My absolute favorite for this kind of interview preparation is Steven Skiena's The Algorithm Design Manual. More than any other book it helped me understand just how astonishingly commonplace graph problems are -- they should be part of every working programmer's toolkit. The book also covers basic data structures and sorting algorithms, which is a nice bonus. Every 1 – pager has a simple picture, making it easy to remember.

"My absolute favorite for this kind of interview preparation is Steven Skiena's The Algorithm Design Manual. More than any other book it helped me understand just how astonishingly commonplace … graph problems are -- they should be part of every working programmer's toolkit. The book also covers basic data structures and sorting algorithms, which is a nice bonus. "Steven Skiena's Algorithm Design Manual retains its title as the best and most comprehensive practical algorithm guide to help identify and solve problems. This newly expanded and updated third edition of the best-selling classic continues to take the "mystery" out of designing algorithms, and analyzing their efficiency.

"Graphs have become very powerful, they're fundamental, and they're only becoming more and more important," said Dr. Steven Skiena, Director of the AI Institute at Stony Brook University. Graphs are a fundamental part of computer science, and they are only getting larger. Analyzing graphs using machine learning is powerful but requires translating them into numerical values. That's where graph algorithms come in. In this week's five-minute interview (conducted at GraphConnect 2018 in NYC), we spoke with Dr. Steven Skiena, author of The Algorithm Design Manual and The Data Science Design Manual, about his work on DeepWalk, slated to be incorporated into the Neo4j graph algorithm library.

A Fulbright Scholar and distinguished professor in the Department of Computer Science will head up Stony Brook University's new Institute for AI-Driven Discovery and Innovation. Steven Skiena, a computational biologist (among other talents) in his 30th year as a member of the SBU faculty, will direct the artificial intelligence-focused program, part of the university's College of Engineering and Applied Science. The institute will be the center point for myriad Stony Brook-based AI research efforts focused on the notion that artificial intelligence "should amplify human intelligence instead of replacing it," under an "overarching vision" the university calls "Human-Machine Symbiosis." "By leveraging their respective strengths to compensate each other's weaknesses, the human-machine partnership becomes mutually beneficial and far more potent at problem-solving than what either can do in isolation," the university said in a statement. What sounds like a "Star Trek" villain's monologued plot reveal is not nearly so ominous, notes Fotis Sotiropoulos, dean of the College of Engineering and Applied Sciences, who believes "intelligent machines" can greatly benefit human engineers, for instance.

About a week ago, Stanford University researchers posted online a study on the latest dystopian AI: They'd made a machine learning algorithm that essentially works as gaydar. After training it with tens of thousands of photographs from dating sites, the algorithm could perform better than a human judge in specific instances. For example, when given photographs of a gay white man and a straight white man taken from dating sites, the algorithm could guess which one was gay more accurately than actual people participating in the study.* They wanted to protect gay people. "[Our] findings expose a threat to the privacy and safety of gay men and women," wrote Michal Kosinski and Yilun Wang in the paper.

Graph embedding methods represent nodes in a continuous vector space, preserving information from the graph (e.g. by sampling random walks). There are many hyper-parameters to these methods (such as random walk length) which have to be manually tuned for every graph. In this paper, we replace random walk hyper-parameters with trainable parameters that we automatically learn via backpropagation. In particular, we learn a novel attention model on the power series of the transition matrix, which guides the random walk to optimize an upstream objective. Unlike previous approaches to attention models, the method that we propose utilizes attention parameters exclusively on the data (e.g. on the random walk), and not used by the model for inference. We experiment on link prediction tasks, as we aim to produce embeddings that best-preserve the graph structure, generalizing to unseen information. We improve state-of-the-art on a comprehensive suite of real world datasets including social, collaboration, and biological networks. Adding attention to random walks can reduce the error by 20% to 45% on datasets we attempted. Further, our learned attention parameters are different for every graph, and our automatically-found values agree with the optimal choice of hyper-parameter if we manually tune existing methods.

About a week ago, Stanford University researchers posted online a study on the latest dystopian AI: They'd made a machine learning algorithm that essentially works as gaydar. After training the algorithm with tens of thousands of photographs from a dating site, the algorithm could, for example, guess if a white man in a photograph was gay with 81 percent accuracy. They wanted to protect gay people. "[Our] findings expose a threat to the privacy and safety of gay men and women," wrote Michal Kosinski and Yilun Wang in the paper. They built the bomb so they could alert the public about its dangers.

Stanford's review board approved Kosinski and Wang's study. "The vast, vast, vast majority of what we call'big data' research does not fall under the purview of federal regulations," says Metcalf. Take a recent example: Last month, researchers affiliated with Stony Brook University and several major internet companies released a free app, a machine learning algorithm that guesses ethnicity and nationality from a name to about 80 percent accuracy. The group also went through an ethics review at the company that provided training list of names, although Metcalf says that an evaluation at a private company is the "weakest level of review that they could do."