from the University of British Columbia and the University of Alberta has found that at least some machine learning applications can learn from far fewer examples than has been assumed. In their paper published in the journal Nature Machine Intelligence, the group describes testing they carried out with machine learning applications created to predict certain types of molecular structures. Machine learning can be used in a wide variety of applications--one of the most well-known is learning to spot people or objects in photographs. Such applications typically require huge amounts of data for training. In this new effort, the researchers have found that in some instances, machine learning applications do not need such huge amounts of data to be useful.