What if you could use artificial intelligence to measure someone’s attention and response to content and ads—by analyzing their facial expression?
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New users can start creating straight away. Lessons learned from deployment and improvements to our safety systems make wider availability possible.
Starting later today, we are removing the waitlist for the DALL·E beta so users can sign up and start using it immediately. More than 1.5M users are now actively creating over 2M images a day with DALL·E—from artists and creative directors to authors and architects—with over 100K users sharing their creations and feedback in our Discord community.
Responsibly scaling a system as powerful and complex as DALL·E—while learning about all the creative ways it can be used and misused—has required an iterative deployment approach.
Since we first previewed the DALL·E research to users in April, users have helped us discover new uses for DALL·E as a powerful creative tool. Artists, in particular, have provided important input on DALL·E’s features.
Their feedback inspired us to build features like Outpainting, which lets users continue an image beyond its original borders and create bigger images of any size, and collections—so users can create in all new ways and expedite their creative processes.
Learning from real-world use has allowed us to improve our safety systems, making wider availability possible today. In the past months, we’ve made our filters more robust at rejecting attempts to generate sexual, violent and other content that violates our content policy and built new detection and response techniques to stop misuse.
We are currently testing a DALL·E API with several customers and are excited to soon offer it more broadly to developers and businesses so they can build apps on this powerful system.
We can't wait to see what users from around the world create with DALL·E. Sign up today and start creating.
source https://openai.com/blog/dall-e-now-available-without-waitlist/
Natural language generation (NLG) is taking the world by storm as a wave of billion-dollar language startups are upending how humans create content and interact with machines.
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At the Marketing AI Institute, we’ve had webinars, blog posts, MAICON sessions, AI Academy for Marketers lessons, and more on ethical AI. But are the right questions being asked? Is the time and care necessary really being put into the implementation of AI-powered technologies? We’ve talked about it and read about it in academics, blog posts, and content, but when it comes to the practical side of things, what we need to help us are examples of ethical and trustworthy AI implementation.
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We’ve trained and are open-sourcing a neural net called Whisper that approaches human level robustness and accuracy on English speech recognition.
Whisper is an automatic speech recognition (ASR) system trained on 680,000 hours of multilingual and multitask supervised data collected from the web. We show that the use of such a large and diverse dataset leads to improved robustness to accents, background noise and technical language. Moreover, it enables transcription in multiple languages, as well as translation from those languages into English. We are open-sourcing models and inference code to serve as a foundation for building useful applications and for further research on robust speech processing.
The Whisper architecture is a simple end-to-end approach, implemented as an encoder-decoder Transformer. Input audio is split into 30-second chunks, converted into a log-Mel spectrogram, and then passed into an encoder. A decoder is trained to predict the corresponding text caption, intermixed with special tokens that direct the single model to perform tasks such as language identification, phrase-level timestamps, multilingual speech transcription, and to-English speech translation.
Other existing approaches frequently use smaller, more closely paired audio-text training datasets, or use broad but unsupervised audio pretraining. Because Whisper was trained on a large and diverse dataset and was not fine-tuned to any specific one, it does not beat models that specialize in LibriSpeech performance, a famously competitive benchmark in speech recognition. However, when we measure Whisper’s zero-shot performance across many diverse datasets we find it is much more robust and makes 50% fewer errors than those models.
About a third of Whisper’s audio dataset is non-English, and it is alternately given the task of transcribing in the original language or translating to English. We find this approach is particularly effective at learning speech to text translation and outperforms the supervised SOTA on CoVoST2 to English translation zero-shot.
We hope Whisper’s high accuracy and ease of use will allow developers to add voice interfaces to a much wider set of applications. Check out the paper, model card, and code to learn more details and to try out Whisper.
Spotify is the world’s most popular audio streaming service with 433 million users (including 188 million paying subscribers) across 183 different markets—and artificial intelligence is absolutely essential to the company’s success.
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If you’ve attended our Intro to AI for Marketers course or have searched for articles on piloting your first project with AI, you’d see that natural language processing (NLP) and natural language generation (NLG) are two of the most tangible and understandable ways for marketers to get started with AI.
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Editor’s Note: This post is sponsored content from AiAdvertising.
When it comes to job tenure, the average Chief Marketing Officer (CMO) faces a lot of uncertainty.
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Digital fatigue is real. We’re all experiencing it as marketers and as consumers. As brands work to gain share of voice in a fragmented digital marketplace, they’re looking in alternate and unexpected places to reach consumers. And it’s a great idea. With some semblance of normalcy returning to the world, consumer travel and activities on the rise, and most adults in need of a digital detox, we marketers need to look outside computers and phone screens to reach customers and prospects.
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In the last few decades, we’ve experienced a rapid evolution of AI. Since its humble beginnings in 1956, artificial intelligence has transformed from simple predictive models to powerful machines, fueled by deep learning.
Today, AI has become much more feasible for organizations, thanks to foundational models made available by tech giants like Google and Meta. The focus has shifted from gathering large amounts of data to using the right data in a responsible way.
So, what does the future of AI look like?
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