This week on The Marketing AI Show, Marketing AI Institute founder/CEO Paul Roetzer and Chief Content Officer Mike Kaput talk about three AI stories in the news and add their take on these developments and what it means for marketers and business leaders.
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In a 2021 survey, 86% of businesses said that AI was becoming a “mainstream technology” at their company, while another poll showed that adoption rates of machine learning methods were at 45%. Digital marketing is one of the industries that can benefit greatly from this technology, harnessing it for advanced segmentation, personalization, and SEO optimization.
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In the world of digital marketing, data is everything. It helps you understand your customers, optimize your campaigns, and make smarter business decisions. Not to mention, it’s a critical prerequisite for using AI in your marketing.
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Technology, the metaverse, and artificial intelligence: they’re in the news now more than ever. As much as we talk about AI, we appreciate the reminder also need to keep the human front and center. And it can reap amazing results.
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![How AI + Predictive Analytics Drive Marketing Strategy [Video]](https://www.marketingaiinstitute.com/hubfs/Atalia%20Horenshtien-1.jpg)
We’ve long preached the idea of marketer + machine, and as marketing departments become increasingly accountable for business outcomes, marketers need support. How can AI support marketing, increase performance and ROI, and how can we integrate AI into daily business activities?
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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.
