Arend Hintze at The Conversation: “…We need to overcome the boundaries that define the four different types of artificial intelligence, the barriers that separate machines from us – and us from them. Type I AI: Reactive machines The most basic types of AI systems are purely reactive, and have the ability neither to form memories nor to use past experiences to inform current decisions. Deep Blue, IBM’s chess-playing supercomputer, which beat international grandmaster Garry Kasparov in the late 1990s, is the perfect example of this type of machine. Deep Blue can identify the pieces on a chess board and... (More >)
AI Ethics: The Future of Humanity
Report by sparks & honey: “Through our interaction with machines, we develop emotional, human expectations of them. Alexa, for example, comes alive when we speak with it. AI is and will be a representation of its cultural context, the values and ethics we apply to one another as humans. This machinery is eerily familiar as it mirrors us, and eventually becomes even smarter than us mere mortals. We’re programming its advantages based on how we see ourselves and the world around us, and we’re doing this at an incredible pace. This shift is pervading culture from our perceptions of... (More >)
Power to the People: Addressing Big Data Challenges in Neuroscience by Creating a New Cadre of Citizen Neuroscientists
Jane Roskams and Zoran Popović in Neuron: “Global neuroscience projects are producing big data at an unprecedented rate that informatic and artificial intelligence (AI) analytics simply cannot handle. Online games, like Foldit, Eterna, and Eyewire—and now a new neuroscience game, Mozak—are fueling a people-powered research science (PPRS) revolution, creating a global community of “new experts” that over time synergize with computational efforts to accelerate scientific progress, empowering us to use our collective cerebral talents to drive our understanding of our brain….(More)” ... (More >)
Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective
Nikolaos Aletras et al at Peer J. Computer Science: “Recent advances in Natural Language Processing and Machine Learning provide us with the tools to build predictive models that can be used to unveil patterns driving judicial decisions. This can be useful, for both lawyers and judges, as an assisting tool to rapidly identify cases and extract patterns which lead to certain decisions. This paper presents the first systematic study on predicting the outcome of cases tried by the European Court of Human Rights based solely on textual content. We formulate a binary classification task where the input of our... (More >)
Artificial Intelligence can streamline public comment for federal agencies
John Davis at the Hill: “…What became immediately clear to me was that — although not impossible to overcome — the lack of consistency and shared best practices across all federal agencies in accepting and reviewing public comments was a serious impediment. The promise of Natural Language Processing and cognitive computing to make the public comment process light years faster and more transparent becomes that much more difficult without a consensus among federal agencies on what type of data is collected – and how. “There is a whole bunch of work we have to do around getting government to... (More >)
Crowdsourcing Gun Violence Research
Penn Engineering: “Gun violence is often described as an epidemic, but as visible and shocking as shooting incidents are, epidemiologists who study that particular source of mortality have a hard time tracking them. The Centers for Disease Control is prohibited by federal law from conducting gun violence research, so there is little in the way of centralized infrastructure to monitor where, how,when, why and to whom shootings occur. Chris Callison-Burch, Aravind K.Joshi Term Assistant Professor in Computer and InformationScience, and graduate studentEllie Pavlick are working to solve this problem. They have developed the GunViolence Database, which combines machine learning... (More >)
The Promise of Artificial Intelligence: 70 Real-World Examples
Report by the Information Technology & Innovation Foundation: “Artificial intelligence (AI) is on a winning streak. In 2005, five teams successfully completed the DARPA Grand Challenge, a competition held by the Defense Advanced Research Projects Agency to spur development of autonomous vehicles. In 2011, IBM’s Watson system beat out two longtime human champions to win Jeopardy! In 2016, Google DeepMind’s AlphaGo system defeated the 18-time world-champion Go player. And thanks to Apple’s Siri, Microsoft’s Cortana, Google’s Google Assistant, and Amazon’s Alexa, consumers now have easy access to a variety of AI-powered virtual assistants to help manage their daily lives.... (More >)
Social Machines: The Coming Collision of Artificial Intelligence, Social Networking, and Humanity
Book by James Hendler and Alice Mulvehill: “Will your next doctor be a human being—or a machine? Will you have a choice? If you do, what should you know before making it? This book introduces the reader to the pitfalls and promises of artificial intelligence in its modern incarnation and the growing trend of systems to “reach off the Web” into the real world. The convergence of AI, social networking, and modern computing is creating an historic inflection point in the partnership between human beings and machines with potentially profound impacts on the future not only of computing but... (More >)
Combining Satellite Imagery and Machine Learning to Predict Poverty
From the sustainability and artificial intelligence lab: “The elimination of poverty worldwide is the first of 17 UN Sustainable Development Goals for the year 2030. To track progress towards this goal, we require more frequent and more reliable data on the distribution of poverty than traditional data collection methods can provide. In this project, we propose an approach that combines machine learning with high-resolution satellite imagery to provide new data on socioeconomic indicators of poverty and wealth. Check out the short video below for a quick overview and then read the paper for a more detailed explanation of how... (More >)
Artificial intelligence is hard to see
Kate Crawford and Meredith Whittaker on “Why we urgently need to measure AI’s societal impacts“: “How will artificial intelligence systems change the way we live? This is a tough question: on one hand, AI tools are producing compelling advances in complex tasks, with dramatic improvements in energy consumption, audio processing, and leukemia detection. There is extraordinary potential to do much more in the future. On the other hand, AI systems are already making problematic judgements that are producing significant social, cultural, and economic impacts in people’s everyday lives. AI and decision-support systems are embedded in a wide array of... (More >)
