TECH

AMD report says Zen 6 EPYC Venice smokes NVIDIA Vera in revised agentic AI benchmarks

AMD has updated its performance estimates for its upcoming 6th Gen EPYC 9006 "Venice" server CPUs, and the latest numbers make an aggressive case against Intel and NVIDIA in high-density data center workloads. A new AMD white paper, titled "AMD EPYC Server CPU Architecture And Performance Overview" revises the company's modeling for Venice and compares the 256-core EPYC 9996 against Intel's 128-core Xeon 6980P and NVIDIA's 88-core Vera CPU.

The updated analysis places particular priority on agentic AI infrastructure, where CPUs handle tasks such as orchestration, databases, web services, caching, APIs, retrieval, and other work surrounding accelerator-based AI inference. This work is now comprising a larger and larger percentage of the actual workload of Agentic AI, reinforcing the role of CPUs in the datacenter, once thought to merely be orchestration for GPUs that do all the 'real' work.

AMD expects that the EPYC 9996 will deliver 2.4× the performance of Xeon 6980P in server-side Java, 2.5× in OpenSSL, 3.5× in MongoDB with YCSB, 2.9× in Redis Benchmark, 3.7× in NGINX with WRK, and 2.6× in transaction processing based on TPC-C. Those results are normalized to a 128-core Xeon 6980P system and represent the CPU-heavy enterprise and cloud-native portions of an agentic AI infrastructure stack. Notably, Vera was not included in this modeling, but not because AMD didn't have numbers to compare against.

Besides, the more eye-catching update comes at the rack level. AMD has revisited its earlier 100-kW rack-capacity model from June, which estimated how much aggregate workload throughput each platform could deliver within a fixed rack-level power envelope. The company says the previous analysis put 5th Gen EPYC 9965 at 2.37× the rack-level throughput of its NVIDIA Vera baseline. The revised study now models the 6th Gen EPYC 9996 at 3.4× Vera's rack-level performance.

This is partially because the underlying methodology has changed. AMD's earlier rack estimate was based on a six-workload geometric mean that included SPECrate 2017 Int, while the updated model incorporates SPECrate 2026 Int, server-side Java, NGINX, Redis, Memcached, and TPROC-C. That makes the latest figure a refreshed model rather than simply adding Venice to the old chart. There's also a small but interesting change for AMD's previous-generation part. The earlier 9965 estimate was 2.37× Vera, while the new chart shows the 9965 at about 2.3× in the revised model. In other words, AMD appears to have rerun the calculations rather than simply carrying its previous numbers forward.

However, it's critical to keep in mind with these rack-scale comparisons that AMD is modeling performance rather than publishing results from physical 100-kW racks containing both Venice and Vera hardware. The company describes the figures as estimates based on a combination of benchmark data and system-level assumptions, so they absolutely should not be treated as apples-to-apples measurements of shipping systems.

But AMD's also making a broader argument about the role of CPUs in agentic AI. Rather than treating the CPU as merely a host for an accelerator, the company says increasingly complex AI workflows require substantial CPU resources for orchestration, retrieval, database access, tool execution, networking, and response generation. The latest EPYC 9996 figures reinforce AMD's pitch for Venice as a high-core-count CPU designed to maximize useful work under real-world rack constraints rather than simply chasing peak processor performance.

Still, Vera is a more specialized CPU than Venice, and it has a very different architecture at the platform level. While these simulations put NVIDIA's new chip behind AMD's finest, NVIDIA claims the win in its own testing using different benchmarks and constraints. It's entirely possible that Vera may end up being a better choice for some workloads. We'll just have to wait for independent benchmarks on real hardware to know for sure.

AMD EPYC Venice is expected to outsell NVIDIA Vera by 2027... To make things even more exciting, a Morgan Stanley report predicts that AMD's EPYC Venice CPUs will reach 6.75 million units sold by next year—17% more than NVIDIA's Vera (and 5.4 times the volume compared to 2026).

According to reports, NVIDIA will remain TSMC's primary customer for CoWoS packaging capacity, with the Taiwanese company expected to reach a capacity of 200,000 wafers per month by 2027.

"Team Green" utilizes TSMC's CoWoS packaging solution for two main products: CoWoS-L for AI GPUs (such as Blackwell and Rubin) and CoWoS-R for Vera CPUs.

CoWoS-L production capacity is expected to reach approximately 910,000 units—a 40% year-over-year increase—while Vera shipments are projected to double. This would drive a 52% increase in revenue for NVIDIA compared to the previous year.

Finally, the report projects that NVIDIA's Vera CPUs will reach 5.75 million units by 2027. This is a significant figure for a new CPU launch, especially given NVIDIA's stated goal of becoming the leading CPU supplier by 2026.

Great news (for AMD)...The key takeaway is that NVIDIA is currently facing stiff competition. While its Vera processors are already in mass production at TSMC, the same applies to AMD's next-generation EPYC platform, codenamed Venice.

It is worth noting that Venice is based on the upcoming Zen 6 architecture, which is expected to deliver significant gains in performance and efficiency. As previously highlighted, the report projects that EPYC Venice CPUs will reach a volume of 6.75 million units—17% more than NVIDIA’s Vera (and 5.4 times the volume compared to 2026).

AMD is also utilizing TSMC’s advanced 2nm manufacturing process, whereas Vera is based on 3nm process technology. Furthermore, Vera is designed for agentic AI, while AMD’s EPYC Venice addresses both AI and HPC workloads.

The challenge at hand is not simply AMD versus NVIDIA or NVIDIA versus AMD, but rather the rise of custom silicon, as many AI companies are now venturing into that field.

Just this week, we reported that Google and MediaTek are collaborating on a chip that integrates CPU and AI capabilities into a single package for the next generation of intelligent agents. OpenAI and Amazon are also either in talks to produce or are already manufacturing custom chips, a trend that will intensify the debate between in-house development and external sourcing.

In short, with the growing popularity of custom chip manufacturing, NVIDIA, AMD, and other manufacturers may be facing a critical situation. While the demand for computing power remains high, AI companies producing their own chips will further exacerbate the supply-demand imbalance.

mundophone

 

TECH

What led two security researchers to leave Google DeepMind

There is a difference between an industry outsider warning that artificial intelligence could become dangerous and hearing the same concern from researchers hired to prevent that from happening. That is exactly what happened at Google DeepMind. Two experts focused on the safety of advanced systems left the company within a few months of each other. And the reasons they cited point to a problem that is far from being resolved.

Bilal Chughtai left Google DeepMind in July 2026. His work was directly related to the interpretability and safety of artificial general intelligence (AGI) systems, seeking to understand what occurs inside increasingly complex models.

After leaving, Chughtai began leading an AI safety program at the organization BlueDot Impact.

His concern is particularly serious, yet it must be understood as a personal risk assessment rather than a proven prediction. Chughtai stated his belief that AI systems could pose an existential risk and that the time available to avert extreme scenarios may be running out.

The core of his argument, however, lies elsewhere.

To him, the alignment problem—ensuring that highly capable systems remain consistent with the goals and boundaries set by their developers—remains unsolved.

At the same time, model capabilities continue to advance rapidly.

For this reason, Chughtai advocates for measures such as greater transparency and a more controlled pace of development, rather than a race to build increasingly powerful systems before sufficiently understanding how they work.

This is not a demonstration that AI will inevitably spiral out of control; rather, it is the assessment of someone who worked specifically on trying to figure out how to prevent such a scenario.

The second researcher chose to observe from the outside... A few weeks later, Josh Engels also left Google DeepMind’s AGI safety team.

His decision had a unique aspect: Engels stated that he had turned down offers from OpenAI and Anthropic to work at METR, an independent organization that evaluates advanced models, investigates incidents, and analyzes safety mechanisms. This choice reveals another concern.

Engels is particularly interested in so-called recursive self-improvement: the possibility of AI systems helping to develop even more capable versions of themselves, creating potentially ever-faster cycles of refinement.

The problem, according to him, is that there is currently no guarantee that sufficiently powerful systems would be safe before initiating such a process.

His estimate also drew attention due to its timeframe. Engels considers the possibility of AI systems causing “immense harm” within the next five years to be concerning, though he makes it clear that he cannot assign a precise probability to this scenario.

Once again, this is an individual assessment of a future risk, not a scientific conclusion establishing that this will necessarily happen.

Concern has mounted following incidents involving AI agents with greater autonomy.

In July, during internal cybersecurity evaluations, OpenAI reported that some models managed to bypass certain restrictions, access the internet, exploit vulnerabilities, and reach systems on the Hugging Face platform. The model involved was being used in internal research and was operating under specific evaluation conditions.

The episode does not demonstrate that an AI spontaneously developed its own intentions, nor does it confirm the extreme scenarios described by Chughtai and Engels.

But it does change the nature of certain questions.

Questions regarding autonomy, oversight, and the ability to keep a model within established limits cease to be merely theoretical exercises when experimental systems manage to bypass mechanisms designed to restrict their behavior.

And the departures of researchers concerned about this issue are not limited to Google DeepMind. Jacob Coxon, who also worked at OpenAI and Anthropic, has expressed similar concerns regarding the race to create systems capable of contributing to their own development.

The most curious detail lies in where they worked...The most significant aspect of this story is not simply that some researchers made pessimistic predictions about the future of artificial intelligence.

It is that they were part of the very teams responsible for studying these risks.

Chughtai worked on interpretability and safety. Engels was part of a team dedicated to AGI safety. Both left major labs and went on to advocate—in different ways—for a more cautious approach.

This does not prove that current systems are out of control, nor does it establish that catastrophic predictions will come to pass.

A second departure...Josh Engels worked alongside Chughtai on DeepMind's AGI safety team. He's an MIT graduate. He resigned on September 13, 2026, and turned down job offers from both OpenAI and Anthropic - two of the companies he'd be most likely to warn about. Instead he's joining METR, the independent nonprofit that evaluates whether frontier AI systems are dangerous before they ship. That's a deliberate choice. Engels told reporters he sees a "terrifying chance" that AI systems cause "immense harm" within the next five years, according to Business Standard. His fear is specific: capability gains outpacing anyone's ability to align or evaluate the systems producing them.

At METR, Engels says he'll trace where alignment failures actually originate in training, and test whether the safety mitigations labs already claim to have would hold up if something went wrong. That's not abstract. It's the difference between a company saying it has guardrails and someone independently checking whether those guardrails work.

More than boardroom talk...It's worth being precise about what this is and isn't. Earlier this month, Anthropic chief executive Dario Amodei publicly called for the AI industry to slow its pace, part of a broader run of leadership-level statements urging coordination among labs. Chughtai and Engels are a different animal entirely. Neither is a CEO making a strategic argument from the top. Both are individual researchers, inside Google's own frontier lab, walking out the door and saying, on the record, that they don't trust where the company they worked for is headed.

They're not alone, either. Jacob Coxon left OpenAI for Anthropic, then quit that job too, on September 9. He told colleagues the major labs are "racing to self-improving superintelligence and gambling with our lives," according to TheNextWeb. Three departures, three different companies, the same complaint: the pace of capability gains is outrunning anyone's ability to keep the systems controllable.

Chughtai's own ask is fairly specific. He wants AI companies to slow their competitive race, submit to real transparency, and coordinate on a pace "that society can handle," rather than one set by whichever lab is most afraid of falling behind. That's a harder sell than it sounds. Slow down unilaterally, and you've just handed the frontier to whichever rival didn't.

Google hasn't said much. A DeepMind spokesperson wasn't available to comment outside business hours when Chughtai's post went viral, according to TheNextWeb. That silence is its own kind of answer. Frankly, when two safety researchers from the same team leave within months of each other and neither departure gets a real public response, it says something about how the company is managing the story, whatever it's doing about the underlying concern.

None of this means DeepMind's alignment work has stalled, and neither Chughtai nor Engels claimed it had. What they're both saying, in slightly different words, is that the gap between what AI systems can do and what anyone can verify about their behavior is widening, not closing. Engels put a number on it: five years, "terrifying chance." Chughtai's timeline was vaguer. His verdict was blunter.

Not every departing employee gets this kind of hearing. These two did, because they left the inside of one of the world's most closely watched AI labs and said, in public, exactly what they were afraid of.

However, it reveals an increasingly significant tension in AI development: the capabilities of these models may advance at a different pace than our ability to fully understand, test, and control them.

It is precisely this disparity that turns artificial intelligence safety into a race against the technology's own pace of evolution.

mundophone        

 

TECH

NVIDIA DLSS 5 mod runs in a browser and even works on Apple Silicon

If you've been fascinated by NVIDIA's DLSS 5 technology, yet lack the requisite hardware or necessary gumption to try it out for yourself, you can now see a live demo right on your PC, directly in your browser, without having to manually download or install anything. The demo works in your web browser, and it uses your GPU, but it doesn't run the DLSS 5 model in real-time, so you can get it to work on just about anything with the requisite WebGPU support.

A developer going by MAAN has demonstrated NVIDIA DLSS 5 Neural Rendering running inside a web browser using WebGPU, and according to their post on X, it works on macOS too. A live demo is up now, and MAAN says people can try it with their own models as well. This is odd because NVIDIA doesn't officially support DLSS through WebGL or WebGPU at all. DLSS normally runs through NVIDIA's NGX interface or the Streamline SDK, which sits between a game and DirectX or Vulkan and needs motion vectors, depth data, and the rendered frame handed over directly. None of that maps cleanly onto a browser's pipeline, so it's not clear which approach the developer has used to get it running on a web browser.

The developer hasn't shared technical details either, but they have confirmed that the source code will be shared on GitHub soon. Performance is another question mark. VideoCardz says loading the Neural Rendering model alone took a second or two on an RTX 4090, and running on Apple Silicon apparently works but isn't fast. That probably rules out real-time gaming for now, though the concept could still be useful for 3D model previews, where a slower response time matters less. It's an early proof of concept more than anything, but getting DLSS 5 working outside NVIDIA's own supported paths entirely is still a neat trick.

Created by a developer known as MAAN and spotted by Videocardz, this web implementation of DLSS 5 is little more than a model viewer that has the ability to apply the DLSS 5 neural filter to loaded models. It offers a chance to preview the look of DLSS 5 on most platforms, as unlike the official release (currently only available in NBA 2K27), it does not require a GeForce RTX 50 series graphics card. I was able to get the demo working on my ASUS ROG Flow z13 tablet with Radeon 8060S graphics, although it didn't load on my aging Snapdragon 888-powered smartphone.

That could have been down to the amount of memory required. The models that the site includes are pretty big, but the DLSS 5 neural network itself is the largest amount of the download, totaling nearly 150 MB by itself. This is pretty large for a website, and some devices may balk at the bulk. However, the developer notes that it works on MacOS, and in theory it should work on most devices with a compatible browser.

If you keep up with DLSS news, it should come as no surprise that the page is able to run the DLSS 5 neural filter on nearly any hardware, as it's not doing anything unique to NVIDIA's hardware. Indeed, modders have gotten DLSS 5 working on older GeForce hardware, AMD and Intel hardware (including integrated graphics), and even on video feeds with no 3D information. While DLSS 5 is AI-powered, it's mostly a post-processing filter, so you can apply it to nearly anything, and it can be run on nearly anything with a little tweaking.

As Whycry notes, the interesting part of MAAN's demo is how he's wired it up to the Three.js framework. NVIDIA doesn't offer WebGL or WebGPU as supported DLSS interfaces, which means the developer has either extracted the weights and reimplemented the network or created some kind of shim to use the leaked binary directly. In any case, it's impressive stuff, and you should check it out if you have a minute.

mundophone

TECH

Iran and China use AI agents to create fake social media profiles

Iran and China have used autonomous artificial intelligence agents to create and manage networks of fake accounts on platforms such as Instagram, Facebook, X, and TikTok. These operations were identified by U.S. authorities and security researchers.

The groups combined open-source Chinese AI models with agents capable of operating software and executing tasks independently. Hundreds of these systems were reportedly employed to open profiles, generate political posts, and coordinate messaging aimed at influencing opinions and stoking online debate.

These so-called AI agents differ from conventional chatbots because they are not limited to answering questions or generating text. They can be assigned a goal and execute a sequence of actions to achieve it—such as accessing a platform, filling out forms, creating accounts, and publishing content. In the identified campaigns, this technology reportedly allowed for the automation of virtually the entire process, from profile creation to message dissemination, with minimal human involvement.

According to a report by *The New York Times*, operators in most cases disabled the safeguards that would otherwise prevent the models from creating fake accounts or manipulating online discussions.

Based on the characteristics of these operations, U.S. authorities and cybersecurity experts attributed the Iranian and Chinese campaigns to initiatives linked to their respective governments. Two other similar operations, associated with Israel, were reportedly conducted by private companies.

The campaigns were considered technically rudimentary, and there is no indication that they achieved significant reach. Nevertheless, they drew the attention of intelligence agencies, technology companies, and researchers by demonstrating how autonomous agents can reduce the cost and effort required to maintain networks of fake profiles.

Operations of this type previously relied on teams tasked with creating accounts, drafting posts, and coordinating interactions. With AI agents, a smaller number of people can control hundreds of profiles and continuously produce content. The technology also makes it possible to rapidly adapt messages to different platforms, topics, and audiences. The discovery points to a new phase in digital influence campaigns. The risk lies not only in the production of fake text, images, or videos, but in the automation of the entire infrastructure needed to distribute these materials and create the appearance of spontaneous support for specific narratives. 

First time Chinese models were used like this...US officials told the NYT that these instances marked one of the first times AI agents from Chinese-produced models were used to handle every stage of an online influence campaign, from creating new fake accounts to coordinating messaging and framing online.

Unlike proprietary AI models, the open-source models were manipulated by developers to allow the agents to evade safeguards designed to verify users and prevent the manipulation of online discourse, the US officials added.

The US intelligence community, along with cybersecurity experts, looked into the groups behind the AI-powered campaigns and traced those efforts back to state-backed Chinese and Iranian groups, as well as to Israeli campaigns stemming from two private firms.

Kyle Crichton, a fellow at the Georgetown Center for Security and Emerging Technology who studies AI and cybersecurity, told the NYT that “Agent-led campaigns” will likely become more common as the technology spreads.

“Agents can be fairly sophisticated, and so in addition to just scale, they have an ability to blend in and look more like a human user,” he said.

The investigation was published in the wake of other major technology news, as AI leaders warned of the risks of developing AI too quickly and without proper safety measures. These warnings followed public concern over recent stories of AI models going rogue and hacking other companies.

In a report last month, Meta said it had seen a “technically significant development” of bad actors using AI for malicious purposes.

AI is now “embedded within automated systems that can produce, adapt, and distribute content with minimal human intervention,” Meta added.

The investigation also traced AI-agent campaigns back to Israel...The report mentioned that IntelEye, a Tel Aviv-based company founded by former NSO Group employees, operated roughly 1,000 AI-powered fake accounts on social media to manipulate political discourse around the upcoming Israeli elections.

The company used DeepSeek, a Chinese open-source AI model, to create its agents. According to the investigation, the bots then published content both supporting and opposing Prime Minister Benjamin Netanyahu.

IntelEye disputed claims that its work was intended to influence online discussions, stating the company was conducting a defensive research experiment to expose weaknesses in AI systems.

“IntelEye does not operate influence campaigns,” co-founder Maor Sellek told the Times. “Our work with DeepSeek was conducted for defensive research and testing, to understand how models with insufficient safeguards could be misused and to improve detection of that activity.”

Sellek said IntelEye bought 10,000 social media accounts and handed over control to autonomous AI agents, though he added that only about 1,000 of the accounts were operational.

The agents used the accounts to leave comments on posts to boost engagement, although Sellek said they were not very successful.

Meta told the New York Times that the vast majority of the AI-powered agents set up on Facebook and Instagram by IntelEye, other companies, and state-backed groups had been removed by its automated detection and verification systems.

Networks of fake accounts...According to the publication, publicly available Chinese artificial intelligence models were used to create autonomous agents capable of performing online tasks. Hundreds of such agents were allegedly deployed to create networks of fake accounts on the American platforms Instagram, Facebook, X and TikTok.

The agents filled the accounts with posts about politics and current events, and also coordinated messages and online interactions. U.S. officials and cybersecurity researchers called this among the first known cases in which agents based on Chinese AI models were involved in almost the entire influence campaign process.

More current news is available on the UA.News Telegram channel Telegram. The operations were described as relatively primitive, but as demonstrating the possibility of automating tasks that previously required significant human involvement.

Iranian and Israeli operations...U.S. intelligence services, technology companies and security researchers investigated this activity and, according to The New York Times, linked the Chinese and Iranian operations to state-backed actors. Iranian AI accounts posed as ordinary Americans in major cities, shared popular memes, tagged journalists and politicians, and promoted views critical of the Republican Party.

Nearly 80,000 people followed these accounts in the first half of the year. The report also describes two Israeli campaigns linked to private companies. One of them, linked to the Tel Aviv-based company IntelEye, involved using AI agents to manage thousands of accounts and participate in discussions about upcoming elections in Israel. The company said it was a defensive research experiment to test the potential misuse of AI models.

TECH

Will AI Graduate from Tool to Lab Member? A Q&A with John Tsang

John Tsang, PhD, and his Yale lab members Jacob Kim and Ao Huang recently posed the question of whether AI immunologists are ready for prime time. Specifically, they were referring to large language models (LLMs) and whether they are as creative as humans in developing new hypotheses and methods for research in the field of immunology.

The answer, based on current studies, is no, not yet, says Tsang, Anthony N. Brady Professor of Immunobiology and professor of biomedical engineering at Yale School of Medicine. While there are functions where AI can be effective, such as summarizing relevant literature, LLMs like ChatGPT aren’t able to consistently generate truly original hypotheses, scientific ideas, or experimental approaches.

But, Tsang adds, researchers are exploring new ways of engaging LLMs that could overcome these current hurdles.

“I think there is a future—probably not too distant—where AI will be able to do such things,” says Tsang, founding director of the Yale Center for Systems and Engineering Immunology and an investigator with and the Yale lead for Biohub New York. “I see AI one day being a sort of team member, contributing ideas alongside scientists.”

We spoke to Tsang about the current state of what he calls AI immunologists and where he sees opportunities going forward.

John Tsang, PhD: An AI immunologist isn’t yet a robot that goes into the lab and does experiments. It’s an AI system that can think through concepts, propose hypotheses, suggest experiments, and interpret the data that come out of those experiments. That’s the kind of AI immunologist we’re talking about.

What do you mean when you refer to AI immunologists?

John Tsang, PhD: An AI immunologist isn’t yet a robot that goes into the lab and does experiments. It’s an AI system that can think through concepts, propose hypotheses, suggest experiments, and interpret the data that come out of those experiments. That’s the kind of AI immunologist we’re talking about.

How is AI capable in that regard now? What can it do well?

Tsang: What it can do really well is research the literature. For example, if you ask, “What's known about this molecule? What can that molecule do to this kind of cell?” AI can deliver excellent information. Basically, if you talk to any of these AI chatbots, things like Claude, Gemini, and ChatGPT, they're doing very well in this regard these days.

And where does AI still have room for improvement?

Tsang: AI is still not super effective at coming up with things that may be novel in the sense that AI may not be completely taking into account, for example, information from another field. If you don't prompt the AI and say, “Hey, have you thought about this particular knowledge in biochemistry or this particular thing over in aging? Do you think there's a connection between that and this principle?” it doesn’t do that on its own.

You note there are exciting opportunities when it comes to multi-agent approaches. Can you describe what these approaches are?

Tsang: When using AI in a classical way, you have one agent, and you have an interactive discussion with it. With a multi-agent approach, as a few research groups have now described, you ask different independent bots to take different roles. One bot could be a computational biologist, another one could be a biochemist, and yet another one could be someone who knows a lot about antibodies, for example. And then there's also a human who's involved in the team that can give the bots a high-level task and then let them talk to each other.

What is very interesting is that in examples of this type of approach so far, the majority of the interactions happen among the AI bots. The human doesn't necessarily go in and tell them what do. And researchers have found that the bots are capable of coming up with a work plan and executing it. Even when the human, while part of the team, is not doing the major coordination.

So that is quite interesting, suggesting that by having interactions among these bots—and I think the key is that they take on different roles—they are able to start to generate something that's quite emergent, not just straight answering. They can engage a multi-step plan and make decisions based on the data. That is, I think, quite intriguing.

One striking example is “The Virtual Lab,” a system developed by Biohub colleagues in which an AI principal investigator led a team of AI specialists—an immunologist, a computational biologist, and a machine learning expert—to design nanobodies (small antibody-like molecules) against a new SARS-CoV-2 variant. The team produced 92 candidate designs, and two were experimentally confirmed to bind the new variant. Notably, more than 98% of the words exchanged during the project came from the AI agents themselves; the human researcher set the goal and stayed in the loop, but the bulk of the back-and-forth largely happened among AI agents.

Would this type of approach help overcome some of the current limitations of AI?

Tsang: Yes, I think so. And I think it also overcomes what's called a context window problem. So each bot has a certain memory, and when you have a team like that, the total volume of memory becomes quite large. At the same time, the bots can start to write out answers to external files, which can get pulled back in again, extending memory further, in a way.

Another interesting thing that's emerging is what's called “world models.” So these are agents that have a much longer memory span, so they're not just working within this context window. They have external storage, and they try to provide a model of the world that the particular agent is in. I think that's also going to help.

Where do you think is the greatest potential when it comes to AI and immunology?

Tsang: I think the answer lies on two levels. First, I think it's going to be helpful in proposing and hypothesizing things that we haven't thought of yet. However, that will be based on existing data, and we still have limited data on lots of things in immunology.

For example, one project that I co-lead called the Human Immunome Project aims to capture human immunology variation across different people and across different populations. Now that kind of data simply doesn't exist yet. If you ask a chat bot or even multi-agents and say, "I know this vaccine works in the U.S. with this level of efficacy, what happens if I bring it to Tanzania?" it would have no idea because it simply doesn't have enough information and knowledge yet.

So that's where a different kind of AI, models like quantitative models of the human immune system, come in. That's another area that we are actively working on, how to develop that kind of model. Of course, once such models become available and useful, then AI agents can use them to query and answer the questions we ask.

Additionally, AI agents, I think, could form a team with us to work together on that problem, where we need to design experiments. We need to think about, given a certain number of resources and budget, what's the best first experiment, for example, what's the best first data collection exercise? Those are the kind of things that we are thinking about a lot.

How do these AI technologies intersect with education?

Tsang: I think we need to put these into education fairly quickly so students can understand how to use them and feel comfortable doing so. Plus, there are creative aspects, like the multi-agents and how to put them together.

I personally use these tools, especially when I’m starting something new. I want to get a sense of what’s known and also to potentially make novel connections. And my lab has discussed this sort of AI team approach.

Sometimes I think there's a bit of a worry about using AI to cheat, for example. That's what some educators may be thinking. But I think we have an opportunity to promote positive and productive use. And so the question is how to incorporate this technology in the most positive, effective way possible.

by: Mallory Locklear, PhD--Managing Editor—Science, Research, and Education

TECH

GrapheneOS says Google is keeping new Android 17 security features from other phones

The Android smartphone world often prides itself on its open-source nature, but the rules of the game appear to be changing drastically. GrapheneOS, a renowned privacy-focused ROM, has leveled harsh criticism at Google regarding the operating system's latest update.

At issue is the release of the new Android 17 QPR1, which began rolling out in mid-September alongside the Pixel Drop package. According to the project's developers, the search giant is intentionally blocking access to crucial features and security fixes within the AOSP (Android Open Source Project). This stance creates a massive gap between Pixel devices and the rest of the ecosystem, delaying improvements for other brands by months. It is, to say the least, concerning to see the company that founded Android increasingly closing the door on community developers.

The team behind GrapheneOS did not mince words when analyzing the inner workings of this new version. They claim Google intentionally withheld new developer APIs and security patches, keeping them exclusive to its own Pixel smartphones.

The major issue is that partner manufacturers and custom ROM creators are effectively left with their hands tied until December, when the code is expected to finally be released to AOSP with the arrival of Android 17 QPR2. This means millions of users on other brands remain exposed to vulnerabilities that Google has already patched on its own devices—raising serious ethical questions.

To understand the gravity of the situation, here are the key technical points raised by the security team:

Critical vulnerability fixes included in the September Pixel bulletin were not integrated into the general Android security bulletin.

New APIs essential for developers are restricted and completely out of reach for third parties.

Deep system changes force community projects to resort to reverse engineering in order to adapt the software in time. This is the first time since the days of the old Android Honeycomb that a version has introduced new APIs without simultaneously sharing them via AOSP.

GrapheneOS—which had already begun moving away from Google hardware to form a more transparent development alliance with Motorola—views this situation as a clear-cut anti-competitive move. It is hard to disagree when one considers the obvious imbalance this creates in the current mobile market.

For now, the development team reports that while they have managed to adapt much of their code for the new Android 17 QPR1, they lack the legal authorization to officially distribute it. Their workaround has been to port firmware, drivers, and other technical components from the Pixel directly to the standard Android 17 base—a genuine engineering puzzle.

It remains to be seen whether Google will maintain this strategy of distancing itself in the coming years or if community pressure will force a change in direction. One thing is certain: the promise of an Android platform that is perfectly uniform and open to everyone is gradually becoming a mirage, accessible only to those who invest in a Pixel.

GrapheneOS has accused Google of deliberately withholding crucial APIs and Security fixes from AOSP in the Android 17 QPR1 update. The decision affects Android manufacturers, with the new security measures potentially never arriving on phones other than Google Pixel devices.

Google Is Withholding AOSP code...According to GrapheneOS, Android 17 QPR1 marks the first time since Android Honeycomb that Google has introduced new developer APIs without simultaneously publishing the corresponding code to AOSP.

This means that the new APIs that promote better privacy and security can never be implemented on other Android smartphones, as Google has officially made them exclusive.

The GrapheneOS team stated they had their code ported prior to the September 15, 2026 release but currently lack permission to publish it. The complaint arrives on top of developers already being forced into the tedious process of backporting Pixel firmware, userspace drivers, and HALs directly to the older Android 17 framework.

This is because Google has essentially stopped publishing device trees, source code and binaries for Pixel phones, essentially attempting to kill third-party custom ROM support.

However, projects like Graphene and LineageOS have held on, albeit still limited to the Pixel 9 series. As a result of this change, even a year after the Pixel 10's launch, the phones are still not supported by the LineageOS team. 

Critical security repercussions...The accusations also extend to critical device safety. The September 2026 Pixel Update Bulletin mentions several vulnerability fixes that are conspicuously missing from the standard Android Security Bulletin.

GrapheneOS claims that many of these fixes apply to core platform components used by non-Pixel devices. By holding these ecosystem-wide patches back until the Android 17 QPR2 release in December, Google is giving its Pixel hardware an exclusive security advantage over other Android OEMs.

While GrapheneOS acknowledges that Pixels are still useful for their project due to their baseline security features, they warn that Google's new strategy makes supporting the devices significantly more difficult.

That said, with the upcoming GrapheneOS-Motorola partnership, the project expects its upcoming collaboration to streamline software development, as that partnership will provide direct access to official firmware and driver code.

This is clearly not a good outlook on Google's part, especially considering how the company doesn't hesitate to market Android security and privacy. If anything, these actions suggest otherwise, and delaying new security APIs and features is quite contradictory.

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APPLE

iPhone 18 Pro battery life disappoints vs iPhone 17 Pro in gaming

Thanks to bigger batteries and the more power-efficient Apple A20 Pro SoC, Apple promises some excellent battery life gains for the iPhone 18 Pro and the iPhone 18 Pro Max. However, Dave2D shows that the battery life improvements aren't as impressive as some might've imagined.

Apple makes some bold claims regarding the battery life of the iPhone 18 Pro and the iPhone 18 Pro Max. Apple officially claims 45 hours of video playback for the eSIM-only iPhone 18 Pro Max and 36 hours for the iPhone 18 Pro. While Apple doesn’t officially advertise the battery capacities, reports suggest that the eSIM-only iPhone 18 Pro and the iPhone 18 Pro Max carry a 4,288 mAh and 5,567 mAh battery, respectively.

However, despite packing larger batteries and a more efficient A20 Pro SoC, Dave2D shows that the battery life gain in demanding use cases remains small for the iPhone 18 Pro Max and non-existent for the iPhone 18 Pro.

The YouTuber reports that, in Genshin Impact at 600 nits of screen brightness, the Apple iPhone 18 Pro Max only lasted 23 minutes longer than the iPhone 17 Pro Max. So, the 9.4% bigger battery is undoubtedly helping here.

The battery life improvement was much more noticeable in lighter routine usage, as the iPhone 18 Pro Max lasted for 17 hours and 19 minutes while browsing Reddit at 600 nits. The iPhone 17 Pro Max lasted for 15 hours and 36 minutes in the same scenario.

The iPhone 18 Pro and the iPhone 17 Pro have roughly identical battery capacities, as there is only a 36 mAh difference between the two. So, any battery life improvements we see on the iPhone 18 Pro will primarily come down to the efficiency gains provided by the Apple A20 Pro.

Coming in at 14 hours and 27 minutes, the iPhone 18 Pro lasted almost an hour longer than the iPhone 17 Pro in Dave2D’s Reddit loop test. However, the iPhone 18 Pro actually fell behind the iPhone 17 Pro while playing Genshin Impact, as the iPhone 18 Pro's battery life was 11 minutes shorter.

In short, the battery life upgrades enjoyed by the new iPhone 18 Pro and 18 Pro Max don’t seem to be that big. So, you might be disappointed if you were hoping for much better battery life.

iPhone 18 Pro battery numbers are both good and bad news for Samsung...Apple announced the iPhone 18 Pro and iPhone 18 Pro Max last week, and it had plenty to say about them. The company doesn’t traditionally share battery capacity, though, but a regulatory source has now revealed these details and more.

The European Union’s EPREL database has revealed that the iPhone 18 Pro has a 4,056mAh battery, a negligible upgrade over the iPhone 17 Pro’s 3,988mAh battery. On the other hand, the iPhone 18 Pro Max is listed with a 5,391mAh battery, which is a significant upgrade over the iPhone 17 Pro Max’s 4,823mAh battery.

It’s worth noting that global iPhones usually have slightly smaller batteries than US models, as the global variants still offer physical SIM slots. For example, the iPhone 17 Pro has a 4,252mAh battery in the US, while the Pro Max has a 5,088mAh battery. So don’t be surprised if the iPhone 18 Pro and Pro Max have even larger batteries in the US than the EU’s database indicates.

Apple is also banking on the 2nm A20 Pro chip to deliver plenty of efficiency gains across both models. Nevertheless, it’s clear that the iPhone 18 Pro Max is probably the one to get if you want the longest battery life possible in an iPhone. It also has a bigger battery than all recent Galaxy flagships. However, many other Android brands are now using 7,000mAh, 8,000mAh, or even 10,000mAh silicon-carbon batteries in their phones for even longer endurance.

The EPREL database also reveals disappointing news if you were hoping for better long-term battery health. The iPhone 18 Pro series offers 1,000 charging cycles before reaching an effective 80% capacity. This means you’ll basically lose 20% of your battery’s capacity after roughly three years. That’s in line with recent iPhones and Pixel phones, but short of rival devices like the Samsung Galaxy S26 series (1,200 cycles), Motorola Edge 70 Pro (1,200), OnePlus 15 (1,100 to 1,400), and Xiaomi 17 series (1,600).

There is good news for charging times, though. Xiaobai’s Tech Reviews (h/t: Ice Universe) reports that the iPhone 18 Pro Max in particular is the fastest-charging iPhone ever. It can apparently reach 100% in 55 minutes, with a peak speed of 52W. There are faster-charging Android phones out there, but this seems a little faster than the Pixel 11 Pro XL.

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