The answer could determine who gets to shape the future of the technology.
Suddenly, “open source” is the latest buzzword in AI circles.
Category: robotics/AI – Page 515
The company says it’s proof that quality AI models don’t have to include controversial copyrighted content.
Brands want to use generative AI to personalize their marketing efforts — but they are also deathly afraid of AI going off message and ruining their brand. At its annual Summit conference in Las Vegas, Adobe today announced GenStudio, a new application that helps brands create content and measure its performance, with generative AI — and the promise of brand safety — at its center.
Currently, the tool is mostly focused on helping social, paid media and lifecycle marketers that want to create social media posts, email campaigns and display ads, with support for creating entire websites coming soon.
Adobe wants GenStudio, which it first previewed last September, to be an end-to-end solution to help marketers tailor their content to different channels and audience segments. It includes tools for content creation, managing campaigns and analytics. But to generate personalized content, you need a supply chain that makes it easy to generate a lot of on-brand content — and then the tools to measure how this content performs.
A select group of artists, designers and filmmakers have now had a couple of months to play with OpenAI’s new Sora text-to-video tool, and on Monday, OpenAI shared some of their creations and first impressions.
“As great as Sora is at generating things that appear real, what excites us is its ability to make things that are totally surreal,” Toronto-based multimedia production company Shy Kids said in a statement accompanying Air Head, a short film it made with Sora. The word surreal aptly describes the video, which stars a guy with a yellow balloon for a noggin.
“I am literally filled with hot air,” he says.
Amazon is enhancing shopping and payment experiences with generative AI in its Amazon One system, which recognizes customer palm scans.
As generative AI becomes more prevalent in our day to day lives, it will also impact our careers and change how companies hire and retain talent.
Nice.
Anthropic’s Claude 3 Opus has knocked OpenAI’s GPT-4 off the top of the chatbot leaderboard for the first time.
According to the Chatbot Arena Leaderboard, Anthropic’s Claude 3 Opus has taken the top spot from OpenAI’s GPT-4 for the first time. Claude 3 Opus now ranks first based on how real people rate chatbot skills. GPT-4 has been pushed down to second place.
The Chatbot Arena is a benchmark platform created by the Large Model System Organization (LMSYS) to compare the performance of large language models. The Arena pits different models against each other in secret, randomized battles. Users rate the models and vote for the answer they like best. This makes the rankings very useful because they are based on what users prefer.
A recent study published in the journal PNAS Nexus sheds light on the impact of text-to-image generative AI tools like Midjourney, Stable Diffusion, and DALL-E on the artistic process. The researchers discovered that these AI systems tend to enhance artists’ productivity and lead to more favorable evaluations of their work by peers.
Interestingly, while the average novelty in AI-assisted artwork content declined over time, peak content novelty increased. The findings point to the possibility of “generative synesthesia,” a blending of human creativity and AI capabilities to unlock heightened levels of artistic expression.
The advent of generative AI has sparked a heated debate within artistic communities. While some view these technologies as a threat to the intrinsic human ability to create, others recognize their potential to augment human creativity. The research aimed to unravel whether AI adoption enables artists to produce more creative content and under what conditions it leads to more valuable artistic creations.
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Timestamps:
00:00 — Intro.
00:33 — New Blackwell GPU Explained.
06:35 — Demand for AI Chips.
07:01 — New 4 trillion transistors Chip.
10:04 — Why do we need Huge Chips?
14:04 — New Analog Chip Explained
Summary: Neural networks, regardless of their complexity or training method, follow a surprisingly uniform path from ignorance to expertise in image classification tasks. Researchers found that neural networks classify images by identifying the same low-dimensional features, such as ears or eyes, debunking the assumption that network learning methods are vastly different.
This finding could pave the way for developing more efficient AI training algorithms, potentially reducing the significant computational resources currently required. The research, grounded in information geometry, hints at a more streamlined future for AI development, where understanding the common learning path of neural networks could lead to cheaper and faster training methods.