Whenever we look at any online course, they take off with linear regression and this is a concept that most of us know, that is, an equation of a line initially and then gradually fitting of the best fit line. The application of this algorithm is used in machine learning as a way to predict results in the future given the feature vectors, x. So, why is the cost function a squared cost function? Why not have an absolute cost function? Well, there are plenty of reasons as to why we consider this, but when we derive this mathematically, we come across the concept of exponential families under general linear models, which generalize the notion of loss functions for any given model, and thus the square function is actually an exponential family curve.

We present Web-STAR, an online platform for story understanding built on top of the STAR reasoning engine for STory comprehension through ARgumentation. The platform includes a web-based IDE, integration with the STAR system, and a web service infrastructure to support integration with other systems that rely on story understanding functionality to complete their tasks. The platform also delivers a number of "social" features, including a community repository for public story sharing with a built-in commenting system, and tools for collaborative story editing that can be used for team development projects and for educational purposes.

Like a zombie, it keeps coming back. Like zombie movies, it enjoys growing popularity by defying logic and common sense. Chicago and Stockton (CA) have launched the most recent proposals for Universal Basic Income (UBI). That the idea appeals to cities that have gone bankrupt or have unsustainable financial prospects should give us pause. Universal Basic Income is "…a periodic cash payment unconditionally delivered to all on an individual basis, without means-test or work requirement."

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.