One objection to conventional AI ethics is that it slows innovation. This presentation responds by reconfiguring ethics as an innovation accelerator. The critical elements develop from a contrast between Stability AI's Diffusion and OpenAI's Dall-E. By analyzing the divergent values underlying their opposed strategies for development and deployment, five conceptions are identified as common to acceleration ethics. Uncertainty is understood as positive and encouraging, rather than discouraging. Innovation is conceived as intrinsically valuable, instead of worthwhile only as mediated by social effects. AI problems are solved by more AI, not less. Permissions and restrictions governing AI emerge from a decentralized process, instead of a unified authority. The work of ethics is embedded in AI development and application, instead of functioning from outside. Together, these attitudes and practices remake ethics as provoking rather than restraining artificial intelligence.

This is an archived copy of the 2019-2020 catalog. To access the most recent version of the catalog, please visit http://bulletin.iit.edu. A maximum of three courses may be shared between the Artificial Intelligence minor and the Computational Structures minor.

Recent advancements in quantum computing have driven the scientific community's quest to solve a certain class of complex problems for which quantum computers would be better suited than traditional supercomputers. To improve the efficiency with which quantum computers can solve these problems, scientists are investigating the use of artificial intelligence approaches. In a new study, scientists at the U.S. Department of Energy's (DOE) Argonne National Laboratory have developed a new algorithm based on reinforcement learning to find the optimal parameters for the Quantum Approximate Optimization Algorithm (QAOA), which allows a quantum computer to solve certain combinatorial problems such as those that arise in materials design, chemistry and wireless communications. "It's a bit like having a self-driving car in traffic; the algorithm can detect when it needs to make adjustments in the'dials' it uses to do the computation." "Combinatorial optimization problems are those for which the solution space gets exponentially larger as you expand the number of decision variables," said Argonne computer scientist Prasanna Balaprakash.

The Illinois Institute of Technology College of Science is pleased to announce the establishment of the Dr. Frederica Darema Lecture Series in Computer Science. This permanent fund will help advance female and minority early-stage computer science researchers at U.S. academic institutions. The lecture series is designed to encourage women and individuals from under-represented groups to pursue academic careers in computer sciences, and focuses on providing speaking opportunities for tenure track assistant professors (or the equivalent) at U.S. institutions in their fourth to sixth year. Lectureships may also be awarded to exceptional junior researchers in U.S. federal or industrial research laboratories in their third to fifth years of career, following doctoral/postdoctoral studies. Artificial Intelligence researcher Judy Hoffman, assistant professor in the School of Interactive Computing at Georgia Tech, will be the inaugural guest speaker for the Frederica Darema Lecture Series giving a lecture on "How Dataset Bias Leads to Learned Model Failures."

Machine Learning, often abbreviated to ML, is a form of learning in which systems use complex computer algorithms to acquire knowledge or skill automatically without being programmed directly. It is considered as a type of AI (Artificial Intelligence) since machines are built with the idea to learn and make decisions from the available data and even improve themselves from experience without requiring human involvement. This is mainly used to maximize the machine's performance. The idea behind ML is based on mathematics, computer science, and statistics. Additionally, great scientists such as Andrey Markov, Thomas Bayes, and Carl Friedrich Gauss have contributed in the invention of statistical models like Markov Chains, Bayes Theorem, and the method of Least-Square respectively which are used a great deal in the Machine Learning algorithms.

When self-driving cars get here, they'll make our commutes more efficient and allow us to get the kids to soccer practice without disrupting mom and dad's work days. They'll conserve resources, boost mobility for seniors and others who can't, and make deadly traffic accidents all but disappear. But the impact of self-driving cars will go deeper than even that, according to researchers at the Illinois Institute of Technology, who've begun to study the potential ultra-long-range impacts of self-driving cars on urban environments. Everything from sidewalks and curbs to streets, building designs, urban layouts, and living patterns will change as computers take the wheel. "We're looking at the broader urban effects--and urban opportunities--of this technology," says Illinois Tech architect Marshall Brown, one of the team members in the Chicago school's Driverless Cities Project. "It's in the news a lot, but nobody's been discussing what it will actually do to cities." Just six percent of long-range transportation plans in major US cities are factoring the impact of autonomous cars, according to a report released in the fall by the National League of Cities.

"There is a stigma against market timing. This stigma existed for good reasons, but the explosion of vast data sets and new analytical techniques has now made timing the market possible. Just as it was considered irresponsible to time the market over the last 30 years, it will be considered irresponsible NOT to time the market in the next 30 years." Light food and drinks provided. The speakers will be Blair Hull, most recently founder of Ketchum Trading, and Matthew Dixon of the Illinois Institute of Technology's Stuart School of Business and founder of Quiota, LLC.

When self-driving cars get here, they'll make our commutes more efficient and allow us to get the kids to soccer practice without disrupting mom and dad's work days. They'll conserve resources, boost mobility for seniors and others who can't, and make deadly traffic accidents all but disappear. But the impact of self-driving cars will go deeper than even that, according to researchers at the Illinois Institute of Technology, who've begun to study the potential ultra-long-range impacts of self-driving cars on urban environments. Everything from sidewalks and curbs to streets, building designs, urban layouts, and living patterns will change as computers take the wheel. "We're looking at the broader urban effects--and urban opportunities--of this technology," says Illinois Tech architect Marshall Brown, one of the team members in the Chicago school's Driverless Cities Project. "It's in the news a lot, but nobody's been discussing what it will actually do to cities." Just six percent of long-range transportation plans in major US cities are factoring the impact of autonomous cars, according to a report released in the fall by the National League of Cities.