AI is Leveling Up the World’s Most Beloved Games

AI is leveling up the world’s most beloved games, as the latest advancements in neural rendering, NVIDIA RTX, and digital human technologies equip game developers to take innovative leaps in their work.

New AI Tools and Technologies

At this year’s GDC conference, running March 17-21 in San Francisco, NVIDIA is revealing new AI tools and technologies to supercharge the next era of graphics in games.

Key Announcements

• New neural rendering advancements with Unreal Engine 5 and Microsoft DirectX
• NVIDIA DLSS 4 now available in over 100 games and apps, making it the most rapidly adopted NVIDIA game technology of all time
• A Half-Life 2 RTX demo coming Tuesday, March 18

Neural Shaders Enable Photorealistic, Living Worlds With AI

The next era of computer graphics will be based on NVIDIA RTX Neural Shaders, which allow the training and deployment of tiny neural networks from within shaders to generate textures, materials, lighting, volumes, and more. This results in dramatic improvements in game performance, image quality, and interactivity, delivering new levels of immersion for players.

Neural Rendering Advancements

At the CES trade show earlier this year, NVIDIA introduced RTX Kit, a comprehensive suite of neural rendering technologies for building AI-enhanced, ray-traced games with massive geometric complexity and photorealistic characters. Now, at GDC, NVIDIA is expanding its powerful lineup of neural rendering technologies, including with Microsoft DirectX support and plug-ins for Unreal Engine 5.

DLSS 4 – The Most Rapidly Adopted NVIDIA Game Technology of All Time

DLSS 4 debuted with the release of GeForce RTX 50 Series GPUs. Over 100 games and apps now feature support for DLSS 4, making it the most rapidly adopted NVIDIA game technology of all time. DLSS 4 introduced Multi-Frame Generation, which uses AI to generate up to three additional frames per traditionally rendered frame, working with the complete suite of DLSS technologies to multiply frame rates by up to 8x over traditional brute-force rendering.

Half-Life 2 RTX Demo and RTX Remix Official Release

Half-Life 2 RTX is a community-made remaster of the iconic first-person shooter Half-Life 2. A playable Half-Life 2 RTX demo will be available on Tuesday, March 18, for free download from Steam for Half-Life 2 owners. The demo showcases Orbifold Studios’ work in the eerily sensational maps of Ravenholm and Nova Prospekt, with significantly improved assets and textures, full ray tracing, DLSS 4 with Multi-Frame Generation, and RTX neural rendering technologies.

NVIDIA ACE Technologies Enhance Game Characters with AI

The NVIDIA ACE suite of RTX-accelerated digital human technologies brings game characters to life with generative AI. NVIDIA ACE autonomous game characters add autonomous teammates, nonplayer characters (NPCs), and self-learning enemies to games, creating new narrative possibilities and enhancing player immersion.

Conclusion

The latest advancements in AI, neural rendering, and digital human technologies are revolutionizing the gaming industry. With NVIDIA’s new AI tools and technologies, game developers can take their work to the next level, creating more immersive and engaging experiences for players.

Frequently Asked Questions

Q: What is NVIDIA RTX?
A: NVIDIA RTX is a set of technologies for building AI-enhanced, ray-traced games with massive geometric complexity and photorealistic characters.

Q: What is DLSS 4?
A: DLSS 4 is the latest version of NVIDIA’s Deep Learning Super Sampling technology, which uses AI to generate multiple frames per traditionally rendered frame, resulting in improved performance and image quality.

Q: What is NVIDIA ACE?
A: NVIDIA ACE is a set of RTX-accelerated digital human technologies that bring game characters to life with generative AI, adding autonomous teammates, nonplayer characters (NPCs), and self-learning enemies to games.

How to Safely Use Generative AI (Gen AI)

It’s increasingly difficult to avoid artificial technology (AI) as it becomes more commonplace. A prime example is Google searches showcasing AI responses. AI safety is more important than ever in this age of technological ubiquity. So, as an AI user, how can you safely use generative AI (Gen AI)?

The Shortcomings of Large Language Models (LLMs)

Carnegie Mellon School of Computer Science assistant professors Maarten Sap and Sherry Tongshuang Wu took to the SXSW stage to inform people about the shortcomings of large language models (LLMs), the type of machine learning model behind popular generative AI tools, such as ChatGPT, and how people can exploit these technologies more effectively.

Optimizing Your AI Use

The tweaks you can implement into your everyday interactions with AI are simple. They will protect you from AI’s shortcomings and help you get more out of AI chatbots, including more accurate responses. Keep reading to learn about the five things you can do to optimize your AI use, according to the experts.

1. Give AI Better Instructions

Because of AI’s conversational capabilities, people often use underspecified, shorter prompts, like chatting with a friend. The problem is that when under instructions, AI systems may infer the meaning of your text prompt incorrectly, as they lack the human skills that would allow them to read between the lines.

To illustrate this issue, in their session, Sap and Wu told a chatbot they were reading a million books, and the chatbot took it literally instead of understanding the person was superfluous. Sap shared that in his research he found that modern LLMs struggle to understand non-literal references in a literal way over 50% of the time.

The best way to circumvent this issue is to clarify your prompts with more explicit requirements that leave less room for interpretation or error. Wu suggested thinking of chatbots as assistants, instructing them clearly about exactly what you want done. Even though this approach might require more work when writing a prompt, the result should align more with your requirements.

2. Double-Check Your Responses

If you have ever used an AI chatbot, you know they hallucinate, which describes outputting incorrect information. Hallucinations can happen in different ways, either outputting factually incorrect responses, incorrectly summarizing given information, or agreeing with false facts shared by a user.

Sap said hallucinations happen between 1% and 25% of the time for general, daily use cases. The hallucination rates are even higher for more specialized domains, such as law and medicine, coming in at greater than 50%. These hallucinations are difficult to spot because they are presented in a way that sounds plausible, even if they are nonsensical.

The models often reaffirm their responses, using markers such as "I am confident" even when offering incorrect information. A research paper cited in the presentation said AI models were certain yet incorrect about their responses 47% of the time.

As a result, the best way to protect against hallucinations is to double-check your responses. Some tactics include cross-verifying your output with external sources, such as Google or news outlets you trust, or asking the model again, using different wording, to see if the AI outputs the same response.

3. Keep the Data You Care About Private

Gen AI tools are trained on large amounts of data. They also require data to continue learning and become smarter, more efficient models. As a result, models often use their outputs for further training.

The issue is that models often regurgitate their training data in their responses, meaning your private information could be used in someone else’s responses, exposing your private data to others. There is also a risk when using web applications because your private information is leaving your device to be processed in the cloud, which has security implications.

The best way to maintain good AI hygiene is to avoid sharing sensitive or personal data with LLMs. There will be some instances where the assistance you want may involve using personal data. You can also redact this data to ensure you get help without the risk. Many AI tools, including ChatGPT, have options that allow users to opt out of data collection. Opting out is always a good option, even if you don’t plan on using sensitive data.

4. Watch How You Talk About LLMs

The capabilities of AI systems and the ability to talk to these tools using natural language have led some people to overestimate the power of these bots. Anthropomorphism, or the attribution of human characteristics, is a slippery slope. If people think of these AI systems as human-adjacent, they may trust them with more responsibility and data.

One way to help mitigate this issue is to stop attributing human characteristics to AI models when referring to them, according to the experts. Instead of saying, "the model thinks you want a balanced response," Sap suggested a better alternative: "The model is designed to generate balanced responses based on its training data."

5. Think Carefully About When to Use LLMs

Although it may seem like these models can help with almost every task, there are many instances in which they may not be able to provide the best assistance. Although benchmarks are available, they only cover a small proportion of how users interact with LLMs.

LLMs may also not work the best for everyone. Beyond the hallucinations discussed above, there have been recorded instances in which LLMs make racist decisions or support Western-centric biases. These biases show models may be unfit to assist in many use cases.

As a result, the solution is to be thoughtful and careful when using LLMs. This approach includes evaluating the impact of using an LLM to determine whether it is the right solution to your problem. It is also helpful to look at what models excel at certain tasks and to employ the best model for your requirements.

Conclusion

In conclusion, while AI is becoming increasingly prevalent, it is essential to use it safely and effectively. By following the five tips outlined above, you can optimize your AI use and avoid potential pitfalls. Remember to give AI better instructions, double-check your responses, keep your data private, watch how you talk about LLMs, and think carefully about when to use LLMs. By doing so, you can get the most out of these powerful tools and avoid potential issues.

FAQs

Q: What are the potential risks of using generative AI?
A: The potential risks of using generative AI include hallucinations, bias, and the exposure of private data.

Q: How can I avoid these risks?
A: You can avoid these risks by giving AI better instructions, double-checking your responses, keeping your data private, and being thoughtful and careful when using LLMs.

Q: Are LLMs always accurate?
A: No, LLMs are not always accurate. They can hallucinate, produce biased responses, and regurgitate their training data.

Q: How can I get the most out of LLMs?
A: You can get the most out of LLMs by following the five tips outlined above, evaluating the impact of using an LLM, and employing the best model for your requirements.