Why it’s so hard to use AI to diagnose cancer

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Why it’s so hard to use AI to diagnose cancer

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Peering into the body to find and diagnose cancer is all about spotting patterns. Radiologists use x-rays and magnetic resonance imaging to illuminate tumors, and pathologists examine tissue from kidneys, livers, and other areas under microscopes and look for patterns that show how severe a cancer is, whether particular treatments could work, and where the malignancy may spread. In theory, artificial intelligence should be great at helping out. “Our job is pattern recognition,” says Andrew Norgan, a pathologist and medical director of the Mayo Clinic’s digital pathology platform. “We look at the slide and we gather pieces of information that have been proven to be important.” Visual analysis is something that AI has gotten quite good at since the first image recognition models began taking off nearly 15 years ago. Even though no model will be perfect, you can imagine a powerful algorithm someday catching something that a human pathologist missed, or at least speeding up the process of getting a diagnosis. We’re starting to see lots of new efforts to build such a model—at least seven attempts in the last year alone—but they all remain experimental. What will it take to make them good enough to be used in the real world? Details about the latest effort to build such a model, led by the AI health company Aignostics with the Mayo Clinic, were published on arXiv earlier this month. The paper has not been peer-reviewed, but it reveals much about the challenges of bringing such a tool to real clinical settings. The model, called Atlas, was trained on 1.2 million tissue samples from 490,000 cases. Its accuracy was tested against six other leading AI pathology models. These models compete on shared tests like classifying breast cancer images or grading tumors, where the model’s predictions are compared with the correct answers given by human pathologists. Atlas beat rival models on six out of nine tests. It earned its highest score for categorizing cancerous colorectal tissue, reaching the same conclusion as human pathologists 97.1% of the time. For another task, though—classifying tumors from prostate cancer biopsies—Atlas beat the other models’ high scores with a score of just 70.5%. Its average across nine benchmarks showed that it got the same answers as human experts 84.6% of the time. Let’s think about what this means. The best way to know what’s happening to cancerous cells in tissues is to have a sample examined by a pathologist, so that’s the performance that AI models are measured against. The best models are approaching humans in particular detection tasks but lagging behind in many others. So how good does a model have to be to be clinically useful? “Ninety percent is probably not good enough. You need to be even better,” says Carlo Bifulco, chief medical officer at Providence Genomics and co-creator of GigaPath, one of the other AI pathology models examined in the Mayo Clinic study. But, Bifulco says, AI models that don’t score perfectly can still be useful in the short term, and could potentially help pathologists speed up their work and make diagnoses more quickly. What obstacles are getting in the way of better performance? Problem number one is training data. “Fewer than 10% of pathology practices in the US are digitized,” Norgan says. That means tissue samples are placed on slides and analyzed under microscopes, and then stored in massive registries without ever being documented digitally. Though European practices tend to be more digitized, and there are efforts underway to create shared data sets of tissue samples for AI models to train on, there’s still not a ton to work with. Without diverse data sets, AI models struggle to identify the wide range of abnormalities that human pathologists have learned to interpret. That includes for rare diseases, says Maximilian Alber, cofounder and CTO of Aignostics. Scouring the publicly available databases for tissue samples of particularly rare diseases, “you’ll find 20 samples over 10 years,” he says. Around 2022, the Mayo Clinic foresaw that this lack of training data would be a problem. It decided to digitize all of its own pathology practices moving forward, along with 12 million slides from its archives dating back decades (patients had consented to their being used for research). It hired a company to build a robot that began taking high-resolution photos of the tissues, working through up to a million samples per month. From these efforts, the team was able to collect the 1.2 million high-quality samples used to train the Mayo model. This brings us to problem number two for using AI to spot cancer. Tissue samples from biopsies are tiny—often just a couple of millimeters in diameter—but are magnified to such a degree that digital images of them contain more than 14 billion pixels. That makes them about 287,000 times larger than images used to train the best AI image recognition models to date. “That obviously means lots of storage costs and so forth,” says Hoifung Poon, an AI researcher at Microsoft who worked with Bifulco to create GigaPath, which was featured in Nature last year. But it also forces important decisions about which bits of the image you use to train the AI model, and which cells you might miss in the process. To make Atlas, the Mayo Clinic used what’s referred to as a tile method, essentially creating lots of snapshots from the same sample to feed into the AI model. Figuring out how to select these tiles is both art and science, and it’s not yet clear which ways of doing it lead to the best results. Thirdly, there’s the question of which benchmarks are most important for a cancer-spotting AI model to perform well on. The Atlas researchers tested their model in the challenging domain of molecular-related benchmarks, which involves trying to find clues from sample tissue images to guess what’s happening on a molecular level. Here’s an example: Your body’s mismatch repair genes are of particular concern for cancer, because they catch errors made when your DNA gets replicated. If these errors aren’t caught, they can drive the development and progression of cancer. “Some pathologists might tell you they kind of get a feeling when they think something’s mismatch-repair deficient based on how it looks,” Norgan says. But pathologists don’t act on that gut feeling alone. They can do molecular testing for a more definitive answer. What if instead, Norgan says, we can use AI to predict what’s happening on the molecular level? It’s an experiment: Could the AI model spot underlying molecular changes that humans can’t see? Generally no, it turns out. Or at least not yet. Atlas’s average for the molecular testing was 44.9%. That’s the best performance for AI so far, but it shows this type of testing has a long way to go. Bifulco says Atlas represents incremental but real progress. “My feeling, unfortunately, is that everybody’s stuck at a similar level,” he says. “We need something different in terms of models to really make dramatic progress, and we need larger data sets.” Deeper Learning OpenAI has created an AI model for longevity science AI has long had its fingerprints on the science of protein folding. But OpenAI now says it’s created a model that can engineer proteins, turning regular cells into stem cells. That goal has been pursued by companies in longevity science, because stem cells can produce any other tissue in the body and, in theory, could be a starting point for rejuvenating animals, building human organs, or providing supplies of replacement cells. Why it matters: The work was a product of OpenAI’s collaboration with the longevity company Retro Labs, in which Sam Altman invested $180 million. It represents OpenAI’s first model focused on biological data and its first public claim that its models can deliver scientific results. The AI model reportedly engineered more effective proteins, and more quickly, than the company’s scientists could. But outside scientists can’t evaluate the claims until the studies have been published. Read more from Antonio Regalado. Bits and Bytes What we know about the TikTok ban The popular video app went dark in the United States late Saturday and then came back around noon on Sunday, even as a law banning it took effect. (The New York Times) Why Meta might not end up like X X lost lots of advertising dollars as Elon Musk changed the platform’s policies. But Facebook and Instagram’s massive scale make them hard platforms for advertisers to avoid. (Wall Street Journal) What to expect from Neuralink in 2025 More volunteers will get Elon Musk’s brain implant, but don’t expect a product soon. (MIT Technology Review) A former fact-checking outlet for Meta signed a new deal to help train AI models Meta paid media outlets like Agence France-Presse for years to do fact checking on its platforms. Since Meta announced it would shutter those programs, Europe’s leading AI company, Mistral, has signed a deal with AFP to use some of its content in its AI models. (Financial Times) OpenAI’s AI reasoning model “thinks” in Chinese sometimes, and no one really knows why While it comes to its response, the model often switches to Chinese, perhaps a reflection of the fact that many data labelers are based in China. (Tech Crunch)

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

Peering into the body to find and diagnose cancer is all about spotting patterns. Radiologists use x-rays and magnetic resonance imaging to illuminate tumors, and pathologists examine tissue from kidneys, livers, and other areas under microscopes and look for patterns that show how severe a cancer is, whether particular treatments could work, and where the malignancy may spread.

In theory, artificial intelligence should be great at helping out. “Our job is pattern recognition,” says Andrew Norgan, a pathologist and medical director of the Mayo Clinic’s digital pathology platform. “We look at the slide and we gather pieces of information that have been proven to be important.”

Visual analysis is something that AI has gotten quite good at since the first image recognition models began taking off nearly 15 years ago. Even though no model will be perfect, you can imagine a powerful algorithm someday catching something that a human pathologist missed, or at least speeding up the process of getting a diagnosis. We’re starting to see lots of new efforts to build such a model—at least seven attempts in the last year alone—but they all remain experimental. What will it take to make them good enough to be used in the real world?

Details about the latest effort to build such a model, led by the AI health company Aignostics with the Mayo Clinic, were published on arXiv earlier this month. The paper has not been peer-reviewed, but it reveals much about the challenges of bringing such a tool to real clinical settings.

The model, called Atlas, was trained on 1.2 million tissue samples from 490,000 cases. Its accuracy was tested against six other leading AI pathology models. These models compete on shared tests like classifying breast cancer images or grading tumors, where the model’s predictions are compared with the correct answers given by human pathologists. Atlas beat rival models on six out of nine tests. It earned its highest score for categorizing cancerous colorectal tissue, reaching the same conclusion as human pathologists 97.1% of the time. For another task, though—classifying tumors from prostate cancer biopsies—Atlas beat the other models’ high scores with a score of just 70.5%. Its average across nine benchmarks showed that it got the same answers as human experts 84.6% of the time.

Let’s think about what this means. The best way to know what’s happening to cancerous cells in tissues is to have a sample examined by a pathologist, so that’s the performance that AI models are measured against. The best models are approaching humans in particular detection tasks but lagging behind in many others. So how good does a model have to be to be clinically useful?

“Ninety percent is probably not good enough. You need to be even better,” says Carlo Bifulco, chief medical officer at Providence Genomics and co-creator of GigaPath, one of the other AI pathology models examined in the Mayo Clinic study. But, Bifulco says, AI models that don’t score perfectly can still be useful in the short term, and could potentially help pathologists speed up their work and make diagnoses more quickly.

What obstacles are getting in the way of better performance? Problem number one is training data.

“Fewer than 10% of pathology practices in the US are digitized,” Norgan says. That means tissue samples are placed on slides and analyzed under microscopes, and then stored in massive registries without ever being documented digitally. Though European practices tend to be more digitized, and there are efforts underway to create shared data sets of tissue samples for AI models to train on, there’s still not a ton to work with.

Without diverse data sets, AI models struggle to identify the wide range of abnormalities that human pathologists have learned to interpret. That includes for rare diseases, says Maximilian Alber, cofounder and CTO of Aignostics. Scouring the publicly available databases for tissue samples of particularly rare diseases, “you’ll find 20 samples over 10 years,” he says.

Around 2022, the Mayo Clinic foresaw that this lack of training data would be a problem. It decided to digitize all of its own pathology practices moving forward, along with 12 million slides from its archives dating back decades (patients had consented to their being used for research). It hired a company to build a robot that began taking high-resolution photos of the tissues, working through up to a million samples per month. From these efforts, the team was able to collect the 1.2 million high-quality samples used to train the Mayo model.

This brings us to problem number two for using AI to spot cancer. Tissue samples from biopsies are tiny—often just a couple of millimeters in diameter—but are magnified to such a degree that digital images of them contain more than 14 billion pixels. That makes them about 287,000 times larger than images used to train the best AI image recognition models to date.

“That obviously means lots of storage costs and so forth,” says Hoifung Poon, an AI researcher at Microsoft who worked with Bifulco to create GigaPath, which was featured in Nature last year. But it also forces important decisions about which bits of the image you use to train the AI model, and which cells you might miss in the process. To make Atlas, the Mayo Clinic used what’s referred to as a tile method, essentially creating lots of snapshots from the same sample to feed into the AI model. Figuring out how to select these tiles is both art and science, and it’s not yet clear which ways of doing it lead to the best results.

Thirdly, there’s the question of which benchmarks are most important for a cancer-spotting AI model to perform well on. The Atlas researchers tested their model in the challenging domain of molecular-related benchmarks, which involves trying to find clues from sample tissue images to guess what’s happening on a molecular level. Here’s an example: Your body’s mismatch repair genes are of particular concern for cancer, because they catch errors made when your DNA gets replicated. If these errors aren’t caught, they can drive the development and progression of cancer.

“Some pathologists might tell you they kind of get a feeling when they think something’s mismatch-repair deficient based on how it looks,” Norgan says. But pathologists don’t act on that gut feeling alone. They can do molecular testing for a more definitive answer. What if instead, Norgan says, we can use AI to predict what’s happening on the molecular level? It’s an experiment: Could the AI model spot underlying molecular changes that humans can’t see?

Generally no, it turns out. Or at least not yet. Atlas’s average for the molecular testing was 44.9%. That’s the best performance for AI so far, but it shows this type of testing has a long way to go.

Bifulco says Atlas represents incremental but real progress. “My feeling, unfortunately, is that everybody’s stuck at a similar level,” he says. “We need something different in terms of models to really make dramatic progress, and we need larger data sets.”

Deeper Learning

OpenAI has created an AI model for longevity science

AI has long had its fingerprints on the science of protein folding. But OpenAI now says it’s created a model that can engineer proteins, turning regular cells into stem cells. That goal has been pursued by companies in longevity science, because stem cells can produce any other tissue in the body and, in theory, could be a starting point for rejuvenating animals, building human organs, or providing supplies of replacement cells.

Why it matters: The work was a product of OpenAI’s collaboration with the longevity company Retro Labs, in which Sam Altman invested $180 million. It represents OpenAI’s first model focused on biological data and its first public claim that its models can deliver scientific results. The AI model reportedly engineered more effective proteins, and more quickly, than the company’s scientists could. But outside scientists can’t evaluate the claims until the studies have been published. Read more from Antonio Regalado.

Bits and Bytes

What we know about the TikTok ban

The popular video app went dark in the United States late Saturday and then came back around noon on Sunday, even as a law banning it took effect. (The New York Times)

Why Meta might not end up like X

X lost lots of advertising dollars as Elon Musk changed the platform’s policies. But Facebook and Instagram’s massive scale make them hard platforms for advertisers to avoid. (Wall Street Journal)

What to expect from Neuralink in 2025

More volunteers will get Elon Musk’s brain implant, but don’t expect a product soon. (MIT Technology Review)

A former fact-checking outlet for Meta signed a new deal to help train AI models

Meta paid media outlets like Agence France-Presse for years to do fact checking on its platforms. Since Meta announced it would shutter those programs, Europe’s leading AI company, Mistral, has signed a deal with AFP to use some of its content in its AI models. (Financial Times)

OpenAI’s AI reasoning model “thinks” in Chinese sometimes, and no one really knows why

While it comes to its response, the model often switches to Chinese, perhaps a reflection of the fact that many data labelers are based in China. (Tech Crunch)

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The new AIP members will “further strengthen the partnership’s technology leadership as the platform seeks to invest in new and expanded AI infrastructure. Nvidia will also continue in its role as a technical advisor to AIP, leveraging its expertise in accelerated computing and AI factories to inform the deployment of next-generation AI data center infrastructure,” the group’s statement said. “Additionally, GE Vernova and NextEra Energy have agreed to collaborate with AIP to accelerate the scaling of critical and diverse energy solutions for AI data centers. GE Vernova will also work with AIP and its partners on supply chain planning and in delivering innovative and high efficiency energy solutions.” The group claimed, without offering any specifics, that it “has attracted significant capital and partner interest since its inception in September 2024, highlighting the growing demand for AI-ready data centers and power solutions.” The statement said the group will try to raise “$30 billion in capital from investors, asset owners, and corporations, which in turn will mobilize up to $100 billion in total investment potential when including debt financing.” Forrester’s Nguyen also noted that the influence of two of the new members — xAI, owned by Elon Musk, along with Nvidia — could easily help with fundraising. Musk “with his connections, he does not make small quiet moves,” Nguyen said. “As for Nvidia, they are the face of AI. Everything they do attracts attention.” Info-Tech’s Bickley said that the astronomical dollars involved in genAI investments is mind-boggling. And yet even more investment is needed — a lot more.

IBM broadens access to Nvidia technology for enterprise AI

The IBM Storage Scale platform will support CAS and now will respond to queries using the extracted and augmented data, speeding up the communications between GPUs and storage using Nvidia BlueField-3 DPUs and Spectrum-X networking, IBM stated. The multimodal document data extraction workflow will also support Nvidia NeMo Retriever microservices. CAS will be embedded in the next update of IBM Fusion, which is planned for the second quarter of this year. Fusion simplifies the deployment and management of AI applications and works with Storage Scale, which will handle high-performance storage support for AI workloads, according to IBM. IBM Cloud instances with Nvidia GPUs In addition to the software news, IBM said its cloud customers can now use Nvidia H200 instances in the IBM Cloud environment. With increased memory bandwidth (1.4x higher than its predecessor) and capacity, the H200 Tensor Core can handle larger datasets, accelerating the training of large AI models and executing complex simulations, with high energy efficiency and low total cost of ownership, according to IBM. In addition, customers can use the power of the H200 to process large volumes of data in real time, enabling more accurate predictive analytics and data-driven decision-making, IBM stated. IBM Consulting capabilities with Nvidia Lastly, IBM Consulting is adding Nvidia Blueprint to its recently introduced AI Integration Service, which offers customers support for developing, building and running AI environments. Nvidia Blueprints offer a suite pre-validated, optimized, and documented reference architectures designed to simplify and accelerate the deployment of complex AI and data center infrastructure, according to Nvidia. The IBM AI Integration service already supports a number of third-party systems, including Oracle, Salesforce, SAP and ServiceNow environments.

Nvidia’s silicon photonics switches bring better power efficiency to AI data centers

Nvidia typically uses partnerships where appropriate, and the new switch design was done in collaboration with multiple vendors across different aspects, including creating the lasers, packaging, and other elements as part of the silicon photonics. Hundreds of patents were also included. Nvidia will licensing the innovations created to its partners and customers with the goal of scaling this model. Nvidia’s partner ecosystem includes TSMC, which provides advanced chip fabrication and 3D chip stacking to integrate silicon photonics into Nvidia’s hardware. Coherent, Eoptolink, Fabrinet, and Innolight are involved in the development, manufacturing, and supply of the transceivers. Additional partners include Browave, Coherent, Corning Incorporated, Fabrinet, Foxconn, Lumentum, SENKO, SPIL, Sumitomo Electric Industries, and TFC Communication. AI has transformed the way data centers are being designed. During his keynote at GTC, CEO Jensen Huang talked about the data center being the “new unit of compute,” which refers to the entire data center having to act like one massive server. That has driven compute to be primarily CPU based to being GPU centric. Now the network needs to evolve to ensure data is being fed to the GPUs at a speed they can process the data. The new co-packaged switches remove external parts, which have historically added a small amount of overhead to networking. Pre-AI this was negligible, but with AI, any slowness in the network leads to dollars being wasted.

Critical vulnerability in AMI MegaRAC BMC allows server takeover

“In disruptive or destructive attacks, attackers can leverage the often heterogeneous environments in data centers to potentially send malicious commands to every other BMC on the same management segment, forcing all devices to continually reboot in a way that victim operators cannot stop,” the Eclypsium researchers said. “In extreme scenarios, the net impact could be indefinite, unrecoverable downtime until and unless devices are re-provisioned.” BMC vulnerabilities and misconfigurations, including hardcoded credentials, have been of interest for attackers for over a decade. In 2022, security researchers found a malicious implant dubbed iLOBleed that was likely developed by an APT group and was being deployed through vulnerabilities in HPE iLO (HPE’s Integrated Lights-Out) BMC. In 2018, a ransomware group called JungleSec used default credentials for IPMI interfaces to compromise Linux servers. And back in 2016, Intel’s Active Management Technology (AMT) Serial-over-LAN (SOL) feature which is part of Intel’s Management Engine (Intel ME), was exploited by an APT group as a covert communication channel to transfer files. OEM, server manufacturers in control of patching AMI released an advisory and patches to its OEM partners, but affected users must wait for their server manufacturers to integrate them and release firmware updates. In addition to this vulnerability, AMI also patched a flaw tracked as CVE-2024-54084 that may lead to arbitrary code execution in its AptioV UEFI implementation. HPE and Lenovo have already released updates for their products that integrate AMI’s patch for CVE-2024-54085.

Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs). In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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