AI programs are constructed within a complex framework that includes a computer's hardware and operating system, programming languages, and often general frameworks for representing and reasoning.
In order to resist attacks, various methods have been proposed. A category of defense methods improve network's training regime to counter adversarial attacks. The most common method is adversarial training [23, 31] with adversarial examples added to the training data. In , a defense method called Min-Max optimization is introduced to augment the training data with first-order attack samples. There are also some model defense methods that target at removing adversarial perturbation by transforming the input images before feeding them to the network [24, 1, 18].
Neural architecture search (NAS) -- selecting which neural model to use for your learning problem -- is a promising but computationally expensive direction for automating and democratizing machine learning. The weight-sharing method, whose initial success at dramatically accelerating NAS surprised many in the field, has come under scrutiny due to its poor performance as a surrogate for full model-training (a miscorrelation problem known as rank disorder) and inconsistent results on recent benchmarks. In this post, we give a quick overview of weight-sharing and argue in favor of its continued use for NAS. First-generation NAS methods were astronomically expensive due to the combinatorially large search space, requiring the training of thousands of neural networks to completion. Then, in their 2018 ENAS (for Efficient NAS) paper, Pham et al. introduced the idea of weight-sharing, in which only one shared set of model parameters is trained for all architectures.
Modern cyber-physical systems (CPS), such as our energy infrastructure, are becoming increasingly complex: An ever-higher share of Artificial Intelligence (AI)-based technologies use the Information and Communication Technology (ICT) facet of energy systems for operation optimization, cost efficiency, and to reach CO2 goals worldwide. At the same time, markets with increased flexibility and ever shorter trade horizons enable the multi-stakeholder situation that is emerging in this setting. These systems still form critical infrastructures that need to perform with highest reliability. However, today's CPS are becoming too complex to be analyzed in the traditional monolithic approach, where each domain, e.g., power grid and ICT as well as the energy market, are considered as separate entities while ignoring dependencies and side-effects. To achieve an overall analysis, we introduce the concept for an application of distributed artificial intelligence as a self-adaptive analysis tool that is able to analyze the dependencies between domains in CPS by attacking them. It eschews pre-configured domain knowledge, instead exploring the CPS domains for emergent risk situations and exploitable loopholes in codices, with a focus on rational market actors that exploit the system while still following the market rules.
In this paper we consider the selection of covariate adjustment variables for off-policy evaluation (Precup et al., 2000) in single time contextual decision making problems. Specifically, we consider the choice of variables that suffice for estimating the value of a point exposure contextual policy by the method of covariate adjustment, when the available data come from a different policy. We assume a causal graphical model with, possibly, hidden variables in which at least one valid adjustment set is fully observable. The value of a policy, also known as the interventional mean, is defined asthe mean ofan outcome (reward)under the policy. In the statistics literature, a policy is referred to as a dynamic treatment regime (Robins, 1993; Murphy et al., 2001; Robins, 2004; Schulte et al., 2014). A practical application of the methods described in this paper is in the design of planned observational studies. Investigators designing such study might use the existing graphical criteria for identifying the class of candidate valid covariate adjustment sets (Pearl, 2000; Kuroki and Miyakawa, 2003; Shpitser et al., 2010), and then apply the methods described in this paper to select an adjustment set that satisfies one of three optimality criteria that we consider here. Each criterion is defined by selecting the observable adjustment set that yields the non-parametrically adjusted estimator with smallest asymptotic variance among those that control for observable adjustment sets in a given class, specifically the class of (i) all adjustment sets, (ii) all minimal adjustment sets, or (iii) all adjustment sets that have minimum cardinality.
According to VentureBeat, AI researchers at Uber have recently posted a paper to Arxiv outlining a new platform intended to assist in the creation of distributed AI models. The platform is called Fiber, and it can be used to drive both reinforcement learning tasks and population-based learning. Fiber is designed to make large-scale parallel computation more accessible to non-experts, letting them take advantage of the power of distributed AI algorithms and models. Fiber has recently been made open-source on GitHub, and it's compatible with Python 3.6 or above, with Kubernetes running on a Linux system and running in a cloud environment. According to the team of researchers, the platform is capable of easily scaling up to hundreds or thousands of individual machines.
This work proposes a novel Graph-based neural ArchiTecture Encoding Scheme, a.k.a. GATES, to improve the predictor-based neural architecture search. Specifically, different from existing graph-based schemes, GATES models the operations as the transformation of the propagating information, which mimics the actual data processing of neural architecture. GATES is a more reasonable modeling of the neural architectures, and can encode architectures from both the "operation on node" and "operation on edge" cell search spaces consistently. Experimental results on various search spaces confirm GATES's effectiveness in improving the performance predictor. Furthermore, equipped with the improved performance predictor, the sample efficiency of the predictor-based neural architecture search (NAS) flow is boosted.
Today machines with artificial intelligence (AI) are becoming more prevalent in society. Across many fields, AI has taken over numerous tasks that humans used to do earlier. As the reference is to human intelligence, artificial intelligence is being modified into what humans can do. However, the technology has not yet matched the level of utmost wisdom possessed by humans and it seems like it is not going to achieve the milestone any time sooner. To replace human beings at most jobs, machines need to exhibit what we intuitively call "common sense".
A preprint paper coauthored by Uber AI scientists and Jeff Clune, a research team leader at San Francisco startup OpenAI, describes Fiber, an AI development and distributed training platform for methods including reinforcement learning (which spurs AI agents to complete goals via rewards) and population-based learning. The team says that Fiber expands the accessibility of large-scale parallel computation without the need for specialized hardware or equipment, enabling non-experts to reap the benefits of genetic algorithms in which populations of agents evolve rather than individual members. Fiber -- which was developed to power large-scale parallel scientific computation projects like POET -- is available in open source as of this week, on Github. It supports Linux systems running Python 3.6 and up and Kubernetes running on public cloud environments like Google Cloud, and the research team says that it can scale to hundreds or even thousands of machines. As the researchers point out, increasing computation underlies many recent advances in machine learning, with more and more algorithms relying on distributed training for processing an enormous amount of data.
Self-quarantined employees are forcing organizations to allow access to critical data remotely. Coronavirus is presenting organizations with a unique opportunity to adopt modern security protocols and enable an efficient remote workforce. Fear of Coronavirus infections has resulted in organizations ruling out large meetings. Healthy individuals are in home-quarantine for weeks at a time, even though they are not necessarily thought to carry the virus. This large number of individuals complying with house arrest is putting a strain on many organizations that have not shifted their working styles to accommodate large-scale remote workers.