At the present scenario, the AI and machine learning scene are at a relatively early stage in India, where most of the AI and machine learning roles tend …

A group of researchers from MIT Computer Science & Artificial Intelligence Laboratory (CSAIL) have proposed a simple framework for performing different image reconstruction tasks using the state-of-the-art generative model StyleGAN2. It's common for machine learning researchers to train models in a supervised setting for solving downstream prediction and image reconstruction tasks. For example, in the task of super-resolution, which aims to obtain high-resolution output images from low-resolution versions, classical methods train models on pairs of low-resolution and high-resolution images. However, such end-to-end methods can also require re-training whenever there is a distribution shift in the inputs or relevant latent variables. Distribution shifts can easily occur for example in the input x-ray images collected from a hospital if the hospital's medical scanners are upgraded, or as the patients contributing the images age due to improved healthcare. Given the prohibitively high computation resources required to re-train end-to-end approaches when distribution shifts occur, how else might researchers build ML models that are both easy to train and robust to distribution shifts?

For several years, there have been many discussions on AI's capability. Many believed that AI outperforms humans in solving a few sectors. As the technology in its infancy, researchers are expecting human-like autonomous systems in the next coming years. OpenAI strategy has a leading posture in the artificial intelligence research space. The goal is on advancing digital intelligence in a way that can benefit humanity as a whole.

We develop a new framework for learning variational autoencoders and other deep generative models that balances generative and discriminative goals. Our framework optimizes model parameters to maximize a variational lower bound on the likelihood of observed data, subject to a task-specific prediction constraint that prevents model misspecification from leading to inaccurate predictions. We further enforce a consistency constraint, derived naturally from the generative model, that requires predictions on reconstructed data to match those on the original data. We show that these two contributions - prediction constraints and consistency constraints - lead to promising image classification performance, especially in the semi-supervised scenario where category labels are sparse but unlabeled data is plentiful. Our approach enables advances in generative modeling to directly boost semi-supervised classification performance, an ability we demonstrate by augmenting deep generative models with latent variables capturing spatial transformations. We develop broadly applicable methods for learning flexible models of high-dimensional data, like images, that are paired with (discrete or continuous) labels. We are particularly interested in semisupervised learning (Zhu, 2005; Oliver et al., 2018) from data that is sparsely labeled, a common situation in practice due to the cost or privacy concerns associated with data annotation. Given a large and sparsely labeled dataset, we seek a single probabilistic model that simultaneously makes good predictions of labels and provides a high-quality generative model of the high-dimensional input data. Strong generative models are valuable because they can allow incorporation of domain knowledge, can address partially missing or corrupted data, and can be visualized to improve interpretability. Prior approaches for the semi-supervised learning of deep generative models include methods based on variational autoencoders (VAEs) (Kingma et al., 2014; Siddharth et al., 2017), generative adversarial networks (GANs) (Dumoulin et al., 2017; Kumar et al., 2017), and hybrids of the two (Larsen et al., 2016; de Bem et al., 2018; Zhang et al., 2019). While these all allow sampling of data, a major shortcoming of these approaches is that they do not adequately use labels to inform the generative model.

The idea of artificial general intelligence as we know it today starts with a dot-com blowout on Broadway. Twenty years ago--before Shane Legg clicked with neuroscience postgrad Demis Hassabis over a shared fascination with intelligence; before the pair hooked up with Hassabis's childhood friend Mustafa Suleyman, a progressive activist, to spin that fascination into a company called DeepMind; before Google bought that company for more than half a billion dollars four years later--Legg worked at a startup in New York called Webmind, set up by AI researcher Ben Goertzel. Today the two men represent two very different branches of the future of artificial intelligence, but their roots reach back to common ground. Even for the heady days of the dot-com bubble, Webmind's goals were ambitious. Goertzel wanted to create a digital baby brain and release it onto the internet, where he believed it would grow up to become fully self-aware and far smarter than humans.

Generative Adversarial Networks (GANs) have recently advanced face synthesis by learning the underlying distribution of observed data. However, it will lead to a biased image generation due to the imbalanced training data or the mode collapse issue. Prior work typically addresses the fairness of data generation by balancing the training data that correspond to the concerned attributes. In this work, we propose a simple yet effective method to improve the fairness of image generation for a pre-trained GAN model without retraining. We utilize the recent work of GAN interpretation to identify the directions in the latent space corresponding to the target attributes, and then manipulate a set of latent codes with balanced attribute distributions over output images. We learn a Gaussian Mixture Model (GMM) to fit a distribution of the latent code set, which supports the sampling of latent codes for producing images with a more fair attribute distribution. Experiments show that our method can substantially improve the fairness of image generation, outperforming potential baselines both quantitatively and qualitatively. The images generated from our method are further applied to reveal and quantify the biases in commercial face classifiers and face super-resolution model.

Machine learning models are commonly trained end-to-end and in a supervised setting, using paired (input, output) data. Classical examples include recent super-resolution methods that train on pairs of (low-resolution, high-resolution) images. However, these end-to-end approaches require re-training every time there is a distribution shift in the inputs (e.g., night images vs daylight) or relevant latent variables (e.g., camera blur or hand motion). In this work, we leverage state-of-the-art (SOTA) generative models (here StyleGAN2) for building powerful image priors, which enable application of Bayes' theorem for many downstream reconstruction tasks. Our method, called Bayesian Reconstruction through Generative Models (BRGM), uses a single pre-trained generator model to solve different image restoration tasks, i.e., super-resolution and in-painting, by combining it with different forward corruption models. We demonstrate BRGM on three large, yet diverse, datasets that enable us to build powerful priors: (i) 60,000 images from the Flick Faces High Quality dataset \cite{karras2019style} (ii) 240,000 chest X-rays from MIMIC III and (iii) a combined collection of 5 brain MRI datasets with 7,329 scans. Across all three datasets and without any dataset-specific hyperparameter tuning, our approach yields state-of-the-art performance on super-resolution, particularly at low-resolution levels, as well as inpainting, compared to state-of-the-art methods that are specific to each reconstruction task. We will make our code and pre-trained models available online.

I recently started a new newsletter focus on AI education. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Safety is one of the emerging concerns in deep learning systems. In the context of deep learning systems, safety is related to building agents that respect safety dynamics in a given environment.

Reinforcement Learning (RL) possesses immense potential and is doubtless one of the most dynamic and stimulating fields of research in Artificial Intelligence. RL is considered as a game-changer in Data Science, particularly after observing the winnings of AI agents AlphaGo Zero and OpenAI Five against top human champions. However, RL is not restricted to games. The progress in Reinforcement Learning, especially during the last few years, has been sensational. RL is everywhere now, ranging from resource management to chemistry, from healthcare to finance, and from Recommender Systems to more advanced applications in stock prediction.

Keeping up with the trend of many recent years, Deep Learning in 2020 continued to be one of the fastest-growing fields, darting straight ahead into the Future of Work. The developments were manifold and on multiple fronts. OpenAI, the AI Research organization, declared PyTorch as its new standard Deep Learning framework. PyTorch will increase its research productivity at scale on GPUs. With PyTorch backing it, OpenAI cut down its generative modeling iteration time from weeks to days. Megvii Technology, a China-based startup, said that it would make its Deep Learning framework open-source.