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Marvin Minsky

Communications of the ACM

Marvin Minsky, an American scientist working in the field of artificial intelligence (AI) who co-founded vthe Massachusetts Institute of Technology (MIT) AI laboratory, wrote several books on AI and philosophy, and was honored with the ACM A.M. Turing Award, passed away on Sunday, Jan. 24, 2016 at the age of 88. Born in New York City, Minsky attended the Ethical Culture Fieldston School, the Bronx High School of Science, and Phillips Academy, before entering the U.S. Navy in 1944. After leaving the service, he attended Harvard University, where he earned a bachelor's degree in mathematics in 1950. He then went to Princeton University, where he built the first randomly wired neural network learning machine, the Stochastic Neural Analog Reinforcement Calculator (SNARC), before earning his Ph.D in mathematics there in 1954. Doctorate in hand, Minsky was admitted to the group of Junior Fellows at Harvard, where he invented the confocal scanning microscope for thick, light-scattering specimens, decades in advance of the lasers and computer power needed to make it useful; today, it is in wide use in the biological sciences.



The 2018 Survey: AI and the Future of Humans

#artificialintelligence

"Please think forward to the year 2030. Analysts expect that people will become even more dependent on networked artificial intelligence (AI) in complex digital systems. Some say we will continue on the historic arc of augmenting our lives with mostly positive results as we widely implement these networked tools. Some say our increasing dependence on these AI and related systems is likely to lead to widespread difficulties. Our question: By 2030, do you think it is most likely that advancing AI and related technology systems will enhance human capacities and empower them? That is, most of the time, will most people be better off than they are today? Or is it most likely that advancing AI and related technology systems will lessen human autonomy and agency to such an extent that most people will not be better off than the way things are today? Please explain why you chose the answer you did and sketch out a vision of how the human-machine/AI collaboration will function in 2030.


Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI

arXiv.org Artificial Intelligence

The rapid advancement of artificial intelligence (AI) is changing our lives in many ways. One application domain is data science. New techniques in automating the creation of AI, known as AutoAI or AutoML, aim to automate the work practices of data scientists. AutoAI systems are capable of autonomously ingesting and pre-processing data, engineering new features, and creating and scoring models based on a target objectives (e.g. accuracy or run-time efficiency). Though not yet widely adopted, we are interested in understanding how AutoAI will impact the practice of data science. We conducted interviews with 20 data scientists who work at a large, multinational technology company and practice data science in various business settings. Our goal is to understand their current work practices and how these practices might change with AutoAI. Reactions were mixed: while informants expressed concerns about the trend of automating their jobs, they also strongly felt it was inevitable. Despite these concerns, they remained optimistic about their future job security due to a view that the future of data science work will be a collaboration between humans and AI systems, in which both automation and human expertise are indispensable.


The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City

AAAI Conferences

Studying the social dynamics of a city on a large scale has tra- ditionally been a challenging endeavor, requiring long hours of observation and interviews, usually resulting in only a par- tial depiction of reality. At the same time, the boundaries of municipal organizational units, such as neighborhoods and districts, are largely statically defined by the city government and do not always reflect the character of life in these ar- eas. To address both difficulties, we introduce a clustering model and research methodology for studying the structure and composition of a city based on the social media its res- idents generate. We use data from approximately 18 million check-ins collected from users of a location-based online so- cial network. The resulting clusters, which we call Livehoods, are representations of the dynamic urban areas that comprise the city. We take an interdisciplinary approach to validating these clusters, interviewing 27 residents of Pittsburgh, PA, to see how their perceptions of the city project onto our findings there. Our results provide strong support for the discovered clusters, showing how Livehoods reveal the distinctly charac- terized areas of the city and the forces that shape them.