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).

Though scientists have long known that mice can pick out scents -- the smell of food, say, or the odor of a predator -- they have been at a loss to explain how they are able to perform that seemingly complex task so easily. But a new study, led by Venkatesh Murthy, professor of molecular and cellular biology, suggests that the means of processing smells may be far simpler than researchers realized. Using a machine-learning algorithm, Murthy and colleagues were able to "train" a computer to recognize the neural patterns associated with various scents, and to identify whether specific odors were present in a mix of smells. The study is described in a Sept. 1 paper in the journal Neuron. Along with Murthy, the paper was co-authored by Alexander Mathis, Dan Rokni, and Vikrant Kapoor, postdoctoral fellows working in Murthy's lab, and Professor Matthias Bethge from the Werner Reichardt Centre for Integrative Neuroscience & Institute of Theoretical Physics in Germany.

Waiopehu College pupils, from left, Kate Nicholson, Niko Tofa and Sammy Heyward. More than 100 teens have explored the ever-evolving world of artificial intelligence. The 130 budding young scientists, from Freyberg High School, Manawatu College and Waiopehu College, learned about the benefits and difficulties faced in a technology-rich future at a conference in Levin on Friday. Mechanical masseuses and construction robots that could work in all weather conditions and give workers a sleep-in were among ideas the pupils – aged 11 to 13 – came up with for the future. Centre for Science and Citizenship founders Dr Deborah Stevens, left, and Dr Lynne Bowyer.

I describe here the projects that I worked on, as well as career progress, starting 25 years ago as a PhD student in statistics, until today, and the transformation from statistician to data scientist that occurred slowly and started more than 20 years ago. This also illustrates many applications of data science, most are still active. My interest in mathematics started when I was 7 or 8, I remember being fascinated by the powers of 2 in primary school, and later purchasing cheap russian math books (Mir publisher) translated in French, for my entertainement. In high school, I participated in the mathematical olympiads, and did my own math research during math classes, rather than listening to the very boring lessons. When I attended college, I stopped showing up in the classroom altogether - afterall, you could just read the syllabus, memorize the material before the exam and regurgitate it at the exam.

The overall goal is to target treatment specifically to each individual so that clinical outcomes for that individual are optimized. One direction of attack is to use patient data to discover decision rules which specify the treatment to use as a function of a vector of features from the patient. Regression and classification are important statistical tools for estimating such rules based on either observational data or data from a randomized trial, and machine learning can help with this because of its ability to artfully handle high dimensional feature spaces with potentially complex interactions.

The use of robotic tutors in primary school classrooms is one step closer according to research recently published in the open access journal Frontiers in Computational Neuroscience. Dr Imbernòn Cuadrado and his co-workers at the Department of Artificial Intelligence in Madrid have developed an integrated computational architecture (ARTIE) for use with software applications in schools. "The main goal of our work was to design a system that can detect the emotional state of primary school children interacting with educational software and make pedagogic interventions with a robot tutor that can ultimately improve the learning experience," says Luis Imbernòn Cuadrado. Online educational resources are becoming increasingly common in the classroom, although they have not taken into sufficient account that the learning ability of primary school children is particularly sensitive to their emotional state. This is perhaps where robot tutors can step in to assist teachers.