{"id":1070,"date":"2022-01-14T20:37:04","date_gmt":"2022-01-14T20:37:04","guid":{"rendered":"https:\/\/mailinvest.blog\/index.php\/2022\/01\/14\/ai-weekly-what-can-ai-tell-us-about-social-unrest-virus-structures-and-carbon-emissions\/"},"modified":"2022-01-14T20:37:04","modified_gmt":"2022-01-14T20:37:04","slug":"ai-weekly-what-can-ai-tell-us-about-social-unrest-virus-structures-and-carbon-emissions","status":"publish","type":"post","link":"https:\/\/mailinvest.blog\/index.php\/2022\/01\/14\/ai-weekly-what-can-ai-tell-us-about-social-unrest-virus-structures-and-carbon-emissions\/","title":{"rendered":"AI Weekly: What can AI tell us about social unrest, virus structures, and carbon emissions?"},"content":{"rendered":"

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Applying data science to predict unrest. AI that can anticipate the next variant of COVID-19\u2019s structure. Reducing carbon emissions from planes using algorithms. That\u2019s a few of the headlines in AI this week, which ran the gamut from the dour (how AI might prevent the next attack on the U.S. Capitol) to the uplifting (making air travel greener). It\u2019s caveated optimism, but nonetheless a breath of fresh air in a community that\u2019s becoming increasingly cynical about the technology\u2019s potential to do good.<\/p>\n

Wired first reported<\/a> that a researcher at the University of North Carolina ran simulations using AI systems, including Alphabet-owned DeepMind\u2019s AlphaFold and the University of Washington\u2019s RoseTTAFold, to predict the protein structure of the Omicron variant of COVID-19. Ford managed to predict one structure that was \u201cpretty much right\u201d \u2014 an impressive feat, given that he arrived at his conclusions before scientists were able to map Omicron\u2019s structure properly.<\/p>\n

AI promises to expedite certain processes in drug discovery<\/a> and virology, for example identifying compounds to treat conditions<\/a> for which medications remain elusive. But as Sriram Subramaniam, a professor at the University of British Columbia who studied Omicron samples, told The Register,<\/a> having access to a real sample still beats algorithmic models. AI still can\u2019t predict things like the strength of new virus variants\u2019 binding to host cells, for instance, or the infectiousness of those variants.<\/p>\n

Predicting social unrest<\/h2>\n

Could AI perhaps predict events like the January 6 attack on the U.S. Capitol? A piece<\/a> in The Washington Post this week investigates the premise. While the consensus is mixed, some researchers believe that algorithms can serve as early indicators of violence in regions ahead of major political conflicts.<\/p>\n

Unrest prediction, also known as conflict prediction, is a burgeoning field in academia and industry. It and its practitioners, such as the University of Central Florida\u2019s CoupCast, aim to design systems that consider variables (e.g., the role of a leader encouraging a mob, long-term democratic history) to determine whether, for example, electoral violence might occur.<\/p>\n

Those who are bullish about the technology say that it\u2019s already revealed surprising insights, like the fact that social media conflict is an unreliable indicator of real-world unrest. But others caution that it\u2019s little better than chance in terms of accuracy \u2014 and that it could be used to justify crackdowns on peaceful protests.<\/p>\n

\u201cActors react,\u201d Roudabeh Kishi, director of innovation at the nonprofit Armed Conflict Location & Event Data Project, a group engaged in conflict prediction research, told<\/a> The Post. \u201cIf people are shifting their tactics, a model trained on historical data will miss it.\u201d<\/p>\n

Reducing jet emissions<\/h2>\n

The global aviation industry produces around 2% of all human-generated carbon dioxide emissions. If they were a country, all the airlines in the industry \u2014 some of which run thousands<\/a> of nearly-empty flights to keep valuable airport slots \u2014 would rank among the top ten in the world.<\/p>\n

Like other greenhouse gases, carbon dioxide drives climate change, leading to extreme weather, larger wildfires, disease from smog and air pollution, food supply disruptions, and other effects. In an effort to combat this, some airlines, including<\/a> Air France, Norwegian, Malaysia Airlines, Cebu Pacific, Go Air, and Atlas Air, are turning to algorithms trained on data from billions of flights to identify emissions-reducing opportunities. Openairlines\u2019 SkyBreathe \u2014 the system recently adopted by Air France \u2014 can reportedly reduce total fuel consumption by up to 5%.<\/p>\n

Other startups, like Flyways, are creating AI-powered platforms that attempt to optimize aircraft routing, giving suggestions on how and where to fly planes. During a six-month pilot program at Alaska Airlines, Flyways claims to have shaved off five minutes from flights and saved 480-thousand gallons of jet fuel on average.<\/p>\n

Some critics argue that airlines aren\u2019t going far enough; they call for a phase-out of short-haul flights in Europe, among other footprint-reducing measures. But considering<\/a> the long road ahead to meaningfully cutting the world\u2019s carbon output, every bit helps.<\/p>\n

\u201cIf you went a teeny bit slower, you were on time, you had a gate, and because you went a teeny bit slower the airplane actually burned less fuel, that might be a win\/win combination for both the guest and the operation and sustainability impact,\u201d Diana Birkett Rakow, senior VP of sustainability at Alaska Airlines, told<\/a> ABC News.<\/p>\n

For AI coverage, send news tips to\u00a0Kyle Wiggers<\/a> \u2014 and be sure to subscribe to the AI Weekly newsletter<\/a>\u00a0and bookmark our AI channel,\u00a0The Machine<\/a>.<\/p>\n

Thank<\/p>\n

AMP
s for reading,<\/p>\n

Kyle Wiggers<\/p>\n

AI Staff Writer<\/p>\n

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