One of the biggest obstacles in the adoption of Artificial Intelligence is that it cannot explain what a prediction is based on. These machine-learning systems are so-called black boxes when the reasoning for a decision is not self-evident to a user. Meike Nauta, Ph.D. candidate at the Data Science group within the EEMCS faculty of the University of Twente, created a model to address the black-box nature of deep learning models. Algorithms can already make accurate predictions, such as medical diagnoses, but they cannot explain how they arrived at such a prediction. In recent years, a lot of attention has been paid to the explainable AI field.
Nevertheless, the recent advances in machine learning triggered genuine interest, as machine learning offer promising preliminary results and open the way to a wide range of new functions for avionics systems, for instance in the area of autonomous flying. In this paper we investigate on how existing certification and regulation techniques, can (or cannot) handle software development that includes parts obtained by machine learning. Nowadays a large aircraft cockpit offers many avionic complex functions: flight controls, navigation, surveillance, communications, displays... Their design has required a top down iterative approach from aircraft level downward, thus the functions are performed by systems of systems, with each system decomposed into subsystems that may contain a collection of software and hardware items. Therefore, any avionic development considers 3 levels of engineering: (i) Function, (ii) System/Subsystem and (iii) Item. The development process of each engineering level relies on several decades of experience and good practices that keep on being adapted today.
It's been a relatively tame week for stocks this week, and Wednesday was no different. The Dow Jones rose 3 points, the S&P 500 continued trading just below its record high and gained nearly 0.2%, while the Nasdaq NDAQ gained 0.4%. Reopening plays like Carnival Corp. and American Airlines AAL led the way, while meme stocks once again had another manic day. Clover Health rose another 23% following yesterday's 85% rally, while Wendy's rose again after gaining 25% yesterday. For investors looking to find the best opportunities, the deep learning algorithms at Q.ai have crunched the data to give you a set of Top Buys.
Deep learning is gaining increasing popularity for spatiotemporal forecasting. However, prior works have mostly focused on point estimates without quantifying the uncertainty of the predictions. In high stakes domains, being able to generate probabilistic forecasts with confidence intervals is critical to risk assessment and decision making. Hence, a systematic study of uncertainty quantification (UQ) methods for spatiotemporal forecasting is missing in the community. In this paper, we describe two types of spatiotemporal forecasting problems: regular grid-based and graph-based. Then we analyze UQ methods from both the Bayesian and the frequentist point of view, casting in a unified framework via statistical decision theory. Through extensive experiments on real-world road network traffic, epidemics, and air quality forecasting tasks, we reveal the statistical and computational trade-offs for different UQ methods: Bayesian methods are typically more robust in mean prediction, while confidence levels obtained from frequentist methods provide more extensive coverage over data variations. Computationally, quantile regression type methods are cheaper for a single confidence interval but require re-training for different intervals. Sampling based methods generate samples that can form multiple confidence intervals, albeit at a higher computational cost.
Knowledge distillation (KD) is a successful approach for deep neural network acceleration, with which a compact network (student) is trained by mimicking the softmax output of a pre-trained high-capacity network (teacher). In tradition, KD usually relies on access to the training samples and the parameters of the white-box teacher to acquire the transferred knowledge. However, these prerequisites are not always realistic due to storage costs or privacy issues in real-world applications. Here we propose the concept of decision-based black-box (DB3) knowledge distillation, with which the student is trained by distilling the knowledge from a black-box teacher (parameters are not accessible) that only returns classes rather than softmax outputs. We start with the scenario when the training set is accessible. We represent a sample's robustness against other classes by computing its distances to the teacher's decision boundaries and use it to construct the soft label for each training sample. After that, the student can be trained via standard KD. We then extend this approach to a more challenging scenario in which even accessing the training data is not feasible. We propose to generate pseudo samples distinguished by the teacher's decision boundaries to the largest extent and construct soft labels for them, which are used as the transfer set. We evaluate our approaches on various benchmark networks and datasets and experiment results demonstrate their effectiveness. Codes are available at: https://github.com/zwang84/zsdb3kd.
Recently, considerable literature has grown up around the theme of few-shot named entity recognition (NER), but little published benchmark data specifically focused on the practical and challenging task. Current approaches collect existing supervised NER datasets and re-organize them to the few-shot setting for empirical study. These strategies conventionally aim to recognize coarse-grained entity types with few examples, while in practice, most unseen entity types are fine-grained. In this paper, we present Few-NERD, a large-scale human-annotated few-shot NER dataset with a hierarchy of 8 coarse-grained and 66 fine-grained entity types. Few-NERD consists of 188,238 sentences from Wikipedia, 4,601,160 words are included and each is annotated as context or a part of a two-level entity type. To the best of our knowledge, this is the first few-shot NER dataset and the largest human-crafted NER dataset. We construct benchmark tasks with different emphases to comprehensively assess the generalization capability of models. Extensive empirical results and analysis show that Few-NERD is challenging and the problem requires further research. We make Few-NERD public at https://ningding97.github.io/fewnerd/.
Instance-based interpretation methods have been widely studied for supervised learning methods as they help explain how black box neural networks predict. However, instance-based interpretations remain ill-understood in the context of unsupervised learning. In this paper, we investigate influence functions , a popular instance-based interpretation method, for a class of deep generative models called variational auto-encoders (VAE). We formally frame the counter-factual question answered by influence functions in this setting, and through theoretical analysis, examine what they reveal about the impact of training samples on classical unsupervised learning methods. We then introduce VAE-TracIn, a computationally efficient and theoretically sound solution based on Pruthi et al. , for VAEs. Finally, we evaluate VAE-TracIn on several real world datasets with extensive quantitative and qualitative analysis.
With the growing popularity of Android devices, Android malware is seriously threatening the safety of users. Although such threats can be detected by deep learning as a service (DLaaS), deep neural networks as the weakest part of DLaaS are often deceived by the adversarial samples elaborated by attackers. In this paper, we propose a new semi-black-box attack framework called one-feature-each-iteration (OFEI) to craft Android adversarial samples. This framework modifies as few features as possible and requires less classifier information to fool the classifier. We conduct a controlled experiment to evaluate our OFEI framework by comparing it with the benchmark methods JSMF, GenAttack and pointwise attack. The experimental results show that our OFEI has a higher misclassification rate of 98.25%. Furthermore, OFEI can extend the traditional white-box attack methods in the image field, such as fast gradient sign method (FGSM) and DeepFool, to craft adversarial samples for Android. Finally, to enhance the security of DLaaS, we use two uncertainties of the Bayesian neural network to construct the combined uncertainty, which is used to detect adversarial samples and achieves a high detection rate of 99.28%.
While artificial intelligence (AI) applications are becoming increasingly capable of solving even the most complex of our problems requiring human-like cognition, the black box of artificial intelligence makes it difficult for us to understand how these systems actually go about solving these problems. Although humans had always known about the existence of fire, it was only when we learned to control or "tame" fire that we really kickstarted the journey of rapid technological progress and evolution we currently find ourselves in. Now, over a million years later, we find ourselves at a similar juncture--albeit faced with an entity that we created instead of a natural phenomenon. The creation of artificial intelligence, undoubtedly, is a step into an era of unprecedented growth unlike any we've seen before. But, a true leap can only be achieved once we "tame" the technology, as it were, by first illuminating the black box of artificial intelligence that will enable us to better control the outcomes affected by the technology.
Gupta, Abhishek, Royer, Alexandrine, Wright, Connor, Khan, Falaah Arif, Heath, Victoria, Galinkin, Erick, Khurana, Ryan, Ganapini, Marianna Bergamaschi, Fancy, Muriam, Sweidan, Masa, Akif, Mo, Butalid, Renjie
The 3rd edition of the Montreal AI Ethics Institute's The State of AI Ethics captures the most relevant developments in AI Ethics since October 2020. It aims to help anyone, from machine learning experts to human rights activists and policymakers, quickly digest and understand the field's ever-changing developments. Through research and article summaries, as well as expert commentary, this report distills the research and reporting surrounding various domains related to the ethics of AI, including: algorithmic injustice, discrimination, ethical AI, labor impacts, misinformation, privacy, risk and security, social media, and more. In addition, The State of AI Ethics includes exclusive content written by world-class AI Ethics experts from universities, research institutes, consulting firms, and governments. Unique to this report is "The Abuse and Misogynoir Playbook," written by Dr. Katlyn Tuner (Research Scientist, Space Enabled Research Group, MIT), Dr. Danielle Wood (Assistant Professor, Program in Media Arts and Sciences; Assistant Professor, Aeronautics and Astronautics; Lead, Space Enabled Research Group, MIT) and Dr. Catherine D'Ignazio (Assistant Professor, Urban Science and Planning; Director, Data + Feminism Lab, MIT). The piece (and accompanying infographic), is a deep-dive into the historical and systematic silencing, erasure, and revision of Black women's contributions to knowledge and scholarship in the United Stations, and globally. Exposing and countering this Playbook has become increasingly important following the firing of AI Ethics expert Dr. Timnit Gebru (and several of her supporters) at Google. This report should be used not only as a point of reference and insight on the latest thinking in the field of AI Ethics, but should also be used as a tool for introspection as we aim to foster a more nuanced conversation regarding the impacts of AI on the world.