Leading figures in the field of artificial intelligence discussed its present and potential future impact on individuals and nations at the John F. Kennedy, Jr. Forum Friday. Kennedy School lecturer and national security expert Juliette N. Kayyem introduced the topic by discussing the current prevalence of artificial intelligence and the significant shifts the technology may cause in relations between various groups and industries. She noted the tensions that may arise when trying to find a role for artificial intelligence in everyday life. Panelists then discussed how artificial intelligence has influenced their particular work and society more generally. Edward W. Felten, the deputy chief technology officer of the White House Office of Science and Technology Policy, commented on a recent White House report that explained the challenges faced when trying to incorporate artificial intelligence into the government.

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.

In the previous example we used clustering to see if an apparent pattern exists within Brexit tweets. We found out that we have three distinct patterns, the leave, the referendum, and Brexit. This in itself helps us think that we may even create a classifier that can identify if the tweet writer is pro or agains an issue automatically, with no human intervention. Let's get back to the issues related to clustering. To use the clustering algorithm we had to map 2 tweets at the time to a binary vector.

The technology industry loves throwing around the term machine learning (ML). It's used in a variety of contexts, from technology providers claiming to have "invented the math" behind machine learning, to others applying it to less than scientific outcomes. This doesn't help the fact that the science of ML as it applies to cybersecurity is probably one of the most complex and least understood topics today. To bring some clarity to the topic, let's walk through five key steps you'll need to take to develop and operationalize a true ML system capable of predicting an outcome based on the data it trains on. "Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data."

In explaining his rationale for deposing CEO Adam Nash and reinserting himself in the top spot, Wealthfront co-founder Andy Rachleff put his faith in artificial intelligence over people: "We understand that many older investors who meet the high minimums of the traditional industry will continue to find more comfort in a personal relationship with a traditional advisor and we respect that." Rachleff went on to explain that his company is building an AI-powered offering for a new generation of investors who supposedly won't ever need a human touch. We respect Rachleff and Wealthfront, but disagree fully with this view. We could cite growing competition from deep-pocketed incumbents, but that's not our main objection. Our view is that bionic advice -- where human skill is augmented with technology -- is the future of the advisory business.

Experts with the Word Economic Forum (WEF) say that there will be seven million less jobs available in 2020 than now. Oxford University experts warn that 20 years from now 39 percent of people will lose their jobs. Industrial workers will be the next to be phased out by the onslaught of intelligent robots. WEF experts insist that the loss of jobs in some sectors will made up for by new jobs available on other economic sectors. According to them, there will be 2 million more high-tech jobs available four to five years from now.

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.

Technology is a big part of healthcare. In a 2014 McKinsey survey, more than 75% of patients polled said that they would like to use digital healthcare services, as long as those services meet their needs and provide the level of quality they expect. And yet the healthcare industry lags behind every other sector when it comes to implementing technology. HIPAA Journal writes, "In some cases, the new technology now being introduced by healthcare providers was first introduced in other industry sectors many years ago." A break in that trend has come from the surge of wearable devices. Getting beyond counting strides and counting calories, the healthcare industry has seen tremendous growth in wearable and wireless technologies that can monitor serious diseases.

Imagine a typical day in 2020: Your personal AI assistant wakes you up with a friendly greeting before preparing your favorite breakfast. During your morning workout, it plays new songs that perfectly match your musical tastes. For your driverless commute to work, it has pre-selected a few articles based on the duration of your commute and what you've read in the past. You read the news and realize the presidential election is coming up. Based on a predicted model that takes into account your previously expressed views and data on other voters in your state, your AI assistant recommends you vote for the Democratic candidate.

We introduce the study of fairness in multi-armed bandit problems. Our fairness definition can be interpreted as demanding that given a pool of applicants (say, for college admission or mortgages), a worse applicant is never favored over a better one, despite a learning algorithm's uncertainty over the true payoffs. We prove results of two types. First, in the important special case of the classic stochastic bandits problem (i.e., in which there are no contexts), we provide a provably fair algorithm based on "chained" confidence intervals, and provide a cumulative regret bound with a cubic dependence on the number of arms. We further show that any fair algorithm must have such a dependence. When combined with regret bounds for standard non-fair algorithms such as UCB, this proves a strong separation between fair and unfair learning, which extends to the general contextual case. In the general contextual case, we prove a tight connection between fairness and the KWIK (Knows What It Knows) learning model: a KWIK algorithm for a class of functions can be transformed into a provably fair contextual bandit algorithm, and conversely any fair contextual bandit algorithm can be transformed into a KWIK learning algorithm. This tight connection allows us to provide a provably fair algorithm for the linear contextual bandit problem with a polynomial dependence on the dimension, and to show (for a different class of functions) a worst-case exponential gap in regret between fair and non-fair learning algorithms