Kudos to Jerry Goldman, the other folks at the Oyez Project as well as the Chicago-Kent College of Law for making this free resource available to the public!
From the description: “OYEZTODAY at IIT Chicago-Kent College of Law offers you the latest information and media on the current business of the Supreme Court of the United States. OYEZTODAY provides: easy-to-grasp abstracts for every case granted review, timely and searchable audio of oral arguments + transcripts, and up-to-date summaries of the Court’s most recent decisions including the Court’s full opinions. You will have access to all this information on your iPhone with the ability to share reactions on Facebook, Twitter, or by email. (Recordings of opinion announcements from the bench will follow when the Court releases these files to the National Archives at the start of the Court’s next Term). Chicago-Kent is proud to provide this free service to enhance the public’s understanding of the Supreme Court and current legal controversies.”
From the abstract: “Complex social networks have received increasing attention from researchers. Recent work has focused on mechanisms that produce scale-free networks. We theoretically and empirically characterize the buyer–supplier network of the US economy and find that purely scale-free models have trouble matching key attributes of the network. We construct an alternative model that incorporates realistic features of firms’ buyer–supplier relationships and estimate the model’s parameters using microdata on firms’ self-reported customers. This alternative framework is better able to match the attributes of the actual economic network and aids in further understanding several important economic phenomena.”
From the abstract: “A complex system is a system composed of many interacting parts, often called agents, which displays collective behavior that does not follow trivially from the behaviors of the individual parts. Examples include condensed matter systems, ecosystems, stock markets and economies, biological evolution, and indeed the whole of human society. Substantial progress has been made in the quantitative understanding of complex systems, particularly since the 1980s, using a combination of basic theory, much of it derived from physics, and computer simulation. The subject is a broad one, drawing on techniques and ideas from a wide range of areas. Here I give a short survey and an annotated bibliography of resources for those interested in learning about complex systems.” [By Mark E.J. Newman – Submitted to Amer. J. Physics]
Complex systems is a relatively young subject area and one that is evolving rapidly, but there are nonetheless a number of general references, including books and reviews, that bring together relevant topics in a useful way. ” The paper then has recommended materials on major topics relevant to the study of complex systems including:
Lattices and Networks
Dynamical Systems (including Chaos & Fractals)
Discrete Dynamics and Cellular Automata
Scaling and Criticality
Adaptation and Game Theory
“Classic examples of complex systems include condensed matter systems, ecosystems, the economy and financial markets, the brain, the immune system, granular materials, road traffic, insect colonies, flocking or schooling behavior in birds or fish, the Internet, and even entire human societies.”
“In 2004, Salman Khan, a hedge fund analyst, began posting math tutorials on YouTube. Six years later, he has posted more than 2.000 tutorials, which are viewed nearly 100,000 times around the world. In this TED 2011 Talk, Salman talks about how and why he created the remarkable Khan Academy, a carefully structured series of educational videos offering complete curricula in math and, now, other subjects. He shows the power of interactive exercises, and calls for teachers to consider flipping the traditional classroom script — give students video lectures to watch at home, and do “homework” in the classroom with the teacher available to help.”
This offers a pretty interesting alternative model for education delivery. It is worth checking out!
This is one of the better TED Talks I have seen to date. It is definitely worth watching!
From the abstract: MIT researcher Deb Roy wanted to understand how his infant son learned language — so he wired up his house with videocameras to catch every moment (with exceptions) of his son’s life, then parsed 90,000 hours of home video to watch “gaaaa” slowly turn into “water.” Astonishing, data-rich research with deep implications for how we learn.
From the abstract: “In coming to understand the world—in learning concepts, acquiring language, and grasping causal relations—our minds make inferences that appear to go far beyond the data available. How do we do it? This review describes recent approaches to reverse-engineering human learning and cognitive development and, in parallel, engineering more humanlike machine learning systems. Computational models that perform probabilistic inference over hierarchies of flexibly structured representations can address some of the deepest questions about the nature and origins of human thought: How does abstract knowledge guide learning and reasoning from sparse data? What forms does our knowledge take, across different domains and tasks? And how is that abstract knowledge itself acquired?”
From the site … “A truly random game of Rock-Paper-Scissors would result in a statistical tie with each player winning, tying and losing one-third of the time … However, people are not truly random and thus can be studied and analyzed. While this computer won’t win all rounds, over time it can exploit a person’s tendencies and patterns to gain an advantage over its opponent.
Computers mimic human reasoning by building on simple rules and statistical averages. Test your strategy against the computer in this rock-paper-scissors game illustrating basic artificial intelligence. Choose from two different modes: novice, where the computer learns to play from scratch, and veteran, where the computer pits over 200,000 rounds of previous experience against you.”
From the Abstract: “We demonstrate a substantial improvement on one of the most celebrated empirical laws in the study of language, Zipf’s 75-y-old theory that word length is primarily determined by frequency of use. In accord with rational theories of communication, we show across 10 languages that average information content is a much better predictor of word length than frequency. This indicates that human lexicons are efficiently structured for communication by taking into account interword statistical dependencies. Lexical systems result from an optimization of communicative pressures, coding meanings efficiently given the complex statistics of natural language use.”