Online learning platform EdX; Google’s open-source machine learning platform, TensorFlow; and HarvardX have put together a certification program to train tech professionals to work with tiny machine learning (TinyML). The program is meant to support this specialized segment of development that can include edge computing with smart devices, wildlife tracking, and other sensors. The program comprises a series of courses that can be completed at home.

The idea is to scale machine learning to function in small form, edge devices that use far less power than desktop computers and have limited storage and processing capacity, says Anant Agarwal, CEO of EdX, which was founded by MIT and Harvard. That can include devices that operate on batteries, such as remote sensors, microphones, and cameras set up in the wilderness.

Agarwal says machine learning is transforming the world with such developments as speech recognition, but the early stages of making the technology work posed a challenge. “It was a hog,” he says. “It was a memory hog; it was a computation hog. It was very expensive to run machine learning, but machine learning could do amazing things.”

The capabilities of machine learning can be limited though by access and availability of robust networks with supporting resources. Devices might always not have such connections, Agarwal says. Smartphones and tablets can leverage machine learning because they connect with computers running in the cloud. That type of access might not be feasible in every environment, he says. “This is where TinyML comes in.”

Google got involved to support the certificate program, in part because it may lead to more developers using its TensorFlow machine learning platform, says Josh Gordon, developer advocate on TensorFlow. “One of the goals, in addition to an open source framework, is we care a lot about the developer community,” he says. “We’re hoping that as more people learn how to use the software they will contribute to new examples and applications in the space.” Gordon describes TinyML as greenfield territory that is waiting to be explored. “We’re interested in seeing what types of projects the students come up with,” he says.

TinyML is meant to run machine learning when the footprint of the hardware is literally tiny, Agarwal says, potentially opening the door for new IT ecosystems and more edge computing. “When the device is small, it has to consume very low power and doesn’t have a huge link to the cloud,” he says. For instance, a motion sensor tied to a camera in the wilderness could be triggered to record leopards passing by. “There’s no way you can have a big computer server there with huge batteries to run it,” Agarwal says. “You don’t have a huge internet connection to transmit the data to the cloud where it can be processed. All your computation has to happen right there.”

More support for the development of TinyML could lead to more embedded devices that operate on little power and bandwidth, he says. “This is the Internet of Things in its most compelling form.”

There is already momentum for such innovation, he says, as more sensors in buildings, infrastructure, vehicles, and personal devices record and compute. The data streams those devices produce must still be turned into actionable intelligence, which can be performed through TinyML, Agarwal says.

He sees ways for TinyML to support multiple industries, such as energy companies with sensors that monitor pipelines, aircraft makers that have sensors on actuators on planes, and the technology behind self-driving cars.

The certification course is taught by Google engineers from the TensorFlow group and Harvard professors, Agarwal says, and can be completed within a few months. The pervasive nature of machine learning and AI could make this program useful to many types of engineers, he says, whether they operate in IT, software, hardware, devices, or sensors. “They might find it useful in terms of learning about applications of TinyML,” Agarwal says. “Others may find it useful in terms of how to develop for these applications.”

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The MIT Press and the Harvard Data Science Initiative (HDSI) have announced the launch of the Harvard Data Science Review (HDSR). The open-access journal, published by MIT Press and hosted online via the multimedia platform PubPub, an initiative of the MIT Knowledge Futures group, will feature leading global thinkers in the burgeoning field of data science, making research, educational resources, and commentary accessible to academics, professionals, and the interested public. With demand for data scientists booming, HDSR will provide a centralized, authoritative, and peer-reviewed publishing community to service the growing profession.

The first issue features articles on topics ranging from authorship attribution of John Lennon-Paul McCartney songs to machine learning models for predicting drug approvals to artificial intelligence (AI). Future content will have a similar range of general interest, academic, and professional content intended to foster dialogue among researchers, educators, and practitioners about data science research, practice, literacy, and workforce development. HDSR will prioritize quality over quantity, with a primary emphasis on substance and readability, attracting readers via inspiring, informative, and intriguing papers, essays, stories, interviews, debates, guest columns, and data science news. By doing so, HDSR intends to help define and shape the profession as a scientifically rigorous and globally impactful multidisciplinary field.

Combining features of a premier research journal, a leading educational publication, and a popular magazine, HDSR will leverage digital technologies and advances to facilitate author-reader interactions globally and learning across various media.

The Harvard Data Science Review will serve as a hub for high-quality work in the growing field of data science, noted by the Harvard Business Review as the “sexiest job of the 21st century.” It will feature articles that provide expert overviews of complex ideas and topics from leading thinkers with direct applications for teaching, research, business, government, and more. It will highlight content in the form of commentaries, overviews, and debates intended for a wide readership; fundamental philosophical, theoretical, and methodological research; innovations and advances in learning, teaching, and communicating data science; and short communications and letters to the editor.

The dynamic digital edition is freely available on the PubPub platform to readers around the globe.

Amy Brand, director of the MIT Press, states, “For too long the important work of data scientists has been opaque, appearing mainly in academic journals with limited reach. We are thrilled to partner with the Harvard Data Science Initiative to publish work that will have a deep impact on popular understanding of the growing field of data science. The Review will be an unparalleled resource for advancing data literacy in society.”

Francesca Dominici, the Clarence James Gamble Professor of Biostatistics, Population and Data Science, and David Parkes, the George F. Colony Professor of Computer Science, both at Harvard University, announce, “As codirectors of the Harvard Data Science Initiative, we’re thrilled for the launch of this new journal. With its rigorous and cross-disciplinary thinking, the Harvard Data ScienceReview will advance the new science of data. By sharing stories of positive transformational impact as well as raising questions, this collective endeavor will reveal the contours that will shape future research and practice.”

Xiao-li Meng,the Whipple V.N. Jones Professor of Statistics at Harvard and founding editor-in-chief of HDSR, explains, “The revolutionary ability to collect, process, and apply new analytics to extract powerful insights from data has a tremendous influence on our lives. However, hype and misinformation have emerged as unfortunate side effects of data science’s meteoric rise. The Harvard Data Science Review is designed to cut through the hype to engage readers with substantive and informed articles from the leading data science experts and practitioners, ranging from philosophers of ethics and historians of science to AI researchers and data science educators. In short, it is ‘everything data science and data science for everyone.’”

Elizabeth Langdon-Gray, inaugural executive director of HDSI, comments, “The Harvard Data Science Initiative was founded to foster collaboration in both research and teaching and to catalyze research that will benefit our society and economy. The Review plays a vital part in our effort to empower research progress and education globally and to solve some of the world’s most important challenges.”

The inaugural issue of HDSR will publish contributions from internationally renowned scholars and educators, as well as leading researchers in industry and government, such as Christine Borgman (University of California at Los Angeles), Rodney Brooks (MIT), Emmanuel Candes (Stanford University), David Donoho (Stanford University), Luciano Floridi (Oxford/The Alan Turing Institute), Alan M. Garber (Harvard), Barbara J. Grosz (Harvard), Alfred Hero (University of Michigan), Sabina Leonelli (University of Exeter), Michael I. Jordan (University of California at Berkeley), Andrew Lo (MIT), Maja Matarić (University of Southern California), Brendan McCord (U.S. Department of Defense), Nathan Sanders (WarnerMedia), Rebecca Willett (University of Chicago), and Jeannette Wing (Columbia University).

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