Collaborating Authors


Artificial Intelligence Ranks Alamo Group Inc Among Today's Top Buys


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 have crunched the data to give you a set of Top Buys.

Travel Is Coming Back, and Artificial Intelligence May Be Planning Your Next Flight


There are dozens of routes that Alaska Airlines Flight 1405 can take from Oklahoma City to Seattle, and dispatcher Brad Ward zeroed in on what he thought was the best one, taking into account weather, wind speeds, and other air traffic. But his new colleague at the Alaska Airlines operations center had other thoughts. A storm cell near Oklahoma City was likely to turn into a thunderstorm around the time Flight 1405 took off, and the airspace north of Amarillo would be closed for military exercises. Better to reroute, the young colleague said, suggesting an alternative that Ward admitted was safer and more efficient. The entire conversation lasted just seconds and passed without a word being spoken: a red box lit up on Ward's computer screen when the colleague, an artificial intelligence program he has affectionately nicknamed Algo, had an idea.

Your Call May Be Recorded (and Analyzed by a Bot) WSJD - Technology

"We can now access customer data that's been previously kind of locked away in call recordings," said Ian Jacobs, vice president research director at Forrester Research Inc., which predicts U.S. businesses will spend roughly $7 billion on contact center systems in 2021. "That means we're going to see a flood of new use cases for that data." Companies don't believe they can ignore the call-center experience they provide, despite millennials' so-called phone phobia and the proliferation of chatbots. Some 89% of companies expect phone communication to continue playing a role in customer care, according to industry publication Customer Contact Week. Verizon Communications Inc. is using technology from Afiniti Ltd. that uses artificial intelligence to connect callers with the agents who are calculated to have the best chance of keeping them loyal.

How Machine Learning Is Changing Commercial Flight - Simple Flying


Artificial Intelligence is rolling out across the aviation industry to a greater and greater extent. It could even hold the key to a speedier post-pandemic recovery. Let's take a look at how its branch of machine learning is already impacting everyday aspects of travel, including how tickets are priced, point-to-point routes, fuel consumption optimization, and biometric boarding. "AI is coming and it will have no mercy for any obstacles on its way. Companies can choose to resist and maintain status quo to extend their survival period, or embrace AI and be part of the ongoing revolution," – IATA, AI in Aviation White Paper, 2018.

Enabling Integration and Interaction for Decentralized Artificial Intelligence in Airline Disruption Management Artificial Intelligence

Airline disruption management traditionally seeks to address three problem dimensions: aircraft scheduling, crew scheduling, and passenger scheduling, in that order. However, current efforts have, at most, only addressed the first two problem dimensions concurrently and do not account for the propagative effects that uncertain scheduling outcomes in one dimension can have on another dimension. In addition, existing approaches for airline disruption management include human specialists who decide on necessary corrective actions for airline schedule disruptions on the day of operation. However, human specialists are limited in their ability to process copious amounts of information imperative for making robust decisions that simultaneously address all problem dimensions during disruption management. Therefore, there is a need to augment the decision-making capabilities of a human specialist with quantitative and qualitative tools that can rationalize complex interactions amongst all dimensions in airline disruption management, and provide objective insights to the specialists in the airline operations control center. To that effect, we provide a discussion and demonstration of an agnostic and systematic paradigm for enabling expeditious simultaneously-integrated recovery of all problem dimensions during airline disruption management, through an intelligent multi-agent system that employs principles from artificial intelligence and distributed ledger technology.

American Airlines just revealed the future (you may feel very uncomfortable)


It's going to be different. Airlines offered so much good news last week that it was hard to know how happy to be. Delta, United, and Alaska all made noises about soon breaking even. Gary Kelly, Southwest's CEO chirped that this "feels like the beginning of the end." So along came American Airlines to helpfully prepare customers for what the end might really look like.

Andrei Papancea, CEO at NLX – Interview Series


Andrei Papancea, is the CEO at NLX a comprehensive SaaS platform for building and managing AI-powered conversational applications at scale. Previously, he built the Natural Language Understanding platform for American Express, processing millions of conversations across AmEx's main servicing channels. You grew up in Romania and started programming when you were 10 years old. What attracted you to programming at such a young age? It started off as curiosity: I've always been intrigued about how things worked and since my family has just gotten a computer, I wanted to figure out how it worked.

On the Subbagging Estimation for Massive Data Machine Learning

This article introduces subbagging (subsample aggregating) estimation approaches for big data analysis with memory constraints of computers. Specifically, for the whole dataset with size $N$, $m_N$ subsamples are randomly drawn, and each subsample with a subsample size $k_N\ll N$ to meet the memory constraint is sampled uniformly without replacement. Aggregating the estimators of $m_N$ subsamples can lead to subbagging estimation. To analyze the theoretical properties of the subbagging estimator, we adapt the incomplete $U$-statistics theory with an infinite order kernel to allow overlapping drawn subsamples in the sampling procedure. Utilizing this novel theoretical framework, we demonstrate that via a proper hyperparameter selection of $k_N$ and $m_N$, the subbagging estimator can achieve $\sqrt{N}$-consistency and asymptotic normality under the condition $(k_Nm_N)/N\to \alpha \in (0,\infty]$. Compared to the full sample estimator, we theoretically show that the $\sqrt{N}$-consistent subbagging estimator has an inflation rate of $1/\alpha$ in its asymptotic variance. Simulation experiments are presented to demonstrate the finite sample performances. An American airline dataset is analyzed to illustrate that the subbagging estimate is numerically close to the full sample estimate, and can be computationally fast under the memory constraint.

Distributed Bootstrap for Simultaneous Inference Under High Dimensionality Machine Learning

We propose a distributed bootstrap method for simultaneous inference on high-dimensional massive data that are stored and processed with many machines. The method produces a $\ell_\infty$-norm confidence region based on a communication-efficient de-biased lasso, and we propose an efficient cross-validation approach to tune the method at every iteration. We theoretically prove a lower bound on the number of communication rounds $\tau_{\min}$ that warrants the statistical accuracy and efficiency. Furthermore, $\tau_{\min}$ only increases logarithmically with the number of workers and intrinsic dimensionality, while nearly invariant to the nominal dimensionality. We test our theory by extensive simulation studies, and a variable screening task on a semi-synthetic dataset based on the US Airline On-time Performance dataset. The code to reproduce the numerical results is available at GitHub:

Uncertainty Quantification and Propagation for Airline Disruption Management Artificial Intelligence

Disruption management during the airline scheduling process can be compartmentalized into proactive and reactive processes depending upon the time of schedule execution. The state of the art for decision-making in airline disruption management involves a heuristic human-centric approach that does not categorically study uncertainty in proactive and reactive processes for managing airline schedule disruptions. Hence, this paper introduces an uncertainty transfer function model (UTFM) framework that characterizes uncertainty for proactive airline disruption management before schedule execution, reactive airline disruption management during schedule execution, and proactive airline disruption management after schedule execution to enable the construction of quantitative tools that can allow an intelligent agent to rationalize complex interactions and procedures for robust airline disruption management. Specifically, we use historical scheduling and operations data from a major U.S. airline to facilitate the development and assessment of the UTFM, defined by hidden Markov models (a special class of probabilistic graphical models) that can efficiently perform pattern learning and inference on portions of large data sets.