A tweet pinned to the top of Hegde's feed in honor of Modi's birthday calls him "the leader who brought back India's lost glory." On January 7, the account tweeted a screenshot from ChatGPT to its more than 185,000 followers; the tweet appeared to show the AI-powered chatbot making a joke about the Hindu deity Krishna. ChatGPT uses large language models to provide detailed answers to text prompts, responding to questions about everything from legal problems to song lyrics. But on questions of faith, it's mostly trained to be circumspect, responding "I'm sorry, but I'm not programmed to make jokes about any religion or deity," when prompted to quip about Jesus Christ or Mohammed. That limitation appears not to include Hindu religious figures.

Artificial intelligence is here, and it's coming for your job. So promising are the tool's capabilities that Microsoft -- amid laying off 10,000 people -- has announced a "multiyear, multibillion-dollar investment" in the revolutionary technology, which is growing smarter by the day. And the rise of machines leaves many well-paid workers vulnerable, experts warn. "AI is replacing the white-collar workers. I don't think anyone can stop that," said Pengcheng Shi, an associate dean in the department of computing and information sciences at Rochester Institute of Technology.

A Generative Adversarial Network (GAN) with generator $G$ trained to model the prior of images has been shown to perform better than sparsity-based regularizers in ill-posed inverse problems. In this work, we propose a new method of deploying a GAN-based prior to solve linear inverse problems using projected gradient descent (PGD). Our method learns a network-based projector for use in the PGD algorithm, eliminating the need for expensive computation of the Jacobian of $G$. Experiments show that our approach provides a speed-up of $30\text{-}40\times$ over earlier GAN-based recovery methods for similar accuracy in compressed sensing. Our main theoretical result is that if the measurement matrix is moderately conditioned for range($G$) and the projector is $\delta$-approximate, then the algorithm is guaranteed to reach $O(\delta)$ reconstruction error in $O(log(1/\delta))$ steps in the low noise regime. Additionally, we propose a fast method to design such measurement matrices for a given $G$. Extensive experiments demonstrate the efficacy of this method by requiring $5\text{-}10\times$ fewer measurements than random Gaussian measurement matrices for comparable recovery performance.