Einstein published his ideas and became a pivotal element in shifting the way we think about physics - from the Newtonian model to the Quantum - in turn this changed the way we think about the world and allowed us to develop new ways of engaging with the world.
We are at a similar juncture. The development of computational technologies allows us to think about astronomical volumes of data and to make meaning of that data.
The mindshift that occurs is that “the machine is our friend”. The computer, like all machines, extends our capabilities. As a consequence the types of thinking now required in industry are those that get away from thinking like a computer and shift towards creative engagement with possibilities. Logical thinking is still necessary but it starts to be driven by imagination.
Computational thinking and data science change the way we think about defining and solving problems.
Many passionate computer science educators believe computational thinking, problem solving, persistence and analysis learned in computer science helps students do better in other courses, especially math.
There’s a clear correlation
According to College Board data, students who take the AP Computer Science exam earn higher AP Calculus and Statistics scores relative to peers who previously performed at a similar level in math.
Are you fascinated by interdisciplinary work? — Are you into data analysis and model building? If yes, you might be interested in this position. In the last decades Econophysics emerged as a new, interdisciplinary field. Our group has longstanding expertise. We develop models for various issues in the economy, particularly in the financial markets. We apply the same standards as in traditional physics and base our models as much as possible on the empirical information.
Marty Walsh had a problem. Boston’s mayor wanted to address pay disparities between men and women, publicizing, as a first step, the average gap in different Boston industries. Normally, calculating that gap would require taking the actual pay gap at each company in an industry, adding them up, and then dividing by the number of companies to reach an average. But companies’ payrolls are proprietary, because their disclosure could be a boon to competitors, a black eye for the firms, and ammo for disgruntled employees who could sue over pay inequities.
Enter Bestavros, a College of Arts and Sciences computer science professor, who proposed an ingeniously simple algorithm from computer science that will allow the city to calculate those industry pay averages, by gender, from a total of 60 participating employers, without any daylight shining on an individual company’s proprietary information.
“We’re watching you.” This was the warning that the Chicago Police Department gave to more than 400 people on its “Heat List.”
The list, an attempt to identify the people most likely to commit violent crime in the city, was created with a predictive algorithm that focused on factors including, per the Chicago Tribune, “his or her acquaintances and their arrest histories – and whether any of those associates have been shot in the past.”
Algorithms like this obviously raise some uncomfortable questions. Who is on this list and why? Does it take race, gender, education and other personal factors into account? When the prison population of America is overwhelmingly Black and Latino males, would an algorithm based on relationships disproportionately target young men of color?
Transparency in the inputs to such algorithms and how their outputs are used is likely to be an important component of such efforts. Ethical considerations like these have recently been recognized as important problems by the academic community: new courses are being created and meetings like FAT-ML are providing venues for papers and discussions on the topic.
When you look at the very best work happening in iPad classrooms, you'll see students creating media, showcasing their understanding, collaborating with peers, and communicating with broad audiences. The pockets of excellence are ever-present and inspiring. On the whole, however, tablets are most often used to reproduce existing practices—to distribute resources and enable students to take notes.
Past generations of school leaders might have been forgiven for permitting these patterns of technology adoption, but today we have the benefit of history to look back on. We know that without a change in our technology integration strategies, there's no reason to expect that a new device will magically create new teaching practices in schools.
To make the most of the investment in tablet computers, school leaders need to do three things. First, they need to work with their communities to articulate a clear vision for how new technology will improve instruction. Second, they need to help educators imagine how new technologies can support those visions. Finally, they need to support teachers and students on a developmental journey that will take them from using tablets for consumption to using them for curation, creation, and connection."
I am writing a series of blog posts related to the integration of technology in the classroom. Each blog post will include practical examples of how to use a specific tool and integrate it into you...
We are living in revolutionary times. It is urgent that we think of education, children and teaching differently from the past. The classroom needs to be a place of innovation where students are able to connect with others, feel empowered and curious and have a say in their learning. Technology provides us with tools to expand our minds and extend our reach (Sir Ken Robinson, 2014).
In contrast to entropy, which increases monotonically, the "complexity" or "interestingness" of closed systems seems intuitively to increase at first and then decrease as equilibrium is approached. For example, our universe lacked complex structures at the Big Bang and will also lack them after black holes evaporate and particles are dispersed. This paper makes an initial attempt to quantify this pattern. As a model system, we use a simple, two-dimensional cellular automaton that simulates the mixing of two liquids ("coffee" and "cream"). A plausible complexity measure is then the Kolmogorov complexity of a coarse-grained approximation of the automaton's state, which we dub the "apparent complexity." We study this complexity measure, and show analytically that it never becomes large when the liquid particles are non-interacting. By contrast, when the particles do interact, we give numerical evidence that the complexity reaches a maximum comparable to the "coffee cup's" horizontal dimension. We raise the problem of proving this behavior analytically.
Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Automaton Scott Aaronson, Sean M. Carroll, Lauren Ouellette
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