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How can telcos use AI-generated synthetic data to fuel machine learning? Telecommunications companies are sitting on a huge volume of data. Call records, location pings, browsing sessions, and usage patterns can all paint a remarkably detailed picture of how millions of people move through their lives. But regulations like GDPR and CCPA, plus an ever-expanding patchwork of local data residency laws, mean telcos are limited in how they can use much of this data for things like AI and ML projects. Synthetic data, however, could be a workaround. Instead of piping real customer records into machine learning pipelines, telcos are increasingly generating artificial datasets that statistically mirror actual customer behavior without containing real data points. The idea is simple enough — algorithms learn the patterns, distributions, and correlations baked into real data, then spin up entirely new records that preserve those statistical properties while being completely fabricated. Models trained on synthetic data let telcos build and iterate on network optimization, churn prediction, personalized services, and predictive maintenance — none of which requires exposing actual customer information to breach risk or the weight of privacy law. It’s not a perfect solution, and there are genuine trade-offs involved, but for an industry that’s simultaneously heavily regulated and increasingly reliant on AI, synthetic data is one of the most practical paths available right now. How synthetic data generation works Deep learning generative models are the most sophisticated tools available for capturing the complex behavioral dynamics telcos actually care about. These are neural network architectures built to learn the underlying structure of real datasets and reproduce it convincingly. GANs, or Generative Adversarial Networks, are probably the most widely recognized approach. Two neural networks compete with each other — a generator produces synthetic data while a discriminator tries to tell whether the output looks real. That push-and-pull forces the generator toward increasingly realistic records over successive training rounds. GANs shine when it comes to complex, multivariate sequences — exactly the kind of data you’d encounter in location tracking or communication pattern analysis, where multiple variables interact across time. Variational Autoencoders, or VAEs, work differently. They compress real data down into a compact latent representation and then decode it back out as synthetic samples. That compression-decompression cycle is particularly good at capturing probabilistic variation and maintaining structural smoothness, which makes VAEs a strong fit for generating slightly varied behavioral patterns while keeping statistical integrity intact. GANs tend to produce sharper, more specific outputs, while VAEs lean toward smoother, more broadly distributed data. Each has its sweet spot depending on what you’re trying to accomplish. Transformer models, including GPT-based architectures, are also part of the picture. These can process structured customer logs and usage records, learning the relationships and patterns within them. They’re effective for generating task-specific synthetic records with prompt-driven control, letting engineers specify exactly what kind of data they need. The caveat is that transformer-generated outputs often need additional validation to confirm the results are statistically grounded rather than just plausible-sounding. Not everything demands deep learning, though. Rule-based generation still has a role, and sometimes it’s the more appropriate choice. Simulation models replicate real-world processes using predefined rules and variables. Data transformation techniques apply mathematical operations to existing records to create new synthetic data points. Markov chains generate sequential data where each value depends on the previous one — a natural fit for time-series events like location traces or communication session logs. These methods lack the flexibility of neural network approaches, but they’re cheaper, easier to interpret, and in many cases perfectly sufficient for the job. Privacy preservation The reason synthetic data works as a

In a patch of sunlight Verla sits on a wooden folding chair and waits. When the door opens she holds her breath. It is another girl who comes into the room. They lock eyes for an instant, then look away to the floor, the walls. Article continues after advertisement The girl moves stiffly in her weird costume, taking only a few steps into the room. The door has closed behind her. The only spare chair is beside Verla’s, so Verla gets up and moves to the window. It is too much, that she be put so close to a stranger. She stands at the window, looking out through a fly-spotted pane at nothing. There is bright sunlight coming into the room, but only reflected off the white weatherboards of another building just metres away. She presses her face to the glass but can see no windows anywhere along the length of that building. She can feel the other girl behind her in the room, staring at her peculiar clothes. The stiff long green canvas smock, the coarse calico blouse beneath, the hard brown leather boots and long woollen socks. The ancient underwear. It is summer. Verla sweats inside them. She can feel it dawning on the other girl that she is a mirror: that she too wears this absurd costume, looks as strange as Verla does. Verla tries to work out what it was she had been given, scanning back through the vocabulary of her father’s sedatives. Midazolam, Largactil? She wants to live. She tries wading through memory, logic, but can’t grasp anything but the fact that all her own clothes—and, she supposes, the other girl’s—are gone. She blinks a slow glance at the girl. Tall, heavy-lidded eyes, thick brows, long black hair to her waist is all Verla sees before looking away again. But she knows the girl stands there dumbly with her hands by her sides, staring down at the floorboards. Drugged too, Verla can tell from her slowness, her vacancy—this runaway, schoolgirl, drug addict? Nun, for all Verla knows. But somehow, even in this sweeping glance, the girl seems familiar. She understands fear should be thrumming through her now. But logic is impossible, all thinking still glazed with whatever they have given her. Like the burred head on a screw, her thoughts can find no purchase. Article continues after advertisement Verla follows the girl’s gaze. The floorboards glisten like honey in the sun. She has an impulse to lick them. She understands that fear is the only thing now that could conceivably save her from what is to come. But she is cotton-headed, too slow for that. The drug has dissolved adrenaline so completely it almost seems unsurprising to be here, with a stranger, in a strange room, wearing this bizarre olden-day costume. She can do nothing to resist it, cannot understand nor question. It is a kind of dumb relief. But she can listen. Verla strains through her sedation. Somewhere beyond the door is the judder of some domestic motor—a fridge, maybe, or an air-conditioning unit. But the place is stinking hot, primitive. She has no idea where they are. The room is large and light. There are the two wooden folding chairs—empty, the other girl did not sit—against a wall painted milky green, and a blackboard at the other end of the room with a rolled vinyl blind high up at the top of the board. Verla knows without knowing that if she tugged on the ring dangling from the centre of the blind she would pull down a map of Australia, coloured yellow and

Hello, I’m Tom Vander Ark. Lately, I’ve been thinking a lot about how hard it has become to talk across differences in America, especially when the issues are complex, emotional, and constantly amplified by social media. We ask young people to navigate that environment every day, yet we rarely give them the time, tools and permission to practice the skills that make real dialogue possible: building an argument on shared evidence, listening with curiosity, and staying in relationship even when disagreement is real. That’s why this conversation matters, not as a ‘nice to have,’ but as a core part of preparing students for civic life and for the kind of community-building our schools are uniquely positioned to support. I sat down with Dr. Vikki Katz, a communications professor at Chapman University and the director of the new OR Initiative. We talked about the roots of her work, shaped by growing up in South Africa during the dismantling of apartheid, and how that experience informs her optimism about what’s possible when people commit to rebuilding trust. We also explored what civil discourse can look like in middle school and high school classrooms, how educators can be supported through system-wide leadership and professional learning communities, and why digital discernment and civil discourse have to be taught together in an AI-accelerated world. Introduction & Origins Tom Vander Ark: We’re talking about civil discourse today: what it is, why it’s important, and how to teach young people something that we’re not very good at as adults in America. We have the pleasure of speaking with Dr. Vikki Katz. She’s a communications pro at Chapman University. Vikki, what a treat to have you join us. Dr. Vikki Katz: Thank you for having me. Tom Vander Ark: Where did your interest in civil discourse come from? Dr. Vikki Katz: My interest in civil discourse goes all the way back. I grew up in South Africa through the 1980s and early 1990s and was old enough to understand what was happening as apartheid was dismantled, Mandela was released from prison, and South Africans came together to build and reimagine a country. The ways in which South Africa did that really left an imprint on me. I joke that it has made me pathologically optimistic about what is possible, even when it looks impossible. And that really has always been one of the engineering lessons that underlies the work that I’ve done in these kinds of areas. Tom Vander Ark: It’s a beautiful origin story. My last trip to South Africa reminded me that my Dutch Calvinist roots include a lot of culpability for setting up a system that prevented civil discourse. And so that’s one thing I think about when I visit. Tom Vander Ark: South Africa, Vikki, you launched an initiative called the OR Initiative to bring this set of skills—civil discourse skills—to middle school, high school and college students. Tell us about that. Dr. Vikki Katz: So we officially launched in early February 2026, but we spent a year doing foundational work to really listen on the things that we were interested in so that we wouldn’t start building solutions without making sure that we were building things that people really needed. We’ve launched the OR Initiative at Chapman University as a new program, and our mission is to help educators prepare our young people to become more digitally discerning and make it easier for them to build robust and shared evidence bases so that they can learn to engage in civil discourse on a shared base, and be able to get braver about talking

Spider-Man: Brand New Day may be the year’s most highly anticipated movie, but as another day goes by, we still don’t have a trailer for the web-slinger’s long-awaited MCU return.
Sony Pictures appears to be taking a similar approach to how Spider-Man: No Way Home was marketed in 2021. While that’s frustrating for fans, the blockbuster grossed $1.9 billion at a time when theaters were still struggling with the damage caused by the pandemic, so the approach is understandable.
Promo art leaks have offered fans an early look at Spider-Man: Brand New Day’s villains, while rumours about what to expect from the movie continue to swirl. The latest is a little hard to believe, but gives us something to chew on as the long wait for a teaser continues.
According to insider @MyTimeToShineH, there are rumblings that Tom Holland’s wall-crawler dies in the movie. They later followed that up by sharing the cover of Web of Spider-Man #32, part of the iconic Kraven’s Last Hunt storyline, and hinted that we’ll see that moment recreated on screen this summer.
Now, that does admittedly sound far-fetched, but there are a couple of ways for Marvel to recreate this beloved piece of imagery in Spider-Man: Brand New Day.
The first would be during a poison-induced hallucination sequence caused by The Scorpion. We’ve heard rumblings about the movie featuring a scene like that, so it’s the likeliest possibility (another is that Peter Parker has nightmares about his future death while in a web coccoon expected to give him new abilities like organic webbing).
Another is that someone actually buries Spidey, believing him dead, only for the hero to fight his way out of that and back into the land of the living.
For now, though, we’ll have to wait and see whether this pans out when Spider-Man: Brand New Day heads our way later this year.
In Spider-Man: Brand New Day, four years have gone by since we last caught up with our friendly neighborhood hero. Peter Parker is no more, but Spider-Man is at the top of his game, keeping New York City safe. Things are going well for our anonymous hero until an unusual trail of crimes pulls him into a web of mystery larger than he’s ever faced before.
In order to take on what’s ahead, Spider-Man not only needs to be at the top of his physical and mental game, but he must also be prepared to face the repercussions of his past!
Shang-Chi and the Legend of the Ten Rings helmer Destin Daniel Cretton directs Spider-Man: Brand New Day from a script by returning Spider-Man franchise writers Chris McKenna and Erik Sommers.
Tom Holland plays Spider-Man in a cast that also includes Jon Bernthal (The Punisher), Mark Ruffalo (The Hulk), Zendaya (MJ), Sadie Sink, Michael Mando (The Scorpion), Tramell Tillman, Marvin Jones III (Tombstone), Jacob Batalon (Ned Leeds), and Liza Colón-Zayas. Avengers: Doomsday star Florence Pugh is expected to reprise her Thunderbolts* role as Yelena Belova.
Spider-Man: Brand New Day will be released in theaters on July 31, 2026.