Artificial intelligence has been touted by some in the security community as the silver bullet in malware detection. Its proponents say it's superior to traditional antivirus since it can catch new variants and never-before-seen malware--think zero-day exploits--that are the Achilles heel of antivirus. One of its biggest proponents is the security firm BlackBerry Cylance, which has staked its business model on the artificial intelligence engine in its endpoint PROTECT detection system, which the company says has the ability to detect new malicious files two years before their authors even create them. But researchers in Australia say they've found a way to subvert the machine-learning algorithm in PROTECT and cause it to falsely tag already known malware as "goodware." The method doesn't involve altering the malicious code, as hackers generally do to evade detection.

Fast forward 50 years and the processing power of the Apollo Guidance Computer (AGC) that took those men to the surface of the moon is now in your pocket, multiple times over-- in fact, an iPhone 6 could be used to guide 120 million Apollo 11 spacecraft to the moon, all at the same time. This factoid doesn't really do justice to the brilliance of the AGC. Given Moore's law, you could pick anything computational and say that 50 years later, there will exist a machine that can run it 2²⁵ faster. It was people like Margaret Hamilton, the lead for the software team who coded the AGC, who chose not to see current hardware limitations as a barrier, but instead as a challenge. She used the resource available to her at the time to achieve the unthinkable.

The financial services industry has seen an explosive growth in Artificial Intelligence (AI) to supplement, and often supplant, existing processes both customer-facing and internal. Given the potential created by rapid advancements in AI sophistication and functionality, more and more financial services firms are leveraging the technology to deploy new use cases for improved decision-making processes – particularly in the areas of anti-money laundering, fraud prevention, risk management, and lending. While the first wave of AI was generally focused on automating manually-intensive and repetitive tasks, banks are now turning to machine learning systems (ML) to uncover more dynamic ways of interpreting their vast swaths of customer data. Whereas AI, at a fundamental level, permits a machine to imitate intelligent human behavior, ML is a specific application (or subset) of AI that enables systems automatically to learn and improve – e.g., reduce errors or maximize the likelihood that their predictions will be true – without being explicitly programmed to make such adjustments. This development has an exciting potential to expand the products available to underbanked communities and improve services and customer experience as a whole.

In this paper, we propose a new first-order gradient-based algorithm to train deep neural networks. We first introduce the sign operation of stochastic gradients (as in sign-based methods, e.g., SIGN-SGD) into ADAM, which is called as signADAM. Moreover, in order to make the rate of fitting each feature closer, we define a confidence function to distinguish different components of gradients and apply it to our algorithm. It can generate more sparse gradients than existing algorithms do. We call this new algorithm signADAM++. In particular, both our algorithms are easy to implement and can speed up training of various deep neural networks. The motivation of signADAM++ is preferably learning features from the most different samples by updating large and useful gradients regardless of useless information in stochastic gradients. We also establish theoretical convergence guarantees for our algorithms. Empirical results on various datasets and models show that our algorithms yield much better performance than many state-of-the-art algorithms including SIGN-SGD, SIGNUM and ADAM. We also analyze the performance from multiple perspectives including the loss landscape and develop an adaptive method to further improve generalization. The source code is available at https://github.com/DongWanginxdu/signADAM-Learn-by-Confidence.

Value-based argumentation enhances a classical abstract argumentation graph - in which arguments are modelled as nodes connected by directed arrows called attacks - with labels on arguments, called values, and an ordering on values, called audience, to provide a more fine-grained justification of the attack relation. With more than one agent facing such an argumentation problem, agents may differ in their ranking of values. When needing to reach a collective view, such agents face a dilemma between two equally justifiable approaches: aggregating their views at the level of values, or aggregating their attack relations, remaining therefore at the level of the graphs. We explore the strenghts and limitations of both approaches, employing techniques from preference aggregation and graph aggregation, and propose a third possibility aggregating rankings extracted from given attack relations.

Floating centroid method (FCM) offers an efficient way to solve a fixed-centroid problem for the neural network classifiers. However, evolutionary computation as its optimization method restrains the FCM to achieve satisfactory performance for different neural network structures, because of the high computational complexity and inefficiency. Traditional gradient-based methods have been extensively adopted to optimize the neural network classifiers. In this study, a gradient-based floating centroid (GDFC) method is introduced to address the fixed centroid problem for the neural network classifiers optimized by gradient-based methods. Furthermore, a new loss function for optimizing GDFC is introduced. The experimental results display that GDFC obtains promising classification performance than the comparison methods on the benchmark datasets.

One of the major problems in natural language processing (NLP) is the word sense disambiguation (WSD) problem. It is t he task of computationally identifying the right sense of a polysemou s word based on its context. Resolving the WSD problem boosts the accurac y of many NLP focused algorithms such as text classification and machi ne translation. In this paper, we introduce a new supervised algorithm for WSD, that is based on Kernel PCA and Semantic Diffusion Kernel, whi ch is called Diffusion Kernel PCA (DKPCA). DKPCA grasps the semant ic similarities within terms, and it is based on PCA. These prop erties enable us to perform feature extraction and dimension reducti on guided by semantic similarities and within the algorithm. Our empiri cal results on SensEval data demonstrate that DKPCA achieves higher or ver y close accuracy results compared to SVM and KPCA with various well-known kernels when the labeled data ratio is meager. Considering t he scarcity of labeled data, whereas large quantities of unlabeled text ual data are easily accessible, these are highly encouraging first resul ts to develop DKPCA further.

One-Shot Neural architecture search (NAS) attracts broad attention recently due to its capacity to reduce the computational hours through weight sharing. However, extensive experiments on several recent works show that there is no positive correlation between the validation accuracy with inherited weights from the supernet and the test accuracy after re-training for One-Shot NAS. Different from devising a controller to find the best performing architecture with inherited weights, this paper focuses on how to sample architectures to train the supernet to make it more predictive. A single-path supernet is adopted, where only a small part of weights are optimized in each step, to reduce the memory demand greatly. Furthermore, we abandon devising complicated reward based architecture sampling controller, and sample architectures to train supernet based on novelty search. An efficient novelty search method for NAS is devised in this paper, and extensive experiments demonstrate the effectiveness and efficiency of our novelty search based architecture sampling method. The best architecture obtained by our algorithm with the same search space achieves the state-of-the-art test error rate of 2.51\% on CIFAR-10 with only 7.5 hours search time in a single GPU, and a validation perplexity of 60.02 and a test perplexity of 57.36 on PTB. We also transfer these search cell structures to larger datasets ImageNet and WikiText-2, respectively.

The pursuit of explaining and improving generalization in deep learning has elicited efforts both in regularization techniques as well as visualization techniques of the loss surface geometry. The latter is related to the intuition prevalent in the community that flatter local optima leads to lower generalization error. In this paper, we harness the state-of-the-art "filter normalization" technique of loss-surface visualization to qualitatively understand the consequences of using adversarial training data augmentation as the explicit regularization technique of choice. Much to our surprise, we discover that this oft deployed adversarial augmentation technique does not actually result in "flatter" loss-landscapes, which requires rethinking adversarial training generalization, and the relationship between generalization and loss landscapes geometries.

Recent research has helped to cultivate growing awareness that machine learning systems fueled by big data can create or exacerbate troubling disparities in society. Much of this research comes from outside of the practicing data science community, leaving its members with little concrete guidance to proactively address these concerns. This article introduces issues of discrimination to the data science community on its own terms. In it, we tour the familiar data mining process while providing a taxonomy of common practices that have the potential to produce unintended discrimination. We also survey how discrimination is commonly measured, and suggest how familiar development processes can be augmented to mitigate systems' discriminatory potential. We advocate that data scientists should be intentional about modeling and reducing discriminatory outcomes. Without doing so, their efforts will result in perpetuating any systemic discrimination that may exist, but under a misleading veil of data-driven objectivity.