Casey Fiesler, University of Colorado Boulder

A parent asked a question in a private Facebook group in April 2024: Does anyone with a child who is both gifted and disabled have any experience with New York City public schools? The parent received a seemingly helpful answer that laid out some characteristics of a specific school, beginning with the context that “I have a child who is also 2e,” meaning twice exceptional.

On a Facebook group for swapping unwanted items near Boston, a user looking for specific items received an offer of a “gently used” Canon camera and an “almost-new portable air conditioning unit that I never ended up using.”

Both of these responses were lies. That child does not exist and neither do the camera or air conditioner. The answers came from an artificial intelligence chatbot.

According to a Meta help page, Meta AI will respond to a post in a group if someone explicitly tags it or if someone “asks a question in a post and no one responds within an hour.” The feature is not yet available in all regions or for all groups, according to the page. For groups where it is available, “admins can turn it off and back on at any time.”

Meta AI has also been integrated into search features on Facebook and Instagram, and users cannot turn it off.

As a researcher who studies both online communities and AI ethics, I find the idea of uninvited chatbots answering questions in Facebook groups to be dystopian for a number of reasons, starting with the fact that online communities are for people.

Human connections

In 1993, Howard Rheingold published the book “The Virtual Community: Homesteading on the Electronic Frontier” about the WELL, an early and culturally significant online community. The first chapter opens with a parenting question: What to do about a “blood-bloated thing sucking on our baby's scalp.”

Rheingold received an answer from someone with firsthand knowledge of dealing with ticks and had resolved the problem before receiving a callback from the pediatrician's office. Of this experience, he wrote, “What amazed me wasn't just the speed with which we obtained precisely the information we needed to know, right when we needed to know it. It was also the immense inner sense of security that comes with discovering that real people – most of them parents, some of them nurses, doctors, and midwives – are available, around the clock, if you need them.”

This “real people” aspect of online communities continues to be critical today. Imagine why you might pose a question to a Facebook group rather than a search engine: because you want an answer from someone with real, lived experience or you want the human response that your question might elicit – sympathy, outrage, commiseration – or both.

Decades of research suggests that the human component of online communities is what makes them so valuable for both information-seeking and social support. For example, fathers who might otherwise feel uncomfortable asking for parenting advice have found a haven in private online spaces just for dads. LGBTQ+ youth often join online communities to safely find critical resources while reducing feelings of isolation. Mental health support forums provide young people with belonging and validation in addition to advice and social support.

In addition to similar findings in my own lab related to LGBTQ+ participants in online communities, as well as Black Twitter, two more recent studies, not yet peer-reviewed, have emphasized the importance of the human aspects of information-seeking in online communities.

One, led by PhD student Blakeley Payne, focuses on fat people's experiences online. Many of our participants found a lifeline in access to an audience and community with similar experiences as they sought and shared information about topics such as navigating hostile healthcare systems, finding clothing and dealing with cultural biases and stereotypes.

Another, led by Ph.D student Faye Kollig, found that people who share content online about their chronic illnesses are motivated by the sense of community that comes with shared experiences, as well as the humanizing aspects of connecting with others to both seek and provide support and information.

Faux people

The most important benefits of these online spaces as described by our participants could be drastically undermined by responses coming from chatbots instead of people.

As a type 1 diabetic, I follow a number of related Facebook groups that are frequented by many parents newly navigating the challenges of caring for a young child with diabetes. Questions are frequent: “What does this mean?” “How should I handle this?” “What are your experiences with this?” Answers come from firsthand experience, but they also typically come with compassion: “This is hard.” “You're doing your best.” And of course: “We've all been there.”

A response from a chatbot claiming to speak from the lived experience of caring for a diabetic child, offering empathy, would not only be inappropriate, but it would be borderline cruel.

However, it makes complete sense that these are the types of responses that a chatbot would offer. Large language models, simplistically, function more similarly to autocomplete than they do to search engines. For a model trained on the millions and millions of posts and comments in Facebook groups, the “autocomplete” answer to a question in a support community is definitely one that invokes personal experience and offers empathy – just as the “autocomplete” answer in a Buy Nothing Facebook group might be to offer someone a gently used camera.

Keeping chatbots in their lanes

This isn't to suggest that chatbots aren't useful for anything – they may even be quite useful in some online communities, in some contexts. The problem is that in the midst of the current generative AI rush, there is a tendency to think that chatbots can and should do everything.

There are plenty of downsides to using large language models as information retrieval systems, and these downsides point to inappropriate contexts for their use. One downside is when incorrect information could be dangerous: an eating disorder helpline or legal advice for small businesses, for example.

Research is pointing to important considerations in how and when to design and deploy chatbots. For example, one recently published paper at a large human-computer interaction conference found that though LGBTQ+ individuals lacking social support were sometimes turning to chatbots for help with mental health needs, those chatbots frequently fell short in grasping the nuance of LGBTQ+-specific challenges.

Another found that though a group of autistic participants found value in interacting with a chatbot for social communication advice, that chatbot was also dispensing questionable advice. And yet another found that though a chatbot was helpful as a preconsultation tool in a health context, patients sometimes found expressions of empathy to be insincere or offensive.

Responsible AI development and deployment means not only auditing for issues such as bias and misinformation, but also taking the time to understand in which contexts AI is appropriate and desirable for the humans who will be interacting with them. Right now, many companies are wielding generative AI as a hammer, and as a result, everything looks like a nail.

Many contexts, such as online support communities, are best left to humans.

Casey Fiesler, Associate Professor of Information Science, University of Colorado Boulder

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

Gaurav Khanna, University of Rhode Island

Physicists consider black holes one of the most mysterious objects that exist. Ironically, they're also considered one of the simplest. For years, physicists like me have been looking to prove that black holes are more complex than they seem. And a newly approved European space mission called LISA will help us with this hunt.

Research from the 1970s suggests that you can comprehensively describe a black hole using only three physical attributes – their mass, charge and spin. All the other properties of these massive dying stars, like their detailed composition, density and temperature profiles, disappear as they transform into a black hole. That is how simple they are.

The idea that black holes have only three attributes is called the “no-hair” theorem, implying that they don't have any “hairy” details that make them complicated.

Hairy black holes?

For decades, researchers in the astrophysics community have exploited loopholes or work-arounds within the no-hair theorem's assumptions to come up with potential hairy black hole scenarios. A hairy black hole has a physical property that scientists can measure – in principle – that's beyond its mass, charge or spin. This property has to be a permanent part of its structure.

About a decade ago, Stefanos Aretakis, a physicist currently at the University of Toronto, showed mathematically that a black hole containing the maximum charge it could hold – called an extremal charged black hole – would develop “hair” at its horizon. A black hole's horizon is the boundary where anything that crosses it, even light, can't escape.

Aretakis' analysis was more of a thought experiment using a highly simplified physical scenario, so it's not something scientists expect to observe astrophysically. But supercharged black holes might not be the only kind that could have hair.

Since astrophysical objects such as stars and planets are known to spin, scientists expect that black holes would spin as well, based on how they form. Astronomical evidence has shown that black holes do have spin, though researchers don't know what the typical spin value is for an astrophysical black hole.

Using computer simulations, my team has recently discovered similar types of hair in black holes that are spinning at the maximum rate. This hair has to do with the rate of change, or the gradient, of space-time's curvature at the horizon. We also discovered that a black hole wouldn't actually have to be maximally spinning to have hair, which is significant because these maximally spinning black holes probably don't form in nature.

Detecting and measuring hair

My team wanted to develop a way to potentially measure this hair – a new fixed property that might characterize a black hole beyond its mass, spin and charge. We started looking into how such a new property might leave a signature on a gravitational wave emitted from a fast-spinning black hole.

A gravitational wave is a tiny disturbance in space-time typically caused by violent astrophysical events in the universe. The collisions of compact astrophysical objects such as black holes and neutron stars emit strong gravitational waves. An international network of gravitational observatories, including the Laser Interferometer Gravitational-wave Observatory in the United States, routinely detects these waves.

Our recent studies suggest that one can measure these hairy attributes from gravitational wave data for fast-spinning black holes. Looking at the gravitational wave data offers an opportunity for a signature of sorts that could indicate whether the black hole has this type of hair.

Our ongoing studies and recent progress made by Som Bishoyi, a student on the team, are based on a blend of theoretical and computational models of fast-spinning black holes. Our findings have not been tested in the field yet or observed in real black holes out in space. But we hope that will soon change.

LISA gets a go-ahead

In January 2024, the European Space Agency formally adopted the space-based Laser Interferometer Space Antenna, or LISA, mission. LISA will look for gravitational waves, and the data from the mission could help my team with our hairy black hole questions.

Formal adoption means that the project has the go-ahead to move to the construction phase, with a planned 2035 launch. LISA consists of three spacecrafts configured in a perfect equilateral triangle that will trail behind the Earth around the Sun. The spacecrafts will each be 1.6 million miles (2.5 million kilometers) apart, and they will exchange laser beams to measure the distance between each other down to about a billionth of an inch.

LISA will detect gravitational waves from supermassive black holes that are millions or even billions of times more massive than our Sun. It will build a map of the space-time around rotating black holes, which will help physicists understand how gravity works in the close vicinity of black holes to an unprecedented level of accuracy. Physicists hope that LISA will also be able to measure any hairy attributes that black holes might have.

With LIGO making new observations every day and LISA to offer a glimpse into the space-time around black holes, now is one of the most exciting times to be a black hole physicist.

Gaurav Khanna, Professor of Physics, University of Rhode Island

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

Libusha Kelly, Albert Einstein College of Medicine

Viruses are a mysterious and poorly understood force in microbial ecosystems. Researchers know they can infect, kill and manipulate human and bacterial cells in nearly every environment, from the oceans to your gut. But scientists don't yet have a full picture of how viruses affect their surrounding environments in large part because of their extraordinary diversity and ability to rapidly evolve.

Communities of microbes are difficult to study in a laboratory setting. Many microbes are challenging to cultivate, and their natural environment has many more features influencing their success or failure than scientists can replicate in a lab.

So systems biologists like me often sequence all the DNA present in a sample – for example, a fecal sample from a patient – separate out the viral DNA sequences, then annotate the sections of the viral genome that code for proteins. These notes on the location, structure and other features of genes help researchers understand the functions viruses might carry out in the environment and help identify different kinds of viruses. Researchers annotate viruses by matching viral sequences in a sample to previously annotated sequences available in public databases of viral genetic sequences.

However, scientists are identifying viral sequences in DNA collected from the environment at a rate that far outpaces our ability to annotate those genes. This means researchers are publishing findings about viruses in microbial ecosystems using unacceptably small fractions of available data.

To improve researchers' ability to study viruses around the globe, my team and I have developed a novel approach to annotate viral sequences using artificial intelligence. Through protein language models akin to large language models like ChatGPT but specific to proteins, we were able to classify previously unseen viral sequences. This opens the door for researchers to not only learn more about viruses, but also to address biological questions that are difficult to answer with current techniques.

Annotating viruses with AI

Large language models use relationships between words in large datasets of text to provide potential answers to questions they are not explicitly “taught” the answer to. When you ask a chatbot “What is the capital of France?” for example, the model is not looking up the answer in a table of capital cities. Rather, it is using its training on huge datasets of documents and information to infer the answer: “The capital of France is Paris.”

Similarly, protein language models are AI algorithms that are trained to recognize relationships between billions of protein sequences from environments around the world. Through this training, they may be able to infer something about the essence of viral proteins and their functions.

We wondered whether protein language models could answer this question: “Given all annotated viral genetic sequences, what is this new sequence's function?”

In our proof of concept, we trained neural networks on previously annotated viral protein sequences in pre-trained protein language models and then used them to predict the annotation of new viral protein sequences. Our approach allows us to probe what the model is “seeing” in a particular viral sequence that leads to a particular annotation. This helps identify candidate proteins of interest either based on their specific functions or how their genome is arranged, winnowing down the search space of vast datasets.

By identifying more distantly related viral gene functions, protein language models can complement current methods to provide new insights into microbiology. For example, my team and I were able to use our model to discover a previously unrecognized integrase – a type of protein that can move genetic information in and out of cells – in the globally abundant marine picocyanobacteria Prochlorococcus and Synechococcus. Notably, this integrase may be able to move genes in and out of these populations of bacteria in the oceans and enable these microbes to better adapt to changing environments.

Our language model also identified a novel viral capsid protein that is widespread in the global oceans. We produced the first picture of how its genes are arranged, showing it can contain different sets of genes that we believe indicates this virus serves different functions in its environment.

These preliminary findings represent only two of thousands of annotations our approach has provided.

Analyzing the unknown

Most of the hundreds of thousands of newly discovered viruses remain unclassified. Many viral genetic sequences match protein families with no known function or have never been seen before. Our work shows that similar protein language models could help study the threat and promise of our planet's many uncharacterized viruses.

While our study focused on viruses in the global oceans, improved annotation of viral proteins is critical for better understanding the role viruses play in health and disease in the human body. We and other researchers have hypothesized that viral activity in the human gut microbiome might be altered when you're sick. This means that viruses may help identify stress in microbial communities.

However, our approach is also limited because it requires high-quality annotations. Researchers are developing newer protein language models that incorporate other “tasks” as part of their training, particularly predicting protein structures to detect similar proteins, to make them more powerful.

Making all AI tools available via FAIR Data Principles – data that is findable, accessible, interoperable and reusable – can help researchers at large realize the potential of these new ways of annotating protein sequences leading to discoveries that benefit human health.

Libusha Kelly, Associate Professor of Systems and Computational Biology, Microbiology and Immunology, Albert Einstein College of Medicine

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