Anjana Susarla, Michigan State University

It’s not surprising that technology regulation is an important issue in the 2024 U.S. presidential campaign.

The past decade has seen advanced technologies, from social media algorithms to large language model artificial intelligence systems, profoundly affect society. These changes, which spanned the Trump and Biden-Harris administrations, spurred calls for the federal government to regulate the technologies and the powerful corporations that wield them.

As a researcher of information systems and AI, I examined both candidates’ records on technology regulation. Here are the important differences.

Algorithmic harms

With artificial intelligence now widespread, governments worldwide are grappling with how to regulate various aspects of the technology. The candidates offer different visions for U.S. AI policy. One area where there is a stark difference is in recognizing and addressing algorithmic harms from the widespread use of AI technology.

AI affects your life in ways that might escape your notice. Biases in algorithms used for lending and hiring decisions could end up reinforcing a vicious cycle of discrimination. For example, a student who can’t get a loan for college would then be less likely to get the education needed to pull herself out of poverty.

At the AI Safety Summit in the U.K. in November 2023, Harris spoke of the promise of AI but also the perils from algorithmic bias, deepfakes and wrongful arrests. Biden signed an executive order on AI on Oct. 30, 2023, that recognized AI systems can pose unacceptable risks of harm to civil and human rights and individual well-being. In parallel, federal agencies such as the Federal Trade Commission have carried out enforcement actions to guard against algorithmic harms.

By contrast, the Trump administration did not take a public stance on mitigation of algorithmic harms. Trump has said he wants to repeal President Biden’s AI executive order. In recent interviews, however, Trump noted the dangers from technologies such as deepfakes and challenges posed to security from AI systems, suggesting a willingness to engage with the growing risks from AI.

Technological standards

The Trump administration signed the American AI Initiative executive order on Feb. 11, 2019. The order pledged to double AI research investment and established the first set of national AI research institutes. The order also included a plan for AI technical standards and established guidance for the federal government’s use of AI. Trump also signed an executive order on Dec. 3, 2020, promoting the use of trustworthy AI in the federal government.

The Biden-Harris administration has tried to go further. Harris convened the heads of Google, Microsoft and other tech companies at the White House on May 4, 2023, to undertake a set of voluntary commitments to safeguard individual rights. The Biden administration’s executive order contains an important initiative to probe the vulnerablity of very large-scale, general-purpose AI models trained on massive amounts of data. The goal is to determine the risks hackers pose to these models, including the ones that power OpenAI’s popular ChatGPT and DALL-E.

Antitrust

Antitrust law enforcement – restricting or conditioning mergers and acquisitions – is another way the federal government regulates the technology industry.

The Trump administration’s antitrust dossier includes its attempt to block AT&T’s acquisition of Time Warner. The merger was eventually allowed by a federal judge after the FTC under the Trump administration filed a suit to block the deal. The Trump administration also filed an antitrust case against Google focused on its dominance in internet search.

Biden signed an executive order on July 9, 2021, to enforce antitrust laws arising from the anticompetitive effects of dominant internet platforms. The order also targeted the acquisition of nascent competitors, the aggregation of data, unfair competition in attention markets and the surveillance of users. The Biden-Harris administration has filed antitrust cases against Apple and Google.

The Biden-Harris administration’s merger guidelines in 2023 outlined rules to determine when mergers can be considered anticompetitive. While both administrations filed antitrust cases, the Biden administration’s antitrust push appears stronger in terms of its impact in potentially reorganizing or even orchestrating a breakup of dominant companies such as Google.

Cryptocurrency

The candidates have different approaches to cryptocurrency regulation. Late in his administration, Trump tweeted in support of cryptocurrency regulation. Also late in Trump’s administration, the federal Financial Crimes Enforcement Network proposed regulations that would have required financial firms to collect the identity of any cryptocurrency wallet to which a user sent funds. The regulations were not enacted.

Trump has since shifted his position on cryptocurrencies. He has criticized existing U.S. laws and called for the United States to be a Bitcoin superpower. The Trump campaign is the first presidential campaign to accept payments in cryptocurrencies.

The Biden-Harris administration, by contrast, has laid out regulatory restrictions on cryptocurrencies with the Securities and Exchange Commission, which brought about a series of enforcement actions. The White House vetoed the Financial Innovation and Technology for the 21st Century Act that aimed to clarify accounting for cryptocurrencies, a bill favored by the cryptocurrency industry.

Data privacy

Biden’s AI executive order calls on Congress to adopt privacy legislation, but it does not provide a legislative framework to do so. The Trump White House’s American AI Initiative executive order mentions privacy only in broad terms, calling for AI technologies to uphold “civil liberties, privacy, and American values.” The order did not mention how existing privacy protections would be enforced.

Across the U.S., several states have tried to pass legislation addressing aspects of data privacy. At present, there is a patchwork of statewide initiatives and a lack of comprehensive data privacy legislation at the federal level.

The paucity of federal data privacy protections is a stark reminder that while the candidates are addressing some of the challenges posed by developments in AI and technology more broadly, a lot still remains to be done to regulate technology in the public interest.

Overall, the Biden administration’s efforts at antitrust and technology regulation seem broadly aligned with the goal of reining in technology companies and protecting consumers. It’s also reimagining monopoly protections for the 21st century. This seems to be the chief difference between the two administrations.

Anjana Susarla, Professor of Information Systems, Michigan State University

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

Sophie Blondel, University of Tennessee

Fusion energy has the potential to be an effective clean energy source, as its reactions generate incredibly large amounts of energy. Fusion reactors aim to reproduce on Earth what happens in the core of the Sun, where very light elements merge and release energy in the process. Engineers can harness this energy to heat water and generate electricity through a steam turbine, but the path to fusion isn’t completely straightforward.

Controlled nuclear fusion has several advantages over other power sources for generating electricity. For one, the fusion reaction itself doesn’t produce any carbon dioxide. There is no risk of meltdown, and the reaction doesn’t generate any long-lived radioactive waste.

I’m a nuclear engineer who studies materials that scientists could use in fusion reactors. Fusion takes place at incredibly high temperatures. So to one day make fusion a feasible energy source, reactors will need to be built with materials that can survive the heat and irradiation generated by fusion reactions.

Fusion material challenges

Several types of elements can merge during a fusion reaction. The one most scientists prefer is deuterium plus tritium. These two elements have the highest likelihood of fusing at temperatures that a reactor can maintain. This reaction generates a helium atom and a neutron, which carries most of the energy from the reaction.

Humans have successfully generated fusion reactions on Earth since 1952 – some even in their garage. But the trick now is to make it worth it. You need to get more energy out of the process than you put in to initiate the reaction.

Fusion reactions happen in a very hot plasma, which is a state of matter similar to gas but made of charged particles. The plasma needs to stay extremely hot – over 100 million degrees Celsius – and condensed for the duration of the reaction.

To keep the plasma hot and condensed and create a reaction that can keep going, you need special materials making up the reactor walls. You also need a cheap and reliable source of fuel.

While deuterium is very common and obtained from water, tritium is very rare. A 1-gigawatt fusion reactor is expected to burn 56 kilograms of tritium annually. But the world has only about 25 kilograms of tritium commercially available.

Researchers need to find alternative sources for tritium before fusion energy can get off the ground. One option is to have each reactor generating its own tritium through a system called the breeding blanket.

The breeding blanket makes up the first layer of the plasma chamber walls and contains lithium that reacts with the neutrons generated in the fusion reaction to produce tritium. The blanket also converts the energy carried by these neutrons to heat.

Fusion devices also need a divertor, which extracts the heat and ash produced in the reaction. The divertor helps keep the reactions going for longer.

These materials will be exposed to unprecedented levels of heat and particle bombardment. And there aren’t currently any experimental facilities to reproduce these conditions and test materials in a real-world scenario. So, the focus of my research is to bridge this gap using models and computer simulations.

From the atom to full device

My colleagues and I work on producing tools that can predict how the materials in a fusion reactor erode, and how their properties change when they are exposed to extreme heat and lots of particle radiation.

As they get irradiated, defects can form and grow in these materials, which affect how well they react to heat and stress. In the future, we hope that government agencies and private companies can use these tools to design fusion power plants.

Our approach, called multiscale modeling, consists of looking at the physics in these materials over different time and length scales with a range of computational models.

We first study the phenomena happening in these materials at the atomic scale through accurate but expensive simulations. For instance, one simulation might examine how hydrogen moves within a material during irradiation.

From these simulations, we look at properties such as diffusivity, which tells us how much the hydrogen can spread throughout the material.

We can integrate the information from these atomic level simulations into less expensive simulations, which look at how the materials react at a larger scale. These larger-scale simulations are less expensive because they model the materials as a continuum instead of considering every single atom.

The atomic-scale simulations could take weeks to run on a supercomputer, while the continuum one will take only a few hours.

All this modeling work happening on computers is then compared with experimental results obtained in laboratories.

For example, if one side of the material has hydrogen gas, we want to know how much hydrogen leaks to the other side of the material. If the model and the experimental results match, we can have confidence in the model and use it to predict the behavior of the same material under the conditions we would expect in a fusion device.

If they don’t match, we go back to the atomic-scale simulations to investigate what we missed.

Additionally, we can couple the larger-scale material model to plasma models. These models can tell us which parts of a fusion reactor will be the hottest or have the most particle bombardment. From there, we can evaluate more scenarios.

For instance, if too much hydrogen leaks through the material during the operation of the fusion reactor, we could recommend making the material thicker in certain places, or adding something to trap the hydrogen.

Designing new materials

As the quest for commercial fusion energy continues, scientists will need to engineer more resilient materials. The field of possibilities is daunting – engineers can manufacture multiple elements together in many ways.

You could combine two elements to create a new material, but how do you know what the right proportion is of each element? And what if you want to try mixing five or more elements together? It would take way too long to try to run our simulations for all of these possibilities.

Thankfully, artificial intelligence is here to assist. By combining experimental and simulation results, analytical AI can recommend combinations that are most likely to have the properties we’re looking for, such as heat and stress resistance.

The aim is to reduce the number of materials that an engineer would have to produce and test experimentally to save time and money.

Sophie Blondel, Research Assistant Professor of Nuclear Engineering, University of Tennessee

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

Victor Ambros, UMass Chan Medical School

The 2024 Nobel Prize in physiology or medicine goes to Victor Ambros and Gary Ruvkun for their discovery of microRNA, tiny biological molecules that tell the cells in your body what kind of cell to be by turning on and off certain genes.

The Conversation Weekly podcast caught up with Victor Ambros from his lab at the UMass Chan Medical School to learn more about the Nobel-winning research and what comes next. Below are edited excerpts from the podcast.

How did you start thinking about this fundamental question at the heart of the discovery of microRNA, about how cells get the instructions to do what they do?

The paper that described this discovery was published in 1993. In the late 1980s, we were working in the field of developmental biology, studying C. elegans as a model organism for animal development. We were using genetic approaches, where mutations that caused developmental abnormalities were then followed up to try to understand what the gene was that was mutated and what the gene product was.

It was well understood that proteins could mediate changes in gene expression as cells differentiate, divide.

We were not looking for the involvement of any sort of unexpected kind of molecular mechanisms. The fact that the microRNA was the product of this gene that was regulating this other gene in this context was a complete surprise.

There was no reason to postulate that there should be such regulators of gene expression. This is one of those examples where the expectations are that you’re going to find out about more complexity and nuance about mechanisms that we already know about.

But sometimes surprises emerge, and in fact, surprises emerge perhaps surprisingly often.

These C. elegans worms, nematodes, is there something about them that allows you to work with their genetic material more easily? Why are they so key to this type of science?

C. elegans was developed as an experimental organism that people could use easily to, first, identify mutants and then study the development.

It only has about a thousand cells, and all those cells can be seen easily through a microscope in the living animal. But still it has all the various parts that are important to all animals: intestine, skin, muscles, a brain, sensory systems and complex behavior. So it’s quite an amazing system to study developmental processes and mechanisms really on the level of individual cells and what those cells do as they divide and differentiate during development.

Listen to Victor Ambros on The Conversation Weekly podcast.

You were looking at this lin-4 gene. What was your surprising discovery that led to this Nobel Prize?

In our lab, Rosalind Lee and Rhonda Feinbaum were working on this project for several years. This is a very labor intensive process, trying to track down a gene.

And all we had to go by was a mutation to guide us as we gradually homed in on the DNA sequence that contained the gene. The surprises started to emerge when we found that the pieces of DNA that were sufficient to confer the function of this gene and rescue a mutant were really small, only 800 base pairs.

And so that suggested, well, the gene is small, so the product of this gene is going to be pretty small. And then Rosalind worked to pare down the sequence more and to mutate potential protein coding sequences in that little piece of DNA. By a process of elimination, she finally showed that there was no protein that could be expressed from this gene.

And at the same time, we identified this very, very small transcript of only 22 nucleotides. So I would say there was probably a period of a week or two there where these realizations came to the fore and we knew we had something new.

You mentioned Rosalind, she’s your wife.

Yeah, we’ve been together since 1976. And we started to work together in the mid-’80s. And so we’re still working together today.

And she was the first author on that paper.

That’s right. It’s hard to express how wonderful it is to receive such validation of this work that we did together. That is just priceless.

Like it’s a Nobel Prize for her too?

Yes, every Nobel Prize has this obvious limitation of the number of people that they give it to. But, of course, behind that are the folks who worked in the lab – the teams that are actually behind the discoveries are surprisingly large sometimes. In this case, two people in my lab and several people in Gary Ruvkun’s lab.

In a way they’re really the heroes behind this. Our job – mine and Gary’s – is to stand in as representatives of this whole enterprise of science, which is so, so dependent upon teams, collaborations, brainstorming amongst multiple people, communications of ideas and crucial data, you know, all this is part of the process that underlies successful science.

That first week of the discoveries, did you anticipate at that point that this could be such a huge step for our understanding of genes?

Until other examples are found of something new, it’s very hard to know how peculiar that particular phenomenon might be.

We’re always mindful that evolution is amazingly innovative. And so it could have been that this particular small RNA base-pairing to this mRNA of lin-14 gene and turning off production of the protein from lin-14 messenger RNA, that could be a peculiar evolutionary innovation.

The second microRNA was identified in Gary Ruvkun’s lab in 1999, so it was a good six years before the second one was found, also in C. elegans. Really, the watershed discovery was when Ruvkun showed that let-7, the other microRNA, was actually conserved perfectly in sequence amongst all the bilaterian animals. So that meant that let-7 microRNA had been around for, what, 500 million years?

And so it was immediately obvious to the field that there had to be other microRNAs – this was not just a C. elegans thing. There must be others, and that quickly emerged to be the case.

You and Gary Ruvkun had been postdoctoral fellows at the same time at MIT, but by the time you made your respective discoveries, you’d both set up your own labs. Would you call them rival labs, in the same town?

No, I would certainly not call it rival labs. We were working together as postdocs basically on this problem of developmental timing in Bob Horvitz’s lab.

We just basically informally divided up the work. The understanding was, OK, Ambros lab will focus on lin-4 gene, and Ruvkun lab will focus on lin-14, and we anticipated that there would be a point that we would get together and share information about what we’ve learned and see if we could come to a synthesis.

That was the informal plan. It was not really a collaboration. It was certainly not a rivalry. The expectation was that we would divide up the work and then communicate when the time came. There was an expectation in this community of C. elegans researchers that you should share data freely.

Your lab still works on microRNA. What are you investigating? What questions do you still have?

One I find very interesting is a project where we collaborated with a clinician, a geneticist who studies intellectual disability. She had discovered that her patients, children with intellectual disabilities, in certain families carried a mutation that neither of their parents had – a spontaneous mutation – in the protein that is associated with microRNAs in humans called the Argonaute protein.

Each of our genomes contains four genes for Argonautes that are the partners of microRNAs. In fact, this is the effector protein that is guided by the microRNA to its target messenger RNAs. This Argonaute is what carries out the regulatory processes that happen once it finds its target.

These so-called Argonaute syndromes were discovered, where there are mutations in Argonautes, point mutations where only one amino acid changes to another amino acid. They have this very profound and extensive effect on the development of the individual.

And so working with these geneticists, our lab and other labs took those mutations, that were essentially gifted to us by the patient. And then we put those mutations into our system, in our case into C. elegans‘ Argonaute.

I’m excited by the very organized, active partnership between the Argonaute Alliance of families with Argonaute syndromes and the basic scientists studying Argonaute.

How does this collaboration potentially help those patients?

What we’ve learned is that the mutant protein is sort of a rogue Argonaute. It’s basically screwing up the normal process that these four Argonautes usually do in the body. And so this rogue Argonaute, in principle, could be removed from the system by trying to employ some of the technology that folks are developing for gene knockout or RNA interference of genes.

This is promising, and I’m hopeful that the payoff for the patients will come in the years ahead.

Victor Ambros, Professor of Molecular Medicine, UMass Chan Medical School

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