NVIDIA Digital Human Technologies Bring AI Characters to Life
Leading AI Developers Use Suite of NVIDIA Technologies to Create Lifelike Avatars and Dynamic Characters for Everything From Games to Healthcare, Financial Services and Retail Applications
Today is the beginning of our moonshot to solve embodied AGI in the physical world. I’m so excited to announce Project GR00T, our new initiative to create a general-purpose foundation model for humanoid robot learning.
As the FlexOS research study “Generative AI at Work” concluded based on a survey amongst knowledge workers, ChatGPT reigns supreme. … 2. AI Tool Usage is Way Higher Than People Expect – Beating Netflix, Pinterest, Twitch. As measured by data analysis platform Similarweb based on global web traffic tracking, the AI tools in this list generate over 3 billion monthly visits.
With 1.67 billion visits, ChatGPT represents over half of this traffic and is already bigger than Netflix, Microsoft, Pinterest, Twitch, and The New York Times.
Something unusual is happening in America. Demand for electricity, which has stayed largely flat for two decades, has begun to surge.
Over the past year, electric utilities have nearly doubled their forecasts of how much additional power they’ll need by 2028 as they confront an unexpected explosion in the number of data centers, an abrupt resurgence in manufacturing driven by new federal laws, and millions of electric vehicles being plugged in.
The tumult could seem like a distraction from the startup’s seemingly unending march toward AI advancement. But the tension, and the latest debate with Musk, illuminates a central question for OpenAI, along with the tech world at large as it’s increasingly consumed by artificial intelligence: Just how open should an AI company be?
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The meaning of the word “open” in “OpenAI” seems to be a particular sticking point for both sides — something that you might think sounds, on the surface, pretty clear. But actual definitions are both complex and controversial.
In partnership with the National Cancer Institute, or NCI, researchers from the Department of Energy’s Oak Ridge National Laboratory and Louisiana State University developed a long-sequenced AI transformer capable of processing millions of pathology reports to provide experts researching cancer diagnoses and management with exponentially more accurate information on cancer reporting.
DC: Hmmm…given that the militaries of the world have been integrating AI into their arsenals (likely for years), this kind of thing is a bit disturbing for me. Autonomous/self-correcting missiles, robotic tanks, drones, and more…here we come. Ouch.https://t.co/Qljl1U9m9S
— Daniel Christian (he/him/his) (@dchristian5) March 13, 2024
The early vibrations of AI have already been shaking the newsroom. One downside of the new technology surfaced at CNET and Sports Illustrated, where editors let AI run amok with disastrous results. Elsewhere in news media, AI is already writing headlines, managing paywalls to increase subscriptions, performing transcriptions, turning stories in audio feeds, discovering emerging stories, fact checking, copy editing and more.
Felix M. Simon, a doctoral candidate at Oxford, recently published a white paper about AI’s journalistic future that eclipses many early studies. Swinging a bat from a crouch that is neither doomer nor Utopian, Simon heralds both the downsides and promise of AI’s introduction into the newsroom and the publisher’s suite.
Unlike earlier technological revolutions, AI is poised to change the business at every level. It will become — if it already isn’t — the beginning of most story assignments and will become, for some, the new assignment editor. Used effectively, it promises to make news more accurate and timely. Used frivolously, it will spawn an ocean of spam. Wherever the production and distribution of news can be automated or made “smarter,” AI will surely step up. But the future has not yet been written, Simon counsels. AI in the newsroom will be only as bad or good as its developers and users make it.
We proposed EMO, an expressive audio-driven portrait-video generation framework. Input a single reference image and the vocal audio, e.g. talking and singing, our method can generate vocal avatar videos with expressive facial expressions, and various head poses, meanwhile, we can generate videos with any duration depending on the length of input video.
New experimental work from Adobe Research is set to change how people create and edit custom audio and music. An early-stage generative AI music generation and editing tool, Project Music GenAI Control allows creators to generate music from text prompts, and then have fine-grained control to edit that audio for their precise needs.
“With Project Music GenAI Control, generative AI becomes your co-creator. It helps people craft music for their projects, whether they’re broadcasters, or podcasters, or anyone else who needs audio that’s just the right mood, tone, and length,” says Nicholas Bryan, Senior Research Scientist at Adobe Research and one of the creators of the technologies.
There’s a lot going on in the world of generative AI, but maybe the biggest is the increasing number of copyright lawsuits being filed against AI companies like OpenAI and Stability AI. So for this episode, we brought on Verge features editor Sarah Jeong, who’s a former lawyer just like me, and we’re going to talk about those cases and the main defense the AI companies are relying on in those copyright cases: an idea called fair use.
The FCC’s war on robocalls has gained a new weapon in its arsenal with the declaration of AI-generated voices as “artificial” and therefore definitely against the law when used in automated calling scams. It may not stop the flood of fake Joe Bidens that will almost certainly trouble our phones this election season, but it won’t hurt, either.
The new rule, contemplated for months and telegraphed last week, isn’t actually a new rule — the FCC can’t just invent them with no due process. Robocalls are just a new term for something largely already prohibited under the Telephone Consumer Protection Act: artificial and pre-recorded messages being sent out willy-nilly to every number in the phone book (something that still existed when they drafted the law).
EIEIO…Chips Ahoy!— from dashmedia.co by Michael Moe, Brent Peus, and Owen Ritz
Here Come the AI Worms — from wired.com by Matt Burgess Security researchers created an AI worm in a test environment that can automatically spread between generative AI agents—potentially stealing data and sending spam emails along the way.
Now, in a demonstration of the risks of connected, autonomous AI ecosystems, a group of researchers have created one of what they claim are the first generative AI worms—which can spread from one system to another, potentially stealing data or deploying malware in the process. “It basically means that now you have the ability to conduct or to perform a new kind of cyberattack that hasn’t been seen before,” says Ben Nassi, a Cornell Tech researcher behind the research.
World’s largest projection mapping snags Guinness World Record — from inavateonthenet.net A nightly projection mapping display at the Tokyo metropolitan government headquarters has been recognised by Guinness World Records as the largest in the world.
The scammers used digitally recreated versions of an international company’s Chief Financial Officer and other employees to order $25 million in money transfers during a video conference call containing just one real person.
The victim, an employee at the Hong Kong branch of an unnamed multinational firm, was duped into taking part in a video conference call in which they were the only real person – the rest of the group were fake representations of real people, writes SCMP.
As we’ve seen in previous incidents where deepfakes were used to recreate someone without their permission, the scammers utilized publicly available video and audio footage to create these digital versions.
Since we launched Bard last year, people all over the world have used it to collaborate with AI in a completely new way — to prepare for job interviews, debug code, brainstorm new business ideas or, as we announced last week, create captivating images.
Our mission with Bard has always been to give you direct access to our AI models, and Gemini represents our most capable family of models. To reflect this, Bard will now simply be known as Gemini.
A new way to discover places with generative AI in Maps— from blog.google by Miriam Daniel; via AI Valley Here’s a look at how we’re bringing generative AI to Maps — rolling out this week to select Local Guides in the U.S.
Today, we’re introducing a new way to discover places with generative AI to help you do just that — no matter how specific, niche or broad your needs might be. Simply say what you’re looking for and our large-language models (LLMs) will analyze Maps’ detailed information about more than 250 million places and trusted insights from our community of over 300 million contributors to quickly make suggestions for where to go.
Starting in the U.S., this early access experiment launches this week to select Local Guides, who are some of the most active and passionate members of the Maps community. Their insights and valuable feedback will help us shape this feature so we can bring it to everyone over time.
Google Prepares for a Future Where Search Isn’t King — from wired.com by Lauren Goode CEO Sundar Pichai tells WIRED that Google’s new, more powerful Gemini chatbot is an experiment in offering users a way to get things done without a search engine. It’s also a direct shot at ChatGPT.
But a few applications of machine learning stood out as genuinely helpful or surprising — here are a few examples of AI that might actually do some good.
The whole idea that AI might not be a total red flag occurred to me when I chatted with Whispp at a press event. This small team is working on voicing the voiceless, meaning people who have trouble speaking normally due to a condition or illness.
This guide shares strategies and tactics for getting better results from large language models (sometimes referred to as GPT models) like GPT-4. The methods described here can sometimes be deployed in combination for greater effect. We encourage experimentation to find the methods that work best for you.
Some of the examples demonstrated here currently work only with our most capable model, gpt-4. In general, if you find that a model fails at a task and a more capable model is available, it’s often worth trying again with the more capable model.
You can also explore example prompts which showcase what our models are capable of…
The study of frontier AI risks has fallen far short of what is possible and where we need to be. To address this gap and systematize our safety thinking, we are adopting the initial version of our Preparedness Framework. It describes OpenAI’s processes to track, evaluate, forecast, and protect against catastrophic risks posed by increasingly powerful models.
Here’s every major innovation from the last 365 days:
Microsoft: Launched additional OpenAI-powered features, including Copilot for Microsoft Dynamics 365 and Microsoft 365, enhancing business functionalities like text summarization, tone adjustment in emails, data insights, and automatic presentation creation.
Google: Introduced Duet, akin to Microsoft’s Copilot, integrating Gen AI across Google Workspace for writing assistance and custom visual creation. Also debuted Generative AI Studio, enabling developers to craft AI apps, and unveiled Gemini & Bard, a new AI technology with impressive features.
Salesforce: …
Adobe: …
Amazon Web Services (AWS): …
IBM: …
Nvidia: …
OpenAI: …
Meta (Facebook): …
Tencent: …
Baidu: …
News in chatbots — from theneurondaily.com by Noah Edelman & Pete Huang
Here’s what’s on the horizon:
Multimodal AI gets huge. Instead of just typing, more people will talk to AI, listen to it, create images, get visual feedback, create graphs, and more.
AI video gets really good. So far, AI videos have been cool-but-not-practical. They’re getting way better and we’re on the verge of seeing 100% AI-generated films, animations, and cartoons.
AI on our phones. Imagine Siri with the brains of ChatGPT-4 and the ambition of Alexa. TBD who pulls this off first!
GPT-5. ‘Nuff said.
20 Best AI Chatbots in 2024— from eweek.com by Aminu Abdullahi These leading AI chatbots use generative AI to offer a wide menu of functionality, from personalized customer service to improved information retrieval.
Top 20 Generative AI Chatbot Software: Comparison Chart
We compared the key features of the top generative AI chatbot software to help you determine the best option for your company…
Here’s our power rankings of the best chatbots for (non-technical) work:
1: ChatGPT-4—Unquestionably the smartest, with the strongest writing, coding, and reasoning abilities.
T1: Gemini Ultra—In theory as powerful as GPT-4. We won’t know for sure until it’s released in 2024.
2: Claude 2—Top choice for managing lengthy PDFs (handles ~75,000 words), and rarely hallucinates. Can be somewhat stiff.
3: Perplexity—Ideal for real-time information. Upgrading to Pro grants access to both Claude-2 and GPT-4.
T4: Pi—The most “human-like” chatbot, though integrating with business data can be challenging.
T4: Bing Chat—Delivers GPT-4-esque responses, has internet access, and can generate images. Bad UX and doesn’t support PDFs.
T4: Bard—Now powered by Gemini Pro, offers internet access and answer verification. Tends to hallucinate more frequently.
and others…
Midjourney + ChatGPT = Amazing AI Art — from theaigirl.substack.com by Diana Dovgopol and the Pycoach Turn ChatGPT into a powerful Midjourney prompt machine with basic and advanced formulas.
Animate Anyone — from theneurondaily.com by Noah Edelman & Pete Huang
Animate Anyone is a new project from Alibaba that can animate any image to move however you’d like.
While the technology is bonkers (duh), the demo video has stirred up mixed reactions.
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I mean…just check out the (justified) fury on Twitter in response to this research.
To the researchers’ credit, they haven’t released a working demo yet, probably for this exact concern.
DC: Agreed. But don’t expect much help from the American Bar Association! It’s almost 2024 and the vast majority of law schools still can’t offer 100% online-based programs!!!
6. ChatGPT’s hype will fade, as a new generation of tailor-made bots rises up
11. We’ll finally turn the corner on teacher pay in 2024
21. Employers will combat job applicants’ use of AI with…more AI
31. Universities will view the creator economy as a viable career path
What: We’re taking the first steps in Bard’s ability to understand YouTube videos. For example, if you’re looking for videos on how to make olive oil cake, you can now also ask how many eggs the recipe in the first video requires.
Why: We’ve heard you want deeper engagement with YouTube videos. So we’re expanding the YouTube Extension to understand some video content so you can have a richer conversation with Bard about it.
I am not sure who said it first, but there are only two ways to react to exponential change: too early or too late. Today’s AIs are flawed and limited in many ways. While that restricts what AI can do, the capabilities of AI are increasing exponentially, both in terms of the models themselves and the tools these models can use. It might seem too early to consider changing an organization to accommodate AI, but I think that there is a strong possibility that it will quickly become too late.
From DSC: Readers of this blog have seen the following graphic for several years now, but there is no question that we are in a time of exponential change. One would have had an increasingly hard time arguing the opposite of this perspective during that time.
Nvidia’s results surpassed analysts’ projections for revenue and income in the fiscal fourth quarter.
Demand for Nvidia’s graphics processing units has been exceeding supply, thanks to the rise of generative artificial intelligence.
Nvidia announced the GH200 GPU during the quarter.
Here’s how the company did, compared to the consensus among analysts surveyed by LSEG, formerly known as Refinitiv:
Earnings: $4.02 per share, adjusted, vs. $3.37 per share expected
Revenue: $18.12 billion, vs. $16.18 billion expected
Nvidia’s revenue grew 206% year over year during the quarter ending Oct. 29, according to a statement. Net income, at $9.24 billion, or $3.71 per share, was up from $680 million, or 27 cents per share, in the same quarter a year ago.
DC: Anyone surprised? This is why the U.S. doesn’t want high-powered chips going to China. History repeats itself…again. The ways of the world/power continue on.
Pentagon’s AI initiatives accelerate hard decisions on lethal autonomous weapons https://t.co/PTDmJugiE2
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