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How should leaders prepare for AIs accelerating impact on work and everyday life? AI scientist, entrepreneur, and Pioneers of AI podcast host Rana el Kaliouby shares her predictions for the year aheadfrom physical AI entering the real world to what it means to onboard AI into your org chart. El Kaliouby cuts through todays biggest AI headlines, bringing to light the insights that will matter most in the months to come. This is an abridged transcript of an interview from Rapid Response, hosted by former Fast Company editor-in-chief Robert Safian. From the team behind the Masters of Scale podcast, Rapid Response features candid conversations with todays top business leaders navigating real-time challenges. Subscribe to Rapid Response wherever you get your podcasts to ensure you never miss an episode. Let’s look ahead to 2026. You sent me some fascinating thoughts about AI’s next-phase impact on business, and I’d love to take you through them. The first one was the rise of what you called relationship intelligent AI. So everybody’s worried that AI is going to make us less human and take away our human-to-human connections. There is definitely a risk of that. But I think the thing I’m most excited about for 2026 is how AI can actually help us build deeper human connections and more meaningful human experiences. And the way this happens is through AI that can really help you organize your relationships and your network and surface connections that you need and maybe make warm introductions to you. I love connecting with people, but my relationship data is a mess. It’s all in my brain. Some of it is in LinkedIn, some are on WhatsApp. I take a lot of notes when I meet new people and I use an AI note taker. It’s just a mess. It’s very disparate data sources. And I always think of this scene in, do you know The Devil Wears Prada? Have you seen? Oh, that’s exactly what I was thinking of. The character is kind of whispering in your ear. Exactly. It was Anne Hathaway [as Andy], and she was with Meryl Streep [as Miranda] at this gala. And this guy and his partner were moving toward Miranda and she [didnt rememeber their names]. And Andy whispers in Miranda’s ear: “He’s the ambassador and his new wife.” And I was like, “That’s exactly what I need. I need an AI version of Anne Hathaway.” And it’s now doable with LLMs because it’s all this unstructured, messy data that an AI can take all of that, contextualize it, and hopefully be that AI chief of staff for you. Is that like a product? Is that something that you would have to do to turn your chatbot, whatever, your Claude or your ChatGPT into that? Or is it a new product that you think is going to come out that will make that easy for you? There are already a number of new companies that are starting in this space. So one company’s called VIA.AI, it’s a Boston-based company. They do this for sales professionals and BD professionals who have to do this for their work. There’s a company called Goodword that I’m very excited about. They’re doing this for just the average person. Like you and I, we have very strong networks, but how can we organize it? So I’m excited about that one. There’s a company called Boardy that does this for investors and founders. So it’s becoming a thing, and I’m excited to see how these companies take off in 2026. They’re all fairly new, so it’ll be interesting to see how they evolve. Yeah, and whether they can stay ahead of some of the bigger chatbots that may just try to integrate some of this capability into the products they already have. That’s always the case in this kind of evolution of technology: What’s a feature and what’s a company, right? What’s an independent service? Absolutely. When I’m looking at these companies and I’m diligencing them, that’s a key question that I ask. Is this something that the next version of ChatGPT or Gemini is just going to implement? And if the answer is yes, then that’s obviously not a defensible company. But a lot of times there’s this additional moat of data and algorithms that you need to sit on top of these LLMs. And I believe in this relationship intelligence space, I don’t think this is something that just a kind of an off-the-shelf LLM can do. It really needs to know you. It needs to know your data, it needs to know your relationships. And you have to trust it enough to share all that data with it, right? Absolutely. That’s your proprietary data, whether it’s about your business or about you individually. Exactly. And I don’t want this to all go up to OpenAI’s cloud. I want to trust that I have control over these really private relations. If you and I have a conversation about our kids, I don’t necessarily want that to now sit in a general OpenAI cloud and be used to train the next ChatGPT. So that safety and security, appreciating the privacy and the importance of this data, is really key. Another business change you expect in 2026 is the insertion of AI into the org chart. This is about who manages AI, like performance reviews and team culture impacts? Yeah, so this goes back to the thesis that there’s this shift in how AI is creating value, and it’s not a tool anymore. Well, it is a tool. It’ll always be a tool, but it’s not a tool that helps you get work done faster. It could actually take an end-to-end task and get it done for you. And I’ll give a few examples. So I’m an investor in a company called Synthpop, and instead of building a tool that helps healthcare administrators accelerate or really become efficient in how they do patient intake, it just takes the task of patient intake. It does the thing end to end. And so if you then imagine what that means for a hospital or a clinic, it will have a combination of human workers collaborating and working closely with AI coworkers. And so then the question becomes, well, who manages these hybrid teams? Sometimes it’s a human manager, sometimes it’s an AI manager. I’m also an investor in a company called Tough Day, and they sell you AI managers. And then how do you do performance reviews for these hybrid teams? How do you build a culture? Like at Affectiva, my company, culture was our superpower. How do you build a culture when some of your team members are AI and some of your team members are humans? So I think that is going to spur a lot of conversation around how do you build organizations that are combinations of digital agents and human employees? As you talk about this merging of AI agents and humans in work, it brings up that looming question about the impact of AI on jobs and employment. And some numbers are coming out now that make it seem like, “Oh, it’s bad for jobs.” There are other numbers coming out that are like, “Oh, we’re actually hirng more people because of it.” Do you have a prediction about what is going to happen with that in 2026? Is AI going to take over roles that have been done by humans that quickly? We had a really fascinating roundtable discussion at the Fortune Brainstorm AI conference and the headline was like, “Is AI killing entry-level jobs?” And actually, a lot of the Fortune companies and also AI companies that were around the table were basically saying, “No, we’re hiring more entry-level jobs. They’re just not the same jobs that we were traditionally seeing.” And also the career ladders have changed. So my prediction is we’re going to see an entirely different organization where I think if you are able to come in an entry-level position, for example, but work very closely with AI and be AI-native and be AI fluent and be able to wear multiple hats, I think that’s going to go a long way. As opposed to this very siloed job trajectory where you come in, this is your little task, and then you do more of it, and then you go up the career ladder. I think that’s going to change. I think young people are looking for different ways of working, and I think AI is changing all of that anyway. Will there be jobs that will go away? I think so. I can’t remember who said this line, but it’s now very popular: It’s not AI that’s going to take your job. It’s going to be somebody who knows how to use AI. And I believe that to be true.
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By the end of October, David, who works at a roughly 2,000-person finance firm in New York, already knew hed be working during the holiday season this year. Usually at the office, he learned hed at least get to work remotely between December 26 and January 1with the way the financial calendar fell, it was inevitable that he couldnt just disappear for clients (like institutional investors and family offices) during that time. He says the schedule doesnt really bother him. I’m not in a trench in the middle of a battlefield here. I’m not laying bricks, he says. Its not terribly unrealistic work that they’re asking us to do. Mainly, hes expected to respond to emails and move forward client processes already in the works. David (who, like other employees Fast Company spoke with, is using his first name only to avoid professional repercussions) is one of many office workers who stay on the clock during winter holidays. Per a 2023 CalendarLabs survey of more than 1,000 full-time U.S. employees, 24% reported planning to work on Christmas Eve, 12% on Christmas Day, and 27% on New Years Eve. Exclusive data from 2024 and 2025 shared with Fast Company by Stanford University economics professor Nicholas Bloom show that these figures tend to be higher for remote workers, 13.3% of whom work on Christmas Day compared to just 1.9% of those who work in person, while nearly 39% of remote employees work the day after Christmas, versus 16% who work in-office. Many employers dont explicitly require office workers to clock in during this Christmas through New Years period, at least not in a typical 9-to-5 fashion. But a few main factors drive people to do it anyway: They have time-sensitive tasks, their higher-ups continue to work so they feel the need to mirror that behavior, and, during this precarious economic time, they fear not showing up could lead to a layoff. The pattern I see in organizations is consistent, says Gleb Tsipursky, CEO of the future-of-work consultancy Disaster Avoidance Experts. Coverage needs do not stop, and many knowledge workers stay online in some capacity because of deadlines, client expectations, end-of-year closeor simply because they feel they will fall behind if they disconnect. Theres always an expectation that you have some level of availability While its obvious why those working in retail or delivery dont quit from late December through the new year, some knowledge workers still have time-sensitive tasks during the season. In litigation, things come up and you have to deal with them, says Thomas, an attorney working at a law firm in New Jersey. Last December, he had a hearing scheduled for the day after Christmas and had to prepare on short notice. Other times, lawyers will work through the end of the year to meet billable hour requirements. Software engineers, meanwhile, may find themselves suddenly on call to put out code-based fires, and marketing professionals could face unexpected publicity nightmares. David, because he works with high-net-worth clients who tend to retreat to second (or third, or fourth) homes during the holidays, had been told early in his career that work gets quieter when this happens. But hes found that the opposite plays out. [Thats] when people have the most questions, because that’s when they actually read their mail or their statements, he says. There’s always an expectation that you have some level of availability. Though these time-sensitive needs are reasonable drivers for clocking in, Robert Kovach, a work psychologist whos long advised senior executives, says working during this season is often less about the work that needs to be done . . . it’s about [the workers] identity. Working at this time of year almost becomes a proxy for commitment, ambition, indispensability, he says. People do it to signal that theyre reliable and valuable. Responsiveness rewarded Again, this usually doesnt come down to formal office policies. The strongest drivers for people to work during holidays tend to be culture signals and incentive structures, says Tsipursky. Leaders reward responsiveness, for example, by publicly praising those who reply quickly during holidays and using phrases like We can count on you during performance reviews. That responsiveness can look like anything from hourly Slack check-ins to responding same day to emails. Typically, says Kovach, bosses dont mandate this, they model it, like by sending emails at 11 p.m. on Christmas Eve. Per Ryan, a software engineering manager in New York, no one is asked to work during this time of year at his company, but people feel committed to their outcomes and delivery. And even though tech companies, in his experience, rarely set schedules for the holidays . . . outside of on-call support, many employees still work in the competitive industry. The real standard becomes Do not disappear, even if no one says that out loud, Tsipurksy says. That can lead to anxiety-fueled holiday working, often compounded by fears of layoffs that happen all too frequently this time of year. After the efficiency layoff trend that started with Twitter and Elon [Musk] and continued to wipe the industry, roles are scarce and competition has naturally increased, says Ryan. Expectations in his industry, he adds, are high among both peers and management, and people tend to meet them by working harder and more. Plus, theres the added fear of AI taking white-collar jobs, says Kovach. In the economic climate we’re in right now, fear is a big [driver for wanting] to be seen as being productive. This could help explain why remote workers work more this time of yearand during holidays and weekends in general, as Blooms research revealed, during which remote workers office activity can exceed those working in person by up to 41.5 percentage points. Since theyre not literally seen by higher-ups, they spend more time making themselves seen via emails time-stamped on January 1, or slack messages that role in on December 24. Some people enjoy holiday working: Future You appreciates Present You Required or not, plenty of people like heading into the office during holidays. A friend of mine who works at a health insurance company calls it a chill time to come in. In the past, it even paved the way for her to get unique, one-on-one time with a very high up superiorshe got to give them a solo presentation while everyone else was off. Then there are people who dont enjoy the holiday season. They may not have much family or they may get depressed at this time of year, so work offers a positive distraction. Of course, not everyone celebrates Christmas, and they may prefer saving vacation days for other occasions. Younger workers told Fast Company that they plan to have kids in the future but dont yet, so they figured theyd put in their time during the holidays now, build up goodwill, ad take vacation when their situations change down the line. When I take days off, I don’t know what to do with myself, says David, so he finds himself checking his phone for office-related notifications. One of his big pros for working during holidays is that afterward, you don’t come back to a giant hornet’s nest of things you need to do, he says. Future You appreciates Present You for keeping the machine moving. However, these pros are easier to come by in office cultures that arent fueled by passive-aggressive pressure. When leaders do things like schedule optional meetings between Christmas and New Years Day, set immediate post-holiday deadlines, or repeatedly send follow-ups to unanswered messages, it sends a clear messagethat silence has consequences, Tsipursky says. In healthier office environments, companies set explicit expectations, plan coverage rotations, and protect true time off, Tsipursky says. Just as leaders can model working too much over the holidays, so can they set a positive example for stepping away. If a senior person disconnects visibly, everyone else gets permission, Kovach says. Ultimately, superiors can confuse being available with being valuable. Taking time off during holiday periods is often the mental reset people need to work more productively when they return. What [leaders] have to be really careful about, Kovach says, is they’re not unconsciously equating responsiveness, or being on, with performance.
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December 15, 2025the deadline for enrolling in a marketplace plan through the Affordable Care Act for 2026came and went without an agreement on the federal subsidies that kept ACA plans more affordable for many Americans. Despite a last-ditch attempt in the House to extend ACA subsidies, with Congress adjourning for the year on December 19, its looking almost certain that Americans relying on ACA subsidies will face a steep increase in healthcare costs in 2026. As a gerontologist who studies the U.S. healthcare system, Im aware that disagreements about healthcare in America have a long history. The main bone of contention is whether providing healthcare is the responsibility of the government or of individuals or their employers. The ACA, passed in 2010 as the countrys first major piece of health legislation since the passage of Medicare and Medicaid in 1965, represents one more chapter in that long-standing debate. That debate explains why the health law has fueled so much political divisivenessincluding a standoff that spurred a record-breaking 43-day-long government shutdown, which began on October 1, 2025. In my view, regardless of how Congress resolves, or doesnt resolve, the current dispute over ACA subsidies, a durable U.S. healthcare policy will remain out of reach until lawmakers address the core question of who should shoulder the cost of healthcare. The ACAs roots In the years before the ACAs passage, some 49 million Americans15% of the populationlacked health insurance. This number had been rising in the wake of the 2008 recession. Thats because the majority of Americans ages 18 to 64 with health insurance receive their health benefits through their employer. In the 2008 downturn, people who lost their jobs basically lost their healthcare coverage. For those who believed government had a primary role in providing health insurance for its citizens, the growing number of people lacking coverage hit a crisis point that required an intervention. Those who place responsibility on individuals and employers saw the ACA as a perversion of the governments purpose. The political parties could find no common groundand this challenge continues. The major goal of the ACA was to reduce the number of uninsured Americans by about 30 million people, or to about 3% of the U.S. population. It got about halfway there: Today, about 26 million Americans, or 8%, are uninsured, though this number fluctuates based on changes in the economy and federal and state policy. Health insurance for all? The ACA implemented an array of strategies to accomplish this goal. Some were popular, such as allowing parents to keep their kids on their family insurance until age 26. Some were unpopular, such as the mandate that everyone must have insurance. But two strategies in particular had the biggest impact on the number of uninsured. One was expanding the Medicaid program to include workers whose income was below 138% of the poverty line. The other was providing subsidies to people with low and moderate incomes that could help them buy health insurance through the ACA marketplace, a state or federal health exchange through which consumers could choose health insurance plans. Medicaid expansion was controversial from the start. Originally, the ACA mandated it for all states, but the Supreme Court eventually ruled that it was up to each state, not the federal government, to decide whether to do so. As of December 2025, 40 states and the District of Columbia have implemented Medicaid expansion, insuring about 20 million Americans. Meanwhile, the marketplace subsidies, which were designed to help people who were working but could not access an employer-based health plan, were not especially contentious early on. Everyone receiving a subsidy was required to contribute to their insurance plans monthly premium. People earning $18,000 or less annually, which in 2010 was 115% of the income threshold set by the federal government as poverty level, contributed 2.1% of their plans cost, and those earning $60,240, which was 400% of the federal poverty level, contributed 10%. People making more than that were not eligible for subsidies at all. In 2021, legislation passed by the Biden administration to stave off the economic impact of the COVID-19 pandemic increased the subsidy that people could receive. The law eliminated premiums entirely for the lowest income people and reduced the cost for those earning more. And, unlike before, people making more than 400% of the federal poverty levelabout 10% of marketplace enrolleescould also get a subsidy. These pandemic-era subsidies are set to expire at the end of 2025. Cost versus coverage If the COVID-19-era subsidies expire, healthcare costs would increase substantially for most consumers, as ACA subsidies return to their original levels. So someone making $45,000 annually will now need to pay $360 a month for health insurance, increasing their payment by 74%, or $153 monthly. Whats more, these changes come on top of price hikes to insurance plans themselves, which are estimated to increase by about 18% in 2026. With these two factors combined, many ACA marketplace users could see their health insurance costs rise more than 100%. Some proponents of extending COVID-19-era subsidies contend that the rollback will result in an estimated 6 million to 7 million people leaving the ACA marketplace and that some 5 million of these Americans could become uninsured in 2026. Congressional gridlock over a healthcare bill continues. Policies in the tax and spending package signed into law by President Donald Trump in July 2025 are amplifying the challenge of keeping Americans insured. The Congressional Budget Office projects that the Medicaid cuts alone, stipulated in the package, may result in more than 7 million people becoming uninsured. Combined with other policy changes outlined in the law and the rollback of the ACA subsidies, that number could hit 16 million by 2034essentially wiping out the majority of gains in health insurance coverage that the ACA achieved since 2010. Subsidy downsides These enhanced ACA subsidies are so divisive now in part because they have dramatically driven up the federal governments healthcare bill. Between 2021 and 2024, the number of people receiving subsidies doubledresulting in many more people having health insurance, but also increasing federal ACA expenditures. In 2025, almost 22 million Americans who purchased a marketplace plan received a federal subsidy to help with the costs, up from 9.2 million in 2020a 137% increase. Those who oppose the extension counter that the subsidies cost the government too much and fund high earners who dont need government supportand that temporary emergencies, even ones as serious as a pandemic, should not result in permanent changes. Another critique is that employers are using the ACA to reduce their responsibility for employee coverage. Under the ACA, employers with more than 50 employees must provide health insurance, but for companies with fewer employers, that requirement is optional. In 2010, 92% of employers with 25 to 49 workers offered health insurance, but by 2025, that proportion had dropped to 64%, suggesting that companies of this size are allowing the ACA to cover their employees. Diverging solutions The U.S. has the most expensive healthcare system in the world by far. The projected increase in the number of uninsured people over the next 10 years could result in even higher costs, as fewer people get preventive care and delayed healthcare interventions, ultimately leading to more complex medical care Federal policy clearly shapes health insurance coverage, but state-level policies play a role too. Nationally, about 8% of people under age 65 were uninsured in 2023, yet that rate varied widelyfrom 3% in Massachusetts to 18.6% in Texas. States under Republican leadership on average have a higher percentage of uninsured people than do those under Democratic leadership, mirroring the political differences driving the national debate over who is responsible for shouldering the costs of healthcare. With dueling ideologies come dueling solutions. For those who believe that the government is responsible for the health of its citizens, expanding health insurance coverage and financing this expansion through taxes presents a clear approach. For those who say the burden should fall on individuals, reliance on the free market drives the fixon the premise that competition between health insurers and providers offers a more effective way to solve the cost challenges than a government intervention. Without finding resolution on this core issue, the U.S. will likely still be embroiled in this same debate for years, if not decades, to come. Robert Applebaum is a senior research scholar in gerontology at Miami University. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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In the world of the long-running kids show Cyberchase, Motherboard, a sort of digital queen and literal technocrat, is the beneficent but impaired leader of all of cyberspace. She iswe are to understanda legitimate ruler, yet faces constant attacks from the odious Hacker, a green-skinned android who dresses like a vampire and whose only goal is to sow chaos and eventually take control of Motherboards realm, which we might describe as something akin to a metaverse, or ever-expanding digital world. Luckily, a trio of human kids named Inez, Mattie, and Jackiea squadvisit cyberspace frequently, where they embark on missions to help protect the ever-embattled Motherboard from her nemesis. Theyre frequently assisted by Digit, a robotic cybird that guides them through various missions. Cyberchase is a publicly funded STEM-themed program created by the public television channel WNET Thirteen. Its been airing on PBS Kids since 2002. As such, every challenge the squad takes on can be answered with numbers, or at least some kind of mathematical concept. Sometimes, an episode involves a mission with subtraction, fractions, or even negative numbers! The whole point of the squads trials and tribulations is to teach children basic science, technology, engineering, and math concepts through adventures. Sandra Sheppard, who created the show and now serves as executive producer, says its writers keep a close eye on how well U.S. students are doing with math concepts, especially as general math performance in the country continues to decline: Incoming freshmen at the University of San Diego increasingly need remedial math education, according to placement test performance, and national U.S. high school math performance has been sinking for years, according to the National Assessment of Education Progress. Parents report that social media continues to be a major distraction for kids. In response, Cyberchase has adapted its content for the social media age, producing shorts that create snippets of its larger math lessons as well as online gaming content. For its upcoming season, slated to premiere in spring 2026, it has released its first seven-minute episodes, which are intended to find a midpoint between a full episode and short-form content. Fast Company chatted with Sheppard about public television in the age of streaming and TikTok, the value of the PBS Kids brand, and how shes adapting a beloved shows math content to meet American kids where they are. This interview has been edited for length and clarity. [Image: Thirteen] I have a very particular memory of learning about negative numbers before everyone else, and then revealing this secret knowledge that I had learned through Cyberchase. But I didn’t have a smartphone or a computer with the internet until high school. How can you possibly get kids to the show when youre competing with smartphones, chatbots, and TikTok? Over the last decade, if not more, the approach to reaching kids is really very multi-platform, because we know kids are using multiple devices and watching in a myriad of ways. I think our partners at PBS Kids have been great in developing products and tools so that kids can really watch anywhere. Cyberchase is on the PBS Kids Video app and it streams everywhere, including YouTube. We also offer games, and that continues to be a really important part of the learning. Thinking about getting our content where kids are watching is constantly on our mind, as is developing content for those platforms and experimenting on those platforms. That includes shorter-form content, vertical shorts, and different kinds of compilations. Have you had more success with some platforms rather than others? Probably most children watch our content on streaming. That being said, there still is a dedicated audience for linear broadcast. And it’s a very diverse audience. Across the platforms, full episodes continue to be the driver of engagement. That’s not to say short-form content isn’t popular, or compilations aren’t popular, but we find that kids are still really driven by story. A full episode is 22 minutes. We’ve been experimenting this coming season with seven-minute stories, a little more bite-sizeas long as they’re a full narrative, so kids can have that kind of rich experience of watching a full story. Can you talk a little bit more about the seven-minute episodes? They’ll be coming out in March. It allows us to focus on math concepts a little bit more simply in bite-size stories, and really focus on some of the characters that we know our audience loves. There are Buzz and Delete, our bumbling henchmen who are buddies and semi-lovable in their own right. We’ve got a whole series of shorts that feature them in these kind of friendship-oriented adventures. We can, in a short time, focus on a single strategy of subtraction, or focus on how to estimate using weight and why that’s an important tool. That’s not to say we’re moving away from long form, but it’s fun to experiment in that space. Those will be released digitally on all the PBS Kids platforms and YouTube. How do you compete with the whole of the internet using algorithm-driven engagement when trying to get kids to your math-based content? The PBS Kids brand is a very safe and trusted brand. For young children, parents, and families, we still guide many of their viewing experiences. And I think they see us as a trusted source of content. That’s not to say that there’s not lots out there and that it hasn’t become more fragmented. There are loads of choices. [Image: Thirteen] How do you measure the sort of uptake of the idas for kids? Is that something you study to make sure that they’re understanding math? How does that work? We do a lot of initial developmental research, where we put ideas in front of focus groups of kids and families and test them out as early scripts. That gives us the opportunity to tweak up front. But we’ve also done a number of studies with external evaluators to really look at: Are kids learning the specific content in the shows? The good news is that we are really kind of a proven research model in that kids do learn from the series. Something I’ve heard anecdotally from people I know who teach math is that kids seem to really be struggling. Especially after the pandemic, it seems like American students are really doing poorly in math. Whats the role of Cyberchase in that? There certainly have been some national reports from the National Center for Education Statistics and the American Education Panel that have shown some real concerns in terms of math knowledge and gaps. Post-COVID, there have been some widening gaps. Interestingly, in this season we made kind of a renewed commitment to focusing on topics like subtraction, which can be a complex topic for young children. For some, addition comes more readily. Subtraction, especially as a kind of mental map exercise, can be challenging. We are embracing topics that could use some extra support. We live in a world that is changing. We’re all inundated with data, some of it AI-driven, some of it not. We’ve also focused in the last couple of seasons on data science, not only collecting and representing data, but looking at it and making sense of it reasonably. Another topic that we’re tackling this season is fractions. I think that’s a topic that for a lot of kids takes a lot of reinforcement. Patterns are a foundational topic in math and a foundational topic in programming. Giving kids more exposure to patterns, all kinds of patterns, too. I’ll say one other theme that I’m really excited about is connecting math to civics and the community. Certainly some of that involves data, but we have a very special show that’s going to be released called Every Flipper Counts. Its set in this wonderful cyber site of Penguia where the penguins have to pick a new team captain for their belly bowll, and the squad comes in and they introduce voting as a fair way to decide. There’s a lot of math and figuring out how to set up a fair vote. How have the cuts to public media impacted you? As a station, were always looking at ways to be more relevant, more sustainable. We have some wonderful funders of Cyberchase who are very supportive. For decades, Cyberchase did receive funding from the National Science Foundation. And for the moment, that’s not happening. It’s a complicated time and we have to navigate a path forward and find new ways to be smart, be cost effective, and bring in new supporters. Final question: Is Cyberchase the metaverse? It is an imaginary cyber world. The metaverse term came later. It’s a whimsical, vast landscape of these wonderfully rich, imaginary cyber sites. Its given us, as writers, an unbelievable place to go.
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E-Commerce
People and institutions are grappling with the consequences of AI-written text. Teachers want to know whether students work reflects their own understanding; consumers want to know whether an advertisement was written by a human or a machine. Writing rules to govern the use of AI-generated content is relatively easy. Enforcing them depends on something much harder: reliably detecting whether a piece of text was generated by artificial intelligence. Some studies have investigated whether humans can detect AI-generated text. For example, people who themselves use AI writing tools heavily have been shown to accurately detect AI-written text. A panel of human evaluators can even outperform automated tools in a controlled setting. However, such expertise is not widespread, and individual judgment can be inconsistent. Institutions that need consistency at a large scale therefore turn to automated AI text detectors. The problem of AI text detection The basic workflow behind AI text detection is easy to describe. Start with a piece of text whose origin you want to determine. Then apply a detection tool, often an AI system itself, that analyzes the text and produces a score, usually expressed as a probability, indicating how likely the text is to have been AI-generated. Use the score to inform downstream decisions, such as whether to impose a penalty for violating a rule. This simple description, however, hides a great deal of complexity. It glosses over a number of background assumptions that need to be made explicit. Do you know which AI tools might have plausibly been used to generate the text? What kind of access do you have to these tools? Can you run them yourself, or inspect their inner workings? How much text do you have? Do you have a single text or a collection of writings gathered over time? What AI detection tools can and cannot tell you depends critically on the answers to questions like these. There is one additional detail that is especially important: Did the AI system that generated the text deliberately embed markers to make later detection easier? These indicators are known as watermarks. Watermarked text looks like ordinary text, but the markers are embedded in subtle ways that do not reveal themselves to casual inspection. Someone with the right key can later check for the presence of these markers and verify that the text came from a watermarked AI-generated source. This approach, however, relies on cooperation from AI vendors and is not always available. How AI text detection tools work One obvious approach is to use AI itself to detect AI-written text. The idea is straightforward. Start by collecting a large corpus, meaning collection of writing, of examples labeled as human-written or AI-generated, then train a model to distinguish between the two. In effect, AI text detection is treated as a standard classification problem, similar in spirit to spam filtering. Once trained, the detector examines new text and predicts whether it more closely resembles the AI-generated examples or the human-written ones it has seen before. The learned-detector approach can work even if you know little about which AI tools might have generated the text. The main requirement is that the training corpus be diverse enough to include outputs from a wide range of AI systems. But if you do have access to the AI tools you are concerned about, a different approach becomes possible. This second strategy does not rely on collecting large labeled datasets or training a separate detector. Instead, it looks for statistical signals in the text, often in relation to how specific AI models generate language, to assess whether the text is likely to be AI-generated. For example, some methods examine the probability that an AI model assigns to a piece of text. If the model assigns an unusually high probability to the exact sequence of words, this can be a signal that the text was, in fact, generated by that model. Finally, in the case of text that is generated by an AI system that embeds a watermark, the problem shifts from detection to verification. Using a secret key provided by the AI vendor, a verification tool can assess whether the text is consistent with having been generated by a watermarked system. This approach relies on information that is not available from the text alone, rather than on inferences drawn from the text itself. AI engineer Tom Dekan demonstrates how easily commercial AI text detectors can be defeated. Limitations of detection tools Each family of tools comes with its own limitations, making it difficult to declare a clear winner. Learning-based detectors, for example, are sensitive to how closely new text resembles the data they were trained on. Their accuracy drops when the text differs substantially from the training corpus, which can quickly become outdated as new AI models are released. Continually curating fresh data and retraining detectors is costly, and detectors inevitably lag behind the systems they are meant to identify. Statistical tests face a different set of constraints. Many rely on assumptions about how specific AI models generate text, or on access to those models probability distributions. When models are proprietary, frequently updated or simply unknown, these assumptions break down. As a result, methods that work well in controlled settings can become unreliable or inapplicable in the real world. Watermarking shifts the problem from detection to verification, but it introduces its own dependencies. It relies on cooperation from AI vendors and applies only to text generated with watermarking enabled. More broadly, AI text detection is part of an escalating arms race. Detection tools must be publicly available to be useful, but that same transparency enables evasion. As AI text generators grow more capable and evasion techniques more sophisticated, detectors are unlikely to gain a lasting upper hand. Hard reality The problem of AI text detection is simple to state but hard to solve reliably. Institutions with rules governing the use of AI-written text cannot rely on detection tools alone for enforcement. As society adapts to generative AI, we are likely to refine norms around acceptable use of AI-generated text and improve detection techniques. But ultimately, well have to learn to live with the fact that such tools will never be perfect. Ambuj Tewari is a professor of statistics a the University of Michigan. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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