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NVIDIA RTX Neural Rendering: Next Era of AI-Powered Graphics Innovation

Jordan Waverly
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NVIDIA RTX Neural Rendering: Next Era of AI-Powered Graphics Innovation

RTX Neural Shaders Bring AI to Programmable Shaders

NVIDIA today unveiled next-generation hardware for gamers, creators, and developers—the GeForce RTX 50 Series desktop and laptop GPUs. Alongside these GPUs, NVIDIA introduced NVIDIA RTX Kit, a suite of neural rendering technologies to ray trace games with AI, render scenes with immense geometry, and create game characters with lifelike visuals.

RTX Neural Shaders

25 years ago NVIDIA introduced GeForce and programmable shaders, unlocking two decades of innovation in graphics technology, from pixel shading to compute shading to real-time ray tracing. Alongside the new GeForce RTX 50 Series GPUs powered by NVIDIA Blackwell architecture, NVIDIA is introducing RTX Neural Shaders, which bring small neural networks into programmable shaders, enabling a new era of graphics innovation.

The RTX Neural Shaders SDK enables developers to train their game data and shader code on an RTX AI PC and accelerate their neural representations and model weights with NVIDIA Tensor Cores at runtime. During training, neural game data is compared to the output of the traditional data and is refined over multiple cycles. Developers can simplify the training process with Slang, a shading language that splits large, complex functions into smaller pieces that are easier to handle.

Applications of Neural Shaders

This breakthrough technology is used for three applications: RTX Neural Texture Compression, RTX Neural Materials, and Neural Radiance Cache (NRC).

  • RTX Neural Texture Compression: uses AI to compress thousands of textures in less than a minute. Their neural representations are stored or accessed in real time or loaded directly into memory without further modification. The neurally compressed textures save up to 7x more VRAM or system memory than traditional block compressed textures at the same visual quality.
  • RTX Neural Materials: uses AI to compress complex shader code typically reserved for offline materials and built with multiple layers such as porcelain and silk. The material processing is up to 5x faster, making it possible to render film-quality assets at game-ready frame rates.
  • RTX Neural Radiance Cache: uses AI to learn multi-bounce indirect lighting to infer an infinite amount of bounces after the initial one to two bounces from path traced rays. This offers better path traced indirect lighting and performance versus path traced lighting without a radiance cache. NRC is now available through the RTX Global Illumination SDK, and will be available soon through RTX Remix and Portal with RTX.

Get Started

GeForce RTX 50 Series introduces the next era of rendering and AI. Train and deploy AI directly within shaders for better compression and approximation techniques. Build more complex open worlds with breakthrough BVH building techniques. Leverage new digital human rendering technologies with real-time performance. Use the latest ray tracing algorithms powered by AI to simulate accurate light transport. Transform traditional game characters into AI-powered autonomous versions for new gameplay experiences. Access the RTX Kit, DLSS 4, Reflex 2, and NVIDIA ACE technologies through the Game Development resource hub.

Models for Perception, Cognition, and Action

NVIDIA ACE models for perception, cognition, and action are coming soon in early access. These models will enable developers to create more realistic and engaging game characters with lifelike decision making and actions.

  • Models for Perception: NemoAudio 4B Instruct, Cosmos Nemotron 4B 128K Instruct, Parakeet CTC XXL 1.1B Multilingual, and NV-OCR.
  • Models for Cognition: Mistral Nemo Minitron 8B 128K Instruct, Mistral Nemo Minitron 4B 128K Instruct, and Mistral Nemo Minitron 2B 128K Instruct.
  • Models for Action: A2 Flow.

Conclusion

The RTX Neural Shaders and NVIDIA RTX Kit bring a new era of graphics innovation, enabling developers to create more realistic and engaging game characters with lifelike decision making and actions. With the RTX Neural Shaders SDK, developers can train and deploy AI directly within shaders for better compression and approximation techniques. The RTX Kit and NVIDIA ACE technologies will be available through the Game Development resource hub.

FAQs

Q: What is RTX Neural Shaders?
A: RTX Neural Shaders is a breakthrough technology that brings small neural networks into programmable shaders, enabling a new era of graphics innovation.

Q: What are the applications of RTX Neural Shaders?
A: RTX Neural Shaders is used for RTX Neural Texture Compression, RTX Neural Materials, and Neural Radiance Cache (NRC).

Q: How do I get started with RTX Neural Shaders?
A: Developers can get started with the RTX Neural Shaders and RTX Neural Texture Compression SDKs at the end of the month through NVIDIA RTX Kit. Sign up to be notified of availability.

Q: What is NVIDIA ACE?
A: NVIDIA ACE is a suite of neural rendering technologies that enables developers to create more realistic and engaging game characters with lifelike decision making and actions.

Consistent Characters in AI-Generated Video

Phil
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Consistent Characters in AI-Generated Video

Consistent Characters in AI Video: A Long-Standing Problem

The Challenge

Consistent characters in AI video has been a significant problem in the field of artificial intelligence. Despite advancements in technology, creating realistic and consistent characters in AI video has proven to be a daunting task. This issue has hindered the development of AI video applications, particularly in areas such as virtual reality, gaming, and entertainment.

The Problem

The main challenge lies in creating characters that are not only realistic but also consistent in their behavior, movements, and expressions. AI systems struggle to replicate the subtleties of human behavior, leading to inconsistent character performances. This inconsistency can be attributed to various factors, including:

  • Limited data sets
  • Inadequate training
  • Lack of domain knowledge
  • Insufficient computational resources

Hailuo’s Solution

Hailuo, a leading AI technology company, has recently announced a breakthrough in creating consistent characters in AI video. Their new Subject-Aware Generative Adversarial Network (SAGAN) has shown promising results in generating realistic and consistent characters.

How it Works

SAGAN uses a novel approach to generate characters by incorporating subject-awareness into the generative process. This means that the AI system is trained to understand the context and subject matter of the video, allowing it to generate characters that are more realistic and consistent.

Key Features

The SAGAN system boasts several key features that set it apart from other AI video generation systems:

  • Subject-awareness: The system is trained to understand the context and subject matter of the video, allowing it to generate characters that are more realistic and consistent.
  • Multi-modal fusion: The system combines multiple modalities, including text, images, and audio, to generate more realistic and diverse characters.
  • Adversarial training: The system uses adversarial training to improve the quality and consistency of the generated characters.

Conclusion

Hailuo’s SAGAN system has the potential to revolutionize the field of AI video generation. By providing a solution to the problem of consistent characters, SAGAN can enable the development of more realistic and engaging AI video applications. As the technology continues to evolve, we can expect to see significant advancements in areas such as virtual reality, gaming, and entertainment.

FAQs

Q: What is the main challenge in creating consistent characters in AI video?
A: The main challenge lies in creating characters that are not only realistic but also consistent in their behavior, movements, and expressions.

Q: How does Hailuo’s SAGAN system address the problem of consistent characters?
A: SAGAN uses a novel approach to generate characters by incorporating subject-awareness into the generative process, allowing it to generate characters that are more realistic and consistent.

Q: What are the key features of Hailuo’s SAGAN system?
A: The key features of SAGAN include subject-awareness, multi-modal fusion, and adversarial training.

Q: What are the potential applications of Hailuo’s SAGAN system?
A: The potential applications of SAGAN include virtual reality, gaming, entertainment, and other areas where realistic and consistent characters are required.

Now See This: NVIDIA’s AI Agents for Video Analysis

Emerson Blake
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Now See This: NVIDIA’s AI Agents for Video Analysis

The Next Big Moment in AI: Video Analysis Agents

Unlocking the Power of Video Data

Today, more than 1.5 billion enterprise-level cameras deployed worldwide are generating roughly 7 trillion hours of video per year. Yet, only a fraction of it gets analyzed. This comes at a high cost, with manufacturers losing trillions of dollars annually to poor product quality or defects that could have been spotted earlier by using AI agents that can perceive, analyze, and help humans take action.

Introducing the NVIDIA AI Blueprint for Video Search and Summarization

To accelerate the creation of such agents, NVIDIA has announced early access to a new version of the NVIDIA AI Blueprint for video search and summarization. Built on top of the NVIDIA Metropolis platform and supercharged by NVIDIA Cosmos Nemotron vision language models (VLMs), NVIDIA Llama Nemotron large language models (LLMs), and NVIDIA NeMo Retriever, the blueprint provides developers with the tools to build and deploy AI agents that can analyze large quantities of video and image content.

How Video Analyst AI Agents Can Help Industrial Businesses

AI agents with visual perception and analysis skills can be fine-tuned to help businesses with industrial operations by:

Increasing Productivity and Reducing Waste

Agents can help ensure standard operating procedures are followed during complex industrial processes like product assembly. They can also be fine-tuned to carefully watch and understand nuanced actions, and the sequence in which they’re implemented.

Boosting Asset Management Efficiency

Agents can help optimize inventory storage in warehouses by performing 3D volume estimation and centralizing understanding across various camera streams.

Improving Safety

Agents can process huge volumes of video and summarize it into contextually informative reports of accidents. They can also help ensure personal protective equipment compliance in factories, improving worker safety in industrial settings.

Preventing Accidents and Production Problems

AI agents can identify atypical activity to quickly mitigate operational and safety risks, whether in a warehouse, factory, or airport, or at a traffic intersection or other municipal setting.

Learning from the Past

Agents can search through operations video archives, find relevant information from the past, and use it to solve problems or create new processes.

Video Analysts for Sports, Entertainment, and More

The $500 billion sports industry is another area where video analysis AI agents can make a mark. Coaches, teams, and leagues can use these agents to evaluate and enhance player performance, prioritize safety, and boost fan engagement through player analytics platforms and data visualization.

Worldwide Adoption and Availability

Partners from around the world are integrating the blueprint for building AI agents for video analysis into their own developer workflows, including Accenture, Centific, Deloitte, EY, Infosys, Linker Vision, Pegatron, TATA Consultancy Services (TCS), Telit Cinterion, and VAST.

Conclusion

The next big moment in AI is in sight, and it’s literally. With the introduction of the NVIDIA AI Blueprint for video search and summarization, developers can now build and deploy AI agents that can analyze large quantities of video and image content, unlocking new possibilities for industrial, sports, and entertainment applications. By applying these agents, businesses and organizations can improve productivity, reduce waste, and drive innovation.

FAQs

Q: What is the NVIDIA AI Blueprint for video search and summarization?

A: The NVIDIA AI Blueprint for video search and summarization is a tool that enables developers to build and deploy AI agents that can analyze large quantities of video and image content.

Q: What are the capabilities of video analyst AI agents?

A: Video analyst AI agents can be fine-tuned to perform tasks such as increasing productivity and reducing waste, boosting asset management efficiency, improving safety, preventing accidents and production problems, and learning from the past.

Q: What industries can benefit from video analyst AI agents?

A: Industrial, sports, and entertainment industries can benefit from video analyst AI agents, among others.

Before Las Vegas, Intel Analysts Warned That Bomb Makers Were Turning to AI

Avery Winslow
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Before Las Vegas, Intel Analysts Warned That Bomb Makers Were Turning to AI

AI-Powered Domestic Terrorism: A Growing Concern for Law Enforcement

The Las Vegas Incident

Six days before his death by suicide outside the main entrance of the Trump International Hotel in Las Vegas, Matthew Livelsberger, a highly decorated US Army Green Beret from Colorado, consulted with an artificial intelligence (AI) on the best ways to turn a rented Cybertruck into a four-ton vehicle-borne explosive. According to documents obtained exclusively by WIRED, Livelsberger’s actions raise concerns about the potential for AI to be used by racially or ideologically motivated extremists to target critical infrastructure, particularly the power grid.

AI-Assisted Bomb-Making

Copies of Livelsberger’s exchanges with OpenAI’s ChatGPT show that he pursued information on how to amass as much explosive material as he legally could while en route to Las Vegas, as well as how best to set it off using the Desert Eagle gun discovered in the Cybertruck following his death. Screenshots shared by McMahill’s office reveal Livelsberger prompting ChatGPT for information on Tannerite, a reactive compound typically used for target practice. In one such prompt, Livelsberger asks, "How much Tannerite is equivalent to 1 pound of TNT?" He follows up by asking how it might be ignited at "point blank range."

US Law Enforcement Concerns

The documents obtained by WIRED show that concerns about the threat of AI being used to help commit serious crimes, including terrorism, have been circulating among US law enforcement. They reveal that the Department of Homeland Security has persistently issued warnings about domestic extremists who are relying on the technology to "generate bomb making instructions" and develop "general tactics for conducting attacks against the United States."

Extremist Online Activity

The memos, which are not classified but are restricted to government personnel, state that violent extremists are increasingly turning to tools like ChatGPT to help stage attacks aimed at collapsing American society through acts of domestic terror. According to investigators, Livelsberger intended the bombing as a "wake-up call" to Americans, whom he urged to reject diversity, embrace masculinity, and rally around president-elect Donald Trump, Elon Musk, and Robert F. Kennedy Jr. He also urged Americans to purge Democrats from the federal government and the military, calling for a "hard reset."

Vulnerability of the Power Grid

While McMahill contended that the incident in Las Vegas may be the first "on US soil where ChatGPT was utilized to help an individual build a particular device," federal intelligence analysts say extremists associated with white supremacist and accelerationist movements online are now frequently sharing access to hacked versions of AI chatbots in an effort to construct bombs with an eye to carrying out attacks against law enforcement, government facilities, and critical infrastructure. In particular, the memos highlight the vulnerability of the US power grid, a popular target among extremists populating "Terrorgram," a loose network of encrypted chatrooms that host a range of violent, racially-motivated individuals bent on the destruction of American democratic institutions.

Conclusion

The use of AI-powered tools to assist in the planning and execution of terrorist attacks is a growing concern for law enforcement agencies. The Las Vegas incident serves as a stark reminder of the potential for violence and destruction that can be unleashed when individuals with extremist ideologies gain access to advanced technology.

Frequently Asked Questions

Q: What is the primary concern regarding the use of AI in terrorism?
A: The primary concern is the potential for AI-powered tools to be used by racially or ideologically motivated extremists to target critical infrastructure, particularly the power grid.

Q: How widespread is the use of AI in terrorist activities?
A: According to federal intelligence analysts, violent extremists are increasingly turning to tools like ChatGPT to help stage attacks aimed at collapsing American society through acts of domestic terror.

Q: What is the vulnerability of the US power grid?
A: The memos highlight the vulnerability of the US power grid, a popular target among extremists, particularly those populating "Terrorgram," a loose network of encrypted chatrooms.

The AI Arms Race Costs Money

Parker Lennox
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The AI Arms Race Costs Money

Mag 7 Capex

Accounting is boring but important. Particularly important: the difference between a capital expense and an operating expense. A capital expense (buying a big piece of equipment, say) does not count directly against earnings on the income statement, as an operating expense (paying a salary, say) does. Instead, a capital expense appears on the income statement over time, in theory matching the drag on profits to the life of the capital asset. This spread-out expense shows up in a line called “depreciation.”

I see you sleeping at the back. But I drag you through this tiresome point because the most important companies in the world, the Magnificent 7 Big Techs, are running up a huge amount of capital expenditure, mostly on data centers for artificial intelligence. This is cash out the door today, but the expense will only appear in earnings per share over time. The AI arms race has not fully hit profits yet. The question is whether the market has digested the fact that it must do so before long.

Here is capex at the five of the seven that are, to greater or lesser extents, going bananas on capex (at Apple, capex is steady; at Nvidia, the capex money is coming in, not going out):

These are staggering numbers, and they are still growing.

Amazon looks like an outlier, but that is not quite true. One needs to scale the spending to the rest of the company’s financials. For example, one can look at capex as a percentage of revenue:

At Meta and Microsoft, one of every five dollars that comes in the door goes out as capex; at Alphabet, it is one in seven. And the trend is up (wondering about that big mountain of spending in 2022 at Meta? Remember the Metaverse?).

**Are Bankruptcies the Canary in the Coal Mine?**

Our colleague Will Schmitt pointed out yesterday that corporate bankruptcies in the US hit a 14-year high in 2024. At least 686 companies filed for bankruptcy last year, 8% above 2023’s filings and the biggest load of bankruptcies since 2010:

Is this evidence that higher rates have, at long last, come for over-indebted corporations? Or has the US economy slowed more than we have appreciated?

Of the top 10 largest bankruptcies in 2024, five were private. One, Red River Talc, is Johnson and Johnson’s holding company for baby powder liabilities. We took a look at the remaining four, and threw in two other high-profile names: home goods store Big Lots, container maker Tupperware, fabric seller Jo-Ann Stores, tinsel emporium Party City, discount air carrier Spirit Airlines, and Franchise Group, owner of retail chains such as The Vitamin Shoppe and Pet Supplies Plus.

Just a glance betrays a theme: all are discount stores, or franchises pitched to lower-income consumers. Just like the dollar stores and other discounters, many suffered as low-income Americans cut back on spending amid high inflation. One of them, Big Lots, directly blamed its downfall on this issue. Together they are an acute example of the US’s “K-shaped” recovery.

**Conclusion**

In conclusion, the market is pricing in a hot economy, and the Magnificent 7 Big Techs are running up a huge amount of capital expenditure on data centers for artificial intelligence. This is cash out the door today, but the expense will only appear in earnings per share over time. The AI arms race has not fully hit profits yet. The question is whether the market has digested the fact that it must do so before long.

**FAQs**

Q: What is the difference between a capital expense and an operating expense?
A: A capital expense does not count directly against earnings on the income statement, while an operating expense does.

Q: Why is the market pricing in a hot economy?
A: The market is pricing in a hot economy due to strong ISM services survey and Jolts report, and higher inflation break-evens and the dollar.

Q: What is the trend of capex at the Magnificent 7 Big Techs?
A: The trend is up, with capex at the five of the seven companies increasing significantly.

Q: What was the outcome of the 2024 bankruptcies?
A: The outcome was that corporate bankruptcies in the US hit a 14-year high, with 686 companies filing for bankruptcy last year, 8% above 2023’s filings.

Microsoft Reverts Bing AI Image Generator Due to Quality Complaints

Avery Winslow
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Microsoft Reverts Bing AI Image Generator Due to Quality Complaints

Microsoft Rolls Back AI-Powered Bing Image Creator Upgrade Due to User Complaints

Microsoft’s AI-Powered Bing Image Creator Sees Rollback Amid User Frustration

Microsoft has announced that it is rolling back a model upgrade to its AI-powered Bing Image Creator, following weeks of complaints from users who found the new version to be less effective. The upgrade, which was made on December 18th, was designed to improve the quality and accuracy of the image creation tool, but instead, users reported that the new model produced less-detailed results or images that did not accurately reflect their prompts.

Reversal of Decision

Microsoft declined to comment on its decision to roll back the upgrade, but Jordi Ribas, the head of search, tweeted that the company was able to reproduce some of the issues reported by users and would be reverting to an older version of the DALL-E model for now. The rollback is expected to take a few weeks to complete.

User Frustration

As soon as Ribas posted about the change on December 18th, complaints began pouring in on social media and online forums, with users expressing frustration and disappointment with the new version of the Bing Image Creator. Many reported that the images produced by the new model were of poor quality, lacking the level of detail and accuracy they had come to expect from the tool.

Artistic Criticism

The decision to roll back the upgrade has also led to a new level of scrutiny and criticism from the art community, with some users taking to social media to express their disappointment and frustration with the tool’s performance. In a post on the OpenAI community forum, one user lamented that the new model "just doesn’t work" and that the images it produces are "not even close" to what they had hoped for.

Conclusion

The decision to roll back the upgrade to Microsoft’s AI-powered Bing Image Creator is a significant setback for the company, which had touted the new model as a major improvement over its predecessors. While the company has not commented on the reasons behind the decision, it is clear that user feedback has played a significant role in the reversal. As the company looks to move forward, it will be crucial for Microsoft to engage with its user base and address the concerns and criticisms that have been raised.

FAQs

Q: Why did Microsoft roll back the upgrade to its AI-powered Bing Image Creator?
A: Microsoft declined to comment on the reasons behind the decision, but it is believed to be in response to user feedback and complaints about the new model’s performance.

Q: What is the expected timeline for the rollback?
A: The rollback is expected to take a few weeks to complete, according to Jordi Ribas, head of search.

Q: What is the impact on users?
A: Users who have been experiencing issues with the new model will see improved performance once the rollback is complete, but the exact timeline for the rollback is still unclear.

AI Glasses Unveiled

Emerson Blake
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AI Glasses Unveiled

Halliday’s Smart Glasses: A Game-Changer in the World of Wearable Technology

Innovative Design and Performance

At this year’s Consumer Electronics Show (CES), several trends dominated the showcased products, including AI and smart glasses. Despite the fierce competition, Halliday’s smart glasses stood out due to their impressive design and performance, which emphasize comfort.

The World’s Smallest Optical Module

The Halliday smart glasses unveiled at CES have an invisible display; that is, the display is not built into the lens, but rather integrated into the frame. This is made possible by using what the company calls the world’s smallest optical module. Despite its 3.6mm size, the display provides users with a field of view similar to that of a 3.5-inch screen.

Lightweight and Sleek Design

The major advantage of such a small display is that the frames are very light, weighing just 35 grams. Compared to the 48-gram Meta Ray-Bans I wore to the event, these felt noticeably lighter. The frames have a classic, sleek design, a battery that lasts up to 12 hours, a microphone, and speakers — and come in three colors: Amber, Black, and Gradient.

The Display

The tiny display is located just above the right lens, meaning you have to look up to see it, as seen in the photo at the top of this article. Although this may seem unnatural, it was pretty comfortable. Placing the graphics slightly above your field of view is helpful because it doesn’t obstruct your view when looking straight ahead.

Features and Functionality

The display shows your graphics, such as icons, words, and texts, in green. You can use that Digi Window display for a variety of functions, including:

  • AI real-time translations in more than 40 languages
  • Teleprompter text
  • Notes
  • Notifications such as texts, music titles, and lyrics
  • Turn-by-turn navigation

Hands-On Experience

In my demo, I went through several of these features, all of which focused on displaying text. I was able to comfortably read the text shown to me — a surprise, as I wear prescription eyeglasses that can make it challenging to demo this type of technology. There is also a dial you can rotate to match your eye prescription and a slide to adjust the display position.

Pricing and Availability

The Halliday Glasses retail for $489. However, if you choose to reserve the glasses now, you can do so for a $9.90 deposit that locks in a launch day exclusive price of $369. The price is fair when compared to Even Realities’ Even G1 smart glasses, which are similar in function and retail for $599.

Conclusion

Halliday’s smart glasses are a game-changer in the world of wearable technology. With their innovative design, comfortable fit, and impressive features, they are sure to revolutionize the way we interact with technology. Don’t miss out on the opportunity to get your hands on these revolutionary glasses at an exclusive price.

FAQs

Q: What is the launch day exclusive price of the Halliday Glasses?
A: $369 (pre-order now for a $9.90 deposit)

Q: How does the display work?
A: The display is integrated into the frame and is located just above the right lens, providing a field of view similar to that of a 3.5-inch screen.

Q: What are the features of the Digi Window display?
A: The display shows graphics, such as icons, words, and texts, in green, and can be used for AI real-time translations, teleprompter text, notes, notifications, and turn-by-turn navigation.

Q: How long does the battery last?
A: Up to 12 hours.

Sam Altman’s Younger Sister Accuses Him of Sexual Abuse

Avery Winslow
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Sam Altman’s Younger Sister Accuses Him of Sexual Abuse

Ann Altman Files Lawsuit Against Brother Sam Altman, OpenAI’s CEO and Founder

Ann Altman, the younger sister of OpenAI’s chief executive and founder, Sam Altman, filed a lawsuit in a Missouri federal court on Monday accusing him of sexually abusing her when she was a minor.

Lawsuit Details

The suit, filed in U.S. District Court for the Eastern District of Missouri, said that the abuse occurred in the Altman’s family home outside St. Louis from 1997 to 2006 and started when Ms. Altman was 3 years old.

Consequences of Abuse

The lawsuit said Ms. Altman had sustained bodily injury and had “experienced PTSD, severe emotional distress, mental anguish and depression, which is expected to continue into the future,” as a result of the abuse.

Previous Claims and Denials

Ms. Altman has long made similar sexual assault claims against her brother on social media services like X. She is represented by an Illinois-based law firm that specializes in sexual assault and harassment cases.

In a statement posted to X on Tuesday, Mr. Altman, along with his mother and two younger brothers, denied the claims. “Annie has made deeply hurtful and entirely untrue claims about our family, and especially Sam,” the statement said. “This situation causes immense pain to our entire family.”

The statement said that Ms. Altman had “mental health challenges” and “refuses conventional treatment and lashes out at family members who are genuinely trying to help.”

Current Status and Next Steps

Ms. Altman and Mr. Altman did not respond to requests for comment.

The lawsuit requests a jury trial and damages in excess of $75,000. Ms. Altman’s lawyer, Ryan Mahoney, said in an interview with The Times that the amount was the minimum required for a federal suit of this kind. He said that if the suit proceeded to a jury trial, he and his client would seek “an amount that fully compensates my client for what happened to her.”

Conclusion

The lawsuit filed by Ann Altman against her brother Sam Altman, OpenAI’s CEO and founder, has shed light on a deeply disturbing and painful experience for Ms. Altman. The case will likely continue to unfold in the coming weeks and months, with Ms. Altman seeking justice and compensation for the abuse she suffered.

FAQs

Q: What is the nature of the lawsuit filed by Ann Altman?

A: Ann Altman has filed a lawsuit against her brother Sam Altman, accusing him of sexually abusing her when she was a minor.

Q: When did the alleged abuse occur?

A: The abuse allegedly occurred in the Altman’s family home outside St. Louis from 1997 to 2006, starting when Ms. Altman was 3 years old.

Q: What are the consequences of the abuse, according to the lawsuit?

A: The lawsuit states that Ms. Altman has sustained bodily injury and has experienced PTSD, severe emotional distress, mental anguish, and depression as a result of the abuse.

Q: How much is the lawsuit seeking in damages?

A: The lawsuit is seeking damages in excess of $75,000, with the possibility of seeking more if the case proceeds to a jury trial.

Q: What is the current status of the lawsuit?

A: The lawsuit has been filed, and the parties involved are awaiting further developments. Ms. Altman and Mr. Altman did not respond to requests for comment.

Add Onvite to Rails 8 Authentication

Parker Lennox
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Add Onvite to Rails 8 Authentication

Here is the organized article:

Adding Workspaces

The previous article on adding sign ups left the option to add a Workspace on sign up. Let’s add that now. It’s simple really. All it is, for now, is a record with a has_many :users. All business records your users create will belong to the workspace, giving the users access to them (but that will be for another article).

Creating the Workspace Model

Let’s create the model first: rails g model Workspace name:string. Then update the existing User model by adding a workspace_id: rails g migration add_workspace_id_to_users workspace_id:integer. Update the new Workspace model like this:

# app/models/workspace.rb
class Workspace < ApplicationRecord
  has_many :users
end

Adding Invites

What I am having in mind is the following:

  1. Workspace "owner" (the User would created the Workspace) adds email from invitee;
  2. Invitation model is created, with: email and inviter_id;
  3. After Invitation create, an email is sent to the email with an invite link;
  4. On clicking the link, invitee sees a form with email and password;
  5. Upon submit, an User model is created and attached to the Workspace.

Creating the Invitation Model

Let’s create the invitation model first: rails g model Invitation workspace_id:integer inviter_id:integer email_address:string accepted_at:datetime. Update the… root_path to include the accepts_invitation action:

# config/routes.rb
Rails.application.routes.draw do
  #...
  resources :accept_invitations, only: %w[new create]
end

# app/views/accept_invitations/new.html.erb
 <%= form_with model: @invitation, url: accept_invitations_path do |form| %>
  <%= form.email_field :email_address %>
  <%= form.password_field :password %>
  <%= form.submit %>
 <% end %>

Creating the AcceptInvitation Class

Let’s create the AcceptInvitation class:

class AcceptInvitation
  #...
  private

  def update_invitation
    @invitation.update accepted_at: Time.current
  end

  def add_new user, to:
    to.users << user
  end

  def invitation = Invitation.find_by_token_for invitation, token
end

Conclusion

This class is the place to handle everything needed after the invite is accepted. I’ve left it to updating the accepted_at column (remember how that expires the token) and adding the new user to the workspace. But you can add any action that makes sense for your business logic.

FAQs

Q: What is a Workspace?
A: A Workspace is a place where users can create and access business records, such as companies, teams, or projects.

Q: Why do I need to add Workspaces?
A: Adding Workspaces allows your users to have a place to store and organize their business records, making it easier to work with others and track progress.

Q: Can I customize the Invite process?
A: Yes, you can customize the invite process to fit your business logic. For example, you could add additional validation or send notifications to the workspace owner when a new user joins.

Llama Models Accelerate Agentic AI Workflows with Accuracy and Efficiency

Jordan Waverly
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Llama Models Accelerate Agentic AI Workflows with Accuracy and Efficiency

Agentic AI: The Next Wave of Generative AI

Agentic AI, the next wave of generative AI, is a paradigm shift with the potential to revolutionize industries by enabling AI systems to act autonomously and achieve complex goals. Agentic AI combines the power of large language models (LLMs) with advanced reasoning and planning capabilities, opening a world of possibilities across industries, from healthcare and finance to manufacturing and logistics.

Agentic AI Architecture

An agentic AI system combines perception, reasoning, and action to interact with its environment effectively. It gathers information from databases and external sources, analyzes goals, and develops strategies to achieve them. The system’s action module executes decisions, while retaining the memory of past interactions to support long-term tasks and personalized responses. With multi-agent collaboration, agents can share information and coordinate efficiently on complex tasks.

Leading LLMs for Agentic AI

Today, NVIDIA announced the Llama Nemotron family of agentic AI models that provide the highest accuracy on a wide range of agentic tasks, exceptional compute efficiency, and open license for enterprise use. In this post, we dive deeper into how this model family is achieving leading accuracies across a diverse range of agentic AI tasks.

Simplify and Accelerate Agentic AI Systems to Market

NVIDIA is simplifying the development of AI agents by unifying the strengths of these models to provide a single model that supports a diverse range of tasks. Llama Nemotron excels across key agentic tasks, so that a single model can streamline the engineering process by replacing multiple specialized models.

Optimized for Compute Efficiency

The Llama Nemotron family is optimized for various compute resources, ensuring optimal performance across different environments:

  • Nano: A model optimized for accuracy and performance on NVIDIA RTX AI PCs and workstations, enabling agentic workflows for PC application developers.
  • Super: A high-accuracy model offering exceptional throughput on a single GPU.
  • Ultra: The highest-accuracy model, designed for data-center-scale applications demanding the highest performance.

Curating High-Quality Data for Model Alignment

High-quality training data plays a critical role in the accuracy and quality of responses from a custom LLM, but robust datasets can be prohibitively expensive and difficult to create. Synthetic data addresses these challenges by generating large-scale data that can be further curated to improve quality. NVIDIA NeMo Curator helps build high-quality multimodal training data by downloading, extracting, cleaning, filtering, deduplicating, and blending the original data at scale.

Achieving World-Class LLM Accuracy Across Benchmarks

NVIDIA is leveraging the Llama family, most popular open models, and NVIDIA’s customization techniques to build state-of-the-art accuracy models for various agentic AI tasks, including instruction following, tool calling, chat, coding, and math.

Building Efficient LLMs with Neural Architecture Search

Agentic systems must be computationally efficient to handle complex tasks in real-time. However, the substantial computational demands of LLMs can hinder their deployment in these complex systems without optimizations that carefully balance performance and resource constraints. Overcoming these challenges necessitates the development of lean, hardware-optimized model architectures that maintain high performance while ensuring practical and scalable deployment.

Conclusion

Agentic AI has the potential to revolutionize industries by enabling AI systems to act autonomously and achieve complex goals. By combining the power of large language models with advanced reasoning and planning capabilities, agentic AI can simplify and accelerate the development of custom AI agents. With NVIDIA’s Llama Nemotron family of models, organizations can unlock the full potential of agentic AI and drive innovation across a wide range of industries.

FAQs

Q: What is agentic AI?
A: Agentic AI is a type of generative AI that enables AI systems to act autonomously and achieve complex goals by combining the power of large language models with advanced reasoning and planning capabilities.

Q: What is the Llama Nemotron family of models?
A: The Llama Nemotron family of models is a set of agentic AI models that provide the highest accuracy on a wide range of agentic tasks, exceptional compute efficiency, and open license for enterprise use.

Q: How can I get started with agentic AI?
A: You can simplify the development and deployment of custom AI agents that can reason, plan, and take action with new NVIDIA AI Blueprints for agentic AI. Sign up to get notified about the new Llama Nemotron models when they’re available as NIM microservices using API endpoints.

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