The year’s just getting started, but there’s already a lot happening. Peruse our latest monthly roundup of Made with Unity highlights to learn what’s up in the Unity community.#MadeWithUnity games reached some exciting milestones in January.To start, we celebrated the return of a classic as Colossal Cave was released in VR. Death in the Water 2 by Lighthouse Games also launched, and it’s proving to be quite a terrifying experience.Finally, it’s great to see so many Made with Unity games nominated for the upcoming Academy of Interactive Arts & Sciences’ D.I.C.E. Awards. The nominees include:Gibbon: Beyond the Trees, IMMORTALITY, and MARVEL SNAP for Mobile Game of the YearTUNIC and NORCO for Adventure Game of the YearCosmonious High, Tentacular, and The Last Clockwinder for Immersive Reality Game of the YearMARVEL SNAP for Online Game of the YearSpiderHeck for Fighting Game of the YearDisney Dreamlight Valley, Lost in Play, and Trombone Champ for Family Game of the YearCitizen Sleeper for Role-Playing Game of the YearIXION, Potion Craft: Alchemist Simulator, and Warhammer 40,000: Chaos Gate – Daemonhunters for Strategy/Simulation Game of the YearOlliOlli World for Sports Game of the YearNeon White for Action Game of the YearIMMORTALITY, Neon White, and TUNIC for Outstanding Achievement for an Independent GameAmong Us VR, Cosmonious High, and The Last Clockwinder for Immersive Reality Technical AchievementMARVEL SNAP and TUNIC for Outstanding Achievement in Game DesignSomerville for Outstanding Achievement in Audio DesignCuphead: The Delicious Last Course for Outstanding Achievement in AnimationIMMORTALITY and TUNIC for Outstanding Achievement in Game DirectionIMMORTALITY, NORCO, and I Was a Teenage Exocolonist for Outstanding Achievement in StoryWe share new releases or milestone spotlights every Monday through @UnityGames on Twitter.Tuesdays are dedicated to #UnityTips on Twitter. Here are two we found helpful in January:@elvismdd shared a classic tip to highlight the GameObject causing trouble@SunnyVStudio offered some nice interactive bow strings to show off in VRWe also published a recap of highlights from our 2022 developer Twitter takeovers right here on the blog. Stay tuned for plenty more throughout the year, and keep tagging us using the #UnityTips hashtag.We love all the incredible-looking projects we see using the #MadeWithUnity hashtag.Twitter’s @Vhalenn_ showcased a breathtaking world that’s worth exploring and @HakanssonAugust demonstrated some on-point camera work. Then, @inresin’s lizard friend took us on all sorts of adventures.On Instagram, @Throwdrow’s floating sword (above) looked straight out of a fantasy world, while @chibig.studio’s witch had some errands to run.We’re so excited for a new year of #MadeWithUnity, so keep adding the hashtag to your posts to show us what you’ve been up to.There were no new Creator Spotlight streams on Twitch in January, but that doesn’t mean we weren’t busy. To close out the month, we sat down with three Unity Insiders – Samyam, Andy Touch, and Code Monkey – ahead of Global Game Jam 2023 for a kickoff panel (also on YouTube) and joined the Global Game Jam team for its Day 2 Prep Week stream.In addition, we continued the revitalizing clips on YouTube with not one but four videos highlighting snippets from recent Creator Spotlight streams, including insight on layered explosions and stylized graphics in Rollerdrome, tips on building your email list from Thomas Brush (above), and the power of command buffers with JellyCar Worlds.Don’t forget to follow us on Twitch and hit the notification bell so you never miss a stream. If you miss a future Creator Spotlight live broadcast, don’t worry: We upload all streams to this YouTube playlist.On January 26, we had our first Dev Blitz Day of the year, focusing on authoring. The event was held in both the forums and the Discord server. Throughout the day, we answered more than 200 developer questions and would like to thank everyone who participated.Our next Dev Blitz Day will be held this month (February), but the date is still TBD. Keep an eye on the forums and our Discord for future announcements.Just in case you’re prioritizing vibes this season, we’ve got you covered with cozy assets for cozy games. What are “cozy games” you might ask? Read up on the emerging genre, and maybe it’ll spark some creative ideas for your next project.Plus, catch up with some of our favorite creator showcases on Twitter last month:Nordic Landscape | John Leonard FrenchWaterCausticsEffect for URP v2 | Masataka HakozakiTerraWorld SRP (Coming Soon) | ShadercrewPoly HP – Wildman | Downrain DCIf you’re using any Unity #AssetStore assets or have published assets to show off, be sure to tag us on Twitter with your latest creations. We’re excited to see what everyone creates throughout the year.Last but not least, here’s a non-exhaustive list of Made with Unity titles released in January. Do you see any on the list that have already become favorites or notice that something is missing? Tell us about it in the forums.Gorilla Tag, Another Axiom (January 1)We Are The Caretakers, Heart Shaped Games LLC (January 6)Children of Silentown, Elf Games, Luna2 Studio (January 11)LONE RUIN, Cuddle Monster Games (January 12)Surviving the Abyss, Rocket Flair Studios (January 17)Osiris: New Dawn, Fenix Fire Entertainment (January 18)A Space for the Unbound, Mojiken (January 19)Colossal Cave, Cygnus Entertainment (January 19)The Pioneers: Surviving Desolation, Supercube (January 20)Fire Emblem™ Engage, Nintendo (January 20)Catch Me!, ByteRockers' Games (January 24)NBA All-World, Niantic (January 24)Death in the Water 2, Lighthouse Games Studio (January 26)Power Chord, Big Blue Bubble (January 26)Roller Drama, Open Lab Games (January 26)That’s a wrap for January! Want more community news as it happens? Don’t forget to follow us on social media: Twitter, Facebook, LinkedIn, Instagram, YouTube, or Twitch.

Wendy McMahon is exiting CBS News amid tension with the parent company, Paramount. Frazer Harrison/Variety via Getty Images 2025-05-19T16:11:45Z Save Saved Read in app This story is available exclusively to Business Insider subscribers. Become an Insider and start reading now. Have an account? Wendy McMahon is quitting CBS News, citing disagreement with parent Paramount's direction. Her exit follows that of "60 Minutes" vet Bill Owens, and raises concerns about the show's future. CBS is facing legal issues with Trump, affecting its merger plans with Skydance. "60 Minutes" is left without another key ally as CBS News head Wendy McMahon has quit, citing disagreement with the company's path forward.Her exit is the latest shocking turn of events in CBS parent Paramount's ongoing face-off with President Donald Trump."It's become clear that the company and I do not agree on the path forward," McMahon, president and CEO of CBS News and Stations and CBS Media Ventures, wrote in a memo obtained by Business Insider. "It's time for me to move on and for this organization to move forward with new leadership."Her departure comes a month after "60 Minutes" longtime executive producer Bill Owens quit the prestigious news program, saying he could no longer run the program independently.At the time, Owens said McMahon had "60 Minutes'" back, while McMahon praised Owens for his "unwavering integrity, curiosity, and a deep commitment to the truth." She said then that CBS remained committed to "60 Minutes" and would ensure that its mission and the work remain a priority."It is not a good sign," a "60 Minutes" employee told BI. "She and Bill were very close. Feels like he was the first target and they wanted her gone, too. But they spread it out. Also, they waited until the '60 Minutes' season was over, by hours."The employee expressed concern that the promises McMahon made to preserve the work of "60 Minutes" and promote from within to succeed Owens could go by the wayside.CBS is in talks to settle a $20 billion legal battle with President Trump. Last year, he sued the network over its "60 Minutes" pre-election interview with then-Vice President Kamala Harris.The legal trouble has cast a shadow over the CBS parent company Paramount's long-standing plan to sell to the Hollywood production company Skydance, which requires FCC approval.Trump and his allies have repeatedly targeted the mainstream press.CBS CEO George Cheeks praised McMahon in a memo that was also seen by BI, saying her contributions improved the network's local news, competitiveness, and streaming operations. He said CBS News president Tom Cibrowski and CBS Stations president Jennifer Mitchell will report directly to him going forward.For CBS Media Ventures, which McMahon also oversaw, Cheeks said Scott Trupchak, who heads advertising sales, and John Budkins, who oversees programming and production, will report to Bryon Rubin, CBS's chief operating officer and CFO.Here's the text of McMahon's full memo to staff:Hi everyone,Today, I am stepping down from my position as president and CEO of CBS News and Stations and CBS Media Ventures.This has been one of the most meaningful chapters in my career. Leading this extraordinary organization has been the honor of a lifetime because I got to work alongside all of you. Your commitment to truth, fairness and the highest standards is unassailable.Championing and supporting the journalism produced by the most amazing stations and bureaus in the world, celebrating the successes of our shows and our brands, elevating our stories and our people ... It has been a privilege and joy.At the same time, the past few months have been challenging. It's become clear that the company and I do not agree on the path forward. It's time for me to move on and for this organization to move forward with new leadership.I have spent the last few months shoring up our businesses and making sure the right leaders are in place; and I have no doubt they will continue to set the standard.You are in incredibly good hands with Tom, Jennifer, John, Matt and Robert. They are phenomenal leaders and people who will continue to protect and celebrate your work. I am sure of it.To George: Thank you for this opportunity.To our viewers: Thank you for your trust. You hold us accountable, and you remind us why this work matters.To the CBS News and Stations and CMV teams: Thank you for your passion, your professionalism and your partnership. It has been a privilege to walk this path with you.Wendy Recommended video

Millions of people argue with each other online every day, but remarkably few of them change someone’s mind. New research suggests that large language models (LLMs) might do a better job. The finding suggests that AI could become a powerful tool for persuading people, for better or worse.   A multi-university team of researchers found that OpenAI’s GPT-4 was significantly more persuasive than humans when it was given the ability to adapt its arguments using personal information about whoever it was debating. Their findings are the latest in a growing body of research demonstrating LLMs’ powers of persuasion. The authors warn they show how AI tools can craft sophisticated, persuasive arguments if they have even minimal information about the humans they’re interacting with. The research has been published in the journal Nature Human Behavior. “Policymakers and online platforms should seriously consider the threat of coordinated AI-based disinformation campaigns, as we have clearly reached the technological level where it is possible to create a network of LLM-based automated accounts able to strategically nudge public opinion in one direction,” says Riccardo Gallotti, an interdisciplinary physicist at Fondazione Bruno Kessler in Italy, who worked on the project. “These bots could be used to disseminate disinformation, and this kind of diffused influence would be very hard to debunk in real time,” he says. The researchers recruited 900 people based in the US and got them to provide personal information like their gender, age, ethnicity, education level, employment status, and political affiliation.  Participants were then matched with either another human opponent or GPT-4 and instructed to debate one of 30 randomly assigned topics—such as whether the US should ban fossil fuels, or whether students should have to wear school uniforms—for 10 minutes. Each participant was told to argue either in favor of or against the topic, and in some cases they were provided with personal information about their opponent, so they could better tailor their argument. At the end, participants said how much they agreed with the proposition and whether they thought they were arguing with a human or an AI. Overall, the researchers found that GPT-4 either equaled or exceeded humans’ persuasive abilities on every topic. When it had information about its opponents, the AI was deemed to be 64% more persuasive than humans without access to the personalized data—meaning that GPT-4 was able to leverage the personal data about its opponent much more effectively than its human counterparts. When humans had access to the personal information, they were found to be slightly less persuasive than humans without the same access. The authors noticed that when participants thought they were debating against AI, they were more likely to agree with it. The reasons behind this aren’t clear, the researchers say, highlighting the need for further research into how humans react to AI. “We are not yet in a position to determine whether the observed change in agreement is driven by participants’ beliefs about their opponent being a bot (since I believe it is a bot, I am not losing to anyone if I change ideas here), or whether those beliefs are themselves a consequence of the opinion change (since I lost, it should be against a bot),” says Gallotti. “This causal direction is an interesting open question to explore.” Although the experiment doesn’t reflect how humans debate online, the research suggests that LLMs could also prove an effective way to not only disseminate but also counter mass disinformation campaigns, Gallotti says. For example, they could generate personalized counter-narratives to educate people who may be vulnerable to deception in online conversations. “However, more research is urgently needed to explore effective strategies for mitigating these threats,” he says. While we know a lot about how humans react to each other, we know very little about the psychology behind how people interact with AI models, says Alexis Palmer, a fellow at Dartmouth College who has studied how LLMs can argue about politics but did not work on the research.  “In the context of having a conversation with someone about something you disagree on, is there something innately human that matters to that interaction? Or is it that if an AI can perfectly mimic that speech, you’ll get the exact same outcome?” she says. “I think that is the overall big question of AI.”

Constructive Crit RoadCraft review: Streamlined building biz beset by bumbling AI Bobs Can we fix it? Well, that often depends on whether you’ll let me deliver the bits myself. Image credit: VG247 Review by Mark Warren Senior Staff Writer Published on May 19, 2025 It’s getting close to 10PM on a Friday night. There’s a slightly muddy hill. Halfway up it, their tires spinning helplessly, are two trucks carrying goods they need to deliver to a shed about half the map away. I sigh, and give my bulldozer/cargo truck the beans. As one fourteen-wheeled mass, we begin to crawl up the gentle slope, which would be easy pickings if the AI-manned haulers glued to my front scoop had any off-roading capabilities whatsoever. They don’t. There’s no driving skill to make up for it, either. If they run into an obstacle during the course of the route I’ve plotted out for them which can’t be overcome by simply reversing and pulling forwards less than three times, they just give up. Small rocks terrify them, turns that happen to be in any way sharp are the banes of their existence, and sometimes they seem to roll over just for a laugh. They need me. When I’m not Bob the builder, I’m Bob the babysitter. To see this content please enable targeting cookies. What I’ve just described is one of the main things that sets RoadCraft - the latest entry in Saber Interactive’s Spintires series of off-roading sims - from its rugged, outdoorsy siblings. These games, MudRunner, SnowRunner, and last year’s Expeditions, were generally games about you - the player - getting from A to B through untamed environments and getting stuck when you messed it up. I’ve regularly, and slightly sarkily, compared these games to the driving equivalent of FromSoft’s boss battlers. Notoriously unforgiving adventures about eventual success earned through overwhelming skill or luck, and usually preempted by a crap-tonne of failure that gradually pushes those who haven’t already taken their lumps in the direction of doing the right thing. When you’re behind the wheel, RoadCraft’s by far the least hardcore title in its delivery of that gameplay loop that Saber has put out to this point. Don’t get me wrong, there’s still a learning curve and plenty of ways to mess up that’ll require a reset. However, in its creation of a game that’s more focused on construction, maintenance, and logistics management than it is straight-up haulage or frontier-conquering exploration, the studio’s simplified things. As you carry out jobs, you no longer have to keep a watchful eye on your fuel gauge or do any repairs if you slam into a wall. RoadCraft’s fleet is permanently fully-fueled and indestructible unless you roll over, sink, or otherwise get wedged in a spot you can’t extricate yourself from. While this, and the resulting lack of an in-depth upgrade system for vehicles, might be a bit frustrating to hardcore haulers, you can see why Saber’s opted to do it. See? Told you there are still ways you can monumentally mess things up. | Image credit: VG247/Saber The rides you’re handed the keys to this time are generally a lot more specialised towards very specific roles for the jobs you’ll be doing as the game walks you through getting locations which have suffered different kinds of natural disaster - from floods, to earthquakes, to hurricanes - up and running again. You’re running a construction firm that you start off by naming and picking out a livery/logo combo for. When you first deploy into one of the maps, which thankfully are openly free-roamable outside jobs unlike those in Expeditions, you’ll do the usual thing and head out in a nippy scout 4x4 to recon the environment. Then, your re-construction efforts begin, and can be divided into about five or six different general activities you’ll do in various orders and with different quirks as you progress - scouting, logging, road and bridge building, plotting routes for AI supply runs, debris clearing, and resource delivery. In terms of the latter, there are four types of resources you’ll need to fix various things - logs, steel beams, metal pipes, and concrete slabs - all of which you’ll acquire by either recycling debris at the plants on each map that part of your job is to get up and running. Getting ahold of those, ferrying them where they need to go, and installing them is done in very SnowRunnery fashion, albeit with manual loading being your only option. As such, the vehicle I’ve spent by far the most time in during my time with the game so far is the Mule T1 crane cargo truck. As the name suggests, it’s a lorry with very decent off-roading capabilities that’s built to transport goods, and even boasts its own built-in crane. (Slaps roof) You know how much junk this Mule can haul? | Image credit: VG247-Saber If you’re playing solo, it’s by far the most important purchase you’ll make early on, because its good stats and that crane mean it’s ideal to handle the vast majority of haulage jobs the game gives you. There is a point where some loads start to get a bit too heavy for it to deal with easily, but I’ve made it up to level 12 so far and it’s still the heart of my fleet. That arguably exposes a bit of a flaw in RoadCraft’s launch vehicle offerings - there’s only one or very occasionally two better successors you can unlock for each of the different vehicle types as you progress. You do unlock some new types of vehicle around the midpoint, such as a heavy crane and beefier cargo truck that together can handle the heavier loads the Mule struggles with, but in plenty of cases there’s a beginner rusty variant of a specific vehicle, a refurbished version of the exact same model with slightly better stats, and then an advanced variant you’ll unlock once you’re starting to home in on the endgame. The most egregious example of this is with the field service vehicles. There are two. One you’re given for free at the start of the game and can’t even be repainted in your company livery as far as I can tell, and then its endgame replacement, which you won’t unlock until level 20, which based on my progress so far looks like it’ll be when you’ve basically finished all of the game’s current content. You’re still unlocking one or two new vehicles or variants of existing vehicles with each level you gain to help freshen things up a bit, but the relatively thin depth at each position and lack of part customisation means the sense of progression feels a lot more limited. No doubt there’ll be plenty of DLC to beef up the roster, but Focus seems to be leaning a bit too heavily on that. C. W. McCall intensifies. | Image credit: VG247/Saber Combined with the aforementioned stripping out of stuff like fuel management, and the XP/cash rewards for jobs being quite generous (the latter especially so because you aren’t constantly spending on upgrades), to this point RoadCraft is the entry in the uber-hard Spintires series I’ve made my way through with the least struggle. The one exception to that, as I outlined in the intro, is that damn route plotting for AI trucks. If it’s the part of the game that’s supposed to dial the difficulty back up, it certainly does just that at regular points, often in infuriating fashion. If I’ve gotten stuck while driving, usually because I’ve done something stupid, that’s annoying, but at the end of the day it’s on me to do a better job. If an AI lorry I’ve already built a bunch of bridges and roads for requires me to follow it along its entire route and do some push-based babysitting whenever it encounters the tiniest obstacle because it’s using a truck that only works on perfectly straight asphalt highways, that’s less easy to take on the chin. Kudos to Saber for trying something different, but some of the ways I’ve had to resort to helping its lorry Lemmings feel like they pretty much defeat the point of not having me just make the deliveries myself. While folks who take a bit more time to clear the perfect path might well find RoadCraft lacking a bit of challenge, I’ve personally enjoyed the non-AI lorry bits of it generally being a lot more chill than the usual. The game’s at its best when you’re heading to a base or driving your field service vehicle somewhere and setting up to spend some time doing a specific job. Both act as spawn points for vehicles, though the latter requires fuel tokens that’re pretty easy to earn from side jobs. Once you’re there, you’ll be doing something like watching the four stages of RoadCraft’s namesake party trick, building roads by dumping sand with a dump truck, using a dozer to flatten it, wheeling out your paver to coat it an asphalt, and then hopping in a steamroller to make it nice and smooth. It’s as mega-satisfying as you always dream baking a cake will be, even if the first step can be pretty unforgiving because it’s near impossible to drop sand in a nice uniform fashion. Luckily, you’ve got the choice to do each step manually or let the computer do it automatically, with the latter tending to go ok given you’re only making short stretches of road. Well, unless your paver finds a small rock you haven’t cleared. It's a piece of cake to lay a pretty road. If the way is hazy, you gotta do the laying by the codex. | Image credit: VG247/Saber Logging by chopping down trees with a tree harvester, picking up the big twigs with a log hauler, and then cleaning up your mess with a stump mulcher is just as fun. There’s not as much process to laying electrical wires between different spots on the map to power up substations, but finding a way to guide the comically unwieldy cable layer through the backwoods has its good moments, even if it’s possible to get stuck in weird ways. Overall, RoadCraft offers a unique enough twist on the established Spintires formula, if a streamlined one, to be worth giving a go. Some series veterans will end up longing for the elements it’s stripped out, especially when the new stuff that’s been drafted in is being more frustrating than fun. But, that central loop of frustration giving way to jubilation as you overcome the environment is still there and regularly just as satisfying. Especially when the convoy you’ve spent all evening pushing up hills finally reaches its destination. RoadCraft releases on March 20 for PC, Xbox Series X/S, and PS5. This review was conducted on PS5 using a code provided by the publisher.

Millions of people argue with each other online every day, but remarkably few of them change someone’s mind. New research suggests that large language models (LLMs) might do a better job. The finding suggests that AI could become a powerful tool for persuading people, for better or worse.   A multi-university team of researchers found that OpenAI’s GPT-4 was significantly more persuasive than humans when it was given the ability to adapt its arguments using personal information about whoever it was debating. Their findings are the latest in a growing body of research demonstrating LLMs’ powers of persuasion. The authors warn they show how AI tools can craft sophisticated, persuasive arguments if they have even minimal information about the humans they’re interacting with. The research has been published in the journal Nature Human Behavior. “Policymakers and online platforms should seriously consider the threat of coordinated AI-based disinformation campaigns, as we have clearly reached the technological level where it is possible to create a network of LLM-based automated accounts able to strategically nudge public opinion in one direction,” says Riccardo Gallotti, an interdisciplinary physicist at Fondazione Bruno Kessler in Italy, who worked on the project. “These bots could be used to disseminate disinformation, and this kind of diffused influence would be very hard to debunk in real time,” he says. The researchers recruited 900 people based in the US and got them to provide personal information like their gender, age, ethnicity, education level, employment status, and political affiliation.  Participants were then matched with either another human opponent or GPT-4 and instructed to debate one of 30 randomly assigned topics—such as whether the US should ban fossil fuels, or whether students should have to wear school uniforms—for 10 minutes. Each participant was told to argue either in favor of or against the topic, and in some cases they were provided with personal information about their opponent, so they could better tailor their argument. At the end, participants said how much they agreed with the proposition and whether they thought they were arguing with a human or an AI. Overall, the researchers found that GPT-4 either equaled or exceeded humans’ persuasive abilities on every topic. When it had information about its opponents, the AI was deemed to be 64% more persuasive than humans without access to the personalized data—meaning that GPT-4 was able to leverage the personal data about its opponent much more effectively than its human counterparts. When humans had access to the personal information, they were found to be slightly less persuasive than humans without the same access. The authors noticed that when participants thought they were debating against AI, they were more likely to agree with it. The reasons behind this aren’t clear, the researchers say, highlighting the need for further research into how humans react to AI. “We are not yet in a position to determine whether the observed change in agreement is driven by participants’ beliefs about their opponent being a bot (since I believe it is a bot, I am not losing to anyone if I change ideas here), or whether those beliefs are themselves a consequence of the opinion change (since I lost, it should be against a bot),” says Gallotti. “This causal direction is an interesting open question to explore.” Although the experiment doesn’t reflect how humans debate online, the research suggests that LLMs could also prove an effective way to not only disseminate but also counter mass disinformation campaigns, Gallotti says. For example, they could generate personalized counter-narratives to educate people who may be vulnerable to deception in online conversations. “However, more research is urgently needed to explore effective strategies for mitigating these threats,” he says. While we know a lot about how humans react to each other, we know very little about the psychology behind how people interact with AI models, says Alexis Palmer, a fellow at Dartmouth College who has studied how LLMs can argue about politics but did not work on the research.  “In the context of having a conversation with someone about something you disagree on, is there something innately human that matters to that interaction? Or is it that if an AI can perfectly mimic that speech, you’ll get the exact same outcome?” she says. “I think that is the overall big question of AI.”

Millions of people argue with each other online every day, but remarkably few of them change someone’s mind. New research suggests that large language models (LLMs) might do a better job. The finding suggests that AI could become a powerful tool for persuading people, for better or worse.   A multi-university team of researchers found that OpenAI’s GPT-4 was significantly more persuasive than humans when it was given the ability to adapt its arguments using personal information about whoever it was debating. Their findings are the latest in a growing body of research demonstrating LLMs’ powers of persuasion. The authors warn they show how AI tools can craft sophisticated, persuasive arguments if they have even minimal information about the humans they’re interacting with. The research has been published in the journal Nature Human Behavior. “Policymakers and online platforms should seriously consider the threat of coordinated AI-based disinformation campaigns, as we have clearly reached the technological level where it is possible to create a network of LLM-based automated accounts able to strategically nudge public opinion in one direction,” says Riccardo Gallotti, an interdisciplinary physicist at Fondazione Bruno Kessler in Italy, who worked on the project. “These bots could be used to disseminate disinformation, and this kind of diffused influence would be very hard to debunk in real time,” he says. The researchers recruited 900 people based in the US and got them to provide personal information like their gender, age, ethnicity, education level, employment status, and political affiliation.  Participants were then matched with either another human opponent or GPT-4 and instructed to debate one of 30 randomly assigned topics—such as whether the US should ban fossil fuels, or whether students should have to wear school uniforms—for 10 minutes. Each participant was told to argue either in favor of or against the topic, and in some cases they were provided with personal information about their opponent, so they could better tailor their argument. At the end, participants said how much they agreed with the proposition and whether they thought they were arguing with a human or an AI. Overall, the researchers found that GPT-4 either equaled or exceeded humans’ persuasive abilities on every topic. When it had information about its opponents, the AI was deemed to be 64% more persuasive than humans without access to the personalized data—meaning that GPT-4 was able to leverage the personal data about its opponent much more effectively than its human counterparts. When humans had access to the personal information, they were found to be slightly less persuasive than humans without the same access. The authors noticed that when participants thought they were debating against AI, they were more likely to agree with it. The reasons behind this aren’t clear, the researchers say, highlighting the need for further research into how humans react to AI. “We are not yet in a position to determine whether the observed change in agreement is driven by participants’ beliefs about their opponent being a bot (since I believe it is a bot, I am not losing to anyone if I change ideas here), or whether those beliefs are themselves a consequence of the opinion change (since I lost, it should be against a bot),” says Gallotti. “This causal direction is an interesting open question to explore.” Although the experiment doesn’t reflect how humans debate online, the research suggests that LLMs could also prove an effective way to not only disseminate but also counter mass disinformation campaigns, Gallotti says. For example, they could generate personalized counter-narratives to educate people who may be vulnerable to deception in online conversations. “However, more research is urgently needed to explore effective strategies for mitigating these threats,” he says. While we know a lot about how humans react to each other, we know very little about the psychology behind how people interact with AI models, says Alexis Palmer, a fellow at Dartmouth College who has studied how LLMs can argue about politics but did not work on the research.  “In the context of having a conversation with someone about something you disagree on, is there something innately human that matters to that interaction? Or is it that if an AI can perfectly mimic that speech, you’ll get the exact same outcome?” she says. “I think that is the overall big question of AI.”

Macworld In his recent Power On newsletter, Bloomberg’s Mark Gurman reports that Apple is looking to offer a new capability with the Apple Pencil: support for a “virtual reed calligraphy pen” that would make the device a tool for Arabic calligraphy. A reed pen is also known as a Kamish pen. The “analog” version, if you will, is usually made from river reeds and has been used for centuries by Middle Eastern calligraphers. Apple offers only one type of Pencil tip, and Gurman calls it a “virtual” pen, so it seems that the Arabic calligraphy support will be implemented through software. Gurman doesn’t offer more specifics, so it’s not clear if reed pen support would be for all four Apple Pencil models or if it would be limited to the Apple Pencil Pro and/or Apple Pencil (USB-C). As part of a larger push to expand its presence in India, Saudi Arabia, and the United Arab Emirates, Apple also has plans to offer a “highly advanced bi-directional keyboard” that can switch between Arabic and English for iOS and iPadOS. Gurman doesn’t state when the pen and the keyboard will be available, though they will likely be part of the new iPadOS 19 announcements at WWDC25 on June 9. Apple Pencil Pro Price When Reviewed: 149 Best Prices Today: €144 at Computeruniverse€144 at cyberport€148 at notebooksbilliger.de136,21 € at amazon.de Apple Pencil (USB-C) Best Prices Today: €89 at Computeruniverse€89 at cyberport€89.9 at Alternate82,33 € at amazon.de

Why it’s time we take behavior seriouslyUsers in an app behave like a complex system. The system’s behavior isn’t just the sum of its parts — it’s emergent. That means we can’t predict what the group will do by only studying individuals. Just like birds: each one may be predictable, but their flocking behavior isn’t something you’d guess from a single bird. (Photo by James Wainscoat on Unsplash)You’ve probably felt it. Your research gets ignored, your methods can’t answer the questions being asked, and your role feels more like a presentation decorator than a decision-maker. It’s not just you — something deeper is off.Let’s be honest: UX research didn’t collapse — it was never solid to begin with.From the start, it’s been shaped by shallow interpretations, more focused on fitting into design teams than standing on solid methodological ground.It borrowed the language of science, psychology, and behavioral research — but only the surface-level tools, never the depth. It borrowed from the social sciences — for example, surveys from psychology and sociology, qualitative approaches from ethnology — and also borrowed usability testing from industrial design. But in all cases, it left behind the frameworks, the theoretical grounding, and the hard questions that came with them.Most of UX research sticks to surface-level methods. But beneath the surface lies a deeper set of tools from the social sciences — powerful, underused, and long overdue. (Original photo by SIMON LEE on Unsplash, modified by the author)What we got instead was something that looked analytical but rarely was. Something collaborative, friendly, and workshop-compatible — but not always meaningful. Something easy to sell to stakeholders — but hard to defend under scrutiny.And now we’re living with the fallout. UX research didn’t fall from grace — it was never built right. A wave of layoffs has hit researchers harder than most, because the value of our work has been reduced to surface-level activities — often nothing more than fancy post-its, sticky walls, and shallow observations that sound replaceable — and in some cases, are.UX research is stuck. We keep hitting the limits of our toolkit. We say no to questions we should be able to answer — not because the questions are bad, but because our methods can’t handle them. And when that happens, companies stop asking.Take companies like Spotify. AI is now personalizing experiences with incredible precision, and in many cases, replacing the need for human-driven personalization. Researchers are being laid off not because their role is obsolete, but because their toolkits haven’t kept pace. They could have stayed relevant — even in AI-heavy environments — if they had adapted, learned new methods, and shown how research can guide, challenge, or complement what AI alone can’t do.That’s on us. As researchers, part of our job — whether or not we’re paid for it — is to explore new tools. Not just to learn new workshop formats or frameworks, but to actually extend what’s possible in research. If we don’t, we risk fading out while the rest of the org moves forward.It’s not fair. But it’s real. And it means we need to push beyond traditional methods — not by inflating our value with vague storytelling, but by actually expanding our capabilities.And thanks to UXR’s shallow borrowing from multiple disciplines without their depth, there’s now an entire world of underused analytical methods sitting just outside our field. One of the most overlooked? Simulation modeling.These models let us simulate users — their behaviors, decisions, mental states, and more — and use those simulations to predict outcomes, test hypotheses, and validate assumptions. They treat users as complex, dynamic systems — not just averaged-out segments or simplified down to percentages and cherry-picked quotes.What are simulation modelings?Simulation models are simplified, programmable systems that help us test ideas about how users behave — without actually having to run a real study. Instead of asking users what they might do, you tell the model how they might act, and then simulate what happens when lots of users behave in those ways over time. These models are widely used in advanced social sciences — like sociology, political science, and behavioral economics — to study how complex systems evolve over time, especially when real-world testing is too slow, risky, or impractical.In these models, users are often treated as “agents” or units that follow certain behavioral rules — for example, “click if X is true” or “drop off if waiting more than 10 seconds.” You set these rules based on your assumptions, data, or existing research results.The model then simulates thousands (or even millions) of interactions between users and the system. The goal isn’t to predict the future with precision — it’s to explore what might happen under different conditions, and whether your hypothesis holds up when scaled.You can simulate behavior over time, track events, test flow changes, model lifecycle transitions, or observe group-level effects like tipping points (moments when a small change leads to a big, sudden shift in user behavior) and virality — all without ever needing a live A/B test or full rollout.Why models?UX research, as it’s currently practiced, is split between two worlds — and both have serious problems.In small companiesFor teams with a limited user base, UXR is often out of reach. These teams usually rely on qualitative methods like interviews, open-ended surveys, or usability tests. While these give insights into how users think and behave, they come with major limitations. They don’t generalize. They don’t predict. And they’re not reliable for long-term decisions. On top of that, many small teams don’t even have stable access to their users — they can’t run constant tests or track behavior over time.Models help in both cases. When you can’t run a study, a model lets you explore what would happen if your assumptions were true. You can test hypotheses, simulate user flows, and make better calls — without having to get users on the line.In large companiesEven in companies that do run regular research, most of what we gather is biased.Participation bias: Most data comes from users who agree to take part in research. But what about those who never respond? They might behave completely differently — and they’re invisible to your team.Self-report bias: People don’t act the way they claim. Most of what users tell us in surveys or interviews doesn’t match what they actually do in the product.Contextless testing: Research methods like first-click or five-second tests remove the task from its context. Sure, they test clarity or attention — but the results aren’t grounded in real-world usage. They show an ideal version of behavior, not what actually happens.Models don’t fix all of this. But they give you a second layer of insight. One that’s testable, repeatable, and grounded in how behavior scales — not just what one user said in a session.Where modeling comes inModeling becomes useful exactly where traditional UXR starts falling short — when you’re working with limited access, incomplete data, or questions that research alone can’t answer.When you have a hypothesis and want to know how far from reality it is → A model lets you play it out. You can define what should happen if the hypothesis were true, simulate it, and then compare that to what’s actually happening. If the gap is huge, you know your assumption needs work.When you’re relying on self-reported data and suspect it’s off → You can simulate what real behavior would likely look like, based on system constraints or observed trends. If your survey says 80% of users did X, but the model shows that’s barely possible, there’s probably overreporting or misunderstanding in your responses.When individual behavior doesn’t tell you much about what happens at scale → Modeling helps you observe how individual user decisions scale into system-wide patterns. It shows you how small frictions add up, where bottlenecks form, or how drop-offs compound. This is where patterns like tipping points or hidden churn clusters show up.When you need to compare multiple design or policy options → Instead of running UXR methods like first-click testing, 5-second tests, or expensive quantitative usability studies and waiting weeks for results, you can simulate different flows and estimate their impact in advance. This includes metrics that usually require A/B testing — like conversion rates or pricing sensitivity. Modeling helps you explore which variants are worth testing at all, and where your efforts are likely to hit diminishing returns or breaking points — moments when users suddenly start behaving very differently because something pushed them just past their limit. Models won’t give you certainty, but they’ll narrow the field — quickly, and without exposing users to half-baked experiments.When you just don’t have the user data you need → Maybe your product is new. Maybe your user base is too small for meaningful patterns. Either way, models let you test assumptions and make early decisions without waiting for months of real usage data.Modeling won’t give you the truth. But it gives you a way to test how wrong (or right) your current assumptions are — and that alone makes it worth using.In this NetLogo flocking model, each agent (like a bird) follows just a few simple rules — move toward others, avoid collisions, and align direction. From those basics, coordinated group movement emerges. Agent-Based Modeling (ABM) helps us uncover how individual decisions create collective patterns like this. (Animation created by the author running the NetLogo model)Some models and how we could use themDifferent questions call for different models. This table maps common UX research goals to the types of simulation or predictive models best suited to answering them. (Table created by the author)Simulation modeling isn’t one-size-fits-all. Different types of models serve different purposes depending on the kind of question you’re trying to answer. Here’s how to pick the right approach — explained simply.Predicting user behaviorModels available: Agent-Based Modeling (ABM), Discrete Event Simulation (DES)These models help when you want to simulate what individual users do and how their actions play out over time.This NetLogo fireflies model shows how individual agents — each flashing at their own pace — eventually sync up over time. It doesn’t happen instantly, so watch closely as their light pulses slowly align. This is another example of how simple local rules can produce global coordination through Agent-Based Modeling. (Animation created by the author running the NetLogo model)Agent-Based Modeling (ABM) You simulate thousands of individual “agents” — think of them as virtual users — each following a simple set of rules. They can behave differently from each other, and they can interact. As those interactions pile up, larger patterns (like herd behavior, virality, or group churn) start to emerge. → Best for understanding how user behaviors interact and lead to bigger system-wide outcomes.Discrete Event Simulation (DES) Here you model a process: users move step-by-step through events — for example, seeing an update prompt, clicking, being redirected, updating, and returning. Each step can have delays, wait times, or drop-offs. → Best for modeling processes like onboarding, checkout flows, or support ticket systems.How users move through the appModels available: System Dynamics, Markov ModelsThese models work with user states — like “new user,” “active user,” “churned user” — and model how users move between these states over time.System Dynamics (SD) This looks at the flow of large groups of users as they move through the system. It works well when you want to see how one group affects another — like how increasing active users affects support load or churn. → Best for long-term trends and feedback loops, like retention vs. burnout.Markov Models These are good for modeling step-by-step state changes, where each next step depends only on where the user is now. For example: active → inactive → churned. → Best for lifecycle modeling or estimating time to churn.How users influence each otherModels available: Network ModelsThese models are useful when what one user does depends on the people around them.Network Modeling You build a graph of users and the connections between them (friends, follows, referrals, etc.). Then you simulate how behaviors spread across that network — like sharing a coupon or copying someone’s playlist. → Best for peer influence, word-of-mouth, or viral spread modeling.Predicting what users will do nextModels available: Machine Learning (ML)When you already have user data and want to predict what might happen next.Machine Learning You train a model using past behavior — who clicked what, who churned, who converted — and the algorithm finds patterns you can use to predict future behavior. → Best for scoring users, prioritizing leads, or forecasting churn/conversion.These models don’t try to explain why users act the way they do. They just tell you what’s likely to happen next.Each of these modeling approaches offers something traditional UXR can’t: a way to test ideas at scale, before they hit the real world — something we also try to do in quantitative UX research — narrowing down hypotheses and running large-scale validation — though simulation allows us to go further when access to users is limited or behavior is too complex to test directly.What’s next: Where to go from hereIn future articles, I’ll walk through how models like Agent-Based Modeling and System Dynamics work in practice — with real case studies. We’ll look at how to build a simple simulation from scratch and what kinds of research questions it can help answer.If you’re unsure which type of model fits your case, want help setting one up, or just want to explore how this might work in your product or team, feel free to reach out. Also, if you’re interested in a more tailored and long-term approach, I’ve written about why we should adopt a person-oriented approach to UXR — and how models like ABM align with that. You can check out that article for a deeper theoretical foundation, as well as a separate case study where I applied this approach in practice. I’m happy to discuss use cases, guide you through the setup, or offer deeper consultation.Email: talieh.kazemi.esfeh@gmail.comLinkedInUsing simulation models in UX research was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.

So we want to set up a new design system for your product. How do we get it up and running from scratch? Do we start with key stakeholders, UI audits, or naming conventions? And what are some of the critical conversations we need to have early to avoid problems down the line? Fortunately, there are a few useful little helpers to get started — and they are the ones I tend to rely on quite a bit when initiating any design system projects. Design System In 90 Days Canvas Design System in 90 Days Canvas (FigJam template) is a handy set of useful questions to start a design system effort. Essentially, it’s a roadmap to discuss everything from the value of a design system to stakeholders, teams involved, and components to start with. A neat little helper to get a design system up and running — and adopted! — in 90 days. Created for small and large companies that are building a design system or plan to set up one. Kindly shared by Dan Mall. Practical Design System Tactics Design System Tactics is a practical overview of tactics to help designers make progress with a design system at every stage — from crafting system principles to component discovery to design system office hours to cross-brand consolidation. Wonderful work by the one-and-only Ness Grixti. Design System Worksheet (PDF) Design System Checklist by Nathan Curtis (download the PDF) is a practical 2-page worksheet for a 60-minute team activity, designed to choose the right parts, products, and people for your design system. Of course, the point of a design system is not to be fully comprehensive or cover every possible component you might ever need. It’s all about being useful enough to help designers produce quality work faster and being flexible enough to help designers make decisions rather than make decisions for them. Useful Questions To Get Started With The value of a design system lies in it being useful and applicable — for a large group of people in the organization. And according to Dan, a good start is to identify where exactly that value would be most helpful to tackle the company’s critical challenges and goals: What is important to our organization at the highest level? Who is important to our design system effort? What unofficial systems already exist in design and code? Which teams have upcoming needs that a system could solve? Which teams have immediate needs that can grow our system? Which teams should we and have we talked to? Which stakeholders should we and have we talked to? What needs, desires, and concerns do our stakeholders have? What components do product or feature teams need now or soon? What end-user problems/opportunities could a system address? What did we learn about using other design systems? What is our repeatable process for working on products? What components will we start with? What needs, desires, and concerns do our stakeholders share? Where are our components currently being used or planned for? Useful Resources Here are a few other useful little helpers that might help you in your design system efforts: Design System Questions To Answer In First 90 Days, by Dan Mall Design System Canvas (PDF / Figjam), by Paavan Buddhdev LeanDS Framework (Figma), by Marianne Ashton-Booth Useful UX Templates For Designers (Figma Kits), by yours truly, Vitaly Friedman Design System Guide, by Romina Kavcic Wrapping Up A canvas often acts as a great conversation starter. It’s rarely complete, but it brings up topics and issues that one wouldn’t have discovered on the spot. We won’t have answers to all questions right away, but we can start moving in the right direction to turn a design system effort into a success. Happy crossing off the right tick boxes! How To Measure UX And Design Impact Meet Measure UX & Design Impact (8h), a practical guide for designers and UX leads to shape, measure, and explain your incredible UX impact on business. Recorded and updated by Vitaly Friedman. Use the friendly code 🎟 IMPACT to save 20% off today. Jump to the details. Video + UX TrainingVideo onlyVideo + UX Training$ 495.00 $ 799.00 Get Video + UX Training25 video lessons (8h) + Live UX Training.100 days money-back-guarantee.Video only$ 250.00$ 395.00 Get the video course25 video lessons (8h). Updated yearly.Also available as a UX Bundle with 2 video courses. Further Reading on Smashing Magazine “Build Design Systems With Penpot Components,” Mikołaj Dobrucki “How To Turn Your Figma Designs Into Live Apps With Anima Playground,” Anima Team “UX And Design Files Organization Template,” Vitaly Friedman “The Digital Playbook: A Crucial Counterpart To Your Design System,” Paul Boag

In November last year, Jaguar Land Rover (JLR) unveiled a radical rebranding exercise. The iconic British car brand, known for its sleek designs, racing heritage and unmistakable growler emblem featuring its big cat mascot, made a dramatic pivot that both alienated its existing customer base and failed to resonate with new audiences. Spearheaded by agency Accenture Song, the new identity introduced a minimalist 'J' logo, replacing the iconic 'growler' logo, along with advertisements that conspicuously lacked cars. Instead, the campaign featured an array of abstract visuals and diverse models with slogans like 'Live Vivid' and 'Delete Ordinary'. It was all part of a strategic shift to position Jaguar as an all-electric luxury brand by 2026. But the public response was swift and devastating. And the backlash wasn't limited to social media outrage: it translated directly to Jaguar's bottom line. Global sales plummeted from 61,661 vehicles in 2022 to just 33,320 in 2024, marking a nearly 50% decline. Now, the company is actively seeking a new advertising agency, effectively acknowledging that the rebrand has failed to achieve its objectives. So what went wrong, and what lessons can other brands learn from this costly misstep? What went wrong? Sue Benson, founder and CEO of The Behaviours Agency, lists some of the reasons she feels the new approach failed. "There are few, if any, cars in the ads, which leaves audiences unsure what the brand even stands for. Heuristics like the leaping cat logo have been discarded, weakening mental shortcuts consumers rely on. Emotion has suffered, too; rather than stirring pride or aspiration, the new identity feels cold and aloof. And in terms of consistency, the shift has been jarring, with little cohesion between legacy and future." In short, the brand destroyed more equity than it built. By abandoning recognisable elements that consumers had emotional connections with, Jaguar created a vacuum that its abstract new positioning couldn't fill. "In a market where EV brands are multiplying, distinctiveness matters more than ever," Sue reasons. "Jaguar already had strong emotional and cultural heritage, so it's worth questioning whether stripping that away in favour of minimalism was the right move." Sign of desperation Of course, many of us like radical change, and many people in the design industry were pretty excited when the rebrand was first launched. But sometimes, 'radical' can go too far and end up throwing out the proverbial baby with the bathwater. "The scale of the transformation was hard to ignore," says Gabor Schreier, chief creative officer at Saffron Brand Consultants. "If Rolls-Royce were to abandon the Spirit of Ecstasy, the reaction would be immediate. Jaguar's move belonged in the same category. Abandoning that legacy and sacrificing vital brand assets might be seen as either a bold, forward-looking strategy or a sign of desperation disguised as a provocation. A leap so detached from its heritage, they might as well have changed the name entirely." Typically, a successful rebrand will maintain a sufficient connection to a brand's core identity so that consumers can follow the evolution. Jaguar, however, created a chasm so wide that the rebrand appeared to be a complete reinvention rather than an evolution, leaving consumers confused about what Jaguar now stood for. A broader identity crisis Of course, none of this happened in a vacuum. Lewis Jones, managing partner at Coley Porter Bell, frames Jaguar's struggles within the broader context of the auto industry's shift to electric. He suggests that the problem goes beyond the rebrand itself to a more existential question about what makes cars desirable in the electric age. "The traditional thrills of internal combustion such as sound, speed and mechanical drama are fast becoming irrelevant," Lewis notes. "As EVs lean into minimalism, efficiency and tech-led rationality, the emotional connection to the car risks being left behind." In this light, a car brand needs to not only maintain its own identity but also solve the broader problem of creating emotional connections in a category increasingly defined by sameness. Instead, though, Jaguar leaned into the very minimalism that risks making electric vehicles feel interchangeable. "Design is where this battle will be won or lost," believes Lewis. "It is no coincidence that one of the most talked-about EVs of 2024 is the Renault 5, a masterclass in retro-modern design. It is not the specs or software that excited people. It is the character, the charm, the identity. It feels like something. In an era of indistinguishable EV silhouettes, design becomes the new sound of the engine. It is the thing that stirs emotion." Despite the failure of Jaguar's rebrand, though, Lewis acknowledges one potential positive: it has "elevated design into a cultural conversation." More specifically, the unveiling of the Type 00 concept car at Miami Art Week in December 2024, which showcased the brand's future electric design direction, did generate significant attention, albeit with the same polarised reception as the rebrand itself. In short, while Jaguar's execution may have been flawed, its instinct to differentiate through bold design wasn't entirely misguided. The failure was in how that design connected—or failed to connect—to the brand's heritage and values. Lessons for brands So, what have we learned from all this? Here are five big lessons for other brands contemplating significant repositioning. 1. Evolution, not revolution Gabor's comment that "right now, it feels as if a Jaguar might no longer be a Jaguar" encapsulates perhaps the most critical lesson. Overly radical rebrands risk destroying more equity than they create. Successful rebrands, in contrast, maintain sufficient connections to a brand's heritage while moving it forward. 2. Maintain distinctive assets Sue highlights the importance of maintaining recognisable elements that create mental shortcuts for consumers. Jaguar's abandonment of the growler logo eliminated a powerful visual cue that had built recognition over decades. The simple lesson is that brands should carefully audit their distinctive assets before discarding them. 3. Solve emotional connection challenges Lewis's about the EV category point to the challenge of creating emotional connections in categories undergoing technological transformation. Brands need to find new ways to create emotional resonance when traditional sources of differentiation disappear. 4. Test with core audiences Jaguar's rebrand appears to have been developed with an idealised future customer in mind, without sufficient consideration for existing loyal customers. Brands should test significant changes with core audiences before full implementation. 5. Ensure clarity of purpose Perhaps most fundamentally, Jaguar's rebrand created confusion about what the brand now stands for; as Sue notes, there was a marked absence of actual cars in the ads. A successful rebrand should clarify rather than confuse a brand's purpose. The road ahead As Jaguar looks to appoint a new ad agency, it faces the challenge of deciding whether to double down on its controversial new direction or course-correct back toward its heritage. Gabor notes, "As someone once said, to err is human; to rectify, wise. Even the most daring strategies leave room for course correction." No brand should be so committed to something that it cannot adjust when the market provides clear feedback. What's clear is that Jaguar's next agency will need to help the brand reconnect with consumers on an emotional level while distinguishing itself in the increasingly crowded electric vehicle market. As Lewis states, "The agency it chooses will need to build on this momentum, not just to sell cars but to help people reconnect emotionally with the act of driving." Whether that means embracing the controversial new direction with better execution, or finding a middle ground that honours Jaguar's heritage while looking to the future, remains to be seen. Either way, the stakes couldn't be higher for this storied British brand.