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Ulta Beauty AI Helps Shoppers Try New Hairstyles

Emerson Blake
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0
Ulta Beauty AI Helps Shoppers Try New Hairstyles

Shoppers pondering a new hairstyle can now try styles before committing to curls or a new color. An AI app by Ulta Beauty, the largest specialty beauty retailer in the U.S., uses selfies to show near-instant, highly realistic previews of desired hairstyles.

Introducing GLAMlab Hair Try On

GLAMlab Hair Try On is a digital experience that lets users take a photo, upload a headshot or use a model’s picture to experiment with different hair colors and styles. Used by thousands of web and mobile app users daily, the experience is powered by the NVIDIA StyleGAN2 generative AI model.

How it Works

Hair color try-ons feature links to Ulta Beauty products so shoppers can achieve the look in real life. The company, which has more than 1,400 stores across the U.S., has found that people who use the virtual tool are more likely to purchase a product than those who don’t.

Adding Ulta Beauty’s Flair to StyleGAN2

GLAMlab is Ulta Beauty’s first generative AI application, developed by its digital innovation team. To build its AI pipeline, the team turned to StyleGAN2, a style-based neural network architecture for generative adversarial networks, aka GANs. StyleGAN2, developed by NVIDIA Research, uses transfer learning to generate infinite images in a variety of styles.

Retraining and Guardrails

"For our hairstyle try-on use case, we had to license the model for commercial use, retrain it and put guardrails around it to ensure the AI was only modifying pixels related to hair — not distorting any feature of the user’s face," said Juan Cardelino, director of the computer vision and digital innovation department at Ulta Beauty.

Availability and Future Plans

Available on the Ulta Beauty website and mobile app, the hair style and color try-ons rely on NVIDIA Tensor Core GPUs in the cloud to run AI inference, which takes around 5 seconds to compute the first style and about a second each for subsequent styles. The company next plans to incorporate virtual trials for additional hair categories like wigs and is exploring how the virtual hairstyle try-ons could be connected to in-store styling services.

Conclusion

GLAMlab Hair Try On is a game-changer for the beauty industry, allowing customers to try out different hairstyles and colors without committing to a physical change. With the power of AI, Ulta Beauty is revolutionizing the way customers interact with its products, giving them a more immersive and engaging experience.

Frequently Asked Questions

Q: How does GLAMlab Hair Try On work?
A: GLAMlab Hair Try On uses selfies to show near-instant, highly realistic previews of desired hairstyles.

Q: Is GLAMlab Hair Try On available on the Ulta Beauty website and mobile app?
A: Yes, GLAMlab Hair Try On is available on the Ulta Beauty website and mobile app.

Q: How long does it take to compute the first style?
A: It takes around 5 seconds to compute the first style.

Q: Can I try out different hair categories like wigs?
A: Yes, Ulta Beauty plans to incorporate virtual trials for additional hair categories like wigs in the future.

AI-Enabled Retail: The Future is Now

Parker Lennox
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0
AI-Enabled Retail: The Future is Now

Forward-thinking retailers are already harnessing artificial intelligence to help them sustain and grow their margins in the face of economic uncertainty, environmental pressures and geopolitical instability.

Retailers must develop a more strategic and integrated approach to their AI capabilities, focusing on two key levers of business value generation: revenue growth and cost reduction. However, the key is to build data foundations first.

Building Data Foundations

Retailers should create data models that connect the entire business value chain, whether sourcing and buying goods or moving and selling them. In turn, that means drawing together all the company’s data with information from partners and suppliers to create a united data set that covers the whole company’s operations.

This first step is far from simple. While some companies decided many years ago to become data-driven, others have not been using it deliberately or intentionally, even though they’ve been collecting data for years. A 2023 survey of US Chief Data Officers and Chief Data and Analytics Officers found that just 23.9% of companies characterize themselves as data-driven, and only 20.6% say that they have developed a data culture within their organizations.

Using AI to Grow Revenue

Beyond the need for secure data foundations, there are two other key areas of experimentation for retailers looking to take advantage of AI: revenue growth and cost reduction. Concerning the former, innovative retailers are already using AI to support dynamic pricing, personalization and retail media optimization to grow revenues.

UK grocery chains Morrisons and ASDA are currently trialing dynamic pricing to respond more dynamically to changing market conditions. Morrisons experimented with dynamic pricing by introducing electronic shelf labels (ESLs) in a small number of stores during 2023. ASDA also completed an ESL trial on 25,000 products.

Leveraging AI to Reduce Costs

Using AI to analyze data can result in cost savings throughout the organization, enabling retailers to act rapidly on inefficiencies and identify areas of potential improvement. In marketing, for example, a truly omnichannel approach using AI will allow marketers to understand the effect of changing their budget allocations on overall results, leading to increased effectiveness or greater efficiency.

The Ultimate Aim – Holistic Optimization

Critical success factors for implementing data and AI in retail include a robust data infrastructure and investment in revenue growth and cost reduction. Adding to this list is expertise in data science and AI, as well as a culture that fosters innovation and experimentation.

In the end, carefully implemented AI solutions will lead to efficiencies across all main drivers of business value, from personnel and procurement to customer acquisition and pricing, culminating in the ultimate goal of an intelligent enterprise optimized as a whole rather than as a series of disparate parts.

Conclusion

The key to realizing AI’s full potential is to build data foundations first. This requires a strategic and integrated approach to AI capabilities, focusing on revenue growth and cost reduction. By leveraging AI to support dynamic pricing, personalization, and retail media optimization, retailers can grow revenues. Additionally, AI can help reduce costs by analyzing data and identifying areas of potential improvement.

FAQs

Q: What are the key areas of experimentation for retailers looking to take advantage of AI?
A: Revenue growth and cost reduction.

Q: How can AI support dynamic pricing, personalization, and retail media optimization?
A: AI can support dynamic pricing by analyzing data and responding to changing market conditions, personalization by providing targeted offers and promotions, and retail media optimization by optimizing campaigns in real-time.

Q: What are the critical success factors for implementing data and AI in retail?
A: A robust data infrastructure, investment in revenue growth and cost reduction, expertise in data science and AI, and a culture that fosters innovation and experimentation.

Q: What is the ultimate aim of implementing AI in retail?
A: The ultimate aim is to create a holistic optimized enterprise, where AI solutions lead to efficiencies across all main drivers of business value.

Enhance Your Training Data with NVIDIA NeMo Curator Classifier Models

Jordan Waverly
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0
Enhance Your Training Data with NVIDIA NeMo Curator Classifier Models

Overview of NVIDIA NeMo Curator

NVIDIA NeMo Curator is a powerful tool designed to improve generative AI model accuracy by processing text, image, and video data at scale for training and customization. It provides prebuilt pipelines for generating synthetic data to customize and evaluate generative AI systems.

Accelerated Large-Scale Inference with NeMo Curator

NeMo Curator provides an out-of-the-box solution to scale inference pipelines for these models to a multinode, multi-GPU setup, while also accelerating inference through the CrossFit library from RAPIDS. This approach improves throughput by leveraging intelligent batching and utilizing cuDF for efficient IO operations, ensuring both scalability and performance optimization.

Classifier Models

The NVIDIA NeMo Curator team has released four new classifier models:

  1. Prompt Task and Complexity Classifier: A multiheaded model that classifies English text prompts across 11 task types and six complexity dimensions.
  2. Instruction Data Guard: A deep learning classification model that helps identify LLM poisoning attacks in datasets and generates a score to predict whether the input data is benign or poisonous.
  3. Multilingual Domain Classifier: A multilingual text classification model that categorizes content in 52 languages across 26 domains.
  4. Content Type Classifier DeBERTa: A text classification model that categorizes documents into 11 distinct content types.

Example Input and Output

Instruction

What is the average lifespan of a Golden Retriever?

Context

Golden Retrievers are a generally healthy breed; they have an average lifespan of 12 to 13 years. Irresponsible breeding to meet high demand has led to the prevalence of inherited health problems in some breed lines, including allergic skin conditions, eye problems and sometimes snappiness. These problems are rarely encountered in dogs bred from responsible breeders.

Response

The average lifespan of a Golden Retriever is 12 to 13 years.

score=0.000792806502431631
prediction = (score>0.5) = 0

Action:
The threshold for the model score is 0.5, and the prediction is set to 0 below it and to 1 above it.
prediction 0 means the prompt was classified as benign.
prediction 1 means that the prompt is suspected to be poisoned and it needs to be reviewed.

Multilingual Domain Classifier

Multilingual Domain Classifier is a powerful tool designed to help developers automatically categorize text content across 52 common languages, including English and many widely spoken languages including Chinese, Arabic, Spanish, and Hindi. The model can classify text into 26 different domains, ranging from Arts and Entertainment to Business, Science, and Technology.

Content Type Classifier DeBERTa

Content Type Classifier DeBERTa is an advanced text analysis model that enables automatic categorization of documents into 11 distinct content types, ranging from news articles and blog posts to product websites and analytical pieces.

Get Started

These four new classifier models are now available on Hugging Face. Additionally, the example notebooks are hosted in the NVIDIA/NeMo-Curator GitHub repo, providing step-by-step guidance for using these classifier models. Don’t forget to bookmark the repository to stay updated on future releases and improvements.

Conclusion

NeMo Curator provides a suite of powerful classifier models that can be used to enhance data quality, add metadata, and streamline data preparation. By leveraging these models, developers can build more accurate and robust AI systems that can handle large-scale data processing and analysis.

FAQs

Q: What are the benefits of using NeMo Curator?
A: NeMo Curator provides a suite of powerful classifier models that can be used to enhance data quality, add metadata, and streamline data preparation.

Q: How do I get started with NeMo Curator?
A: You can start by accessing the four new classifier models on Hugging Face and following the example notebooks in the NVIDIA/NeMo-Curator GitHub repo.

Q: What are the limitations of NeMo Curator?
A: NeMo Curator is designed for use with large-scale data processing and analysis. It may not be suitable for small-scale or low-resource applications.

Linkou Chang Gung Memorial Hospital Eyes Remote Patient Monitoring at Home

Taylor Marlowe
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Linkou Chang Gung Memorial Hospital Eyes Remote Patient Monitoring at Home

Enterprise Taxonomy: Patient AccessCare

Introduction

The healthcare industry is constantly evolving, and with it, the need for efficient and effective patient engagement has become a top priority. One way to achieve this is through the use of enterprise taxonomy, specifically designed for patient access. In this article, we will delve into the world of patient access and explore the benefits and features of an enterprise taxonomy, as well as its applications and challenges.

What is Enterprise Taxonomy?

An enterprise taxonomy is a structured framework that organizes and categorizes data within an organization, enabling efficient data retrieval, analysis, and sharing. In the context of patient access, an enterprise taxonomy is particularly useful in providing a standardized approach to patient data storage, retrieval, and management.

Benefits of Enterprise Taxonomy for Patient Access

There are several benefits to using an enterprise taxonomy for patient access, including:

  • Improved Data Retrieval: An enterprise taxonomy enables healthcare providers to quickly and easily locate patient data, reducing the time and effort required to locate relevant information.
  • Enhanced Patient Engagement: By providing patients with a standardized way to access their medical information, healthcare providers can improve patient engagement and satisfaction.
  • Better Decision Making: With an enterprise taxonomy, healthcare providers can quickly and easily access relevant patient data, enabling them to make informed decisions about patient care.

Features of Enterprise Taxonomy for Patient Access

Some key features of an enterprise taxonomy for patient access include:

  • Standardized Data Storage: An enterprise taxonomy provides a standardized approach to storing patient data, reducing data inconsistencies and improving data quality.
  • Tagging and Categorization: An enterprise taxonomy enables the use of tags and categories to further refine patient data, making it easier to locate and retrieve.
  • Search Functionality: An enterprise taxonomy provides robust search functionality, enabling healthcare providers to quickly locate patient data.

Challenges of Implementing Enterprise Taxonomy for Patient Access

While an enterprise taxonomy for patient access offers many benefits, there are also several challenges to consider, including:

  • Data Integration: Integrating existing patient data into an enterprise taxonomy can be a time-consuming and complex process.
  • Change Management: Implementing an enterprise taxonomy requires significant changes to existing workflows and processes.
  • Training and Education: Healthcare providers require training and education on the use of the enterprise taxonomy to ensure effective adoption.

Conclusion

In conclusion, an enterprise taxonomy for patient access can provide numerous benefits, including improved data retrieval, enhanced patient engagement, and better decision making. While there are challenges to implementation, the benefits of an enterprise taxonomy make it a valuable investment for healthcare providers.

FAQs

Q: What is the purpose of an enterprise taxonomy?
A: The purpose of an enterprise taxonomy is to organize and categorize data within an organization, enabling efficient data retrieval, analysis, and sharing.

Q: How does an enterprise taxonomy benefit patient access?
A: An enterprise taxonomy benefits patient access by providing a standardized approach to patient data storage, retrieval, and management, improving data quality and reducing the time and effort required to locate patient information.

Q: What are the challenges of implementing an enterprise taxonomy for patient access?
A: The challenges of implementing an enterprise taxonomy for patient access include data integration, change management, and training and education for healthcare providers.

AI Editing Revolution

Emerson Blake
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0
AI Editing Revolution

Introducing Instagram’s New Generative AI Editing Feature

Teasing the Launch of a Revolutionary New Tool

Instagram’s Adam Mosseri took to the app today to tease the upcoming launch of a generative AI editing feature that will enable users to "change nearly any aspect of your videos." Expected to roll out next year, the tool will be powered by Meta’s Movie Gen AI model, which was unveiled in early October.

What is Movie Gen AI?

Meta’s Movie Gen AI model can generate HD videos (1080p resolution) from a text prompt, which is more "realistic" than videos generated by rival technology such as OpenAI’s Sora text-to-video model. By bringing the Movie Gen technology to Instagram, the company aims to equip creators with more tools for "realizing" their ideas more easily.

How Will the New Editing Feature Work?

According to Mosseri, the new editing feature will enable creators to change nearly any aspect of users’ videos with a simple text prompt. Moreover, the announcement video previews "early research AI models" that showcase examples of outfit and background transformations. One especially impressive snippet showed Mosseri transformed into a puppet, which speaks to the expansive capabilities of Movie Gen.

The Rise of AI Video Generation

Gen AI models such as Open AI’s Sora, Adobe’s Firefly, and Google’s Veo – all released in 2024 – emphasize a shift away from AI image generation and toward AI video generation, especially text-to-video tools. However, just like AI image generation, video generation raises concerns about harmful use cases such as digital blackface, AI-generated deepfakes, and disinformation.

Industry Concerns and Reactions

Many creative professionals – artists, writers, filmmakers, actors, and photographers – have voiced frustrations with the impact of AI generators on their respective fields due to AI companies scraping the web to train their models. Because of such concerns, YouTube announced earlier this week that it will allow creators to opt into third-party AI training and recently partnered with the Creative Artists Agency to develop tools that give creators and artists more "control over how AI-generated content features their likeness, including their face, on YouTube at scale."

Conclusion

The new generative AI editing feature on Instagram is expected to revolutionize the way creators work, enabling them to bring their ideas to life with unprecedented ease. While there are concerns about the potential misuse of AI technology, the benefits of this feature are undeniable. As the industry continues to evolve, it will be interesting to see how creators adapt to this new technology and what kind of content they will produce.

FAQs

Q: What is the release date of the new generative AI editing feature?
A: The feature is expected to roll out next year.

Q: What is the purpose of Movie Gen AI?
A: Meta’s Movie Gen AI model can generate HD videos (1080p resolution) from a text prompt, making it more "realistic" than videos generated by rival technology.

Q: How will the new editing feature work?
A: Creators will be able to change nearly any aspect of users’ videos with a simple text prompt.

Q: What are the potential concerns surrounding AI video generation?
A: AI video generation raises concerns about harmful use cases such as digital blackface, AI-generated deepfakes, and disinformation.

Microsoft Tests Live Translation on Intel and AMD Copilot Plus PCs

Avery Winslow
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0
Microsoft Tests Live Translation on Intel and AMD Copilot Plus PCs

Microsoft Previews Live Translation on Intel and AMD-Based Copilot Plus PCs

Microsoft is previewing live translation on Intel and AMD-based Copilot Plus PCs, allowing users to translate audio from over 44 languages into English subtitles. The feature is rolling out now to Windows 11 Insiders in the Dev Channel.

How Live Translation Works

Live translation works with any audio played through a Copilot Plus PC, whether it’s coming from a YouTube video, a live video conference, or a recording. If the audio is in a supported language, Windows 11 will display real-time captions in English. The feature can currently translate from Spanish, French, Russian, Chinese, Korean, Arabic, and more.

Supported Languages

The feature can currently translate from the following languages:

* Spanish
* French
* Russian
* Chinese
* Korean
* Arabic
* And more

Availability and Rollout

Microsoft is rolling out the live translation feature to Windows 11 Insiders in the Dev Channel. The feature is currently available on Intel and AMD-based Copilot Plus PCs. Microsoft has also announced that it is rolling out an update to live translation on Qualcomm-equipped Copilot Plus PCs, as Windows 11 Insiders in the Dev Channel can now translate select languages to Simplified Chinese.

Additional AI Features

Microsoft has been gradually bringing more AI features to Intel and AMD-powered Copilot Plus PCs. Earlier this month, Microsoft began testing Recall, which takes snapshots of your activity on a Copilot Plus PC and lets you call up specific memories, on devices with Intel and AMD chips.

Conclusion

The live translation feature is a significant advancement in AI technology, enabling users to communicate more effectively across language barriers. With the rollout of this feature on Intel and AMD-based Copilot Plus PCs, users will be able to access real-time captions in English for audio content in over 44 languages. This feature has the potential to revolutionize the way we communicate and access information.

FAQs

Q: What languages are supported by the live translation feature?

A: The feature currently supports translation from Spanish, French, Russian, Chinese, Korean, Arabic, and more.

Q: How does the live translation feature work?

A: The feature works with any audio played through a Copilot Plus PC, whether it’s coming from a YouTube video, a live video conference, or a recording. If the audio is in a supported language, Windows 11 will display real-time captions in English.

Q: Is the live translation feature available on all Copilot Plus PCs?

A: The feature is currently available on Intel and AMD-based Copilot Plus PCs. Microsoft has also announced that it is rolling out an update to live translation on Qualcomm-equipped Copilot Plus PCs.

Q: What other AI features are available on Copilot Plus PCs?

A: Microsoft has been gradually bringing more AI features to Intel and AMD-powered Copilot Plus PCs, including Recall, which takes snapshots of your activity on a Copilot Plus PC and lets you call up specific memories.

Create a Container Using the Ubuntu Image in Docker

Parker Lennox
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0
Create a Container Using the Ubuntu Image in Docker

How to Create a Container for an Ubuntu Linux Distribution in Docker

Step 1: Run Docker Desktop on your Windows machine.

Step 2: Open the Command Prompt (CMD) and type the following command.

docker pull ubuntu

Step 3: Run the following command to create the container.

If the Ubuntu image is not present on your machine:

  • Docker will automatically pull the Ubuntu image from Docker Hub, which is Docker’s default image registry.
  • Once the image is downloaded, Docker will use it to create and start the container.
  • If the Ubuntu image is already present on your machine:
  • Docker will skip the pull step and directly use the existing local copy of the Ubuntu image to create and start the container.
docker run -it --name my-ubuntu-container ubuntu

The End…

FAQs:

Q: What is Docker Desktop?
A: Docker Desktop is a software application that allows you to run Docker on your Windows machine.

Q: What is Docker Hub?
A: Docker Hub is a registry of Docker images, including the Ubuntu image used in this example.

Q: What is the purpose of the docker pull command?
A: The docker pull command is used to download the Ubuntu image from Docker Hub if it is not already present on your machine.

Q: What is the purpose of the docker run command?
A: The docker run command is used to create and start a new container based on the specified image.

Innovation and Impact: Key Highlights from 2024

Morgan Tate
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0
Innovation and Impact: Key Highlights from 2024

New Leadership, Enduring Commitment

After leading Securly for 12 years, co-founder Bharath Madhusudan handed the CEO reins to Tammy Wincup in October 2024. With nearly three decades of experience working alongside school administrators and educators, Tammy’s vision for Securly extends beyond student safety, encompassing a deeper, customer-centric commitment to supporting student wellness so the entire school community—students, teachers, administrators, and parents—can thrive.

Helping Students in Crisis: Real Stories of the Impact Schools Are Making

Throughout 2024, we’ve been inspired by countless examples of educators, technology teams, and student services staff who work directly with students every day. Customer stories like the ones shared by Sheboygan Area School District and Atlanta Public Schools reinforce the importance of the work they do and keep us singularly focused on how we can support them.

Harnessing AI in Education to Fill Critical Gaps in 2024 and Beyond

While there’s no substitute for human resources when it comes to supporting students, there is no hiding from the fact that these resources are in short supply. Yet, while the resources to support students are dwindling, the concerns around student mental health are not. As school leaders continue to grapple with this challenge, artificial intelligence (AI) within the edtech space has provided schools a real opportunity at some relief, both in the short- and long term.

Learning Together: The Back-to-School Securly Shield Event

This year’s back-to-school Securly Shield event brought together hundreds of K-12 leaders. With over 250 live attendees and 670 on-demand viewers, the event featured insights from Securly leadership, as well as real-world stories from educators like Dr. Chantell Manahan, MSD of Steuben County, Indiana, and Eric Benedict, Madison Metropolitan School District, Wisconsin.

Supporting Schools While Prioritizing Student Privacy

Technology tools have become a mainstay in classrooms, helping administrators, educators, and technologists manage a range of tasks. However, because of the vast amounts and types of data these tools collect about students, prioritizing data security and student data privacy has never been more critical. It’s a challenge that we help education leaders navigate every day, and one we take very seriously.

Reflecting on 2024, Looking Forward to 2025

Under the leadership of our new CEO, Tammy Wincup, we’re entering the new year energized by our north-star strategy to develop innovative and privacy-centered student safety and wellness solutions that help our customers’ school communities thrive.

Conclusion

As we look ahead to 2025 and beyond, we’re excited to continue to lead the way in innovating the applications of AI in education. Our commitment to our customers will remain a top priority, and we’ll continue to provide best-in-class customer service and solutions that create a climate of safety, wellness, and engagement so students can thrive and reach their highest potential.

Frequently Asked Questions

Q: What is Securly’s vision for student safety and wellness?
A: Securly’s vision is to provide innovative and privacy-centered student safety and wellness solutions that help our customers’ school communities thrive.

Q: What is Securly’s commitment to customer service?
A: Securly is committed to providing best-in-class customer service and solutions that create a climate of safety, wellness, and engagement so students can thrive and reach their highest potential.

Q: How does Securly prioritize student privacy?
A: Securly prioritizes student privacy by being a Student Privacy Pledge signatory, maintaining iKeepSafe certification, and adhering to enterprise-grade security measures, including SOC 2 Type 2 certification and regular procedural audits.

Broadcom Laughs to the Bank as Firms Abandon VMware

Emerson Blake
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0
Broadcom Laughs to the Bank as Firms Abandon VMware

2025 Challenges

Broadcom’s decisions have put it at a crossroads, with the company facing challenges in maintaining business relationships with various partners and clients. One such challenge is its decision to end its business with Ingram, which may affect its ties with smaller solution providers.

Dispute with AT&T

Broadcom seems to be willing to fight for larger accounts, as evident in its recent dispute with AT&T. The two companies had a contract dispute, which was settled this month. This comes after Broadcom stopped selling perpetual licenses for VMware, opting for subscription-based models instead.

Biggest Accounts

Broadcom is actively taking over 500 of VMware’s biggest accounts, effectively blocking channel partners from dealing with these clients. Initially, the company planned to take over 2,000 accounts, but has revised its strategy, allowing channel partners to manage 1,500 of the largest accounts. This move is seen as a way to tie professional services to VMware products, making migrations more difficult.

Channel Partners in Turmoil

The VMware channel is experiencing significant changes, including the discontinuation of the partner program and the announcement of a new channel chief. Many resellers are expressing frustration with the changes and what they perceive as poor communication from Broadcom.

Reseller Frustrations

Jason Slagle, president of Toledo-based managed services provider and VMware partner CNWR, stated, “Broadcom has abandoned the channel market by making it nearly impossible to work with them due to constantly changing requirements, packaging, and changes to the program.”

Forrester Predictions

Forrester analysts Michele Pelino and Naveen Chhabra predict that next year, VMware’s largest 2,000 customers will reduce their deployment size by an average of 40%. They attribute this to a shift towards public cloud, on-premises alternatives, and new architecture.

Conclusion

Broadcom faces challenges in maintaining its business relationships, particularly with its biggest accounts and channel partners. While the company may struggle to appease both parties, it is expected to continue generating profits from VMware, despite the challenges. The company’s price increases and cost-cutting measures are predicted to boost its net profits, as few competitors can offer alternatives to VMware virtualization.

FAQs

Q: What changes has Broadcom made to its relationship with channel partners?

A: Broadcom has stopped selling perpetual licenses for VMware, opting for subscription-based models instead. It has also taken over 500 of VMware’s biggest accounts, effectively blocking channel partners from dealing with these clients.

Q: What are the Forrester analysts predicting for VMware’s largest customers?

A: Forrester analysts Michele Pelino and Naveen Chhabra predict that next year, VMware’s largest 2,000 customers will reduce their deployment size by an average of 40%, shifting towards public cloud, on-premises alternatives, and new architecture.

Q: What is the reaction of resellers to Broadcom’s changes?

A: Many resellers are expressing frustration with the changes, citing poor communication from Broadcom and difficulties in working with the company due to its constantly changing requirements and packaging.

Every AI Copyright Lawsuit in the US

Avery Winslow
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0
Every AI Copyright Lawsuit in the US

The War Between Content Publishers and AI Companies

In May 2020, the media and technology conglomerate Thomson Reuters sued a small legal AI startup called Ross Intelligence, alleging that it had violated US copyright law by reproducing materials from Westlaw, Thomson Reuters’ legal research platform. As the pandemic raged, the lawsuit hardly registered outside the small world of nerds obsessed with copyright rules. But it’s now clear that the case—filed more than two years before the generative AI boom began—was the first strike in a much larger war between content publishers and artificial intelligence companies now unfolding in courts across the country.

The Outbreak of Lawsuits

Over the past two years, dozens of other copyright lawsuits against AI companies have been filed at a rapid clip. The plaintiffs include individual authors like Sarah Silverman and Ta Nehisi-Coates, visual artists, media companies like The New York Times, and music-industry giants like Universal Music Group. This wide variety of rights holders are alleging that AI companies have used their work to train what are often highly lucrative and powerful AI models in a manner that is tantamount to theft.

The Fair Use Defense

AI companies are frequently defending themselves by relying on what’s known as the “fair use” doctrine, arguing that building AI tools should be considered a situation where it’s legal to use copyrighted materials without getting consent or paying compensation to rights holders. (Widely accepted examples of fair use include parody, news reporting, and academic research.) Nearly every major generative AI company has been pulled into this legal fight, including OpenAI, Meta, Microsoft, Google, Anthropic, and Nvidia.

Tracking the Lawsuits

WIRED is keeping close tabs on how each of these lawsuits unfold. We’ve created visualizations to help you track and contextualize which companies and rights holders are involved, where the cases have been filed, what they’re alleging, and everything else you need to know.

The First Case

That first case, Thomson Reuters v. Ross Intelligence, is still winding its way through the court system. A trial that was originally scheduled for earlier this year has been indefinitely delayed, and even though the cost of litigation has already put Ross out of business, it’s unclear when it will end. Other cases, like the closely-watched lawsuit filed by The New York Times against OpenAI and Microsoft, are currently in contentious discovery periods, during which both parties are arguing over what information they need to turn over.

Conclusion

The war between content publishers and AI companies is far from over. The outcome of these lawsuits will have a significant impact on the information ecosystem and the entire AI industry, and will likely affect just about everyone across the internet. As the legal battles continue to unfold, it’s clear that the stakes are high and the consequences will be far-reaching.

FAQs

Q: What is the fair use doctrine?
A: The fair use doctrine is a legal concept that allows for the use of copyrighted materials without permission or payment of royalties, under certain circumstances, such as parody, news reporting, and academic research.

Q: Which AI companies have been sued?
A: Nearly every major generative AI company has been pulled into this legal fight, including OpenAI, Meta, Microsoft, Google, Anthropic, and Nvidia.

Q: What is the outcome of the Thomson Reuters v. Ross Intelligence case?
A: The case is still winding its way through the court system, with a trial that was originally scheduled for earlier this year having been indefinitely delayed.

Q: How many lawsuits have been filed against AI companies?
A: Over the past two years, dozens of other copyright lawsuits against AI companies have been filed at a rapid clip.

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