In January, Amazon opened Amazon Go, a high-tech, cashierless convenience store in Seattle. There are no checkout lines and few employees. The only requirement to shop is downloading an app. Customers just walk in, load up their bags, and go. There's no need to even scan purchases; cameras positioned overhead take note of items in customers' carts and add them to a virtual bill. Amazon Go is both an interesting novelty -- and a profound challenge to the livelihoods of the more than 3.5 million Americans who work as cashiers. Rumors of a coming wave of similar stores and robot-run factories have provoked apocalyptic predictions of mass unemployment among pundits and politicians.

According to the Times, true artificial intelligence is just around the corner. A year ago, the paper ran a front-page story about the wonders of new technologies, including deep learning, a neurally-inspired A.I. technique for statistical analysis. Then, among others, came an article about how I.B.M.'s Watson had been repurposed into a chef, followed by an upbeat post about quantum computation. On Sunday, the paper ran a front-page story about "biologically inspired processors," "brainlike computers" that learn from experience. This past Sunday's story, by John Markoff, announced that "computers have entered the age when they are able to learn from their own mistakes, a development that is about to turn the digital world on its head."

In the early to mid-90's while my friends were getting into Indie Rock, I was hacking away at robots and getting them to learn to map a room. A computer science graduate student, I programmed LISP algorithms for parsing nursing records in order to predict intervention codes. I was no less a nerd (or to put it a better way, a technology enthusiast) in law school, when I wrote about how natural language processing can improve legal research tools. I didn't put much thought, either as a computer scientist or law student to whether artificial intelligence (AI) should be regulated. Frankly, we were in such the early days of the technology, that AI regulations seemed like science fiction a la Isaac Asimov's three laws of robotics.

Cognitive Science is the study of thought, learning, and mental organization, which draws on aspects of psychology major, linguistics, philosophy, and computer modeling. The Cognitive Science Major/Field is made up of a diverse number of different majors, like linguistics, cognition, neurobiology, artificial intelligence, law, and many more. Cognitive Science Students may ask themselves things like: What job can I get with cognitive science? What does a Cognitive Science Lecture look like? How does the major effect Cognitive Artificial Intelligence?

Irina Raicu is the director of the Internet Ethics program at the Markkula Center for Applied Ethics at Santa Clara University. Over in Santa Clara University's Leavey School of Business, professor Sanjiv Das teaches machine learning to graduate students enrolled in the MS of Information Systems program. As the Spring 2017 quarter was about to start, Subramaniam (Subbu) Vincent (the Tech Lead for the center's Trust Project, and an engineer-journalist with experience in data science) suggested that the two of them might collaborate in an effort to introduce the students to some key questions in data analytics: what do fairness and bias look like in the context of machine learning? And, if bias is detected in a dataset or an algorithm, are there ways to minimize or correct for it? In his hands-on, skill-building course, professor Das asked the students to work in small groups as they practiced predictive modeling on data sets--and proposed the fairness questions as one project option. Five of the groups took him up on the offer.

We consider the problem of minimizing a smooth convex function by reducing the optimization to computing the Nash equilibrium of a particular zero-sum convex-concave game. Zero-sum games can be solved using no-regret learning dynamics, and the standard approach leads to a rate of $O(1/T)$. But we are able to show that the game can be solved at a rate of $O(1/T^2)$, extending recent works of \cite{RS13,SALS15} by using \textit{optimistic learning} to speed up equilibrium computation. The optimization algorithm that we can extract from this equilibrium reduction coincides \textit{exactly} with the well-known \NA \cite{N83a} method, and indeed the same story allows us to recover several variants of the Nesterov's algorithm via small tweaks. This methodology unifies a number of different iterative optimization methods: we show that the \HB algorithm is precisely the non-optimistic variant of \NA, and recent prior work already established a similar perspective on \FW \cite{AW17,ALLW18}.

Minister of Employment, Workforce Development and Labour Patty Hajdu speaks with Lori Sterling, deputy minister of Labour and associate deputy minister of Employment and Social Development, as they appear at a Commons human resources committee hearing on Parliament Hill in Ottawa on Monday, Feb. 12, 2018. Federal officials overseeing billions in benefit payments to millions of Canadians are hoping machine learning tools can solve ongoing snags in the system. Federal officials overseeing billions in benefit payments to millions of Canadians are hoping artificial intelligence can resolve ongoing snags in the system. The government is looking to "push the boundaries" of what artificial intelligence can do to improve a variety of services, including the pace of benefit decisions to Canadians applying for disability pensions, say documents obtained by The Canadian Press under the access to information law. Employment and Social Development Canada is currently facing processes that are "slow, inefficient, inconsistent, and prone to error," reads a presentation about the AI efforts.

The use of facial recognition software is growing in China--s universities, ostensibly to improve security, but concerns are growing that it is used for monitoring students -- including foreing students -- and teachers, creating massive data bases on student attendance and movements around campus. Peking University in Beijing now screens students entering the university--s south-western gate by using a camera to scan their faces in a trial that began at the end of June to see if the technology can replace the use of university identity cards. The system scans through a database of thousands of photographs taken for student and staff identity cards, using a powerful system to match the photograph against a database of thousands of others. Facial recognition devices have already been installed outside the university--s libraries, classrooms, student accommodation, sports facilities and computer centres, but these match a face to an existing photograph of that person on the database rather than sifting through the entire database. Photographs can be retaken in the guard room at the gates if the photos do not quite match, according to the university--s social media account on Sina Weibo, though it does not say what the failure rate is -- in particular for foreign students.

As STEM (science, technology, engineering, and mathematics) education becomes more centered on active learning practices, what happens to students' anxiety levels? Specifically, what aspects of evaluative active learning practices cause student anxiety to increase or decrease? After measuring students' baseline anxiety levels, Cooper et al. conducted semi-structured interviews to explore how students' anxiety levels were altered in an active learning classroom. Results show that the way that the active learning activity is implemented and the extent to which students perceive the activity to be beneficial influence its effect on their anxiety. The authors encourage instructors to consider student anxiety when implementing active learning.

An entrepreneur who follows his father into an established industry is likely to enjoy greater success than peers whose parents didn't work in that field, recent research suggests. At the same time, innovative, high-IQ entrepreneurs are more likely to strike out on their own in search of greater rewards, researchers found after analyzing statistics on young and middle-aged Norwegian men and businesses started in Norway from 1999 to 2007. Their findings indicate that different advantages – inherited industry knowledge for some, great intellectual talent for others – take entrepreneurs on different paths to different kinds of success. "A majority of male entrepreneurs start a firm in the same or a closely related industry as their fathers' industry of employment," the researchers wrote in a National Bureau of Economic Research working paper issued early this year. "This tendency is correlated with intelligence: higher-IQ entrepreneurs are less likely to follow their fathers," they said. In fact, the authors found that entrepreneurs with higher cognitive test scores are far more likely to go into the tech industry.