The book AI Crash Course by Hadelin de Ponteves contains a toolkit of four different AI models: Thompson Sampling, Q-Learning, Deep Q-Learning and Deep Convolutional Q-learning. It teaches the theory of these AI models and provides coding examples for solving industry cases based on these models. InfoQ readers can find an excerpt of AI Crash Course on the publisher's website. InfoQ interviewed Hadelin de Ponteves about using different AI models and how to develop AI skills. InfoQ: Why did you write this book?

Fortinet, a global integrated and automated cybersecurity solutions, today announced FortiAI, an appliance that leverages self-learning Deep Neural Networks (DNN) to speed threat remediation and handle time-consuming, manual security analyst tasks. FortiAI's Virtual Security AnalystÔ embeds a mature cybersecurity artificial intelligence, developed by Fortinet's FortiGuard Labs, directly into an organization's network to deliver sub-second detection of advanced threats. To address the challenges faced by security professionals today, Fortinet is unveiling FortiAI Virtual Security AnalystÔ to accelerate threat remediation. FortiAI handles many of the time consuming, manual tasks currently expected of security professionals, preserving their time for higher-value security functions. FortiAI's self-learning capabilities continue to get smarter once deployed in an organization's network.

Leveraging the advances in deep generative models, ODE2VAE can simultaneously learn the embedding of high dimensional trajectories and infer arbitrarily complex continuous-time latent dynamics. Our model explicitly decomposes the latent space into momentum and position components and solves a second order ODE system, which is in contrast to recurrent neural network (RNN) based time series models and recently proposed black-box ODE techniques. In order to account for uncertainty, we propose probabilistic latent ODE dynamics parameterized by deep Bayesian neural networks. We demonstrate our approach on motion capture, image rotation, and bouncing balls datasets. We achieve state-of-the-art performance in long term motion prediction and imputation tasks.

Modern machine learning methods including deep learning have achieved great success in predictive accuracy for supervised learning tasks, but may still fall short in giving useful estimates of their predictive uncertainty. Quantifying uncertainty is especially critical in real-world settings, which often involve input distributions that are shifted from the training distribution due to a variety of factors including sample bias and non-stationarity. In such settings, well calibrated uncertainty estimates convey information about when a model's output should (or should not) be trusted. Many probabilistic deep learning methods, including Bayesian-and non-Bayesian methods, have been proposed in the literature for quantifying predictive uncertainty, but to our knowledge there has not previously been a rigorous large-scale empirical comparison of these methods under dataset shift. We present a large-scale benchmark of existing state-of-the-art methods on classification problems and investigate the effect of dataset shift on accuracy and calibration.

Deep networks are commonly used to model dynamical systems, predicting how the state of a system will evolve over time (either autonomously or in response to control inputs). Despite the predictive power of these systems, it has been difficult to make formal claims about the basic properties of the learned systems. In this paper, we propose an approach for learning dynamical systems that are guaranteed to be stable over the entire state space. The approach works by jointly learning a dynamics model and Lyapunov function that guarantees non-expansiveness of the dynamics under the learned Lyapunov function. We show that such learning systems are able to model simple dynamical systems and can be combined with additional deep generative models to learn complex dynamics, such as video textures, in a fully end-to-end fashion.

Deep neural networks (DNNs) excel at learning representations when trained on large-scale datasets. Pre-trained DNNs also show strong transferability when fine-tuned to other labeled datasets. However, such transferability becomes weak when the target dataset is fully unlabeled as in Unsupervised Domain Adaptation (UDA). We envision that the loss of transferability may stem from the intrinsic limitation of the architecture design of DNNs. In this paper, we delve into the components of DNN architectures and propose Transferable Normalization (TransNorm) in place of existing normalization techniques.

The KOSMOS platform employs multiple layers of applied deep learning … clinical value through the meaningful application of artificial intelligence.

Adopters of AI in the enterprise are focusing on specific production workloads centered around supervised and deep learning while the number of organizations using AI in production or evaluating the technology jumped to 85 percent of companies polled in an annual survey. Another indication of maturing enterprise AI initiatives is a heavier emphasis on data governance, according to an AI adoption survey released Wednesday (March 18) by O'Reilly Media. More than 26 percent of respondents said they are instituting formal governance processes as concerns about privacy and "trustworthy" AI grow. Nearly 35 percent of those surveyed said they expect to launch AI data governance efforts over the next three years, O'Reilly reported. "AI adoption is proceeding apace," wrote report authors Roger Magoulas and Steve Swoyer.

You already know that Deep Learning is good at vision, translation, playing games, and other tasks. But Neural Networks don't "learn" the way humans do, instead it's just really good at fast pattern matching. Today's research mainly focuses on bigger models with larger datasets, bigger models, and complicated loss functions. But the next revolution is likely going to be more fundamental. Let's take a look at two approaches: adding logic with Stacked Capsule Auto Encoders and Self-Supervised Learning at scale. This about sums up what most AI scientists already know: Deep Learning is really good at doing narrow, pattern based tasks such as object or speech recognition.

Though ERNIE 1.0 (released in March 2019) has been a popular model for text classification, it was ERNIE 2.0 which became the talk of the town in the latter half of 2019. Developed by tech-giant Baidu, ERNIE outperformed Google XLNet and BERT on the GLUE benchmark for English. ERNIE stands for Enhanced Representation through kNowledge IntEgration, and ERNIE 2.0 is an upgraded version of ERNIE 1.0. ERNIE 1.0 was pathbreaking in its own way – it was one of the first models to leverage Knowledge Graphs. This incorporation further enhanced training the model for advanced tasks like Relation Classification and NamedEntityRecognition (NER). Like its predecessor, ERNIE 2.0 brings another innovation to the table in the form of Continual Incremental Multi-task Learning.