Summer is winding down and that means the return to the classroom just around the corner, however, the school supply list isn't the only way to prep for the upcoming academic year. Outfitting your residence with smart home gadgets can make the busy back-to-school-season easier for the whole family. Does your student walk home from school? A digital smart lock can be a lifesaver if your child ever loses his or her house keys. Don't want your child fiddling with the air conditioner after coming home from soccer practice?

This paper presents a physics-based data-driven method to learn predictive reduced-order models (ROMs) from high-fidelity simulations, and illustrates it in the challenging context of a single-injector combustion process. The method combines the perspectives of model reduction and machine learning. Model reduction brings in the physics of the problem, constraining the ROM predictions to lie on a subspace defined by the governing equations. This is achieved by defining the ROM in proper orthogonal decomposition (POD) coordinates, which embed the rich physics information contained in solution snapshots of a high-fidelity computational fluid dynamics (CFD) model. The machine learning perspective brings the flexibility to use transformed physical variables to define the POD basis. This is in contrast to traditional model reduction approaches that are constrained to use the physical variables of the high-fidelity code. Combining the two perspectives, the approach identifies a set of transformed physical variables that expose quadratic structure in the combustion governing equations and learns a quadratic ROM from transformed snapshot data. This learning does not require access to the high-fidelity model implementation. Numerical experiments show that the ROM accurately predicts temperature, pressure, velocity, species concentrations, and the limit-cycle amplitude, with speedups of more than five orders of magnitude over high-fidelity models. Moreover, ROM-predicted pressure traces accurately match the phase of the pressure signal and yield good approximations of the limit-cycle amplitude.

Natural language understanding (NLU) of text is a fundamental challenge in AI, and it has received significant attention throughout the history of NLP research. This primary goal has been studied under different tasks, such as Question Answering (QA) and Textual Entailment (TE). In this thesis, we investigate the NLU problem through the QA task and focus on the aspects that make it a challenge for the current state-of-the-art technology. This thesis is organized into three main parts: In the first part, we explore multiple formalisms to improve existing machine comprehension systems. We propose a formulation for abductive reasoning in natural language and show its effectiveness, especially in domains with limited training data. Additionally, to help reasoning systems cope with irrelevant or redundant information, we create a supervised approach to learn and detect the essential terms in questions. In the second part, we propose two new challenge datasets. In particular, we create two datasets of natural language questions where (i) the first one requires reasoning over multiple sentences; (ii) the second one requires temporal common sense reasoning. We hope that the two proposed datasets will motivate the field to address more complex problems. In the final part, we present the first formal framework for multi-step reasoning algorithms, in the presence of a few important properties of language use, such as incompleteness, ambiguity, etc. We apply this framework to prove fundamental limitations for reasoning algorithms. These theoretical results provide extra intuition into the existing empirical evidence in the field.

--Automated machine learning has gained a lot of attention recently. Building and selecting the right machine learning models is often a multi-objective optimization problem. General purpose machine learning software that simultaneously supports multiple objectives and constraints is scant, though the potential benefits are great. In this work, we present a framework called Autotune that effectively handles multiple objectives and constraints that arise in machine learning problems. Autotune is built on a suite of derivative-free optimization methods, and utilizes multilevel parallelism in a distributed computing environment for automatically training, scoring, and selecting good models. Incorporation of multiple objectives and constraints in the model exploration and selection process provides the flexibility needed to satisfy tradeoffs necessary in practical machine learning applications. Experimental results from standard multi-objective optimization benchmark problems show that Autotune is very efficient in capturing Pareto fronts. These benchmark results also show how adding constraints can guide the search to more promising regions of the solution space, ultimately producing more desirable Pareto fronts. Results from two real-world case studies demonstrate the effectiveness of the constrained multi-objective optimization capability offered by Autotune. There has been increasing interest in automated machine learning (AutoML) for improving data scientists' productivity and reducing the cost of model building. A number of general or specialized AutoML systems have been developed [1]- [7], showing impressive results in creating good models with much less manual effort. Most of these systems only support a single objective, typically accuracy or error, to assess and compare models during the automation process.

State-of-the-art machine learning frameworks support a wide variety of design features to enable a flexible machine learning programming interface and to ease the programmability burden on machine learning developers. Identifying and using a performance-optimal setting in feature-rich frameworks, however, involves a non-trivial amount of performance characterization and domain-specific knowledge. This paper takes a deep dive into analyzing the performance impact of key design features and the role of parallelism. The observations and insights distill into a simple set of guidelines that one can use to achieve much higher training and inference speedup. The evaluation results show that our proposed performance tuning guidelines outperform both the Intel and TensorFlow recommended settings by 1.29x and 1.34x, respectively, across a diverse set of real-world deep learning models.

Artificial Intelligence, or AI for short, has become quite the public buzzword. Companies and investors are pouring money into the field. Universities -- even high schools -- are rushing to start new degree programs or colleges dedicated to AI. Civil society organizations are scrambling to understand the impact of AI technology on humanity, and governments are competing to encourage or regulate AI research and deployment. One country, the United Arab Emirates, even boasts a minister for AI. At the same time, the world's militaries are developing AI-based weaponry to defeat their enemies, police agencies are experimenting with AI as a surveillance tool to identify or interrogate suspects, and companies are testing its ability to replace humans in menial or more meaningful jobs -- all of which may change the equation of life for all of the world's people.

This video is part of an online course, End-to-End Machine Learning with Tensorflow from Google Cloud. About this course: In the first course of this specialization, we will recap what was covered in the Machine Learning with TensorFlow on Google Cloud Platform Specialization. One of the best ways to review something is to work with the concepts and technologies that you have learned.

Have anything in mind that you think is awesome and would fit in this list? Feel free to send a pull request.

Last week, I reviewed Thomas Siebel's surprisingly good Digital Transformation: Survive and Thrive in an Era of Mass Extinction. The components that makeup Siebel's digital transformation are: cloud computing, big data, IoT (internet of things), and AI (artificial intelligence). In that review, I asked if any books examine the impact of these technologies on the future of higher ed? But maybe that is the wrong question. A better approach might be to ask: what are the academic analogs of each of the components of digital transformation?

Today's operators are undergoing vast digital transformations to help shape their roadmaps for future innovation. That includes transforming existing networks to more virtualised environments and preparing for 5G. The new networks must be more robust and agile and at the same time, able to adapt to whatever the future will bring, Anand Gonuguntla, co-founder and CEO of Centina. Operators must also be prepared to manage a continued trend of software as a service and cloud-based service models. Assuring quality of existing and future services becomes both more challenging and more critical to these operators as the competition heats up.