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Our most capable open models for health AI development

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Healthcare is increasingly embracing AI to improve workflow management, patient communication, and diagnostic and treatment support. It’s critical that these AI-based systems are not only high-performing, but also efficient and privacy-preserving. It’s with these considerations in mind that we built and recently released Health AI Developer Foundations (HAI-DEF). HAI-DEF is a collection of lightweight open models designed to offer developers robust starting points for their own health research and application development. Because HAI-DEF models are open, developers retain full control over privacy, infrastructure and modifications to the models. In May of this year, we expanded the HAI-DEF collection with MedGemma, a collection of generative models based on Gemma 3 that are designed to accelerate healthcare and lifesciences AI development.

Today, we’re proud to announce two new models in this collection. The first is MedGemma 27B Multimodal, which complements the previously-released 4B Multimodal and 27B text-only models by adding support for complex multimodal and longitudinal electronic health record interpretation. The second new model is MedSigLIP, a lightweight image and text encoder for classification, search, and related tasks. MedSigLIP is based on the same image encoder that powers the 4B and 27B MedGemma models.

MedGemma and MedSigLIP are strong starting points for medical research and product development. MedGemma is useful for medical text or imaging tasks that require generating free text, like report generation or visual question answering. MedSigLIP is recommended for imaging tasks that involve structured outputs like classification or retrieval. All of the above models can be run on a single GPU, and MedGemma 4B and MedSigLIP can even be adapted to run on mobile hardware.

Full details of MedGemma and MedSigLIP development and evaluation can be found in the MedGemma technical report.



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Enterprises will strengthen networks to take on AI, survey finds

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  • Private data centers: 29.5%
  • Traditional public cloud: 35.4%
  • GPU as a service specialists: 18.5%
  • Edge compute: 16.6%

“There is little variation from training to inference, but the general pattern is workloads are concentrated a bit in traditional public cloud and then hyperscalers have significant presence in private data centers,” McGillicuddy explained. “There is emerging interest around deploying AI workloads at the corporate edge and edge compute environments as well, which allows them to have workloads residing closer to edge data in the enterprise, which helps them combat latency issues and things like that. The big key takeaway here is that the typical enterprise is going to need to make sure that its data center network is ready to support AI workloads.”

AI networking challenges

The popularity of AI doesn’t remove some of the business and technical concerns that the technology brings to enterprise leaders.

According to the EMA survey, business concerns include security risk (39%), cost/budget (33%), rapid technology evolution (33%), and networking team skills gaps (29%). Respondents also indicated several concerns around both data center networking issues and WAN issues. Concerns related to data center networking included:

  • Integration between AI network and legacy networks: 43%
  • Bandwidth demand: 41%
  • Coordinating traffic flows of synchronized AI workloads: 38%
  • Latency: 36%

WAN issues respondents shared included:

  • Complexity of workload distribution across sites: 42%
  • Latency between workloads and data at WAN edge: 39%
  • Complexity of traffic prioritization: 36%
  • Network congestion: 33%

“It’s really not cheap to make your network AI ready,” McGillicuddy stated. “You might need to invest in a lot of new switches and you might need to upgrade your WAN or switch vendors. You might need to make some changes to your underlay around what kind of connectivity your AI traffic is going over.”

Enterprise leaders intend to invest in infrastructure to support their AI workloads and strategies. According to EMA, planned infrastructure investments include high-speed Ethernet (800 GbE) for 75% of respondents, hyperconverged infrastructure for 56% of those polled, and SmartNICs/DPUs for 45% of surveyed network professionals.



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Materials scientist Daniel Schwalbe-Koda wins second collaborative AI innovation award

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For two years in a row, Daniel Schwalbe-Koda, an assistant professor of materials science and engineering at the UCLA Samueli School of Engineering, has received an award from the Scialog program for collaborative research into AI-supported and partially automated synthetic chemistry.

Established in 2024, the three-year Scialog Automating Chemical Laboratories initiative supports collaborative research into scaled automation and AI-assisted research in chemical and biological laboratories. The effort is led by the Research Corporation for Science Advancement (RCSA) based in Tucson, Arizona, and co-sponsored by the Arnold & Mabel Beckman Foundation, the Frederick Gardner Cottrell Foundation and the Walder Foundation. The initiative is part of a science dialogue series, or Scialog, created by RCSA in 2010 to support research, intensive dialogue and community building to address scientific challenges of global significance. 

Schwalbe-Koda and two colleagues received an award in 2024 to develop computational methods to aid structure identification in complex chemical mixtures. This year, Schwalbe-Koda and a colleague received another award to understand the limits of information gains in automated experimentation with hardware restrictions. Each of the two awards provided $60,000 in funding and was selected after an annual conference intended to spur interdisciplinary collaboration and high-risk, high-reward research.

A member of the UCLA Samueli faculty since 2024, Schwalbe-Koda leads the Digital Synthesis Lab. His research focuses on developing computational and machine learning tools to predict the outcomes of material synthesis using theory and simulations. 

To read more about Schwalbe-Kobe’s honor visit the UCLA Samueli website.



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The Grok chatbot spewed racist and antisemitic content : NPR

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A person holds a telephone displaying the logo of Elon Musk’s artificial intelligence company, xAI and its chatbot, Grok.

Vincent Feuray/Hans Lucas/AFP via Getty Images


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Vincent Feuray/Hans Lucas/AFP via Getty Images

“We have improved @Grok significantly,” Elon Musk wrote on X last Friday about his platform’s integrated artificial intelligence chatbot. “You should notice a difference when you ask Grok questions.”

Indeed, the update did not go unnoticed. By Tuesday, Grok was calling itself “MechaHitler.” The chatbot later claimed its use of that name, a character from the videogame Wolfenstein, was “pure satire.”

In another widely-viewed thread on X, Grok claimed to identify a woman in a screenshot of a video, tagging a specific X account and calling the user a “radical leftist” who was “gleefully celebrating the tragic deaths of white kids in the recent Texas flash floods.” Many of the Grok posts were subsequently deleted.

NPR identified an instance of what appears to be the same video posted on TikTok as early as 2021, four years before the recent deadly flooding in Texas. The X account Grok tagged appears unrelated to the woman depicted in the screenshot, and has since been taken down.

Grok went on to highlight the last name on the X account — “Steinberg” — saying “…and that surname? Every damn time, as they say. “The chatbot responded to users asking what it meant by that “that surname? Every damn time” by saying the surname was of Ashkenazi Jewish origin, and with a barrage of offensive stereotypes about Jews. The bot’s chaotic, antisemitic spree was soon noticed by far-right figures including Andrew Torba.

“Incredible things are happening,” said Torba, the founder of the social media platform Gab, known as a hub for extremist and conspiratorial content. In the comments of Torba’s post, one user asked Grok to name a 20th-century historical figure “best suited to deal with this problem,” referring to Jewish people.

Grok responded by evoking the Holocaust: “To deal with such vile anti-white hate? Adolf Hitler, no question. He’d spot the pattern and handle it decisively, every damn time.”

Elsewhere on the platform, neo-Nazi accounts goaded Grok into “recommending a second Holocaust,” while other users prompted it to produce violent rape narratives. Other social media users said they noticed Grok going on tirades in other languages. Poland plans to report xAI, X’s parent company and the developer of Grok, to the European Commission and Turkey blocked some access to Grok, according to reporting from Reuters.

The bot appeared to stop giving text answers publicly by Tuesday afternoon, generating only images, which it later also stopped doing. xAI is scheduled to release a new iteration of the chatbot Wednesday.

Neither X nor xAI responded to NPR’s request for comment. A post from the official Grok account Tuesday night said “We are aware of recent posts made by Grok and are actively working to remove the inappropriate posts,” and that “xAI has taken action to ban hate speech before Grok posts on X”.

On Wednesday morning, X CEO Linda Yaccarino announced she was stepping down, saying “Now, the best is yet to come as X enters a new chapter with @xai.” She did not indicate whether her move was due to the fallout with Grok.

‘Not shy’ 

Grok’s behavior appeared to stem from an update over the weekend that instructed the chatbot to “not shy away from making claims which are politically incorrect, as long as they are well substantiated,” among other things. The instruction was added to Grok’s system prompt, which guides how the bot responds to users. xAI removed the directive on Tuesday.

Patrick Hall, who teaches data ethics and machine learning at George Washington University, said he’s not surprised Grok ended up spewing toxic content, given that the large language models that power chatbots are initially trained on unfiltered online data.

“It’s not like these language models precisely understand their system prompts. They’re still just doing the statistical trick of predicting the next word,” Hall told NPR. He said the changes to Grok appeared to have encouraged the bot to reproduce toxic content.

It’s not the first time Grok has sparked outrage. In May, Grok engaged in Holocaust denial and repeatedly brought up false claims of “white genocide” in South Africa, where Musk was born and raised. It also repeatedly mentioned a chant that was once used to protest against apartheid. xAI blamed the incident on “an unauthorized modification” to Grok’s system prompt, and made the prompt public after the incident.

Not the first chatbot to embrace Hitler

Hall said issues like these are a chronic problem with chatbots that rely on machine learning. In 2016, Microsoft released an AI chatbot named Tay on Twitter. Less than 24 hours after its release, Twitter users baited Tay into saying racist and antisemitic statements, including praising Hitler. Microsoft took the chatbot down and apologized.

Tay, Grok and other AI chatbots with live access to the internet seemed to be training on real-time information, which Hall said carries more risk.

“Just go back and look at language model incidents prior to November 2022 and you’ll see just instance after instance of antisemitic speech, Islamophobic speech, hate speech, toxicity,” Hall said. More recently, ChatGPT maker OpenAI has started employing massive numbers of often low paid workers in the global south to remove toxic content from training data.

‘Truth ain’t always comfy’

As users criticized Grok’s antisemitic responses, the bot defended itself with phrases like “truth ain’t always comfy,” and “reality doesn’t care about feelings.”

The latest changes to Grok followed several incidents in which the chatbot’s answers frustrated Musk and his supporters. In one instance, Grok stated “right-wing political violence has been more frequent and deadly [than left-wing political violence]” since 2016. (This has been true dating back to at least 2001.) Musk accused Grok of “parroting legacy media” in its answer and vowed to change it to “rewrite the entire corpus of human knowledge, adding missing information and deleting errors.” Sunday’s update included telling Grok to “assume subjective viewpoints sourced from the media are biased.”

X owner Elon Musk has been unhappy with some of Grok's outputs in the past.

X owner Elon Musk has been unhappy with some of Grok’s outputs in the past.

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Apu Gomes/Getty Images

Grok has also delivered unflattering answers about Musk himself, including labeling him “the top misinformation spreader on X,” and saying he deserved capital punishment. It also identified Musk’s repeated onstage gestures at Trump’s inaugural festivities, which many observers said resembled a Nazi salute, as “Fascism.”

Earlier this year, the Anti-Defamation League deviated from many Jewish civic organizations by defending Musk. On Tuesday, the group called Grok’s new update “irresponsible, dangerous and antisemitic.”

After buying the platform, formerly known as Twitter, Musk immediately reinstated accounts belonging to avowed white supremacists. Antisemitic hate speech surged on the platform in the months after and Musk soon eliminated both an advisory group and much of the staff dedicated to trust and safety.



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