TheSequence - 💸 Generative AI Fundraising Momentum
Was this email forwarded to you? Sign up here 📝 EditorialGenerative AI has taken the baton as the most exciting area in modern artificial intelligence (AI). Models like DALL-E, Midjourney, Stable Diffusion, and Make-A-Video are transcending the boundaries of AI into mainstream pop culture. The reason for this momentum is that generative AI is not only impressive, but it looks really cool. Multimodal models that can generate images, videos and music are using AI to materialize and augment human creativity. The opportunities for transforming industries with generative AI seem endless, and, not surprisingly, this has quickly become part of the investment thesis of many of the top venture capital firms in the world. From a VC perspective, the fascinating aspect of generative AI is that tremendous value is being created at both the infrastructure and application levels. Just this week, the space crowned two new unicorns. In the infrastructure area, Stability AI, the company behind the Stable Diffusion models and the LAION datasets, raised $101 million at a $1 billion valuation. Stability AI challenged the many concerns around the safety of large text-to-image synthesis models by open-sourcing Stable Diffusion ahead of AI powerhouses like Google, Meta and OpenAI. In the application area, Jasper AI raised $125 million at a $1.7 billion valuation. The startup has been one the pioneers in applying the generative capabilities of models like GPT-3 across different content creation and marketing areas. The fundraising momentum for generative AI is undeniable, and it feels like it is just getting started. 🔺🔻TheSequence Scope – our Sunday edition with the industry’s development overview – is free. To receive high-quality content about the most relevant developments in the ML world every Tuesday and Thursday, please subscribe to TheSequence Edge 🔺🔻 🗓 Next week in TheSequence Edge: Edge#237: we discuss Midjourney, one of the most enigmatic models in the space; explore Microsoft’s LAFITE that can train text-to-image synthesis models without any text data; explain Disco Diffusion, an important open source implementation of diffusion models. Edge#238: we deep dive into ImageNet: DeepMind’s new perception benchmark for deep learning models. 📌 New Webinar! Data-centric approach to improving computer vision model performanceTraining dataset health and overall conditions often are not the focal points for many computer vision teams. When exploring ways of improving model performance, CV teams will most likely opt for solutions like tuning hyper-parameters than exploring datasets for flows. SuperAnnotate tries taking a data-centric view and seeing what impact that will have on our computer vision model’s performance. During this webinar, you’ll be looking at the state-of-art methods for getting the most out of training data and observing the impact on the model’s performance. Now, let’s review the most important developments in the AI industry this week 🔎 ML ResearchNeural Architecture Search for Diverse Tasks Researchers from Carnegie Mellon University (CMU) published a paper discussing DASH, a neural architecture search method for different tasks across several domains →read more Speech Translation for Unwritten Languages Meta AI published a paper detailing a machine translation system for primarily oral languages like Hokkien →read more Understanding Image Quality and Aesthetics Google Research published a paper detailing a multi-scale transformer architecture to understand image quality and aesthetics →read more 🤖 Cool AI Tech ReleasesThe Future of TensorFlow The TensorFlow team outlines their vision and future capabilities they are planning to include in future releases →read more 🛠 Real World MLNetflix Maestro Netflix unveiled details about Maestro, their data and ML workflow orchestration pipeline →read more Google Cloud and DeepMind DeepMind published some details about how their AI research is being applied across the Google Cloud suite of products →read more PyTorch Selective Build The PyTorch team shared some experiences about reducing the model size to adapt to different runtimes →read more 💸 Money in AI
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🔢 Edge#236: Inside DeepMind’s AlphaTensor
Thursday, October 20, 2022
The new AI agent was able to discover new algorithms in a super challenging field of matrix multiplication
🎙Dmitrii Evstiukhin/Provectus: "Four Horsemen of AI Project Failure and How to Deal with Them"
Wednesday, October 19, 2022
Getting to know the experience gained by researchers, engineers, and entrepreneurs doing real ML work is an excellent source of insight and inspiration. Share this interview if you like it. No
🐱🐶 Edge#235: Understanding Meta AI’s Make-A-Scene
Tuesday, October 18, 2022
In this issue: we explain Meta AI's Make-A-Scene; we discuss Meta AI's Make-A-Scene Paper; we explore LAION, one of the most complete training datasets for text-to-image synthesis models. Enjoy
🪟🪟 DALL-E 2 is Coming to Azure! and Other Exciting Microsoft’s ML Announcements
Sunday, October 16, 2022
Weekly news digest curated by the industry insiders
📝➡️📺 Edge#234: Inside Meta AI’s Make-A-Video
Thursday, October 13, 2022
The new model builds on the principles of text-to-image methods to produce visually astonishing videos
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