If you go back a few hundred years, what we take for granted today would seem like magic – being able to talk to people over long distances, to transmit images, flying, accessing vast amounts of data like an oracle. These are all things that would have been considered magic a few hundred years ago.

The future of extreme weather prediction may lie in modernizing a piece of technology from the past. Researchers recently developed a new technique to augment an old-fashioned weather forecasting method with the power of deep learning, a subset of artificial intelligence (AI). Once the deep learning system is fully trained, it is able to predict extreme weather events like heat waves and cold spells with 80% accuracy up to 5 days beforehand. "This is a very inexpensive way of predicting extreme events at least a few days ahead of time," said Ashesh Chattopadhyay, a mechanical engineering graduate student at Rice University in Houston and lead author on the project. The project began when Pedram Hassanzadeh, an assistant professor of mechanical engineering at Rice, realized that extreme weather events like heat waves and cold spells usually arise from very unusual atmospheric circulation patterns that could potentially be taught to a pattern recognition computer program.

The future of extreme weather prediction may lie in modernizing a piece of technology from the past. Researchers recently developed a new technique to augment an old-fashioned weather forecasting method with the power of deep learning, a subset of artificial intelligence (AI). Once the deep learning system is fully trained, it is able to predict extreme weather events like heat waves and cold spells with 80% accuracy up to 5 days beforehand. "This is a very inexpensive way of predicting extreme events at least a few days ahead of time," said Ashesh Chattopadhyay, a mechanical engineering graduate student at Rice University in Houston and lead author on the project. The project began when Pedram Hassanzadeh, an assistant professor of mechanical engineering at Rice, realized that extreme weather events like heat waves and cold spells usually arise from very unusual atmospheric circulation patterns that could potentially be taught to a pattern recognition computer program.

It is predated by three publications only: the seminal work of McCulloch and Pitts (1943) that hypothesized how neurons might work by relying on analogy to electrical circuits; Donald Hebb's book The Organization of Behavior (1949), which famously stipulated the basic principle of association of neurons by means of neural co-activation (i.e., assembling); and Frank Rosenblatt's work on the Perceptron (Rosenblatt, 1958). Importantly, however, the Widrow-Hoff rule was the first one that was successfully applied to real-life problems (e.g., noise cancellation in telephone lines which is used to date; cf., Haykin, 1999). After the initial excitement and until the (more) recent successes, models such as those mentioned above that were inspired biologically or, more specifically, neurally were ignored in favour of machines implementing von Neumann's traditional architecture. During the 1970s, the Widrow-Hoff rule was accidentally rediscovered in Psychology by Rescorla and Wagner (1972) who worked on animal and human learning, and by Kohonen (1972) in his work on Self-Organizing Maps in Computer Science. Finally, the widely known and successful Connectionist Parallel-Distributed Processing Models have the Widrow-Hoff rule as their principal building block (cf., McClelland & Rumelhart, 1986).

Abstract--Technologies of the Internet of Things (IoT) facilitate digital contents such as images being acquired in a massive way. However, consideration from the privacy or legislation perspective still demands the need for intellectual content protection. In this paper, we propose a general deep neural network (DNN) based watermarking method to fulfill this goal. Instead of training a neural network for protecting a specific image, we train on an image set and use the trained model to protect a distinct test image set in a bulk manner. Respective evaluations both from the subjective and objective aspects confirm the supremacy and practicability of our proposed method. To demonstrate the robustness of this general neural watermarking mechanism, commonly used manipulations are applied to the watermarked image to examine the corresponding extracted watermark, which still retains sufficient recognizable traits. To the best of our knowledge, we are the first to propose a general way to perform watermarking using DNN. Considering its performance and economy, it is concluded that subsequent studies that generalize our work on utilizing DNN for intellectual content protection is a promising research trend.

We develop an effective generation of adversarial attacks on neural models that output a sequence of probability distributions rather than a sequence of single values. This setting includes the recently proposed deep probabilistic autoregressive forecasting models that estimate the probability distribution of a time series given its past and achieve state-of-the-art results in a diverse set of application domains. The key technical challenge we address is effectively differentiating through the Monte-Carlo estimation of statistics of the joint distribution of the output sequence. Additionally, we extend prior work on probabilistic forecasting to the Bayesian setting which allows conditioning on future observations, instead of only on past observations. We demonstrate that our approach can successfully generate attacks with small input perturbations in two challenging tasks where robust decision making is crucial: stock market trading and prediction of electricity consumption.

Deep learning in combination with improved training techniques and high computational power has led to recent advances in the field of reinforcement learning (RL) and to successful robotic RL applications such as in-hand manipulation. However, most robotic RL relies on a well known initial state distribution. In real-world tasks, this information is however often not available. For example, when disentangling waste objects the actual position of the robot w.r.t.\ the objects may not match the positions the RL policy was trained for. To solve this problem, we present a novel adversarial reinforcement learning (ARL) framework. The ARL framework utilizes an adversary, which is trained to steer the original agent, the protagonist, to challenging states. We train the protagonist and the adversary jointly to allow them to adapt to the changing policy of their opponent. We show that our method can generalize from training to test scenarios by training an end-to-end system for robot control to solve a challenging object disentangling task. Experiments with a KUKA LBR+ 7-DOF robot arm show that our approach outperforms the baseline method in disentangling when starting from different initial states than provided during training.

Google is using its DeepMind artificial intelligence to use to combat the coronavirus or COVID-19. With the coronavirus still spreading slowly but surely and no cure is yet to be seen, is there any hope that this AI might be able to help find the cure? Read More: READ! Coronavirus Has Two Strains Which Will Make it Even More Difficult to Contain Since The Other Half Doesn't Know They Are Infected Until It's Too Late A post that was published Thursday, DeepMind is now using its AlphaFold system to create "structure predictions of several under-studied proteins associated with SARS-CoV-2, the virus that causes COVID-19." The predictions, however, have not been experimentally verified, but DeepMind is confident that the data will be useful to the scientists who have a better understanding of the novel coronavirus that it will be of use to them. DeepMind stated that understanding a protein's structure usually takes months or even longer.

Google's DeepMind is putting its artificial intelligence systems to a new task: trying to figure out certain properties of the novel coronavirus which has killed thousands in the past couple of months. In a post Thursday, DeepMind (which was acquired by Google in 2014 and is now a subsidiary of Alphabet), said it has put its AlphaFold system to create "structure predictions of several under-studied proteins associated with SARS-CoV-2, the virus that causes COVID-19." These predictions haven't been experimentally verified, DeepMind says, but they may help scientists understand how the coronavirus functions. This, in turn, may be of use when developing a vaccine or cure. DeepMind says that understanding a protein's structure typically takes months or longer.

Welcome to the world of intelligent chatbots: your companion and conversation agents who should make your life smarter. A leading research paper even said that by 2020, the average person would have more conversations with bots than with their spouse. So, be ready to embrace this new life in a year from now. Have you ever tried telling Siri or Google to "find restaurants that don't serve pizza?" At least they are both consistent in that they gave the same answer -- suggesting restaurants that do serve pizza. The first citizen humanoid robot, Sofia, is making her way to every media event, conducting interviews using human-like conversations. How does she compare to these competitors? Well, the truth is far from reality.