Not even Pokémon is safe from AI benchmarking controversy. Last week, a post on X went viral, claiming that Google’s latest Gemini model surpassed Anthropic’s flagship Claude model in the original Pokémon video game trilogy. Reportedly, Gemini had reached Lavendar Town in a developer’s Twitch stream; Claude was stuck at Mount Moon as of late […]

Hello everyone! I’m Luna. I’ve been lurking on the forum and learn a lot from everyone. So I think it’s time for me to step up and show my work :> I’ll be updating my work from time to time and all feedbacks are greatly appreciated! You can check out the HD version at : https://www.artstation.com/artwork/gRBmyZ Thank you everyone. 1 post - 1 participant Read full topic

Housing affordability is a hot button issue in the 2025 New York City mayoral race—Brad Lander, Zohran Mamdani, Scott Stringer, Jessica Ramos, and Zellnor Myrie have all shared housing platforms these past few months, as reported by AN in February.  Andrew Cuomo, after announcing his candidacy March 1, shared his housing plan this weekend. The document, Addressing New York City’s Housing Crisis, was written by Cuomo policy advisor Paul Francis, The New York Times reported. The release was overshadowed, however, by reporting from Hell Gate, which said Cuomo’s team used Chat GPT to pen its platform. Cuomo’s opponents were quick to respond. Over the phone, Cuomo spokesperson Rich Azzopardi, founder of Bulldog Strategies, told AN Chat GPT was used for citation purposes only, and not for writing the plan itself. “We wrote the paper, and yes, we used Chat GPT to get footnotes. This is no different from using Google.” The Plan? Cuomo’s housing plan says it prioritizes gentrification, homelessness, tax abatement programs for affordable housing, tenants protections, and office-to-residential conversions, among other things, but it’s noticeably scant on detail. He says the city needs to preserve and build 500,000 homes over the next 10 years, many of them affordable. Most of the document highlights Cuomo’s past work under Mayor David Dinkins, as Housing and Urban Development secretary during the Clinton administration, and as New York State governor.  The plan alludes to the need to fix NYCHA, but says—twice, verbatim—he will “specifically address his plans for NYCHA in a separate NYCHA Agenda to be released in the coming weeks.” He also opposes rezonings in “low-density neighborhoods,” suggesting he’ll fight new affordable housing in the outer-boroughs. Cuomo notes City of Yes is “an important but ultimately insufficient step towards addressing the housing shortage.”  Zohran Mamdani, an assemblymember from Queens also running for mayor, was vocal about Cuomo’s housing plan, and highlighted its grammatical errors. Mamdani told AN: “New Yorkers are facing a devastating housing crisis that is forcing lifelong residents to flee the city and Andrew Cuomo doesn’t have the decency to write his own housing platform. This plan is half-baked, riddled with typos, incoherent, and most importantly fails to address the astronomical cost-of-living here in New York City.” “Worse, Cuomo is too much of a coward to face the press or public and defend this shoddy agenda,” Mamdani continued. “New Yorkers deserve a mayor who does their homework and will treat the problems they face with the gravity and attention they deserve.” To Mamdani’s point, some platitudes that stood out include: “Market rate housing has a role in the market,” and “Both New York City and the State need to increase their capital commitments in order to make the economics of affordable housing are relatively simple [sic].” And: “New York City has many capital needs, but none is more important than the need for more affordable housing in New York City [sic].” Many paragraphs don’t add up: “Nevertheless, several candidates for mayor this year have either called directly for a rent increase or for other measures that would tilt the scale toward lower rent increases. This is a politically convenient posture, but to be in. Victory if landlords—small landlords in particular—are simply unable to maintain their buildings [sic].” Failed Legacy? “Andrew Cuomo phoned in his housing policy as governor so it’s no surprise he phoned in his housing policy when running for mayor,” former New York City comptroller and current mayoral candidate Scott Stringer told AN. “He was a major cause of the housing problems we’re now facing, and he can’t be trusted to clean up the mess he left. We deserve a mayor who is focused on building affordable housing for New York’s middle-class families, not someone who is going to outsource the job to a computer.” Zellnor Myrie, an assemblymember who represents Central Brooklyn, who recently released a housing report discussing how Cuomo’s past housing policies have negatively impacted Black New Yorkers, told AN: “It’s no surprise Andrew Cuomo can’t write an honest housing plan—because his failed legacy of skyrocketing rent and dwindling housing supply is nothing to brag about.” “Under Cuomo, housing prices rose nearly 80 percent and tens of thousands of Black New Yorkers were pushed out of the city,” Myrie added. “I’m running for Mayor to deliver one million homes—because we need bold, new leadership with plans to tackle the housing crisis Cuomo created. New Yorkers deserve better than the Status Cuomo.” In response to comments from mayoral candidates, Azzopardi highlighted Cuomo’s track record. “Governor Cuomo was Housing and Urban Development secretary under Bill Clinton. He authored a nationally-emulated homelessness fighting plan under Mayor Dinkins,” Azzopardi said. “As Governor, he built and preserved hundreds-of-thousands of affordable housing units in a $100 billion program. Also, as HUD secretary, he not only built affordable housing, he took over problematic housing authorities like in Chicago, and made them work again.” “New Yorkers aren’t stupid. They’re not going to fall for this,” Azzopardi added. “They’re not going to fall for petty attacks by other candidates that have no record of their own to run on. They know Andrew Cuomo is the candidate in this race that has the experience, and the recorded results to make New York City a more affordable and safer place for all.”

One of the many stunning and well-preserved Colonial homes in Canterbury, Connecticut is this residence, the John Carter House on S. Canterbury Road. Records show that the house was built around 1765 for John Carter and his wife, Mary Smith. This house is a good example of the domestic architecture of 18th-century Connecticut and while there have been some changes over time, it continues to exhibit all the major hallmarks of the colonial type, such as a center-chimney plan, clapboard exterior, and five-bay facade. The house originally had a saltbox roof sloping to the rear, but was removed sometime in the 19th or 20th centuries, likely when the projecting Colonial Revival-era porch was added at the entrance. What a spectacular home!

Anna Bliokh/Getty ImagesHave you ever wanted to build your own custom application but didn't want to take the time to do any of the pesky learning that software development requires? If so, a new experimental project from GitHub might just make your dreams come true.GitHub Spark lets you build what the company calls "micro apps" or "sparks." These are very limited custom applications that perform one or two basic tasks. You create them through a chatbot interface, and when you're done, you get a spark you can (someday) share with all your friends. Also: Microsoft is offering free AI skills training for all - and it's not too late to sign upI recently got access to the preview and was able to do some testing. Fundamentally, the tool is extremely limited. But because there's an AI operating underneath, it's possible for the AI to do some very sophisticated AI magic within the very limited interface of Spark. Linking and configuring The first thing you need to do is link your GitHub account to Spark. Point your browser to https://spark.githubnext.com/ and log in with your GitHub account. If you don't have a GitHub account, you'll need to get one. Once you've logged in, you'll need to give permission. This is very similar to any other app that requires permission before first use. Screenshot by David Gewirtz/ZDNETOnce you're in the Spark environment, you'll see a standard chatbot interface. If you click the little control panel icon, you can choose the AI model you want to use. I've had some good success with GPT-4o and coding, so that's what I chose. Screenshot by David Gewirtz/ZDNET What do you want to build? I thought a lot about what sort of app I'd want to build. Examples included habit-tracking applications, an allowance tracker, a map app, and a karaoke night planner. Basically, they were all apps that presented a form consisting of fields and buttons and performed some business logic based on the data being entered. But the entity doing the business logic calculation wasn't a typical forms manager. Instead, it was GPT-4o. So what if my business logic was something insanely complex and difficult for a regular algorithm but easy for an AI -- all wrapped in a very simple UI? I decided I wanted to create a tool that would allow me to paste in a block of code. The app would tell me what the code did, what language it was written in, any observations about areas where there might be a problem, and maybe a detailed breakdown of the lines of code. Think about that. In years past, that would have been a multi-million-dollar project if it could have been done at all. But I just fed Spark a single line: "A tool that examines and explains source code." Screenshot by David Gewirtz/ZDNETThen I hit "Go." The result was the interface shown below. On the left is a pane that theoretically allows you to iterate on what the app will do. The middle pane consists of the code generated from the prompt. And the right pane is the user interface Spark created for the app. Screenshot by David Gewirtz/ZDNETI fed it the buggy regular expression code from my programming tests just to see what it would do. Screenshot by David Gewirtz/ZDNETAs I suspected, GPT-4o was quite capable of analyzing a code snippet. I didn't find the result as useful as I wanted, so I decided to refine what the application would do for me. Customizing the application You make changes through the Iterate field in the leftmost pane. I told GPT-4o that I wanted it to: Display the language of the source codeProvide a short one- to two-sentence description of what the code doesAdd a sentence or two describing any failings of the codeI presented that to Spark in that field and hoped for the best. Screenshot by David Gewirtz/ZDNETThe results were impressive. The app did, in fact, provide me with the information I wanted. You can see that in the pane on the right side of the interface. It identified the language, provided a short description of the code, and outlined a whole bunch of problems with the code. It then provided the detailed explanation of the code that was part of the original requirement prompt, where I asked it to explain the source code. Screenshot by David Gewirtz/ZDNETThe results were impressive, but I didn't like the formatting. Stubborn, thick-headed, and non-responsive It was at this point that Spark began to show its limitations. As you can see in the leftmost pane of the above image, I tried to get Spark to remove the three asterisks at the beginning of each answer. I also tried to get it to turn the critique section into a bulleted list. Finally, I wanted to get rid of the second set of index numbers under the headings. I got the bullets, but Spark or GPT-4o ignored my other requests. My guess is that GPT-4o was writing in Markdown, but Spark's UI didn't parse Markdown correctly. Screenshot by David Gewirtz/ZDNETTo be honest, I have another 20 or so screenshots of my various attempts to get GPT-4o and Spark to clean up that presentation. I was so impressed that I could quickly build an app that explained code, but increasingly more frustrated that I couldn't get it to make a few minor presentation fixes that would have taken five minutes if I were coding it directly. One line of code -- a string replace command replacing three asterisks with the empty string, and another replacing two asterisks with the empty string -- would have cleaned up one presentation issue. Another string replace command, with a simple regular expression that searched for a digit followed by a decimal point at the beginning of a line and replaced it with the empty string, would have fixed all the presentation issues. I tried it, and you can modify the code generated by Spark. But if you do any additional iterations using the AI, any code you modify is overwritten, even if the AI modifications requested are nowhere near your modified code. Sharing is limited Eventually, I gave up on trying to tune the output formatting. Even with slightly ugly output, the tool itself was useful. So I decided I wanted to share it with everyone. You can do this by clicking on the share icon next to the named Spark and choosing to share it. Screenshot by David Gewirtz/ZDNETHere's a link to my Spark (but don't get your hopes up about using it). You can't use it unless you have a GitHub account. Even if you do, you can't use it unless you've been accepted into the Spark preview program. I tried with another GitHub account and got this message. No Spark for you. Screenshot by David Gewirtz/ZDNET How consequential is this? No-code form generators have been available for years. I built one as far back as the early 2000s. Since the UI for such a tool is mostly a matter of choosing the controls (buttons, drop-downs, fields, etc.), along with placement and some pretty paint, it's not a very difficult prospect. While you can only do so much with form-based apps, you can actually build a pretty good variety of apps. These apps are usually of the information management kind, rather than productivity or highly interactive tools. Still, businesses can get a lot done within the confines of a form generator. Adding AI to manage business logic wildly expands the capability of such form generators, as my programming code analyzer showed. But AIs are also incredibly unreliable, so it's not clear you'd want to run mission-critical business logic through an algorithm managed by an AI. But again, for a moderately large subset of applications, this approach could be good enough. And that brings us to the iterative process that Spark offers. Because human-written code gets blasted into oblivion with each AI update, modification and customization the old-fashioned way is impractical. Unfortunately, the AI has a this-far-no-farther mentality, and once it builds some basic business logic, it stubbornly refuses to implement additional tweaks and modifications. That, sadly, makes this tool a mere curiosity, not a useful business resource. But on the other hand, that's not a terribly hard problem to solve. As such, given the very attainable task of increasing the capability of the forms generator and the equally attainable task of making iteration and changes more effective, I think GitHub Spark has the potential to be useful. I'd like to see a way for human-written code to coexist with AI-written code. And I'd like to see a way for Sparks to run as standalone web applications without users having to be part of the GitHub framework. But those are also fairly achievable expectations. The bottom line is that this has the potential for being a usable, if constrained, tool. It's certainly not there yet, but give it a year or so of iteration. It will probably be capable of doing some interesting tasks. I'd say, stay tuned. There's likely more to come. Have you tried GitHub Spark yet, or are you still waiting to be accepted into the preview? If you've had access, what kind of app did you try to build, and how did it go? Were you impressed by the AI's ability to generate logic, or frustrated by the limitations when trying to make adjustments? And if you haven't used Spark, do you think tools like this could change how non-coders approach building apps? Let us know in the comments below. Get the morning's top stories in your inbox each day with our Tech Today newsletter.You can follow my day-to-day project updates on social media. Be sure to subscribe to my weekly update newsletter, and follow me on Twitter/X at @DavidGewirtz, on Facebook at Facebook.com/DavidGewirtz, on Instagram at Instagram.com/DavidGewirtz, on Bluesky at @DavidGewirtz.com, and on YouTube at YouTube.com/DavidGewirtzTV.Artificial Intelligence Editorial standards

ATHENS - AUGUST 27: Liu Xiang of China crosses the finish line as he finished first in the men's ... More 110 metre hurdle final on August 27, 2004 during the Athens 2004 Summer Olympic Games at the Olympic Stadium in the Sports Complex in Athens, Greece. Liu equalled the world record of 12.91 seconds. (Photo by Stuart Hannagan/Getty Images)Getty Images Developers develop. Software application developers program applications by coding in their language of choice, on and to their platform of choice, inside their preferred integrated development environment and through their chosen application engineering methodology. While some or all of those factors may be governed by the team that they find themselves in and so become less of a personal decision, there is a general notion of freedom to be, especially perhaps when it comes to the use of open source toolsets. As laissez-faire as all that sounds, developers are also directed towards a number of system management responsibilities that need to happen to ensure “uptime” is maintained and users get functionality out of the applications and data services that they need to work, or indeed play. At The Point Of Cloud Runtime While all software engineering teams of any reasonable size will have a dedicated security team (and smaller ones obviously won’t always have that luxury), the rise of cloud computing and the Kubernetes container orchestration platform has put more of the control responsibility back in the hands of the cloud development engineer themself. Because cloud and containers move so fast (some are “spun up” into existence for mere minutes), the security consideration must move to the point of application runtime i.e. the point at which an application actively executes and makes calls to the resources that surround it in the environment it is built in. But how do developers know what to work on around security fixes today? Traditionally, this has seen them take a list of issues from the IT security team (all pretty much without context or application environment information) and then attempt to work through a process of reverse engineering logic as they try to understand what’s happening in any given cloud. A New Route To The Root This could mean working through thousands of items spanning different software libraries, different cloud container images, different data feeds and different third-party plugins and more. What developers would like in these scenarios is a way to find the root cause of security issues and be able to prioritize actions to remediate system health. But cloud computing has changed some key fundamentals, so what route do we take to get to the root now? Real-time cloud-native security tools company Sysdig has plenty of opinions to share here. “Sysdig was founded to solve a problem. That problem was the question of how we do observability when we can’t look at a packet [a chunk of data moving over a network with routing information to tell it where to go] in the virtualized and abstracted world of cloud,” said Alex Lawrence, director of cloud security strategy at Sysdig. “We knew that was our mission, because packets don’t lie. But this is not the old days of networking where we could look at network switches to see packets; now, those packets run on someone else’s infrastructure, the cloud services provider. So we know that the system call becomes the lowest common denominator and we have access to that information. If I’m on a server in a virtual machine in the cloud, the system call is the thing that creates the packet. It’s the thing that gives the instruction to write the file.” What Is A System Call? To define this term, a system call is an interface mechanism between an application and its governing infrastructure (often the operating system kernel) that enables the application to access the memory, processing power, data storage or other services that it needs to breathe. Sysdig Lawrence along with founder and chief technology officer Loris Degioanni say that a system call is arguably a richer telemetry source than a packet ever was. This is due to the fact that in any software system, there’s “stuff that happens” without ever becoming a packet. For example, let’s say an application wants to perform a call on a host server in a container. It doesn’t have to leave the cloud container or the host to make this action happen, it all occurs internally. “But if we can ‘instrument’ the system call, we can now know everything happening on that individual host, right? So Sysdig originally was an observability company that was doing all the observability metrics to analyze everything happening on a host cloud server and see what was going on,” said Lawrence. “But then we had customers early on saying, hey, you realize that this has really big security implications too and it’s not just observability. That’s what inspired the company to create project Falco, which is basically like a camcorder that tracks all the things happening inside a cloud. It is system analysis that looks for an abnormal system call that shouldn’t be there, or find the structure of the executables within an application or database query or whatever that shouldn’t be happening in the ‘normal’ course of operations.” One analogy here is likened to being at home and turning the tap on and getting beer or wine out of the faucet instead of water i.e. the thing that is instructed to do something which we would normally expect to happen, is doing something we don’t expect to happen. But this isn’t beer taps, this is what we can now call a cloud-native application protection platform, or CNAPP for short. Falco, As In Eagle-Eyed The Falco project is powered by rules and all those rules are written in the the YAML software language. Now a graduated project housed under the auspices of the Cloud Native Computing Foundation, Falco can be described as an open source runtime security platform that enables software developers to find and react to suspicious behaviour within Linux containers and applications. Falco was conceptualized, designed and built to work with Kubernetes, but its realm and purview is not limited to Kubernetes. This means it is also capable of delivering runtime security monitoring for other container orchestration platforms and standalone container deployments. “Falco’s journey is far from over. As cloud-native security threats grow in complexity, Falco is evolving to meet them head-on. The focus for the coming year is clear: deeper Kubernetes integration, a more sophisticated plugin system… and a shift toward automation in runtime security. Perhaps the most exciting development, though, is the growing synergy between Falco and Stratoshark [a software tool built by the same team that created Wireshark, which analyzes system calls and log messages]. Together, they are setting the foundation for a new security paradigm – one where detection, investigation, and response are seamlessly unified,” wrote Degioanni on his company blog. “Runtime security has always been about visibility, but as Kubernetes environments scale, visibility alone isn’t enough. Falco is tackling this by modernizing its stack, making security more automated and easier to deploy.” He asserts one final note to suggest that Falco and Stratoshark will pioneer a Kubernetes Detection and Response (KDR) approach. Next we will see tighter integration between the tools, automated forensic workflows and collaboration between the Falco and Wireshark communities to redefine open source runtime security. Our Immediate Future, Developer Self-Service Where companies like Sysdig are taking us is towards a future where software developers get more immediate control of system and application health from first principles. While the perceived notion is that programmers care most about “cool functionality” on the road to creating the next killer app, they do in fact care a lot about vulnerability management in the virtual cloud arena. “Taking stock of where we are today, there are vendors that specialize in software system detection & response (think of this like a security camera on your house) and there are vendors who offer security posture management technology (a wider angle view on an IT stack to make sure there are locks on the doors of the house) today. To continue our home security analogy, if your door locks are broken, but no intruders are near your house, then you know how to act accordingly vs a scenario where you’re actually about to lose your possessions. Sysdig was engineered from the start to provide both sides of this weigh-scale so that we can offer a total security platform offering,” said Degioanni. “Our platform now sits at that broader point where we can offer users the most accurate visibility into their cloud IT stack as fast as possible… a combination which is now empowered and accelerated with agentic AI services. To offer a platform technology proposition in this way, Sysdig has collected and correlated vast amounts of data from system calls and posture status (using our backbone and employing a graph database) so that all data and information relationships can be tracked and mapped accurately, quickly and in the most efficient way possible.” We live in a world where software system security is trying to be more automated (through artificial intelligence yes, but also through system-level automation that we probably wouldn’t classify as AI), more hands-off and more self-service. It’s a large part of why we’ve been able to talk so volubly about so-called DevOps as the marriage of shared responsibility between developers and operations staff. The notion of platform engineering and agentless technologies have subsequently followed suit for the same reason. Will we still need IT security teams in the future then? Yes, obviously, they may be able to spend more time refining and finessing the tools inside platforms like Sysdig than chasing vulnerabilities and attacks. It’s all getting a whole lot more granular in computing… and, from a user security perspective, fine-grained is just fine.

The big picture: Clean water is essential, but some pollutants are notoriously tricky to remove. Scientists have developed a breakthrough filtration process using 2D materials and sugar-based chemistry to trap persistent contaminants. Engineers can customize the technology to target specific molecules, offering a scalable solution for safer water worldwide. Researchers at Monash University have introduced a new water filtration technology that could shift the fight against PFAS – chemicals known for their environmental persistence and health risks. Found in products like waterproof clothing and firefighting foams, PFAS are notoriously hard to break down. Traditional treatment methods often fail, especially against the smallest PFAS molecules, which slip through filters and accumulate in ecosystems and human bodies. The Monash team developed a graphene oxide membrane derived from graphite and enhanced it with beta-cyclodextrin, a ring-shaped sugar molecule. The pairing is intentional as beta-cyclodextrin can trap chemical compounds inside its ring-like structure, acting as a molecular cage. The researchers created a highly selective network of nanoscale channels by integrating beta-cyclodextrin into the graphene oxide membrane. These channels act as energy barriers, blocking PFAS molecules – including the elusive short-chain types – while allowing water to flow through efficiently. Lead researcher Eubert Mahofa said the membrane's design overcomes a major challenge in water purification – balancing the removal of tiny, persistent contaminants with maintaining a fast flow of clean water. "Our approach solves this by filtering out and concentrating these harmful chemicals while still allowing water to flow through efficiently," Mahofa said. The membrane's performance remains stable even as water temperature changes, which is essential for real-world applications where conditions can vary. The manufacturing method, known as shear alignment printing, is efficient and scalable, enabling the production of large membrane sheets suited for municipal water treatment plants, industrial facilities, and environmental cleanup efforts. // Related Stories Co-researcher Dr. Sally El Meragawi emphasized that the membrane removes harmful chemicals while preserving essential minerals and nutrients. This ability makes it suitable for drinking water and wastewater treatment, ensuring the water remains safe and healthy for consumption. What sets this technology apart is its adaptability. Researchers can modify the chemical structure of beta-cyclodextrin to target a wide range of pollutants, including pharmaceuticals, pesticides, and heavy metals. Professor Mainak Majumder, who leads the Australian Research Council's Research Hub for Advanced Manufacturing with 2D Materials, explained that this approach could pave the way for a new generation of customizable water filters, each designed to target specific contaminants. Monash University, Clean TeQ Water, and NematiQ – a company specializing in graphene-based technologies – collaborated over several years to develop this breakthrough process. Image credit: Clean TeQ Water

Table of Contents Table of Contents A brief summary of AI training How is Apple planning to fix its AI?  Why is it a crucial step forward? Apple’s AI efforts haven’t made the same kind of impact as Google’s Gemini, Microsoft Copilot, or OpenAI’s ChatGPT. The company’s AI stack, dubbed Apple Intelligence, hasn’t moved the functional needle for iPhone and Mac users, even triggering an internal management crisis at the company.  It seems user data could rescue the sinking ship. Earlier today, the company published a Machine Learning research paper that details a new approach to train its onboard AI using data stored on your iPhone, starting with emails. These emails will be used to improve features such as email summarization and Writing Tools.  Recommended Videos Nirave Gondhia / Digital Trends Before we dig into the specifics, here’s a brief rundown of how AI tools work. The first step is training, which essentially involves feeding a vast amount of human-created data to an “artificial brain.” Think of books, articles, research papers, and more. The more data it is fed, the better its responses get. Related That’s because chatbots, which are technically known as Large Language Models (LLMs), try to understand the pattern and relationship between words. Tools like ChatGPT, which are now integrated within Siri and Apple Intelligence, are essentially word predictors.  But there is only so much data out there to train an AI, and the whole process is pretty time-consuming and expensive. So, why not use AI-generated data to train your AI? Well, as per research, it will technically “poison” the AI models. That means more inaccurate responses, spouting nonsense, and delivering misleading outputs.  Nadeem Sarwar / Digital Trends Instead of relying solely on synthetic data, one can improve the responses of an AI tool by refining and fine-tuning it. The best approach to train an AI assistant, however, is to give it more human data. The data stored on your phone is the richest source for such information, but a company can’t simply do that.  It would be a serious privacy violation and an open invitation to lawsuits.  What Apple intends to do is take an indirect peek at your emails, without ever copying or sending them to its servers. In a nutshell, all your data remains on your phone. Moreover, Apple is not going to technically “read” your emails. Instead, it will simply compare them to a pile of synthetic emails.  The secret sauce here is identifying which synthetic data is the closest match for an email written by a human. That would give Apple an idea about which kind of data is the most realistic way humans engage in a conversation. So far, Apple has “typically” used synthetic data for AI training, reports Bloomberg.  “This synthetic data can then be used to test the quality of our models on more representative data and identify areas of improvement for features like summarization,” the company explains. It could lead to tangible improvements for the responses you get from Siri and Apple Intelligence down the road. Nadeem Sarwar / Digital Trends Based on learnings from realistic human data, Apple aims to improve its email summarization system and a few items in the Writing Tools kit. “The contents of the sampled emails never leave the device and are never shared with Apple,” assures the company. Apple says it has already put similar privacy-first training systems in place for the Genmoji system. Right now, the summaries you get courtesy of Apple Intelligence in Mail can often be quite confusing, and occasionally, downright gibberish. The status quo of app notifications is no different, and it got so bad that Apple had to temporarily pause it after drawing flak from the BBC for misrepresenting news articles.  The situation is so bad that the summarized notifications have become a joke in our team chats. In its bid to summarize conversations or emails, Apple Intelligence often clubs together random sentences that either make no sense, or give an entirely different spin to what’s really happening. The core problem is that AI still struggles with context and human intent. The best way to fix it is by training it on more situation-aware material with proper contextual understanding. Recently, AI models capable of reasoning have arrived on the scene, but they haven’t quite been a magic pill.  Nadeem Sarwar / Digital Trends The method described by Apple sounds like the best of both worlds. “This process allows us to improve the topics and language of our synthetic emails, which helps us train our models to create better text outputs in features like email summaries, while protecting privacy,” says the company.  Now, here is the good part. Apple is not going to read all emails stored on iPhones and Macs across the world. Instead, it is taking an opt-in approach. Only users who have explicitly agreed to share Device Analytics data with Apple will be a part of the AI training process.  You can enable it by following this path: Settings > Privacy & Security > Analytics & Improvements. The company will reportedly kick the plans into action with the upcoming iOS 18.5, iPad 18.5, and macOS 15.5 beta updates. A corresponding build targeted at developers has already been released.  Editors’ Recommendations

The video-game adaptation succeeded at both things cinema struggles to do consistently: sell gobs of tickets and get young people into theaters.

Dare to debate Should we settle Mars, or is it a dumb idea for humans to live off world? Should we all just read A City on Mars and call the whole thing off? Eric Berger – Apr 14, 2025 5:56 pm | 43 An expanded settlement on Mars in the late 2020s, as envisioned by Mars One. Credit: Mars One An expanded settlement on Mars in the late 2020s, as envisioned by Mars One. Credit: Mars One Story text Size Small Standard Large Width * Standard Wide Links Standard Orange * Subscribers only   Learn more Mars is back on the agenda. During his address to a joint session of Congress in March, President Donald Trump said the United States "will pursue our Manifest Destiny into the stars, launching American astronauts to plant the Stars and Stripes on the planet Mars." What does this mean? Manifest destiny is the belief, which was particularly widespread in 1800s America, that US settlers were destined to expand westward across North America. Similarly, then, the Trump administration believes it is the manifest destiny of Americans to settle Mars. And he wants his administration to take steps toward accomplishing that goal. Should the US Prioritize Settling Mars? But should we really do this? I recently participated in a debate with Shannon Stirone, a distinguished science writer, on this topic. The debate was sponsored by Open to Debate, and professionally moderated by Emmy award-winning journalist John Donvan. Spoiler alert: I argued in favor of settlement. I hope you learned as much as I did. Eric Berger Senior Space Editor Eric Berger Senior Space Editor Eric Berger is the senior space editor at Ars Technica, covering everything from astronomy to private space to NASA policy, and author of two books: Liftoff, about the rise of SpaceX; and Reentry, on the development of the Falcon 9 rocket and Dragon. A certified meteorologist, Eric lives in Houston. 43 Comments