Gartner predicts that half of AI projects in finance will be delayed or canceled by 2024 - Your Daily AI Research tl;dr | 2022-06-13
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In this newsletter, I share the most exciting papers I find daily, along with a one-liner summary to help you quickly determine if the paper (and code) is worth investigating. I will also take this opportunity to share daily interesting news in the field. I hope you enjoy the format of this newsletter, and I would gladly take any feedback you have in the comments to improve it.
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1?? BigVGAN: A Universal Neural Vocoder with Large-Scale Training
BigVGAN: A new universal vocoder (GAN-based models that can generate high-fidelity raw audio conditioned on mel spectrogram) that generalizes well under various unseen conditions in zero-shot settings (e.g. new speakers, novel languages, music, noisy environments, etc.), with the largest scale up to 112M parameters, unprecedented in the literature.
Link to the paper: https://arxiv.org/abs/2206.04658
Code: https://github.com/NVIDIA/BigVGAN
2?? Open Challenges in Deep Stereo: the Booster Dataset
A novel high-resolution stereo dataset framing indoor scenes annotated with dense and accurate ground-truth disparities (see image above) with the presence of several specular and transparent surfaces like mirrors or closed TV screens causing much trouble to current SOTA models.
Link to the paper: https://arxiv.org/abs/2206.04671
The dataset: https://cvlab-unibo.github.io/booster-web/
?? Gartner predicts that half of AI projects in finance will be delayed or canceled by 2024
The problem resides in scaling their "AI-based" solution:
"As the number of AI solutions and users grow, so does the complexity in scaling efforts. CFOs who attempt to keep AI in-house will hit a productivity ceiling, as the complexity of maintaining projects tax internal resources and slow or prevent the deployment of new solutions."
What do you think? Are they right, or will we find ways to make these processes more efficient and dodge this productivity ceiling with more innovation and societal changes? Or will there simply be companies focused on developing, maintaining, and scaling AI projects for others?
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Thank you for reading, a fellow AI enthusiast and researcher.