The MIT Statistics and Data Science Center (SDSC), a part of the Institute for Data, Systems, and Society (IDSS), announced two new academic programs today: the MicroMasters program in Statistics and Data Science, and the Interdisciplinary Doctoral Program in Statistics, both beginning in the fall. The MicroMasters program, currently under development by MIT faculty, will be offered online through edX. "Digital technologies are enabling us to bring MIT's data science curriculum to learners around the world regardless of their location or socioeconomic status," says Vice President for Open Learning Sanjay Sarma. The curriculum includes foundational knowledge of data science methods and tools, a deep dive into probability and statistics, and opportunities to learn, implement, and experiment with data analysis techniques and machine learning algorithms. "The demand for data scientists is growing rapidly," says Dean for Digital Learning Krishna Rajagopal.
The experimental classroom is part of a wave of so-called maker spaces popping up at schools across the country. They're designed to facilitate complex play--open-ended, thought-provoking activities that involve tinkering and engineering with no "right" answer. According to Donna Ross, an associate professor of science education at San Diego State University, research suggests that these activities foster problem-solving, critical thinking and team-building skills--all attributes valued by today's employers. As screen time and highly structured activities have colonized children's off hours, "we're trying to build [complex play] into the formal school day," Ross said. The $200,000 space at Haine was funded by a state grant and opened in September; almost 1,500 students in kindergarten through sixth grade use it each week.
The goal of this competition was to develop machine learning models for the prediction of two materials properties, namely the formation energy, which is an indication of the stability of a new material, and the electronic band gap, which determines a material's transparency over the visible range. The developed models can potentially facilitate the discovery of new transparent conductors and allow for advancements in (opto)electronic technologies. Inspired by Jacek Golebiowski, who made valuable contributions to the final solution, Lars used a smooth overlap of atomic positions (SOAP) based descriptor developed by Barto?k et al. [1, 2] to encode information about the crystal structure of the transparent conductive oxides that were studied in this competition. These SOAP features were then used to teach a Neural Network to predict the desired materials properties. Details about the competition and the top three submissions can be found here.
Considered the Holy Grail of automation, data analysis, and robotics, Artificial Intelligence has taken the world by storm as a major field of research and development. Python has surfaced as a dominate language in AI/ML programming because of its simplicity and flexibility, in addition to its great support for open source libraries such as spaCy and TensorFlow. This video course is built for those with a basic understanding of artificial intelligence, introducing them to advanced artificial intelligence projects as they go ahead. The first project introduces natural language processing including part-of-speech tagging and named entity extraction. Wikipedia articles are used to demonstrate the extraction of keywords, and the Enron email archive is mined for mentions and relationships of people, places, and organizations.
As a software engineer at Microsoft, Elena Voyloshnikova's job is to make informed recommendations about how to improve the performance of software engineering tools. But too often, she spends her days manually analyzing the data she needs to make those decisions. Lately, her team has been discussing the potential of building machine learning models to automate that task – creating more time to focus on the decision-making. That's why she was intrigued when she received an email announcing an upcoming AI training session for Microsoft employees. "I asked my manager, 'Can I go to this?'" she said.
The ability to manipulate and understand data is increasingly critical to discovery and innovation. As a result, we see the emergence of a new field--data science--that focuses on the processes and systems that enable us to extract knowledge or insight from data in various forms and translate it into action. In practice, data science has evolved as an interdisciplinary field that integrates approaches from such data-analysis fields as statistics, data mining, and predictive analytics and incorporates advances in scalable computing and data management. But as a discipline, data science is only in its infancy. The challenge of developing data science in a way that achieves its full potential raises important questions for the research and education community: How can we evolve the field of data science so it supports the increasing role of data in all spheres? How do we train a workforce of professionals who can use data to its best advantage? What should we teach them? What can government agencies do to help maximize the potential of data science to drive discovery and address current and future needs for a workforce with data science expertise?
Catherine Stinson is a postdoctoral scholar at the Rotman Institute of Philosophy, at the University of Western Ontario, and former machine-learning researcher. I wrote my first lines of code in 1992, in a high school computer science class. When the words "Hello world" appeared in acid green on the tiny screen of a boxy Macintosh computer, I was hooked. I remember thinking with exhilaration, "This thing will do exactly what I tell it to do!" and, only half-ironically, "Finally, someone understands me!" For a kid in the throes of puberty, used to being told what to do by adults of dubious authority, it was freeing to interact with something that hung on my every word – and let me be completely in charge. For a lot of coders, the feeling of empowerment you get from knowing exactly how a thing works – and having complete control over it – is what attracts them to the job.
WE HUMANS ARE SPECIAL, RIGHT? Can a robot write a symphony? Can a robot turn a canvas into a beautiful masterpiece? OVER SOME 40,000 YEARS, HUMAN CREATIVITY HAS EXPLODED – FROM DRAWINGS ON CAVE WALLS THROUGH THE GREAT ART OF CENTURIES TO COME…. COMPUTATIONAL CREATIVITY IS LEADING US TO ASK NEW QUESTIONS ABOUT HUMAN CREATIVITY. IS THIS ESSENTIAL HUMAN TRAIT TRULY UNIQUE? WILL ARTIFICIAL INTELLIGENCE BE A COMPETITOR? OR CAN IT BE A COLLABORATOR, HELPING US TOWARD STILL UNIMAGINED CREATIONS? SCHAEFER: My first guest is a member of Google Brain's Magenta team. He is currently working on neural network models of sound and music and recently produced a synthesizer that designed its own sounds. SCHAEFER: Also with us, is an Assistant professor at the University of Illinois at Urbana Champaign in the Dept. of Electrical and Computer Engineering. He focuses on several surprising creative domains including the culinary arts and fashion and the theoretical foundations of creativity. SCHAEFER: Also with us is an Associate Professor of psychological and brain science at Dartmouth College. He's interested in the neural basis of imagination and in the evolution of human creativity. A former research fellow at MIT's Media lab and artist in residence at Google, please welcome Sougwen Chung. SCHAEFER: Peter, it seems like there are many possible pros and cons for approaching computational creativity.
Helping students develop skills in both critical thinking and scientific reasoning is fundamental to science education. However, the relationship between these two constructs remains largely unknown. Dowd et al. examined this issue by investigating how students' critical thinking skills related to scientific reasoning in the context of undergraduate thesis writing. The authors used the BioTAP rubric to assess scientific reasoning and the California Critical Thinking Skills Test to assess critical thinking. Results support the role of inference in scientific reasoning in writing, while also revealing other aspects of scientific reasoning (epistemological considerations and writing conventions) not related to critical thinking.