The Computer Science Department's new course focusing on issues of justice, equity, diversity and inclusion in computer science and society got its start when a group of graduate students decided to create the training they wished they had received. And after hundreds of hours of work by 15 Ph.D. students --pilot programs, countless conversations with faculty and students, data gathering, and developing and tweaking course material -- CS-JEDI: Justice, Equity, Diversity and Inclusion is now a required part of the curriculum for incoming Ph.D. students in computer science. It's also being looked at as a model by both other departments in the School of Computer Science and universities elsewhere. The course was created and taught by Abhinav Adduri, Valerie Chen, Judeth Choi, Bailey Flanigan, Paul Göelz, Anson Kahng, Pallavi Koppol, Ananya Joshi, Tabitha Lee, Sara McAllister, Samantha Reig, Ziv Scully, Catalina Vajiac, Alex Wang and Josh Williams -- all doctoral candidates in SCS who represent nearly every department in the school. The team received Carnegie Mellon University's 2022 Graduate Student Service Award and will be honored during the Celebration of Education Award Ceremony on Thursday, April 28.

We've acquired Dessa, a Toronto-based company building machine learning applications that address significant real-world challenges for all types of businesses. Their team of world-class engineers will immediately bolster our machine learning and artificial intelligence capabilities at Square. Machine learning is a critical field in technology today, and we've expanded our machine learning work at Square over time through both in-house development and acquisitions like Eloquent Labs. The acquisition of Dessa will help us further boost our machine learning abilities, improve our products, and ultimately pass on the benefits to our customers around the world. For example, machine learning technology can help us enhance products in areas like customer engagement, risk management, and more.

The programme aims to produce AI engineers with a highly relevant skillset in AI topics. Students will learn how to use and develop intelligent computer systems that can learn from experience, recognise patterns in vast amounts of data and reason strategically in complex decision-making situations. The programme content will deliver a comprehensive range of topics integral to the study of AI. These include machine learning, deep learning, natural language processing, optimisation, anomaly detection and big data processing to name but a few. The programme will start in September 2018 producing the first AI graduates in 2019.

When David Stinson finished high school, in Grand Rapids, Michigan, in 1977, the first thing he did was get a job building houses. After a few years, though, the business slowed. Stinson was then twenty-four, with two children to support. As he explained over lunch recently, that meant finding a job at one of the two companies in the area that offered secure, blue-collar work. "Either I'll be working at General Motors or I'll be working at Steelcase by the end of the year," he vowed in 1984.

Using a new technique we invented to efficiently evolve DNNs, we were surprised to discover that an extremely simple genetic algorithm (GA) can train deep convolutional networks with over 4 million parameters to play Atari games from pixels, and on many games outperforms modern deep reinforcement learning (RL) algorithms (e.g. This result is surprising both because GAs, which are not gradient-based, were not expected to scale well to such large parameter spaces and also because matching or outperforming the state-of-the-art in RL using GAs was not thought to be possible. We further show that modern GA enhancements that improve the power of GAs, such as novelty search, also work at DNN scales and can promote exploration to solve deceptive problems (those with challenging local optima) that stymie reward-maximizing algorithms such as Q-learning (DQN), policy gradients (A3C), ES, and the GA.

Adding further understanding, a companion study confirms empirically that ES (with a large enough perturbation size parameter) acts differently than SGD would, because it optimizes for the expected reward of a population of policies described by a probability distribution (a cloud in the search space), whereas SGD optimizes reward for a single policy (a point in the search space). In practice, SGD in RL is accompanied by injecting parameter noise, which turns points in the search space into clouds (in expectation).

AI is clearly a hot topic for insurers. InsurTech startups are creating compelling new use cases and applications for data, algorithms and AI within the insurance space. It is, indeed, an exciting time for insurance. Yet many traditional insurers may read Rick's article with concern and potential fear. In a recent survey by KPMG International, 91 percent of insurance CEOs admitted being worried about the challenge of integrating automation, AI and cognitive robotics into their existing business and operating models.

AI is clearly a hot topic for insurers. And, as Rick's article aptly points out, InsurTech startups are creating compelling new use cases and applications for data, algorithms and AI within the insurance space. It is, indeed, an exciting time for insurance. Yet many traditional insurers may read Rick's article with concern and potential fear. In a recent survey by KPMG International, 91 percent of insurance CEOs admitted being worried about the challenge of integrating automation, AI and cognitive robotics into their existing business and operating models.

Ignoring everything they've seen throughout the Terminator franchise, a group of Japanese researchers have come up with a computer that writes short stories… and it's actually produced a piece of work that got through the first round of a literary competition. Creative writing as a manufactured commodity – that's a scary thought, but perhaps it's inevitable. Companies love automation: feed a few instructions in one end and get a finished product out of the other. It's always been a popular notion that there are only a handful of stories and that everything written is a variation on those; if that's true then why can't a machine just write a half decent story? And does it even have to be half-decent to sell by the thousands?

Danaher/Institute for Ethics and Emerging TechnologiesAlgorithms are everywhere, and in most ways they make our lives better. In the simplest terms, algorithms are procedures or formulas aimed at solving problems. Implemented on computers, they sift through big databases to reveal compatible lovers, products that please, faster commutes, news of interest, stocks to buy, and answers to queries. Dud dates or boring book recommendations are no big deal. But John Danaher, a lecturer in the law school at the National University of Ireland, warns that algorithmic decision-making takes on a very different character when it guides government monitoring and enforcement efforts.