Determining whether an AI system is maintaining fairness in its predictions requires an understanding of models' short- and long-term effects, which might be informed by disparities in error metrics on a number of static data sets. In some cases, it's necessary to consider the context in which the AI system operates in addition to the aforementioned error metrics, which is why Google researchers developed ML-fairness-gym, a set of components for evaluating algorithmic fairness in simulated social environments. ML-fairness-gym -- which was published in open source on Github this week –is designed to be used to research the long-term effects of automated systems by simulating decision-making using OpenAI's Gym framework. AI-controlled agents interact with digital environments in a loop, and at each step, an agent chooses an action that affects the environment's state. The environment then reveals an observation that the agent uses to inform its next actions, so that the environment models the system and dynamics of a problem and the observations serve as data.

DeepMind Technologies, a Google subsidiary and Artificial Intelligence (AI) firm, disclosed that it will adopt Blockchain technology and make use of Distributed Ledger Technology (DLT).This move will help the company secure patient data more efficiently. DeepMind creates algorithms designed for applications, gaming protocols and stimulation. It earned fame for developing a machine-learning program that can be capable of playing video games. Likewise, DeepMind developed the so-called "Neural Turing Machine" that copies short-term memory of human beings. It signed a five-year contract with Royal Free London NHS Trust recently so it can apply the technology to healthcare. The problem is this accord created some hullabaloo as it allegedly affected confidentiality of patient data.

A Tampa health care expert goes public with a plan to fix America's broken health care system. TAMPA, Fla. – Local health care expert and medical case management consultant Paul Roberts has spent two decades working to develop more efficient ways to improve healthcare, and now the 47-year-old is attempting his most ambitious task – to reform America's health care system. "There's no question about it, our healthcare system is broken and that really is something that all Americans can get behind, regardless of their political ideologies," said Roberts. "It affects all of us and we need to address it before costs skyrocket even more." According to Roberts, his alternative plan will save tax payers an estimated 40 percent over Medicare for All, while enhancing the overall quality of care.

Data protection and privacy have been discussed nonstop as more and more people come to realize just how much personal information they are sharing through the countless apps and websites they regularly visit. It's no longer so surprising to see products you've talked about with friends or concerts you've searched on Google promptly appear as advertisements in your social media feeds. And that has many people concerned. Recent government initiatives such as the EU's General Data Protection Regulation (GDPR) are designed to protect individuals' data privacy, with a core concept being "the right to be forgotten." The bad news is, it's generally difficult to revoke things that have already been shared online or to properly delete such data.

People share millions of photos and videos on Facebook every day, creating some of the most compelling and creative visuals on our platform. Some of that content is manipulated, often for benign reasons, like making a video sharper or audio more clear. But there are people who engage in media manipulation in order to mislead. Manipulations can be made through simple technology like Photoshop or through sophisticated tools that use artificial intelligence or "deep learning" techniques to create videos that distort reality – usually called "deepfakes." While these videos are still rare on the internet, they present a significant challenge for our industry and society as their use increases.

In a call for new employees, Elon Musk says he doesn't care if applicants have a high-school degree as long as they're best-in-class at coding or developing AI. The Tesla and SpaceX CEO made his priorities clear in a Twitter thread advertising a'super fun AI party/hackathon' at his residence that will include other employees who work for Tesla's AI team. Musk said invitations for the party will be sent out soon. In response from one Twitter user who asked whether they will have to get their PhD in order to secure an invite, Musk said education wasn't the priority. 'A PhD is definitely not required.

GENEVA – Europe's physics lab CERN on Wednesday said it had stopped using a Facebook team-chat application because of concerns about handing over data to the U.S. tech giant. CERN said it wound up its Facebook Workplace account on Jan. 31 after the U.S. firm gave it the choice of either paying to use the service or sharing data. "Losing control of our data was unacceptable," CERN said in a blog on Jan. 28, confirmed to AFP by spokeswoman Anais Rassat on Wednesday. CERN said it started using Workplace when it was offered the service for free in 2016. It said some 1,000 members of the CERN community had created accounts and there were around 150 active users each week.

This paper focuses on deep reinforcement learning (DRL)-based energy dispatch for isolated microgrids (MGs) with diesel generators (DGs), photovoltaic (PV) panels, and a battery. A finite-horizon Partial Observable Markov Decision Process (POMDP) model is formulated and solved by learning from historical data to capture the uncertainty in future electricity consumption and renewable power generation. In order to deal with the instability problem of DRL algorithms and unique characteristics of finite-horizon models, two novel DRL algorithms, namely, FH-DDPG and FH-RDPG, are proposed to derive energy dispatch policies with and without fully observable state information. A case study using real isolated microgrid data is performed, where the performance of the proposed algorithms are compared with the myopic algorithm as well as other baseline DRL algorithms. Moreover, the impact of uncertainties on MG performance is decoupled into two levels and evaluated respectively.

We propose Unbalanced GANs, which pre-trains the generator of the generative adversarial network (GAN) using variational autoencoder (VAE). We guarantee the stable training of the generator by preventing the faster convergence of the discriminator at early epochs. Furthermore, we balance between the generator and the discriminator at early epochs and thus maintain the stabilized training of GANs. We apply Unbalanced GANs to well known public datasets and find that Unbalanced GANs reduce mode collapses. We also show that Unbalanced GANs outperform ordinary GANs in terms of stabilized learning, faster convergence and better image quality at early epochs.

Examples include meteorology, biology, and physics. The usual way to model deterministic dynamical systems is by using (partial) differential equations. Typically, differential equations models for a given dynamical system are derived using apriori insights into the problem at hand; then the model is validated using empirical observations. In an era in which massive data-sets pertaining to different fields of science are widely available, an interesting problem is whether it is possible for a useful differential equations model to be learned directly from data, without any major modeling effort required by the researcher. Our goal in this paper is to develop a general methodology for building such differential equations models in contexts in which not all relevant variables are observed, that is, in cases in which the main variable of interest depends on other variables of which no measurements are available. As a concrete example, consider the following problem. RTE, the electricity transmission system operator of France, uses high-level simulations of hourly temperature series to study the impact different climate scenarios have on electricity consumption, and hence on the French electrical power grid.