Elaine Short, Tufts University

You might have heard that artificial intelligence is going to revolutionize everything, save the world and give everyone superhuman powers. Alternatively, you might have heard that it will take your job, make you lazy and stupid, and make the world a cyberpunk dystopia.

Consider another way to look at AI: as an assistive technology – something that helps you function.

With that view, also consider a community of experts in giving and receiving assistance: the disability community. Many disabled people use technology extensively, both dedicated assistive technologies such as wheelchairs and general-use technologies such as smart home devices.

Equally, many disabled people receive professional and casual assistance from other people. And, despite stereotypes to the contrary, many disabled people regularly give assistance to the disabled and nondisabled people around them.

Disabled people are well experienced in receiving and giving social and technical assistance, which makes them a valuable source of insight into how everyone might relate to AI systems in the future. This potential is a key driver for my work as a disabled person and researcher in AI and robotics.

Actively learning to live with help

While virtually everyone values independence, no one is fully independent. Each of us depends on others to grow our food, care for us when we are ill, give us advice and emotional support, and help us in thousands of interconnected ways. Being disabled means having support needs that are outside what is typical and therefore those needs are much more visible. Because of this, the disability community has reckoned more explicitly with what it means to need help to live than most nondisabled people.

This disability community perspective can be invaluable in approaching new technologies that can assist both disabled and nondisabled people. You can't substitute pretending to be disabled for the experience of actually being disabled, but accessibility can benefit everyone.

This is sometimes called the curb-cut effect after the ways that putting a ramp in a curb to help a wheelchair user access the sidewalk also benefits people with strollers, rolling suitcases and bicycles.

Partnering in assistance

You have probably had the experience of someone trying to help you without listening to what you actually need. For example, a parent or friend might “help” you clean and instead end up hiding everything you need.

Disability advocates have long battled this type of well-meaning but intrusive assistance – for example, by putting spikes on wheelchair handles to keep people from pushing a person in a wheelchair without being asked to or advocating for services that keep the disabled person in control.

The disabled community instead offers a model of assistance as a collaborative effort. Applying this to AI can help to ensure that new AI tools support human autonomy rather than taking over.

A key goal of my lab's work is to develop AI-powered assistive robotics that treat the user as an equal partner. We have shown that this model is not just valuable, but inevitable. For example, most people find it difficult to use a joystick to move a robot arm: The joystick can only move from front to back and side to side, but the arm can move in almost as many ways as a human arm.

To help, AI can predict what someone is planning to do with the robot and then move the robot accordingly. Previous research assumed that people would ignore this help, but we found that people quickly figured out that the system is doing something, actively worked to understand what it was doing and tried to work with the system to get it to do what they wanted.

Most AI systems don't make this easy, but my lab's new approaches to AI empower people to influence robot behavior. We have shown that this results in better interactions in tasks that are creative, like painting. We also have begun to investigate how people can use this control to solve problems outside the ones the robots were designed for. For example, people can use a robot that is trained to carry a cup of water to instead pour the water out to water their plants.

Training AI on human variability

The disability-centered perspective also raises concerns about the huge datasets that power AI. The very nature of data-driven AI is to look for common patterns. In general, the better-represented something is in the data, the better the model works.

If disability means having a body or mind outside what is typical, then disability means not being well-represented in the data. Whether it's AI systems designed to detect cheating on exams instead detecting students' disabilities or robots that fail to account for wheelchair users, disabled people's interactions with AI reveal how those systems are brittle.

One of my goals as an AI researcher is to make AI more responsive and adaptable to real human variation, especially in AI systems that learn directly from interacting with people. We have developed frameworks for testing how robust those AI systems are to real human teaching and explored how robots can learn better from human teachers even when those teachers change over time.

Thinking of AI as an assistive technology, and learning from the disability community, can help to ensure that the AI systems of the future serve people's needs – with people in the driver's seat.

Elaine Short, Assistant Professor of Computer Science, Tufts University

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

Sean Rafferty, University at Albany, State University of New York

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 was popcorn discovered? – Kendra, age 11, Penn Yan, New York

You have to wonder how people originally figured out how to eat some foods that are beloved today. The cassava plant is toxic if not carefully processed through multiple steps. Yogurt is basically old milk that's been around for a while and contaminated with bacteria. And who discovered that popcorn could be a toasty, tasty treat?

These kinds of food mysteries are pretty hard to solve. Archaeology depends on solid remains to figure out what happened in the past, especially for people who didn't use any sort of writing. Unfortunately, most stuff people traditionally used made from wood, animal materials or cloth decays pretty quickly, and archaeologists like me never find it.

We have lots of evidence of hard stuff, such as pottery and stone tools, but softer things – such as leftovers from a meal – are much harder to find. Sometimes we get lucky, if softer stuff is found in very dry places that preserve it. Also, if stuff gets burned, it can last a very long time.

Corn's ancestors

Luckily, corn – also called maize – has some hard parts, such as the kernel shell. They're the bits at the bottom of the popcorn bowl that get caught in your teeth. And since you have to heat maize to make it edible, sometimes it got burned, and archaeologists find evidence that way. Most interesting of all, some plants, including maize, contain tiny, rock-like fragments called phytoliths that can last for thousands of years.

Scientists are pretty sure they know how old maize is. We know maize was probably first farmed by Native Americans in what is now Mexico. Early farmers there domesticated maize from a kind of grass called teosinte.

Before farming, people would gather wild teosinte and eat the seeds, which contained a lot of starch, a carbohydrate like you'd find in bread or pasta. They would pick teosinte with the largest seeds and eventually started weeding and planting it. Over time, the wild plant developed into something like what we call maize today. You can tell maize from teosinte by its larger kernels.

There's evidence of maize farming from dry caves in Mexico as early as 9,000 years ago. From there, maize farming spread throughout North and South America.

Popped corn, preserved food

Figuring out when people started making popcorn is harder. There are several types of maize, most of which will pop if heated, but one variety, actually called “popcorn,” makes the best popcorn. Scientists have discovered phytoliths from Peru, as well as burned kernels, of this type of “poppable” maize from as early as 6,700 years ago.

You can imagine that popping maize kernels was first discovered by accident. Some maize probably fell into a cooking fire, and whoever was nearby figured out that this was a handy new way of preparing the food. Popped maize would last a long time and was easy to make.

Ancient popcorn was probably not much like the snack you might munch at the movie theater today. There was probably no salt and definitely no butter, since there were no cows to milk in the Americas yet. It probably wasn't served hot and was likely pretty chewy compared with the version you're used to today.

It's impossible to know exactly why or how popcorn was invented, but I would guess it was a clever way to preserve the edible starch in corn by getting rid of the little bit of water inside each kernel that would make it more susceptible to spoiling. It's the heated water in the kernel escaping as steam that makes popcorn pop. The popped corn could then last a long time. What you may consider a tasty snack today probably started as a useful way of preserving and storing food.

Hello, curious kids! Do you have a question you'd like an expert to answer? Ask an adult to send your question to CuriousKidsUS@theconversation.com. Please tell us your name, age and the city where you live.

And since curiosity has no age limit – adults, let us know what you're wondering, too. We won't be able to answer every question, but we will do our best.

Sean Rafferty, Professor of Anthropology, University at Albany, State University of New York

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

Chris Impey, University of Arizona

Government funding for science is usually immune from political gridlock and polarization in Congress. But, federal funding for science is slated to drop for 2025.

Science research dollars are considered to be discretionary, which means the funding has to be approved by Congress every year. But it's in a budget category with larger entitlement programs like Medicare and Social Security that are generally considered untouchable by politicians of both parties.

Federal investment in scientific research encompasses everything from large telescopes supported by the National Science Foundation to NASA satellites studying climate change, programs studying the use and governance of artificial intelligence at the National Institute of Standards and Technology, and research on Alzheimer's disease funded by the National Institutes of Health.

Studies show that increasing federal research spending benefits productivity and economic competitiveness.

I'm an astronomer and also a senior university administrator. As an administrator, I've been involved in lobbying for research funding as associate dean of the College of Science at the University of Arizona, and in encouraging government investment in astronomy as a vice president of the American Astronomical Society. I've seen the importance of this kind of funding as a researcher who has had federal grants for 30 years, and as a senior academic who helps my colleagues write grants to support their valuable work.

Bipartisan support

Federal funding for many programs is characterized by political polarization, meaning that partisanship and ideological divisions between the two main political parties can lead to gridlock. Science is usually a rare exception to this problem.

The public shows strong bipartisan support for federal investment in scientific research, and Congress has generally followed suit, passing bills in 2024 with bipartisan backing in April and June.

The House passed these bills, and after reconciliation with language from the Senate, they resulted in final bills to direct US$460 billion in government spending.

However, policy documents produced by Congress reveal a partisan split in how Democratic and Republican lawmakers reference scientific research.

Congressional committees for both sides are citing more scientific papers, but there is only a 5% overlap in the papers they cite. That means that the two parties are using different evidence to make their funding decisions, rather than working from a scientific consensus. Committees under Democratic control were almost twice as likely to cite technical papers as panels led by Republicans, and they were more likely to cite papers that other scientists considered important.

Ideally, all the best ideas for scientific research would receive federal funds. But limited support for scientific research in the United States means that for individual scientists, getting funding is a highly competitive process.

At the National Science Foundation, only 1 in 4 proposals are accepted. Success rates for funding through the National Institutes of Health are even lower, with 1 in 5 proposals getting accepted. This low success rate means that the agencies have to reject many proposals that are rated excellent by the merit review process.

Scientists are often reluctant to publicly advocate for their programs, in part because they feel disconnected from the policymaking and appropriations process. Their academic training doesn't equip them to communicate effectively to legislators and policy experts.

Budgets are down

Research received steady funding for the past few decades, but this year Congress reduced appropriations for science at many top government agencies.

The National Science Foundation budget is down 8%, which led agency leaders to warn Congress that the country may lose its ability to attract and train a scientific workforce.

The cut to the NSF is particularly disappointing since Congress promised it an extra $81 billion over five years when the CHIPS and Science Act passed in 2022. A deal to limit government spending in exchange for suspending the debt ceiling made the law's goals hard to achieve.

NASA's science budget is down 6%, and the budget for the National Institutes of Health, whose research aims to prevent disease and improve public health, is down 1%. Only the Department of Energy's Office of Science got a bump, a modest 2%.

As a result, the major science agencies are nearing a 25-year low for their funding levels, as a share of U.S. gross domestic product.

Feeling the squeeze

Investment in research and development by the business sector is strongly increasing. In 1990, it was slightly higher than federal investment, but by 2020 it was nearly four times higher.

The distinction is important because business investment tends to focus on later stage and applied research, while federal funding goes to pure and exploratory research that can have enormous downstream benefits, such as for quantum computing and fusion power.

There are several causes of the science funding squeeze. Congressional intentions to increase funding levels, as with the CHIPS and Science Act, and the earlier COMPETES Act in 2007, have been derailed by fights over the debt limit and threats of government shutdowns.

The CHIPS act aimed to spur investment and job creation in semiconductor manufacturing, while the COMPETES Act aimed to increase U.S competitiveness in a wide range of high-tech industries such as space exploration.

The budget caps for fiscal years 2024 and 2025 remove any possibility for growth. The budget caps were designed to rein in federal spending, but they are a very blunt tool. Also, nondefense discretionary spending is only 15% of all federal spending. Discretionary spending is up for a vote every year, while mandatory spending is dictated by prior laws.

Entitlement programs like Medicare, Medicaid and Social Security are mandatory forms of spending. Taken together, they are three times larger than the amount available for discretionary spending, so science has to fight over a small fraction of the overall budget pie.

Within that 15% slice, scientific research competes with K-12 education, veterans' health care, public health, initiatives for small businesses, and more.

Global competition

While government science funding in the U.S. is stagnant, America's main scientific rivals are rising fast.

Federal R&D funding as a percentage of GDP has dropped from 1.2% in 1987 to 1% in 2010 to under 0.8% currently. The United States is still the world's biggest spender on research and development, but in terms of government R&D as a fraction of GDP, the United States ranked 12th in 2021, behind South Korea and a set of European countries. In terms of science researchers as a portion of the labor force, the United States ranks 10th.

Meanwhile, America's main geopolitical rival is rising fast. China has eclipsed the United States in high-impact papers published, and China now spends more than the United States on university and government research.

If the U.S. wants to keep its status as the world leader in scientific research, it'll need to redouble its commitment to science by appropriately funding research.

Chris Impey, University Distinguished Professor of Astronomy, University of Arizona

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