The authors of the Harrisburg University study make explicit their desire to provide "a significant advantage for law enforcement agencies and other intelligence agencies to prevent crime" as a co-author and former NYPD police officer outlined in the original press release. At a time when the legitimacy of the carceral state, and policing in particular, is being challenged on fundamental grounds in the United States, there is high demand in law enforcement for research of this nature, research which erases historical violence and manufactures fear through the so-called prediction of criminality. Publishers and funding agencies serve a crucial role in feeding this ravenous maw by providing platforms and incentives for such research. The circulation of this work by a major publisher like Springer would represent a significant step towards the legitimation and application of repeatedly debunked, socially harmful research in the real world. To reiterate our demands, the review committee must publicly rescind the offer for publication of this specific study, along with an explanation of the criteria used to evaluate it. Springer must issue a statement condemning the use of criminal justice statistics to predict criminality and acknowledging their role in incentivizing such harmful scholarship in the past. Finally, all publishers must refrain from publishing similar studies in the future.
During my last interview I had a great talk with Daniel McDuff. Daniel's research is at the intersection of psychology and computer science. He is interested in designing hardware and algorithms for sensing human behavior at scale, and in building technologies that make life better. Applications of behavior sensing that he is most excited about are in: understanding mental health, improving online learning and designing new connected devices (IoT). Listen to more about why it is important to collect data from much larger scales and help computers read our emotional state. Key Learning Points: 1. Understanding the impact, intersection, and meaning of Psychology and Computer Science 2. Facial Expression Recognition 3. How to define Artificial Intelligence, Deep Learning, and Machine Learning 4. Applications of behavior sensing with Online Learning, Health, and Connected Devices 5. Visual Wearable sensors and heart health 6. The impact of education and learning 7. How to build computers to measure phycology, our reactions, emotions, etc 8. Daniel is building and utilizing scalable computer vision and machine learning tools to enable the automated recognition and analysis of emotions and physiology. He is currently Director of Research at Affectiva, a post-doctoral research affiliate at the MIT Media Lab and a visiting scientist at Brigham and Womens Hospital. At Affectiva Daniel is building state-of-the-art facial expression recognition software and leading analysis of the world's largest database of human emotion responses. Daniel completed his PhD in the Affective Computing Group at the MIT Media Lab in 2014 and has a B.A. and Masters from Cambridge University. His work has received nominations and awards from Popular Science magazine as one of the top inventions in 2011, South-by-South-West Interactive (SXSWi), The Webby Awards, ESOMAR, the Center for Integrated Medicine and Innovative Technology (CIMIT) and several IEEE conferences. His work has been reported in many publications including The Times, the New York Times, The Wall Street Journal, BBC News, New Scientist and Forbes magazine. Daniel has been named a 2015 WIRED Innovation Fellow.
In Italy, 120 high school students helped solve a centuries-old problem: how to give researchers access to the Vatican Secret Archives, a massive collection of documents detailing the Vatican's activities as far back as the eighth century. That should look pretty great on their college applications. The shelves of the Vatican Secret Archives are about 85 kilometers (53 miles) long and house 35,000 volumes of catalogues. But the documents that researchers have scanned and uploaded take up less than an inch. That's because the Vatican seems to not have wanted to share the information.
On a sunny Monday afternoon in Oakland, AI4All alum Ananya Karthik gathered a few dozen girls to show them how to use the Deep Dream Generator program to fuse images together and create a unique piece of art. OAKLAND -- Through connections made at summer camp, high school students Aarvu Gupta and Lili Sun used artificial intelligence to create a drone program that aims to detect wildfires before they spread too far. Rebekah Agwunobi, a rising high school senior, learned enough to nab an internship at the Massachusetts Institute of Technology's Media Lab, working on using artificial intelligence to evaluate the court system, including collecting data on how judges set bail. Both projects stemmed from the Oakland, Calif.-based nonprofit AI4All, which will expand its outreach to young under-represented minorities and women with a $1 million grant from Google.org, the technology giant's philanthropic arm announced Friday. Artificial intelligence is becoming increasingly commonplace in daily life, found in everything from Facebook's face detection feature for photos to Apple's iPhone X facial recognition.
An Upstate New York school is using facial recognition technology to help it spot possible school shooters or escaped felons on campus. Lockport City School District has installed a surveillance system in a high school, middle school and several elementary schools that scans students' faces to check for matches in its security database. The controversial move has attracted pushback from local parents, privacy advocates and some legislators who say it could invade students' privacy. Each client who chooses to install the system is able to choose which information is loaded into its database. They may source the material from local mugshot databases or images of students who've been expelled.