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How AI could take over elections – and undermine democracy

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An AI-driven political campaign could be all things to all people. Eric Smalley, TCUS; Biodiversity Heritage Library/Flickr; Taymaz Valley/Flickr, CC BY-ND

Could organizations use artificial intelligence language models such as ChatGPT to induce voters to behave in specific ways?

Sen. Josh Hawley asked OpenAI CEO Sam Altman this question in a May 16, 2023, U.S. Senate hearing on artificial intelligence. Altman replied that he was indeed concerned that some people might use language models to manipulate, persuade and engage in one-on-one interactions with voters.

Altman did not elaborate, but he might have had something like this scenario in mind. Imagine that soon, political technologists develop a machine called Clogger – a political campaign in a black box. Clogger relentlessly pursues just one objective: to maximize the chances that its candidate – the campaign that buys the services of Clogger Inc. – prevails in an election.

While platforms like Facebook, Twitter and YouTube use forms of AI to get users to spend more time on their sites, Clogger's AI would have a different objective: to change people's voting behavior.

How Clogger would work

As a political scientist and a legal scholar who study the intersection of technology and democracy, we believe that something like Clogger could use automation to dramatically increase the scale and potentially the effectiveness of behavior manipulation and microtargeting techniques that political campaigns have used since the early 2000s. Just as advertisers use your browsing and social media history to individually target commercial and political ads now, Clogger would pay attention to you – and hundreds of millions of other voters – individually.

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It would offer three advances over the current state-of-the-art algorithmic behavior manipulation. First, its language model would generate messages — texts, social media and email, perhaps images and videos — tailored to you personally. Whereas advertisers strategically place a relatively small number of ads, language models such as ChatGPT can generate countless unique messages for you personally – and millions for others – over the course of a campaign.

Second, Clogger would use a technique called reinforcement learning to generate a succession of messages that become increasingly more likely to change your vote. Reinforcement learning is a machine-learning, trial-and-error approach in which the computer takes actions and gets feedback about which work better in order to learn how to accomplish an objective. Machines that can play Go, Chess and many video games better than any human have used reinforcement learning.

How reinforcement learning works.

Third, over the course of a campaign, Clogger's messages could evolve in order to take into account your responses to the machine's prior dispatches and what it has learned about changing others' minds. Clogger would be able to carry on dynamic “conversations” with you – and millions of other people – over time. Clogger's messages would be similar to ads that follow you across different websites and social media.

The nature of AI

Three more features – or bugs – are worth noting.

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First, the messages that Clogger sends may or may not be political in content. The machine's only goal is to maximize vote share, and it would likely devise strategies for achieving this goal that no human campaigner would have thought of.

One possibility is sending likely opponent voters information about nonpolitical passions that they have in or entertainment to bury the political messaging they receive. Another possibility is sending off-putting messages – for example incontinence advertisements – timed to coincide with opponents' messaging. And another is manipulating voters' social media friend groups to give the sense that their social circles its candidate.

Second, Clogger has no regard for truth. Indeed, it has no way of knowing what is true or false. Language model “hallucinations” are not a problem for this machine because its objective is to change your vote, not to provide accurate information.

Third, because it is a black box type of artificial intelligence, people would have no way to know what strategies it uses.

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The field of explainable AI aims to open the black box of many machine-learning models so people can understand how they work.

Clogocracy

If the Republican presidential campaign were to deploy Clogger in 2024, the Democratic campaign would likely be compelled to respond in kind, perhaps with a similar machine. Call it Dogger. If the campaign managers thought that these machines were effective, the presidential contest might well down to Clogger vs. Dogger, and the winner would be the client of the more effective machine.

Political scientists and pundits would have much to say about why one or the other AI prevailed, but likely no one would really know. The president will have been elected not because his or her policy proposals or political ideas persuaded more Americans, but because he or she had the more effective AI. The content that won the day would have come from an AI focused solely on victory, with no political ideas of its own, rather than from candidates or parties.

In this very important sense, a machine would have won the election rather than a person. The election would no longer be democratic, even though all of the ordinary activities of democracy – the speeches, the ads, the messages, the voting and the counting of votes – will have occurred.

The AI-elected president could then go one of two ways. He or she could use the mantle of election to pursue Republican or Democratic party policies. But because the party ideas may have had little to do with why people voted the way that they did – Clogger and Dogger don't care about policy views – the president's actions would not necessarily reflect the will of the voters. Voters would have been manipulated by the AI rather than freely choosing their political leaders and policies.

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Another path is for the president to pursue the messages, behaviors and policies that the machine predicts will maximize the chances of reelection. On this path, the president would have no particular platform or agenda beyond maintaining power. The president's actions, guided by Clogger, would be those most likely to manipulate voters rather than serve their genuine interests or even the president's own ideology.

Avoiding Clogocracy

It would be possible to avoid AI election manipulation if candidates, campaigns and consultants all forswore the use of such political AI. We believe that is unlikely. If politically effective black boxes were developed, the temptation to use them would be almost irresistible. Indeed, political consultants might well see using these tools as required by their professional responsibility to help their candidates win. And once one candidate uses such an effective tool, the opponents could hardly be expected to resist by disarming unilaterally.

Enhanced privacy protection would help. Clogger would depend on access to vast amounts of personal data in order to target individuals, craft messages tailored to persuade or manipulate them, and track and retarget them over the course of a campaign. Every bit of that information that companies or policymakers deny the machine would make it less effective.

Strong data privacy laws could help steer AI away from being manipulative.

Another solution lies with elections commissions. They could try to ban or severely regulate these machines. There's a fierce debate about whether such “replicant” speech, even if it's political in nature, can be regulated. The U.S.'s extreme free speech tradition leads many leading academics to say it cannot.

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But there is no reason to automatically extend the First Amendment's protection to the product of these machines. The nation might well choose to give machines rights, but that should be a grounded in the challenges of today, not the misplaced assumption that James Madison's views in 1789 were intended to apply to AI.

European Union regulators are moving in this direction. Policymakers revised the European Parliament's draft of its Artificial Intelligence Act to designate “AI systems to influence voters in campaigns” as “high risk” and subject to regulatory scrutiny.

One constitutionally safer, if smaller, step, already adopted in part by European internet regulators and in California, is to prohibit bots from passing themselves off as people. For example, regulation might require that campaign messages come with disclaimers when the content they contain is generated by machines rather than humans.

This would be like the advertising disclaimer requirements – “Paid for by the Sam Jones for Committee” – but modified to reflect its AI origin: “This AI-generated was paid for by the Sam Jones for Congress Committee.” A stronger version could require: “This AI-generated message is being sent to you by the Sam Jones for Congress Committee because Clogger has predicted that doing so will increase your chances of voting for Sam Jones by 0.0002%.” At the very least, we believe voters deserve to know when it is a bot speaking to them, and they should know why, as well.

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The possibility of a system like Clogger shows that the path toward human collective disempowerment may not require some superhuman artificial general intelligence. It might just require overeager campaigners and consultants who have powerful new tools that can effectively push millions of people's many buttons.

Learn what you need to know about artificial intelligence by signing up for our newsletter series of four emails delivered over the course of a week. You can read all our stories on generative AI at TheConversation.com.

Archon Fung consults for Apple .

Lawrence Lessig does not work for, consult, own shares in or receive from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

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By: Archon Fung, Professor of Citizenship and Self-, Harvard Kennedy School
Title: How AI could take over elections – and undermine democracy
Sourced From: theconversation.com/how-ai-could-take-over-elections-and-undermine-democracy-206051
Published Date: Fri, 02 Jun 2023 13:42:24 +0000

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Nationwide test of Wireless Emergency Alert system could test people’s patience – or help rebuild public trust in the system

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Nationwide test of Wireless Emergency Alert system could test people's patience – or help rebuild public trust in the system

A message like this should pop up on your phone on Oct. 4, 2023.
Jeff Greenberg/Education Images/Universal Images Group via Getty Images

Elizabeth Ellcessor, University of Virginia and Hamilton Bean, University of Colorado Denver

The Wireless Emergency Alert system is to have its third nationwide test on Oct. 4, 2023. The Wireless Emergency Alert system is a public safety system that allows authorities to alert people via their mobile devices of dangerous weather, missing and other situations requiring public attention.

Similar tests in 2018 and 2021 caused a degree of public confusion and resistance. In addition, there was confusion around the first test of the U.K. system in April 2023, and an outcry surrounding accidental alert messages such as those sent in Hawaii in January 2018 and in Florida in April 2023.

The federal government lists five types of emergency alerts: National (formerly labeled Presidential), Imminent Threat, Public Safety, America's Missing: Emergency Response (Amber), and Opt-in Test Messages. You can opt out of any except National Alerts, which are reserved for national emergencies. The Oct. 4 test is a National Alert.

We are a media studies researcher and a communications researcher who study emergency alert systems. We believe that concerns about previous tests raise two questions: Is public trust in emergency alerting eroding? And how might the upcoming test rebuild it?

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Confusion and resistance

In an ever-updating digital media environment, emergency alerts appear as part of a constant stream of updates, buzzes, reminders and notifications on people's smartphones. Over-alerting is a common fear in emergency management circles because it can lead people to ignore alerts and not take needed action. The sheer volume of different updates can be similarly overwhelming, burying emergency alerts in countless other messages. Many people have even opted out of alerts when possible, rummaging through settings and toggling off every alert they can find.

Even when people alerts, however, there is potential for confusion and rejection. All forms of emergency alerts rely on the recipients' trust in the people or organization responsible for the alert. But it's not always clear who the sender is. As one emergency manager explained to one of us regarding alerts used during COVID-19: “People were more confused because they got so many different notifications, especially when they don't say who they're from.”

When the origin of an alert is unclear, or the recipient perceives it to have a political bias counter to their own views, people may become confused or resistant to the message. Prior tests and use of the Wireless Emergency Alert system have indicated strong anti-authority attitudes, particularly following the much-derided 2018 test of what was then called the Presidential Alert message class. There are already conspiracy theories online about the upcoming test.

People receive mobile alerts from then- in a ‘Saturday Night Live' sketch aired on Oct. 6, 2018.

Trust in alerts is further reduced by the overall lack of testing and public awareness work done on behalf of the Wireless Emergency Alert system since its launch in June 2012. As warning expert Dennis Mileti explained in his 2018 Federal Emergency Management Agency PrepTalk, routine public tests are essential for warning systems' effectiveness. However, the Wireless Emergency Alert system has been tested at the national level only twice, and there has been little public outreach to explain the system by either the government or technology companies.

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More exposure and info leads to more trust

The upcoming nationwide test may offer a moment that could rebuild trust in the system. A survey administered in the days immediately following the 2021 national test found that more respondents believed that the National Alert message class label would signal more trustworthy information than the Presidential Alert message class label.

Similarly, in contrast to the 2021 test, which targeted only select users, the Oct. 4 test is slated to reach all compatible devices in the U.S. Since users cannot opt out of the National Alert message class, this week's test is a powerful to build awareness about the potential of a functional federal emergency alert system.

The Oct. 4 test message is expected to state, “THIS IS A TEST of the National Wireless Emergency Alert system. No action is needed.” We instead suggest that action is, in fact, urgently needed to help people better understand the rapidly changing mobile alert and warning ecosystem that confronts them. Familiarity with this system is what will allow it to public health and safety, and address the crises of the 21st century.

Here are steps that you can take now to help make the Wireless Emergency Alert system more effective:

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  • The Wireless Emergency Alert system is only one form of emergency alert. Identify which mobile notification systems are used by your local emergency management : , fire and emergency services. Know which systems are opt-in and opt-out, and opt in to those needed. Ensure access to other sources of information during an emergency, such as local radio and television, or National Oceanic and Atmospheric Administration weather radio.

  • Understand the meaning of mobile device notification settings. Just because you are opted in to “Emergency Alerts” on your cellphone does not necessarily mean you are signed up to receive notifications from local authorities. Check the FEMA website for information about the Wireless Emergency Alert system and your local emergency management organizations' websites about opt-in systems.

  • Have a plan for contacting family, friends and neighbors during an emergency. Decide in advance who will help the vulnerable members of your community.

  • Find out if your local emergency management organizations test their alert systems, and make sure to receive those local tests.

  • Anticipate the possibility that mobile systems will be damaged or unavailable during a crisis and prepare essentials for sheltering in place or quick evacuation.

Finally, push back on the lack of information and rise of misinformation about alerts by sharing reliable information about emergency alerts with your family and friends.The Conversation

Elizabeth Ellcessor, Associate Professor of Media Studies, University of Virginia and Hamilton Bean, Associate Professor of Communication, University of Colorado Denver

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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Superconductivity at room temperature remains elusive a century after a Nobel went to the scientist who demonstrated it below -450 degrees Fahrenheit

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Superconductivity at room temperature remains elusive a century after a Nobel went to the scientist who demonstrated it below -450 degrees Fahrenheit

Photograph of the first Solvay Conference in 1911 at the Hotel Metropole. Heike Kamerlingh Onnes is standing third from the right.
Benjamin Couprie/Wikimedia Commons

David D. Nolte, Purdue University

On April 8, 1911, Dutch physicist Heike Kamerlingh Onnes scribbled in pencil an almost unintelligible note into a kitchen notebook: “near enough null.”

The note referred to the electrical resistance he'd measured during a landmark experiment that would later be credited as the discovery of superconductivity. But first, he and his team would need many more trials to confirm the measurement.

Their discovery opened up a world of potential scientific applications. The century since has seen many advances, but superconductivity researchers can take lessons from Onnes' original, Nobel Prize-winning work.

I have always been interested in origin stories. As a physics professor and the author of books on the history of physics, I look for the interesting backstory – the twists, turns and serendipities that lie behind great discoveries.

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The true stories behind these discoveries are usually more chaotic than the rehearsed narratives crafted after the fact, and some of the lessons learned from Onnes' experiments remain relevant today as researchers search for new superconductors that might, one day, operate near room temperature.

Superconductivity

A rare quantum effect that allows electrical currents to flow without resistance in superconducting wires, superconductivity allows for a myriad of scientific applications. These include MRI machines and powerful particle accelerators.

Imagine giving a single push to a row of glass beads strung on a frictionless wire. Once the beads start moving down the wire, they never stop, like a perpetual motion machine. That's the idea behind superconductivity – particles flowing without resistance.

Superconductivity happens when a current experiences no electrical resistance.

For superconductors to work, they need to be cooled to ultra-low temperatures colder than any Arctic blast. That's how Onnes' original work cooling helium to near absolute zero temperature set the stage for his unexpected discovery of superconductivity.

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The discovery

Onnes, a physics professor at the of Leiden in the Netherlands, built the leading low-temperature physics laboratory in the world in the first decade of the 20th century.

His lab was the first to turn helium from a gas to a liquid by making the gas expand and cool. His lab managed to cool helium this way to a temperature of -452 degrees Farenheit (-269 degrees Celsius).

Onnes then began studying the electrical conductivity of metals at these cold temperatures. He started with mercury because mercury in liquid form can conduct electricity, making it easy to fill into glass tubes. At low temperatures, the mercury would freeze solid, creating metallic wires that Onnes could use in his conductivity experiments.

On April 8, 1911, his lab technicians transferred liquid helium into a measurement cryostat – a glass container with a vacuum jacket to insulate it from the room's heat. They cooled the helium to -454 F (-270 C) and then measured the electrical resistance of the mercury wire by sending a small current through it and measuring the voltage.

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It was then that Onnes wrote the cryptic “near enough null” measurement into his kitchen notebook, meaning that the wire was conducting electricity without any measurable resistance.

That date of April 8 is often quoted as the discovery of superconductivity, but the full story isn't so simple, because scientists can't accept a scribbled “near-enough null” as sufficient proof of a new discovery.

In pursuit of proof

Onnes' team performed its next experiment more than six weeks later, on May 23. On this day, they cooled the cryostat again to -454 F (-270 C) and then let the temperature slowly rise.

At first they barely measured any electrical resistance, indicating superconductivity. The resistance stayed small up to -452 F, when it suddenly rose by over a factor of 400 as the temperature inched up just a fraction of a degree.

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The rise was so rapid and so unexpected that they started searching for some form of electrical fault or open circuit that might have been caused by the temperature shifts. But they couldn't find anything wrong. They spent five more months improving their system before again. On Oct. 26 they repeated the experiment, capturing the earlier sudden rise in resistance.

A graph with the resistence of Mercury on the y axis and temperature on the x axis, showing a sharp drop.
The resistance of mercury as recorded on Oct. 26, 1911, by Onnes' lab.
Heike Kamerlingh Onnes via Wikimedia Commons

One later, Onnes presented these results to the first Solvay Conference, and two years later he received his Nobel Prize in physics, recognizing his low-temperature work generally but not superconductivity specifically.

It took another three years of diligent work before Onnes had his irrefutable evidence: He measured persistent currents that did not decay, demonstrating truly zero resistance and superconductivity on April 24, 1914.

New frontiers for critical temperatures

In the decades Onnes' discovery, many researchers have explored how metals act at supercooled temperatures and have learned more about superconductivity.

But if researchers can observe superconductivity only at super low temperatures, it's hard to make anything useful. It is too expensive to operate a machine practically if it works only at -400 F (-240 C).

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So, scientists began searching for superconductors that can work at practical temperatures. For instance, K. Alex Müller and J. Georg Bednorz at the IBM research laboratory in Switzerland figured out that metal oxides like lanthanum-barium-copper oxide, known as LBCO, could be good candidates.

It took the IBM team about three years to find superconductivity in LBCO. But when they did, their work set a new record, with superconductivity observed at -397 F (-238 C) in 1986.

A year later, in 1987, a lab in Houston replaced lanthanum in LBCO with the element yttrium to create YBCO. They demonstrated superconductivity at -292 F. This discovery made YBCO the first practical superconductor, because it could work while immersed in inexpensive liquid nitrogen.

Since then, researchers have observed superconductivity at temperatures as high as -164 F (-109 C), but achieving a room-temperature superconductor has remained elusive.

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Chart of the discoveries of new superconductors plotted as critical temperature versus year of discovery, with each discovery labeled with a shape, color and abbreviation.
Timeline of accomplishments in superconductivity research.
Gingras.ol/Wikimedia Commons, CC BY-NC-SA

In 2023, two groups claimed they had evidence for room-temperature superconductivity, though both reports have been met with sharp skepticism, and both are now in limbo following further scrutiny.

Superconductivity has always been tricky to prove because some metals can masquerade as superconductors. The lessons learned by Onnes a century ago – that these discoveries require time, patience and, most importantly, proof of currents that never stop – are still relevant today.The Conversation

David D. Nolte, Distinguished Professor of Physics and Astronomy, Purdue University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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Tenacious curiosity in the lab can lead to a Nobel Prize – mRNA research exemplifies the unpredictable value of basic scientific research

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Tenacious curiosity in the lab can lead to a Nobel Prize – mRNA research exemplifies the unpredictable value of basic scientific research

Basic research often involves lab work that won't be appreciated until decades down the line.
Sebastian Condrea/Moment via Getty Images

André O. Hudson, Rochester Institute of Technology

The 2023 Nobel Prize in physiology or medicine will go to Katalin Karikó and Drew Weissman for their discovery that modifying mRNA – a form of genetic material your body uses to produce proteins – could reduce unwanted inflammatory responses and allow it to be delivered into cells. While the impact of their findings may not have been apparent at the time of their breakthrough over a decade ago, their work paved the way for the of the Pfizer-BioNTech and Moderna COVID-19 vaccines, as well as many other therapeutic applications currently in development.

We asked André O. Hudson, a biochemist and microbiologist at the Rochester Institute of Technology, to explain how basic research like that of this year's Nobel Prize winners provides the foundations for science – even when its far-reaching effects won't be felt until years later.

What is basic science?

Basic research, sometimes called fundamental research, is a type of investigation with the overarching goal of understanding natural phenomena like how cells work or how birds can fly. Scientists are asking the fundamental questions of how, why, when, where and if in order to bridge a gap in curiosity and understanding about the natural world.

Researchers sometimes conduct basic research with the hope of eventually developing a technology or drug based on that work. But what many scientists typically do in academia is ask fundamental questions with answers that may or may not ever to practical applications.

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Humans, and the animal kingdom as a whole, are wired to be curious. Basic research scratches that itch.

What are some basic science discoveries that went on to have a big influence on medicine?

The 2023 Nobel Prize in physiology or medicine acknowledges basic science work done in the early 2000s. Karikó and Weissman's discovery about modifying mRNA to reduce the body's inflammatory response to it allowed other researchers to leverage it to make improved vaccines.

Another example is the discovery of antibiotics, which was based on an unexpected observation. In the late 1920s, the microbiologist Alexander Fleming was growing a species of bacteria in his lab and found that his Petri dish was accidentally contaminated with the fungus Penicillium notatum. He noticed that wherever the fungus was growing, it impeded or inhibited the growth of the bacteria. He wondered why that was happening and subsequently went on to isolate penicillin, which was approved for medical use in the early 1940s.

This work fed into more questions that ushered in the age of antibiotics. The 1952 Nobel Prize in physiology or medicine was awarded to Selman Waksman for his discovery of streptomycin, the first antibiotic to treat tuberculosis.

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Penicillin was discovered by .

Basic research often involves seeing something surprising, wanting to understand why and deciding to investigate further. Early discoveries start from a basic observation, asking the simple question of “How?” Only later are they parlayed into a medical technology that helps humanity.

Why does it take so long to get from curiosity-driven basic science to a new product or technology?

The mRNA modification discovery could be considered to be on a relatively fast track from basic science to application. Less than 15 years passed between Karikó and Weissman's findings and the vaccines. The importance of their discovery came to the forefront with the pandemic and the millions of lives they saved.

Most basic research won't reach the market until several decades after its initial publication in a science journal. One reason is because it depends on need. For example, orphan diseases that affect only a small number of people will get less attention and than conditions that are ubiquitous in a population, like cancer or diabetes. Companies don't want to spend billions of dollars developing a drug that will only have a small return on their investment. Likewise, because the return on investment for basic research often isn't clear, it can be a hard sell to financially.

Another reason is cultural. Scientists are trained to chase after funding and support for their work wherever they can find it. But sometimes that's not as easy as it seems.

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A good example of this was when the human genome was first sequenced in the early 2000s. A lot of people thought that having access to the full sequence would lead to treatments and cures for many different diseases. But that has not been the case, because there are many nuances to translating basic research to the clinic. What works in a cell or an animal might not translate into people. There are many steps and layers in the to get there.

Why is basic science important?

For me, the most critical reason is that basic research is how we train and mentor future scientists.

In an academic setting, telling “Let's go develop an mRNA vaccine” versus “How does mRNA work in the body” influences how they approach science. How do they design experiments? Do they start the study going forward or backward? Are they argumentative or cautious in how they present their findings?

Close-up of scientist wearing nitrile gloves looking into microscope hovering over Petri dish
There are many steps between translating findings in a lab to the clinic.
Marco VDM/E+ via Getty Images

Almost every scientist is trained under a basic research umbrella of how to ask questions and go through the scientific method. You need to understand how, when and where mRNAs are modified before you can even begin to develop an mRNA vaccine. I believe the best way to inspire future scientists is to encourage them to expand on their curiosity in order to make a difference.

When I was writing my dissertation, I was relying on studies that were published in the late 1800s and early 1900s. Many of these studies are still cited in scientific articles today. When researchers share their work, though it may not be today or tomorrow, or 10 to 20 years from now, it will be of use to someone else in the future. You'll make a future scientist's job a little bit easier, and I believe that's a great legacy to have.

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What is a common misconception about basic science?

Because any immediate use for basic science can be very hard to see, it's easy to think this kind of research is a waste of money or time. Why are scientists breeding mosquitoes in these labs? Or why are researchers studying migratory birds? The same argument has been made with astronomy. Why are we spending billions of dollars putting things into ? Why are we looking to the edge of the universe and studying when they are millions and billions of light years away? How does it affect us?

There is a need for more scientific literacy because not having it can make it difficult to understand why basic research is necessary to future breakthroughs that will have a major effect on society.

In the short term, the worth of basic research can be hard to see. But in the long term, history has shown that a lot of what we take for granted now, such as common medical equipment like X-rays, lasers and MRIs, came from basic things people discovered in the lab.

And it still goes down to the fundamental questions – we're a species that seeks answers to things we don't know. As long as curiosity is a part of humanity, we're always going to be seeking answers.The Conversation

André O. Hudson, Dean of the College of Science, Professor of Biochemistry, Rochester Institute of Technology

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This article is republished from The Conversation under a Creative Commons license. Read the original article.

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