Nicholas Green, Kennesaw State University

Curious Kids is a series for children of all ages. If you have a question you’d like an expert to answer, send it to CuriousKidsUS@theconversation.com.

How many types of insects are there in the world? – Sawyer, age 8, Fuquay-Varina, North Carolina

Exploring anywhere on Earth, look closely and you’ll find insects. Check your backyard and you may see ants, beetles, crickets, wasps, mosquitoes and more. There are more kinds of insects than there are mammals, birds and plants combined. This fact has fascinated scientists for centuries.

One of the things biologists like me do is classify all living things into categories. Insects belong to a phylum called Arthropoda – animals with hard exoskeletons and jointed feet.

All insects are arthropods, but not all arthropods are insects. For instance, spiders, lobsters and millipedes are arthropods, but they’re not insects.

Instead, insects are a subgroup within Arthropoda, a class called “Insecta,” that is characterized by six legs, two antennae and three body segments – head, abdomen and the thorax, which is the part of the body between the head and abdomen.

Most insects also have wings, although a few, like fleas, don’t. All have compound eyes, which means insects see very differently from the way people see. Instead of one lens per eye, they have many: a fly has 5,000 lenses; a dragonfly has 30,000. These types of eyes, though not great for clarity, are excellent at detecting movement.

What is a species?

All insects descend from a common ancestor that lived about about 480 million years ago. For context, that’s about 100 million years before any of our vertebrate ancestors – animals with a backbone – ever walked on land.

A species is the most basic unit that biologists use to classify living things. When people use words like “ant” or “fly” or “butterfly” they are referring not to species, but to categories that may contain hundreds, thousands or tens of thousands of species. For example, about 18,000 species of butterfly exist – think monarch, zebra swallowtail or cabbage white.

Basically, species are a group that can interbreed with each other, but not with other groups. One obvious example: bees can’t interbreed with ants.

But brown-belted bumblebees and red-belted bumblebees can’t interbreed either, so they are different species of bumblebee.

Each species has a unique scientific name – like Bombus griseocollis for the brown-belted bumblebee – so scientists can be sure which species they’re talking about.

Quadrillions of ants

Counting the exact number of insect species is probably impossible. Every year, some species go extinct, while some evolve anew. Even if we could magically freeze time and survey the entire Earth all at once, experts would disagree on the distinctiveness or identity of some species. So instead of counting, researchers use statistical analysis to make an estimate.

One scientist did just that. He published his answer in a 2018 research paper. His calculations showed there are approximately 5.5 million insect species, with the correct number almost certainly between 2.6 and 7.2 million.

Beetles alone account for almost one-third of the number, about 1.5 million species. By comparison, there are “only” an estimated 22,000 species of ants. This and other studies have also estimated about 3,500 species of mosquitoes, 120,000 species of flies and 30,000 species of grasshoppers and crickets.

The estimate of 5.5 million species of insects is interesting. What’s even more remarkable is that because scientists have found only about 1 million species, that means more than 4.5 million species are still waiting for someone to discover them. In other words, over 80% of the Earth’s insect biodiversity is still unknown.

Add up the total population and biomass of the insects, and the numbers are even more staggering. The 22,000 species of ants comprise about 20,000,000,000,000,000 individuals – that’s 20 quadrillion ants. And if a typical ant weighs about 0.0001 ounces (3 milligrams) – or one ten-thousandth of an ounce – that means all the ants on Earth together weigh more than 132 billion pounds (about 60 billion kilograms).

That’s the equivalent of about 7 million school buses, 600 aircraft carriers or about 20% of the weight of all humans on Earth combined.

Many insect species are going extinct

All of this has potentially huge implications for our own human species. Insects affect us in countless ways. People depend on them for crop pollination, industrial products and medicine. Other insects can harm us by transmitting disease or eating our crops.

Most insects have little to no direct impact on people, but they are integral parts of their ecosystems. This is why entomologists – bug scientists – say we should leave insects alone as much as possible. Most of them are harmless to people, and they are critical to the environment.

It is sobering to note that although millions of undiscovered insect species may be out there, many will go extinct before people have a chance to discover them. Largely due to human activity, a significant proportion of Earth’s biodiversity – including insects – may ultimately be forever lost.

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Nicholas Green, Assistant Professor of Biology, Kennesaw State University

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

Nneka Vivian Iduu, Auburn University

In the 20th century, when a routine infection was treated with a standard antibiotic, recovery was expected. But over time, the microbes responsible for these infections have evolved to evade the very drugs designed to eliminate them.

Each year, there are more than 2.8 million antibiotic-resistant infections in the United States, leading to over 35,000 deaths and US$4.6 billion in health care costs. As antibiotics become less effective, antimicrobial resistance poses an increasing threat to public health.

Antimicrobial resistance began to emerge as a serious threat in the 1940s with the rise of penicillin resistance. By the 1990s, it had escalated into a global concern. Decades later, critical questions still remain: How does antimicrobial resistance emerge, and how can scientists track the hidden changes leading to it? Why does resistance in some microbes remain undetected until an outbreak occurs? Filling these knowledge gaps is crucial to preventing future outbreaks, improving treatment outcomes and saving lives.

Over the years, my work as a microbiologist and biomedical scientist has focused on investigating the genetics of infectious microbes. My colleagues and I identified a resistance gene previously undetected in the U.S. using genetic and computational methods that can help improve how scientists detect and track antimicrobial resistance.

Challenges of detecting resistance

Antimicrobial resistance is a natural process where microbes constantly evolve as a defense mechanism, acquiring genetic changes that enhance their survival.

Unfortunately, human activities can speed up this process. The overuse and misuse of antibiotics in health care, farming and the environment push bacteria to genetically change in ways that allow them to survive the drugs meant to kill them.

Early detection of antimicrobial resistance is crucial for effective treatment. Surveillance typically begins with a laboratory sample obtained from patients with suspected infections, which is then analyzed to identify potential antimicrobial resistance. Traditionally, this has been done using culture-based methods that involve exposing microbes to antibiotics in the lab and observing whether they survived to determine whether they were becoming resistant. Along with helping authorities and researchers monitor the spread of antimicrobial resistance, hospitals use this approach to decide on treatment plans.

However, culture-based approaches have some limitations. Resistant infections often go unnoticed until antibiotics fail, making both detection and intervention processes slow. Additionally, new resistance genes may escape detection altogether.

Genomics of antimicrobial resistance

To overcome these challenges, researchers have integrated genomic sequencing into antimicrobial resistance surveillance. Through whole-genome sequencing, we can analyze all the DNA in a microbial sample to get a comprehensive view of all the genes present – including those responsible for resistance. With the computational tools of bioinformatics, researchers can efficiently process vast amounts of genetic data to improve the detection of resistance threats.

Despite its advantages, integrating genomic sequencing into antimicrobial resistance monitoring presents some challenges of its own. High costs, quality assurance and a shortage of trained bioinformaticians make implementation difficult. Additionally, the complexity of interpreting genomic data may limit its use in clinical and public health decision-making.

Establishing international standards could help make whole-genome sequencing and bioinformatics a fully reliable tool for resistance surveillance. The World Health Organization recommends laboratories follow strict quality control measures to ensure accurate and comparable results. This includes using reliable, user-friendly computational tools and shared microbial databases. Additional strategies include investing in training programs and fostering collaborations between hospitals, research labs and universities.

Discovering a resistance gene

Combining whole genome sequencing and bioinformatics, my colleagues and I analyzed Salmonella samples collected from several animal species between 1982 and 1999. We discovered a Salmonella resistance gene called blaSCO-1 that has evaded detection in U.S. livestock for decades.

The blaSCO-1 gene confers resistance to microbes against several critical antibiotics, including ampicillin, amoxicillin-clavulanic acid and, to some extent, cephalosporins and carbapenems. These medications are crucial for treating infections in both humans and animals.

The blaSCO-1 gene likely remained unreported because routine surveillance usually targets well-known resistance genes and it has overlapping functions with other genes. Gaps in bioinformatics expertise may have also hindered its identification.

The failure to detect genes like blaSCO-1 raises concern about its potential role in past treatment failures. Between 2015 and 2018, the Centers for Disease Control and Prevention began implementing whole-genome sequencing for routine surveillance of Salmonella. Studies conducted during this period found that 77% of multistate outbreaks were linked to livestock harboring resistant Salmonella.

These missed genes have significant implications for both food safety and public health. Undetected antimicrobial resistance genes can spread through food animals, contaminated food products, processing environments and agricultural runoff, allowing resistant bacteria to persist and reach humans. These resistant bacteria lead to infections that are harder to treat and increase the risk of outbreaks. Moreover, the global movement of people, livestock and goods means that these resistant strains can easily cross borders, turning local outbreaks into worldwide health threats.

Identifying new resistance genes not only fills a critical knowledge gap, but it also demonstrates how genomic and computational approaches can help detect hidden resistance mechanisms before they pose widespread threats.

Strengthening surveillance

As antimicrobial resistance continues to rise, adopting a One Health approach that integrates human, animal and environmental factors can help ensure that emerging resistance does not outpace humans’ ability to combat it.

Initiatives like the Quadripartite AMR Multi-Partner Trust Fund provide support for programs that strengthen global collaborative surveillance, promote responsible antimicrobial use and drive the development of sustainable alternatives. Ensuring researchers around the world follow common research standards will allow more labs – especially those in low- and middle-income countries – to contribute to global surveillance efforts.

The health of future generations depends on the world’s ability to ensure food safety and protect public health on a global scale. In the ongoing battle between microbial evolution and human innovation, vigilance and adaptability are key to staying ahead.

Nneka Vivian Iduu, Graduate Research Assistant in Pathobiology, Auburn University

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

Anna Choi, Cornell University and Katelyn Mei, University of Washington

When someone sees something that isn’t there, people often refer to the experience as a hallucination. Hallucinations occur when your sensory perception does not correspond to external stimuli.

Technologies that rely on artificial intelligence can have hallucinations, too.

When an algorithmic system generates information that seems plausible but is actually inaccurate or misleading, computer scientists call it an AI hallucination. Researchers have found these behaviors in different types of AI systems, from chatbots such as ChatGPT to image generators such as Dall-E to autonomous vehicles. We are information science researchers who have studied hallucinations in AI speech recognition systems.

Wherever AI systems are used in daily life, their hallucinations can pose risks. Some may be minor – when a chatbot gives the wrong answer to a simple question, the user may end up ill-informed. But in other cases, the stakes are much higher. From courtrooms where AI software is used to make sentencing decisions to health insurance companies that use algorithms to determine a patient’s eligibility for coverage, AI hallucinations can have life-altering consequences. They can even be life-threatening: Autonomous vehicles use AI to detect obstacles, other vehicles and pedestrians.

Making it up

Hallucinations and their effects depend on the type of AI system. With large language models – the underlying technology of AI chatbots – hallucinations are pieces of information that sound convincing but are incorrect, made up or irrelevant. An AI chatbot might create a reference to a scientific article that doesn’t exist or provide a historical fact that is simply wrong, yet make it sound believable.

In a 2023 court case, for example, a New York attorney submitted a legal brief that he had written with the help of ChatGPT. A discerning judge later noticed that the brief cited a case that ChatGPT had made up. This could lead to different outcomes in courtrooms if humans were not able to detect the hallucinated piece of information.

With AI tools that can recognize objects in images, hallucinations occur when the AI generates captions that are not faithful to the provided image. Imagine asking a system to list objects in an image that only includes a woman from the chest up talking on a phone and receiving a response that says a woman talking on a phone while sitting on a bench. This inaccurate information could lead to different consequences in contexts where accuracy is critical.

What causes hallucinations

Engineers build AI systems by gathering massive amounts of data and feeding it into a computational system that detects patterns in the data. The system develops methods for responding to questions or performing tasks based on those patterns.

Supply an AI system with 1,000 photos of different breeds of dogs, labeled accordingly, and the system will soon learn to detect the difference between a poodle and a golden retriever. But feed it a photo of a blueberry muffin and, as machine learning researchers have shown, it may tell you that the muffin is a chihuahua.

When a system doesn’t understand the question or the information that it is presented with, it may hallucinate. Hallucinations often occur when the model fills in gaps based on similar contexts from its training data, or when it is built using biased or incomplete training data. This leads to incorrect guesses, as in the case of the mislabeled blueberry muffin.

It’s important to distinguish between AI hallucinations and intentionally creative AI outputs. When an AI system is asked to be creative – like when writing a story or generating artistic images – its novel outputs are expected and desired. Hallucinations, on the other hand, occur when an AI system is asked to provide factual information or perform specific tasks but instead generates incorrect or misleading content while presenting it as accurate.

The key difference lies in the context and purpose: Creativity is appropriate for artistic tasks, while hallucinations are problematic when accuracy and reliability are required.

To address these issues, companies have suggested using high-quality training data and limiting AI responses to follow certain guidelines. Nevertheless, these issues may persist in popular AI tools.

What’s at risk

The impact of an output such as calling a blueberry muffin a chihuahua may seem trivial, but consider the different kinds of technologies that use image recognition systems: An autonomous vehicle that fails to identify objects could lead to a fatal traffic accident. An autonomous military drone that misidentifies a target could put civilians’ lives in danger.

For AI tools that provide automatic speech recognition, hallucinations are AI transcriptions that include words or phrases that were never actually spoken. This is more likely to occur in noisy environments, where an AI system may end up adding new or irrelevant words in an attempt to decipher background noise such as a passing truck or a crying infant.

As these systems become more regularly integrated into health care, social service and legal settings, hallucinations in automatic speech recognition could lead to inaccurate clinical or legal outcomes that harm patients, criminal defendants or families in need of social support.

Check AI’s work

Regardless of AI companies’ efforts to mitigate hallucinations, users should stay vigilant and question AI outputs, especially when they are used in contexts that require precision and accuracy. Double-checking AI-generated information with trusted sources, consulting experts when necessary, and recognizing the limitations of these tools are essential steps for minimizing their risks.

Anna Choi, Ph.D. Candidate in Information Science, Cornell University and Katelyn Mei, Ph.D. Student in Information Science, University of Washington

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