We outline the “Learning Everywhere" paradigm -- a powerful scientific methodology of coupling learning methods to traditional HPC simulations. We present several examples of “Learning Everywhere” applications, their scientific impact, and effective performance improvements over traditional HPC simulations. Such applications require a fundamental re-examination of scientific programming and systems software. This talk will highlight middleware advances to enable "Learning Everywhere" algorithms and methods as the "natural" extreme-scale programming paradigm.
Despite intensive research over 10 years, Android malware detection still faces substantial challenges including frequent changes in the Android framework, the existence of noisy labels in large-scale up-to-date datasets, and the continuous evolution of Android malware. The consequences of ignoring these challenges are multifold. One is the fast decline of malware detection accuracy over time due to the use of out-of-date malware detection feature sets, the ignorance of new APIs and changed APIs, and the failure of capturing emerging malware patterns.
While much attention has been recently given to the social, ethical and political implications of fairness in artificial intelligence methods, practices and technologies, not much has been said about the formal (conceptual, mathematical, algorithmic) definitions of fairness and their conceptual adequacy. It is not clear, for example, whether group parity/equity captures equality amongst individuals and/or which one is more desirable in a given algorithmic function.
In this talk, I introduce three recent and/or ongoing projects that are representative of the work we do in my lab (Learner Corpus Research and Applied Data Science Lab). The first project investigates the features of academic language using multidimensional analysis (Biber, 1988; 2004) and a wide array of linguistic features extracted using an NLP pipeline with a number of post-processing steps.
Our daily lives are becoming increasingly dependent on a variety of smart cyber-physical infrastructures, such as smart cities and buildings, smart energy grid, smart transportation, smart healthcare, etc. Alongside, smartphones and sensor-based IoTs are empowering humans with fine-grained information and opinion collection through crowdsensing about events of interest, resulting in actionable inferences and decisions. This synergy has led to the cyber-physical-social (CPS) convergence with human in the loop, the goal of which is to improve the “quality” of life.
Deep networks were recently suggested to face the odds between accuracy (on clean natural data) and robustness (on adversarially perturbed data). Such a dilemma is shown to be rooted in the inherently higher sample complexity and/or model capacity, for learning a high-accuracy and robust classifier. In view of that, given a classification task, growing the model capacity appears to help draw a win-win between accuracy and robustness, yet at the expense of model size and latency.
With the maturing of AI and multiagent systems research, we have a tremendous opportunity to direct these advances towards addressing complex societal problems. We focus on the problems of public health and wildlife conservation, and present research advances in multiagent systems to address one key cross-cutting challenge: how to effectively deploy our limited intervention resources in these problem domains. We present our deployments from around the world as well as lessons learned that we hope are of use to researchers who are interested in AI for Social Impact.
Computer hacking—or the unauthorized access to or misuse of computers—has been a standard form of cybercrime for decades. However, in many jurisdictions, we also see law enforcement using similar techniques to gain access to suspects' computers, and several countries have recently enacted laws that allow the police to remotely access such devices.
Organizations have rapidly shifted infrastructure and applications over to public cloud computing services such as AWS, Google Cloud Platform, and Azure. Unfortunately, such services have security models that are substantially different and more complex than traditional enterprise security models. As a result, misconfiguration errors in cloud deployments have led to dozens of well-publicized breaches. This talk describes Thunder CTF, a scaffolded, scenario-based CTF for helping students learn about and practice cloud security skills.
How does the human brain use neural activity to create and represent meanings of the words, phrases, sentences and stories it reads? One way to study this question is to give people text to read while observing their brain activity. We have been doing such experiments with fMRI (1 mm spatial resolution) and MEG (1 msec time resolution) brain imaging, and developing novel machine learning approaches to analyze this data.