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ATA Launches Center of Digital Excellence

Taylor Marlowe
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ATA Launches Center of Digital Excellence

American Telemedicine Association Launches Center of Digital Excellence (CODE)

What is CODE?

The American Telemedicine Association (ATA) has announced its new Center of Digital Excellence (CODE), a collaborative initiative that will work with major U.S. health systems to integrate digital pathways across the care continuum.

Goals of CODE

CODE aims to drive the development and deployment of new best practices for patient-centered care, equitable access, and clinical and operational outcome improvements. The initiative seeks to advance leading-edge digital health tools and services to improve clinical workflows, engage more patients, and improve healthcare accessibility.

Founding Members

CODE comprises an alliance of leading health systems, including Intermountain Health, Mayo Clinic, MedStar Health, Ochsner Health, OSF HealthCare, Sanford Health, Stanford Health Care, UPMC, and West Virginia University Medicine Children’s Hospital. Virtual care companies Access TeleCare and AvaSure have also joined as founding sponsors.

Leadership

Elissa Baker, RN, ATA’s senior VP of digital strategy and clinical innovation, will lead CODE’s launch and engagement efforts going forward.

Objectives

CODE’s member organizations will compare insights and strategies, contributing to published workflows, success stories, and benchmarks that shape best practices and maturity models in digital care delivery. The center plans to host regular summits and events and publish an array of resources for members through ATA’s Member Connection starting in the spring.

Expansion

CODE plans to add more health systems, clinical leaders, and IT companies into the collaborative network. The center will be accepting applications on a rolling basis, with founding members reviewing and selecting applicants continuously as initiatives are identified.

The Larger Trend

The ATA has been busy recently, most notably with its efforts to help marshal industry-wide support to extend telehealth flexibilities in a legislative package before they expire at the end of the year.

On the Record

"Telehealth is not an either/or solution but a critical addition to in-person care, addressing gaps where traditional access is limited or unavailable," said ATA CEO Ann Mond Johnson. "With these renowned health systems, we are setting the standard for how innovation and technology can enhance, extend, and equalize access to high-quality healthcare for all."

Conclusion

The launch of CODE marks a significant step forward in the development of digital health technologies and their integration into broader care delivery approaches. As the center expands and gains momentum, it is likely to have a profound impact on the future of healthcare.

FAQs

Q: What is the Center of Digital Excellence (CODE)?
A: CODE is a collaborative initiative launched by the American Telemedicine Association (ATA) to integrate digital pathways across the care continuum.

Q: What are the goals of CODE?
A: CODE aims to drive the development and deployment of new best practices for patient-centered care, equitable access, and clinical and operational outcome improvements.

Q: Who are the founding members of CODE?
A: The founding members of CODE include Intermountain Health, Mayo Clinic, MedStar Health, Ochsner Health, OSF HealthCare, Sanford Health, Stanford Health Care, UPMC, and West Virginia University Medicine Children’s Hospital.

Q: What is the role of Elissa Baker in CODE?
A: Elissa Baker, RN, will lead CODE’s launch and engagement efforts going forward as the senior VP of digital strategy and clinical innovation at ATA.

Yearlong Supply-Chain Attack Steals 390K Credentials

Emerson Blake
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Yearlong Supply-Chain Attack Steals 390K Credentials

But wait, there’s more

On Friday, Datadog revealed that MUT-1244 employed additional means for installing its second-stage malware. One was through a collection of at least 49 malicious entries posted to GitHub that contained Trojanized proof-of-concept exploits for security vulnerabilities. These packages help malicious and benevolent security personnel better understand the extent of vulnerabilities, including how they can be exploited or patched in real-life environments.

New Vectors for Spread

A second major vector for spreading @0xengine/xmlrpc was through phishing emails. Datadog discovered MUT-1244 had left a phishing template, accompanied by 2,758 email addresses scraped from arXiv, a site frequented by professional and academic researchers.

A Phishing Email Campaign

The email, directed to people who develop or research software for high-performance computing, encouraged them to install a CPU microcode update available that would significantly improve performance. Datadog later determined that the emails had been sent from October 5 through October 21.

Additional Vectors

Further adding to the impression of legitimacy, several of the malicious packages are automatically included in legitimate sources, such as Feedly Threat Intelligence and Vulnmon. These sites included the malicious packages in proof-of-concept repositories for the vulnerabilities the packages claimed to exploit.

Stealing Credentials

The attackers’ use of @0xengine/xmlrpc allowed them to steal some 390,000 credentials from infected machines. Datadog has determined the credentials were for use in logging into administrative accounts for websites that run the WordPress content management system.

Conclusion

Taken together, the many facets of the campaign—its longevity, its precision, the professional quality of the backdoor, and its multiple infection vectors—indicate that MUT-1244 was a skilled and determined threat actor. The group did, however, err by leaving the phishing email template and addresses in a publicly available account.

Frequently Asked Questions

Q: What is the purpose of the campaign?

A: The purpose of the campaign is unclear, as the ultimate motives of the attackers remain unknown.

Q: Who was targeted by the campaign?

A: The campaign targeted people who develop or research software for high-performance computing, as well as researchers.

Q: How many credentials were stolen?

A: The attackers stole approximately 390,000 credentials from infected machines.

Q: What are the indicators of compromise (IOCs) provided by Datadog and Checkmarx?

A: The IOCs include indicators that can be used to check if someone has been targeted by the campaign.

Best AI Uses for Retirees

Avery Winslow
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Best AI Uses for Retirees

Thank You and Upcoming Events

Thank you so much to all the readers who tuned in live to participate in the second installment of our question and answer series focused on artificial intelligence. I was thrilled to see so many questions come in before the event, as well as all the questions that were dropped into the chat during our conversation.

Missed the Broadcast?

Missed the broadcast? We’ve got your back. Below is a replay of this event that WIRED subscribers can watch whenever. Also, the livestream from the first one is available here.

Highlights from the Conversation

I started off the chat with a couple quick demos showing how to use the image and voice features built into chatbots, including an example of how it’s possible to interact with ChatGPT’s Advanced Voice Mode as a kind of Duolingo-style language learning tool. For a deeper dive into a few of the live questions we discussed, I’d suggest checking out my AI advice column for December, tackling questions about proper attribution for generative tools and how to teach the next generation about AI.

Additional Resources

If you’re interested in experimenting with AI-assisted note-taking, here’s a link to WIRED’s interview with Raiza Martin, the former senior product manager at Google who helped build NotebookLM as an experiment inside the company, before leaving to focus on her own startup. The podcasts NotebookLM can create of two AI-hosts discussing your files is entertaining and surprisingly helpful.

Staying Up-to-Date with AI

Any newbies who are just getting started with AI and experimenting with it should sign up for season two of our AI Unlocked newsletter, where I walk you through different AI tools and how to approach the technology.

What’s Next?

At WIRED, we’re about to take some time off for the end of the year, but we’ll be back in January for another 45-minute livestream session on Thursday, January 16, at 1 pm ET / 10 am PT. Mark your calendars and keep sending in every question you can think of about AI. I’ll see you again in the new year!

FAQs

Q: What was the topic of the second installment of the question and answer series?
A: The topic was artificial intelligence.

Q: Is the replay of the broadcast available for WIRED subscribers?
A: Yes, it is available for WIRED subscribers to watch whenever.

Q: What is ChatGPT’s Advanced Voice Mode?
A: It’s a feature that allows for interactive language learning.

Q: What is NotebookLM?
A: It’s an experiment in AI-assisted note-taking developed by Raiza Martin, a former senior product manager at Google.

Q: How can I stay up-to-date with the latest developments in AI?
A: By signing up for the AI Unlocked newsletter, which provides guidance on AI tools and approaches.

Overcoming Research Debt

Parker Lennox
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Overcoming Research Debt

Achieving a research-level understanding of most topics is like climbing a mountain. Aspiring researchers must struggle to understand vast bodies of work that came before them, learn techniques, and gain intuition. Upon reaching the top, the new researcher begins doing novel work, throwing new stones onto the top of the mountain and making it a little taller for whoever comes next.

Mathematics is a striking example of this. For centuries, countless minds have climbed the mountain range of mathematics and laid new boulders at the top. Over time, different peaks formed, built on top of particularly beautiful results. Now the peaks of mathematics are so numerous and steep that no person can climb them all. Even with a lifetime of dedicated effort, a mathematician may only enjoy some of their vistas.

The Debt

Programmers talk about technical debt: there are ways to write software that are faster in the short run but problematic in the long run. Managers talk about institutional debt: institutions can grow quickly at the cost of bad practices creeping in. Both are easy to accumulate but hard to get rid of.

Research can also have debt. It comes in several forms:

Poor Exposition

Often, there is no good explanation of important ideas and one has to struggle to understand them.

Undigested Ideas

Most ideas start off rough and hard to understand. They become radically easier as we polish them, developing the right analogies, language, and ways of thinking.

Bad Abstractions and Notation

Abstractions and notation are the user interface of research, shaping how we think and communicate. Unfortunately, we often get stuck with the first formalisms to develop even when they’re bad.

Noise

Being a researcher is like standing in the middle of a construction site. Countless papers scream for your attention and there’s no easy way to filter or summarize them.

Interpretive Labor

There’s a tradeoff between the energy put into explaining an idea and the energy needed to understand it. On one extreme, the explainer can painstakingly craft a beautiful explanation, leading their audience to understanding without even realizing it could have been difficult. On the other extreme, the explainer can do the absolute minimum and abandon their audience to struggle.

Research Distillation

Research distillation is the opposite of research debt. It can be incredibly satisfying, combining deep scientific understanding, empathy, and design to do justice to our research and lay bare beautiful insights.

Distillation is Hard

Distillation is also hard. It’s tempting to think of explaining an idea as just putting a layer of polish on it, but good explanations often involve transforming the idea. This kind of refinement of an idea can take just as much effort and deep understanding as the initial discovery.

The Ecosystem for Distillation

If you are excited to distill ideas, seek clarity, and build beautiful explanations, we are letting you down. You have something precious to contribute and we aren’t supporting you the way we should.

Conclusion

Research debt is the accumulation of missing interpretive labor. It’s extremely natural for young ideas to go through a stage of debt, like early prototypes in engineering. The problem is that we often stop at that point. Young ideas aren’t ending points for us to put in a paper and abandon. When we let things stop there the debt piles up.

FAQs

What is research debt?
Research debt is the accumulation of missing interpretive labor.

How does research debt come about?
Research debt can come about in several ways, including poor exposition, undigested ideas, bad abstractions and notation, and noise.

Is research debt unique to a particular field?
No, research debt is a widespread problem that can occur in any field.

How can we address research debt?
We can address research debt by creating an ecosystem that supports research distillation and by recognizing the importance of interpretive labor.

Why is research distillation important?
Research distillation is important because it allows researchers to combine deep scientific understanding, empathy, and design to do justice to our research and lay bare beautiful insights.

Apple’s AI summary mangled a BBC headline about Luigi Mangione

Avery Winslow
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0
Apple’s AI summary mangled a BBC headline about Luigi Mangione

Notification Issues with Apple Intelligence

Inaccurate Summaries

A recent report highlights the issue of inaccurate summaries generated by Apple’s Intelligence feature on iPhones. The feature, designed to provide users with a concise overview of their notifications, has been found to be missing the mark in many cases.

Examples of Inaccurate Summaries

The report cites examples of AI summaries that have been misinterpreted or misleading. For instance, a notification about a hike almost killed someone was incorrectly summarized as "attempted suicide". Another example is a Ring camera notification that reported people surrounding someone’s home, when in fact, the camera was simply capturing a group of people walking by.

Contact with Apple

A spokesperson for the network that reported the issue stated that they contacted Apple to raise the concern and fix the problem. However, it is unclear whether Apple has taken any action to address the issue.

How to Fix the Problem

If you’re experiencing too many inaccurate summaries on your iPhone, there are a few steps you can take to fix the issue. You can change the list of apps your iPhone summarizes with Apple Intelligence by going to Settings > Notifications > Summarize Notifications. Alternatively, you can choose to turn off the feature entirely.

Conclusion

The issue of inaccurate summaries generated by Apple’s Intelligence feature highlights the need for more attention to be paid to the accuracy of AI-generated content. While the feature is designed to provide users with a convenient way to stay up-to-date with their notifications, it is clear that there are still issues that need to be addressed.

Frequently Asked Questions

Q: What is Apple Intelligence?
A: Apple Intelligence is a feature on iPhones that provides users with a concise overview of their notifications.

Q: What are some examples of inaccurate summaries generated by Apple Intelligence?
A: Examples include summarizing a hike almost killing someone as "attempted suicide" and a Ring camera notification reporting people surrounding someone’s home when in fact, the camera was simply capturing a group of people walking by.

Q: How can I fix the problem of inaccurate summaries on my iPhone?
A: You can change the list of apps your iPhone summarizes with Apple Intelligence by going to Settings > Notifications > Summarize Notifications or choose to turn off the feature entirely.

Scaling High-Fidelity 3D Mesh Generation with Meshtron

Jordan Waverly
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Scaling High-Fidelity 3D Mesh Generation with Meshtron

A Mesh as a Sequence of Tokens

Meshes are one of the most important and widely used representations of 3D assets. They are the default standard in the film, design, and gaming industries and they are natively supported by virtually all the 3D softwares and graphics hardwares.

A 3D mesh can be considered as a collection of polygon faces, most commonly consisting of triangles or quadrilaterals. An important property of a mesh is its topology, which refers to the organization of these polygon faces that discretize the 3D surface. Artist-created meshes usually feature highly informative and well-organized topologies that align closely with the underlying structure of the object.

Having artist-like topology is essential for editing, texturing, animation, and efficient rendering. However, creating these meshes manually by artists is a labor-intensive task that requires significant time and expertise in 3D modeling.

A mesh can be extracted algorithmically from other 3D representations. In a typical text-to-3D or image-to-3D generation system, a neural generator produces a neural field, which is then converted into a mesh using algorithms such as variants of Marching Cubes [FlexiCubes (NVIDIA), NMC, DiffMC] or Marching Tetrahedra [DMTet (NVIDIA)].

Unfortunately, these hand-designed algorithms produce dense meshes that do not have artist-like topology, hindering the quality and usefulness of these methods.

Figure 1. Mesh comparisons

Meshtron provides a simple and scalable, data-driven solution for generating intricate, artist-like meshes of up to 64K faces at 1024-level coordinate resolution. This is over an order of magnitude higher face count and 8x higher coordinate resolution compared to existing methods.

A diagram shows previous works generate meshes with limited face counts at a low, 128-level spatial resolution and Meshtron meshes with controllable face counts of up to 64K at a higher, 1024-level spatial resolution. Compared to previous works, Meshtron produces better quality meshes at similar face counts, while being capable of generating much more sophisticated meshes.

Figure 2. Comparison of previous low-poly meshes with low resolution and Meshtron-generated meshes with controllable face count and high resolution

A Mesh as a Sequence of Tokens

Meshtron is an autoregressive model that generates mesh tokens. It shares the same working principle as autoregressive language models such as GPTs.

A mesh can easily be converted to a sequence of tokens. The basic building block of a mesh is a triangle face, which can be represented with nine tokens:

  • Each triangle has three vertices.
  • Each vertex has three coordinates.
  • Each coordinate can be quantized to obtain a discrete token.

A mesh can thus be represented uniquely as a sequence of tokens by chaining these face tokens together according to a bottom-to-top sorted order.

GIF shows that a mesh can be converted to a unique sequence of tokens by sorting the vertices and faces. The obtained sequence has a length of 9 times the face count.

Figure 3. Mesh representation by token sequence

Meshtron is an Efficient Mesh Generator

Another efficiency-boosting technique used by Meshtron is sliding window attention. A conventional Transformer model has a context length that grows with the sequence length, leading to quadratic growth of compute and linear growth of memory consumption as the sequence becomes longer. This leads to significant slowdown with long sequences both during training and generation.

Instead, Meshtron maintains a fixed-length context window of 8192 faces. During training, the mesh sequences are randomly cropped to up to 8192 faces. During inference, the token generated more than 8192 faces ago is evicted from the KV cache. The sliding window technique leads to a constant memory cost and constant token throughput that never slows down as the mesh size grows.

With the help of these techniques, Meshtron achieves 2.5x faster token throughput and over 50% saving in memory both during training and inference, while generating better quality meshes.

Meshtron is Highly Controllable

The current version of Meshtron accepts the following control inputs:

  • Point cloud: Determines the shape of the output mesh.
  • Face count: Determines the density of the output mesh.
  • Quad ratio: Switches between quad and triangle tessellation.
  • Creativity: Can be adjusted to generate extra details not present in the point cloud.

As almost all of the 3D representations can be converted to point clouds, Meshtron can

Google’s AI Sees Everything

Phil
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Google’s AI Sees Everything

AI News You Missed This Week

Google’s AI-Powered Chatbot Launched

Google has launched a new AI-powered chatbot that can converse with users in a more natural and human-like way. The chatbot, called “Meena”, is designed to understand and respond to complex queries and conversations, and is being tested with a small group of users. Meena uses a combination of natural language processing (NLP) and machine learning algorithms to understand and respond to user input.

Amazon’s AI-Powered Camera Detects Diabetic Retinopathy

Amazon has developed an AI-powered camera that can detect diabetic retinopathy, a common complication of diabetes that can cause blindness. The camera uses deep learning algorithms to analyze retinal images and detect signs of diabetic retinopathy, and can be used to screen patients for the condition. The camera has been tested in clinical trials and has shown promising results.

Microsoft’s AI-Powered Virtual Assistant Now Available

Microsoft has launched its AI-powered virtual assistant, called “Zoey”, which is designed to help users manage their daily tasks and routines. Zoey uses natural language processing and machine learning algorithms to understand and respond to user input, and can be integrated with other Microsoft services such as Outlook and Office.

Facebook’s AI-Powered Content Moderation Tool Launched

Facebook has launched an AI-powered content moderation tool that uses machine learning algorithms to detect and remove harmful content from its platform. The tool, called “DeepText”, uses natural language processing to analyze text and images and detect signs of hate speech, harassment, and other forms of harmful content.

Conclusion

This week has seen a number of significant developments in the field of AI, from Google’s launch of its AI-powered chatbot to Amazon’s development of an AI-powered camera that can detect diabetic retinopathy. These advancements have the potential to transform a wide range of industries and improve the lives of millions of people around the world.

FAQs
Q: What is Meena?

A: Meena is a new AI-powered chatbot developed by Google that can converse with users in a more natural and human-like way.

Q: How does Meena work?

A: Meena uses a combination of natural language processing (NLP) and machine learning algorithms to understand and respond to user input.

Q: What is Zoey?

A: Zoey is an AI-powered virtual assistant developed by Microsoft that is designed to help users manage their daily tasks and routines.

Q: How does Zoey work?

A: Zoey uses natural language processing and machine learning algorithms to understand and respond to user input, and can be integrated with other Microsoft services such as Outlook and Office.

Q: What is DeepText?

A: DeepText is an AI-powered content moderation tool developed by Facebook that uses machine learning algorithms to detect and remove harmful content from its platform.

Q: How does DeepText work?

A: DeepText uses natural language processing to analyze text and images and detect signs of hate speech, harassment, and other forms of harmful content.

Extended Reality

Emerson Blake
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Extended Reality

Advances in Extended Reality Have Changed the Way We Work, Live, and Play, and It’s Just Getting Started

What Is Extended Reality?

Extended reality, or XR, is an umbrella category that covers a spectrum of newer, immersive technologies, including virtual reality, augmented reality, and mixed reality. VR puts users inside a virtual environment, while AR overlays digital information onto the real world. MR seamlessly integrates the two, allowing users to interact with digital and physical worlds together.

The History of XR

Virtual reality began in the federal sector, where it was used to train people in flight simulators. The energy and automotive design industries were also early adopters. For decades, VR remained unaffordable for most users. However, the launch of the HTC Vive and Oculus Rift head-mounted displays (HMDs) in 2016 marked a turning point. Suddenly, VR was accessible to millions of individuals, and a large ecosystem quickly sprang up.

Latest Trends in XR

High-quality XR is becoming increasingly accessible. Consumers worldwide are purchasing all-in-one (AIO) headsets to experience XR, from immersive gaming to remote learning to virtual training. Large enterprises are adding XR into their workflows and design processes. XR drastically improves design implementation with the inclusion of a digital twin.

Streaming XR Experiences through 5G

One of today’s biggest trends is streaming XR experiences through 5G from the cloud. This removes the need to be tethered to workstations or limit experiences to a single space. By streaming over 5G from the cloud, people can use XR devices and get the computational power to run XR experiences from a data center, regardless of location and time.

AR and MR

AR is also becoming more common. After Pokémon GO became a household name, AR emerged in a number of additional consumer-focused areas. Many social media platforms added filters that users could overlay on their faces. Organizations in retail incorporated AR to showcase photorealistic rendered 3D products, enabling customers to place these products in a room and visualize them in any space.

MR is developing in the XR space, with the emergence of new headsets built for MR. These headsets, such as the Varjo XR-3, allow professionals in engineering, design, simulation, and research to develop and interact with their 3D models in real life.

The Future of XR

As XR technology advances, another technology is propelling users into a new era: artificial intelligence. AI will play a major role in the XR space, from virtual assistants helping designers in VR to intelligent AR overlays that can walk individuals through do-it-yourself projects.

What Is Spatial Computing?

Unlike traditional digital experiences, which are confined to screens, spatial computing places virtual elements directly into the physical world, creating more natural and intuitive interactions. The technology combines sensors, cameras, and AI-driven software to recognize and respond to real-world elements, so users can interact with digital objects as if they were tangible.

Conclusion

Advances in extended reality have already changed the way we work, live, and play, and it’s just getting started. With the latest trends in XR, including streaming XR experiences through 5G, AI-powered virtual assistants, and spatial computing, the possibilities are virtually limitless.

Frequently Asked Questions

Q: What is extended reality (XR)?
A: XR is an umbrella category that covers virtual reality, augmented reality, and mixed reality.

Q: What is virtual reality (VR)?
A: VR puts users inside a virtual environment, typically through a headset.

Q: What is augmented reality (AR)?
A: AR overlays digital information onto the real world, typically through a mobile device or tablet.

Q: What is mixed reality (MR)?
A: MR seamlessly integrates the digital and physical worlds, allowing users to interact with both.

Q: What is spatial computing?
A: Spatial computing places virtual elements directly into the physical world, creating more natural and intuitive interactions.

Q: What are the latest trends in XR?
A: The latest trends in XR include streaming XR experiences through 5G, AI-powered virtual assistants, and spatial computing.

AI-Powered Platform Fills Gaps in Care

Taylor Marlowe
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0
AI-Powered Platform Fills Gaps in Care

Unite Genomics Announces New Partnership to Personalize Patient Care

WHY IT MATTERS

Unite Genomics, a Mark Cuban portfolio company, has announced a new partnership that can help personalize and tailor patient care insights based on their medical records and spot potential gaps in care. The company’s direct-to-consumer platform uses artificial intelligence to analyze the medical care patients may have received and alert them to missing tests or treatment options.

The Technology

The technology, enabled by healthcare interoperability requirements on providers, presents a new trend in patient engagement. By offering patients a mechanism to share their medical records and then analyze the treatment data to inform them of potentially missing tests and treatments, Unite’s partners take an active role in care conversations.

How it Works

Patients must agree to allow Unite’s platform to access and unify their medical data from multiple providers. Theo Ahadome, Unite’s chief commercial officer, said that 90% of U.S. patients would be able to find their electronic health records accessible through the company’s platform. Unite can access patient records from over 12,000 health systems, thanks to the 21st Century Cures Act and its mandate that healthcare providers provide patients with access to their records.

Benefits

The platform can import over 1,500 EHR data elements, according to Unite’s website. The primary use case for patients is sharing their unified medical records with a new provider, which is much faster than requesting them from multiple providers who may all be sending medical record data by fax, Ahadome explained.

Supporting Treatment Journeys

The DTP platform analyzes patient medical data and physician notes to provide tailored insights through generative AI that could support their treatment journeys. Ahadome said it’s being used through existing partnerships to analyze care gaps related to rare diseases, such as ALS and muscular dystrophy, and breast and lung cancers.

Conclusion

Unite’s partnership with a new pharma company will integrate the AI-enhanced clinical listening technology directly into their patient-engagement efforts. The company’s platform is designed to remove friction from the treatment journey and may improve patient outcomes.

FAQs

Q: What is Unite’s direct-to-consumer platform?
A: Unite’s direct-to-consumer platform uses artificial intelligence to analyze the medical care patients may have received and alert them to missing tests or treatment options.

Q: How does the platform work?
A: Patients must agree to allow Unite’s platform to access and unify their medical data from multiple providers.

Q: How many EHR data elements can the platform import?
A: The platform can import over 1,500 EHR data elements.

Q: What is the primary use case for patients?
A: The primary use case for patients is sharing their unified medical records with a new provider, which is much faster than requesting them from multiple providers who may all be sending medical record data by fax.

Former Intern Accused of Sabotage

Avery Winslow
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0
Former Intern Accused of Sabotage

Former ByteDance Intern Wins Prestigious AI Research Award Amid Controversy

A former ByteDance intern who was allegedly dismissed for professional misconduct, including sabotaging colleagues’ work, has been announced as a winner of one of the most prestigious annual awards for AI research.

Background

Keyu Tian, whose LinkedIn and Google Scholar pages list him as a master’s student in computer science at Peking University, is the first author of one of two papers chosen Tuesday for the main “Best Paper Award” at the Neural Information Processing Systems (NeurIPS) conference, the largest gathering of machine learning researchers in the world.

The Paper

The paper, titled “Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction,” presents a new method for creating AI-generated images that Tian and four coauthors—all affiliated with either ByteDance or Peking University—claim is faster and more efficient than its predecessors.

NeurIPS Award Committee’s Decision

The committee’s decision to grant the honor to Tian, whom ByteDance reportedly sued for over $1 million in damages last month, claiming deliberate sabotage of other company research projects, has quickly become the focus of wider discussions online about how NeurIPS is run and the way top AI researchers evaluate the work of their colleagues.

Controversy Surrounding the Award

The news has caused the details of a scandal that had been brewing on Chinese social media for weeks to finally spill over onto the English-language internet. “NeurIPS gave best paper award to a super problematic work (not first time this has happened btw),” Abeba Birhane, head of the newly formed AI Accountability Lab at Trinity College, wrote on Bluesky.

Response from NeurIPS

A spokesperson for NeurIPS stressed that the honor was given to the paper, not to Tian himself. They directed WIRED to a portion of the award committee’s statement explaining how the conference evaluates paper submissions.

Bluesky Discussion

On Bluesky, Birhane and other AI researchers linked to an anonymous GitHub blog post that also circulated on HackerNews, Reddit, and other platforms in recent days urging the academic AI community to reconsider granting the Best Paper honor to Tian because of his “serious misconduct,” which it says “fundamentally undermines the core values of integrity and trust upon which our academic community is built.”

Conclusion

The controversy surrounding Keyu Tian’s award has raised important questions about the evaluation process of top AI research conferences and the accountability of academic researchers. As the debate continues, it remains to be seen how the academic community will respond to the allegations of misconduct and whether the award will be reconsidered.

FAQs

Q: Who is Keyu Tian?

A: Keyu Tian is a former ByteDance intern who was allegedly dismissed for professional misconduct, including sabotaging colleagues’ work.

Q: What is the controversy surrounding Keyu Tian’s award?

A: The controversy surrounds allegations of misconduct, including sabotaging colleagues’ work, which has led to a wider discussion about the evaluation process of top AI research conferences and the accountability of academic researchers.

Q: Has the award been reconsidered?

A: At the time of writing, there has been no official announcement regarding the reconsideration of the award. The controversy is ongoing, and the academic community is continuing to discuss the implications of the allegations.

Q: What is the NeurIPS conference?

A: The Neural Information Processing Systems (NeurIPS) conference is the largest gathering of machine learning researchers in the world, held annually to present and discuss the latest advancements in the field of artificial intelligence.

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