Machine learning may once have been a topic of discussion only for computer scientists and researchers. Now, however, it is a technology businesses are eager to use. The need for machine learning and Artificial Intelligence (AI) is being driven by the massive amount of data being generated today. Statisticians can get insight from this data. But the volume is so large and growing at such a rate, the best way to tackle it is using the very same machines that are in part responsible for creating the data.

Automated machine learning has become a topic of considerable interest over the past several months. A recent KDnuggets blog competition focused on this topic, and generated a handful of interesting ideas and projects. Of note, our readers were introduced to Auto-sklearn, an automated machine learning pipeline generator, via the competition, and learned more about the project in a follow-up interview with its developers. Prior to that competition, however, KDnuggets readers were introduced to TPOT, "your data science assistant," an open source Python tool that intelligently automates the entire machine learning process. For scikit-learn-compatible datasets, TPOT can automatically optimize a series of feature preprocessors and machine learning models that maximize the dataset's cross-validation accuracy, and outputs the optimal model as Python code leveraging scikit-learn.

A major challenge in crowdsourcing evaluation tasks like labeling objects, grading assignments in online courses, etc., is that of eliciting truthful responses from agents in the absence of verifiability. In this paper, we propose new reward mechanisms for such settings that, unlike many previously studied mechanisms, impose minimal assumptions on the structure and knowledge of the underlying generating model, can account for heterogeneity in the agents' abilities, require no extraneous elicitation from them, and furthermore allow their beliefs to be (almost) arbitrary. These mechanisms have the simple and intuitive structure of an output agreement mechanism: an agent gets a reward if her evaluation matches that of her peer, but unlike the classic output agreement mechanism, this reward is not the same across evaluations, but is inversely proportional to an appropriately defined popularity index of each evaluation. The popularity indices are computed by leveraging the existence of a large number of similar tasks, which is a typical characteristic of these settings. Experiments performed on MTurk workers demonstrate higher efficacy (with a $p$-value of $0.02$) of these mechanisms in inducing truthful behavior compared to the state of the art.

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments.

"Every aspect of our lives will be transformed. In short, success in creating AI could be the biggest event in the history of our civilization." From self-driving vehicles to virtual assistants, artificial intelligence (AI) is evolving at a rapid pace. It has the potential for tremendous good – IBM's Watson improving cancer treatment with genomic sequencing as an example. Additionally, AI has been used to bring new art into the world – "Symphonologie" is an orchestra piece created with the help of AI.

On its 250th birthday, November 10, the Rutgers University community statewide will focus on these and many other provocative subjects as it hosts 80 of its alumni, noted for their thought leadership and innovation, for "A Day of Revolutionary Thinking" on the concluding day of activities associated with the university's yearlong celebration of its rich history. The university's special guests – which include a cybersecurity CEO, a biopharmaceutical company founder, a former New Jersey attorney general and an activist-artist – were invited to share their diverse points of view with students and to demonstrate how learning at Rutgers contributed to their successes. In anticipation of their presentations, Rutgers Today invited these innovators to discuss the "Next Big Thing" they envision occurring in their respective fields. Thomas Kennedy, '77, B.S. Electrical and Computer Engineering Given the increase in cybersecurity and the number of everyday items with network connectivity, securing the "internet of things" is imperative, stresses Kennedy, chair and CEO of Raytheon Company, which specializes in defense, civil government and cybersecurity solutions. "This is expanding exponentially with the number of things connected online," he says.

Today's blog post is inspired from an email I received from Jason, a student at the University of Rochester. Jason is interested in building a custom object detector using the HOG Linear SVM framework for his final year project. He understands the steps required to build the object detector well enough -- but he isn't sure how to evaluate the accuracy of his detector once it's trained. His professor mentioned that he should use the Intersection over Union (IoU) method for evaluation, but Jason's not sure how to implement it. My email really helped Jason finish getting his final year project together and I'm sure he's going to pass with flying colors.

Two crucial questions in discussions about the risks of artificial superintelligence are: 1) How much more capable could an AI become relative to humans, and 2) how easily could superhuman capability be acquired? To answer these questions, I will consider the literature on human expertise and intelligence, discuss its relevance for AI, and consider how an AI could improve on humans in two major aspects of thought and expertise, namely mental simulation and pattern recognition. I find that although there are very real limits to prediction, it seems like an AI could still substantially improve on human intelligence, possibly even mastering domains which are currently too hard for humans. In practice, the limits of prediction do not seem to pose much of a meaningful upper bound on an AI's capabilities, nor do we have any nontrivial lower bounds on how much time it might take to achieve a superhuman level of capability. Takeover scenarios with timescales on the order of mere days or weeks seem to remain within the range of plausibility. As AI systems become more advanced, there is the possibility of them reaching superhuman levels of intelligence, eventually breaking out of human control (Bostrom 2014). The answers to these questions will influence the urgency of dealing with questions of superintelligent AI, as well as the correct means of it. If AI systems can rapidly achieve strong capabilities, becoming powerful enough to take control of the world before any human can react, then that implies a very different approach than one where AI capabilities develop gradually over many decades, never getting substantially past the human level (Sotala & Yampolskiy, 2015). Views on these questions vary. Authors such as Bostrom (2014) and Yudkowsky (2008) argue for the possibility of a fast leap in intelligence, with both offering hypothetical example scenarios where an AI rapidly acquires a dominant position over humanity. On the other hand, Anderson (2010) and Lawrence (2016) appeal to fundamental limits on predictability – and thus intelligence – posed by the complexity of the environment. 'Practitioners who have performed sensitivity analysis on time series prediction will know how quickly uncertainty accumulates as you try to look forward in time. There is normally a time frame ahead of which things become too misty to compute any more. Further computational power doesn't help you in this instance, because uncertainty dominates. Reducing model uncertainty requires exponentially greater computation. We might try to handle this uncertainty by quantifying it, but even this can prove intractable.

Have you ever been in a situation where knowing another language would have come in handy? I remember standing on the platform at Tokyo Station watching my train to Nagano -- the last train of the day -- pulling away without me on it. What ensued was a frustrating hour of gestures, confused smiles, and head-shaking as I wandered the station looking for someone who spoke English (my Japanese is unfortunately nonexistent). It would have been really helpful to have a bilingual pal along with me to translate. Bilingual pals can be hard to find, but Google's new translation software may be an equally useful alternative.

Deep data science is a branch of data science that has little if any overlap with closely related fields such as machine learning, computer science, operations research, mathematics, or statistics. Even classical machine learning and statistical techniques such as clustering, density estimation, or tests of hypotheses, have model-free, data-driven, robust versions designed for automated processing (as in machine-to-machine communications), and thus these techniques also belong to deep data science. Note that unlike deep learning, deep data science is not the intersection of data science and artificial intelligence; however, the analogy between deep data science and deep learning is not completely meaningless, in the sense that both deal with automation. For a robust regression that will work even if all the traditional model assumptions are violated, click here. It is simple (it can be implemented in Excel and it is model-free), efficient and very comparable to the standard regression (when the model assumptions are not violated).