A severe, prolonged drought created an agricultural crisis that swept all of the Maya kingdoms into history. That’s the popular narrative for the fall of the ancient Maya.
New research led by Brandeis associate professor of anthropology Charles Golden and professor Andrew Scherer of Brown University suggests the truth may not be so simple.
The team of researchers found signs that communities in the Western Maya Lowlands likely had an abundance of crops and were not struggling for agricultural resources when Maya civilization fell between the 8th and 9th centuries, and the three main kingdoms in the region were each functioning in very different ways. The discoveries were made with lidar, a remote sensor technology that can create a nearly complete picture of ancient ruins hidden to the naked eye below dense tree canopy.
The last major independent data provider has set its sights on being the financial industry’s first port of call for ESG information.
In the past few years Bloomberg has been leveraging its huge corporate and reference data pools to spin off a number of sustainability-related products. The New York-based company now sees itself as a natural data choice of investors as the imperative to allocate capital to addressing the world’s climate and social challenges snowballs.
“We would like to be a system of choice, where our clients can find all the information that they need from an ESG perspective, and be able to integrate ESG in the overall narrative of a particular company,” Patricia Torres, Bloomberg’s Global Head of Sustainable Finance Solutions tells A-Team’s ESG Insight. “We are incorporating ESG scores and data from multiple sources, so people can look at ESG holistically, in combination – and integrated – with other data factors.”
Deep learning programs, such as OpenAI’s GPT-3, continue using more and more GPU chips from Nvidia and AMD — or novel kinds of accelerator chips — to build ever-larger software programs. The accuracy of the programs increases with size, researchers contend.
That obsession with size was on full display Wednesday in the latest industry benchmark results reported by MLCommons, which sets the standard for measuring how quickly computer chips can crunch deep learning code.
Google decided not to submit to any standard benchmark tests of deep learning, which consist of programs that are well-established in the field but relatively outdated. Instead, Google’s engineers showed off a version of Google’s BERT natural language program, which no other vendor used
Just as convincing images of cats can be created using artificial intelligence, new proteins can now be made using similar tools. In a report in Nature, researchers describe the development of a neural network that “hallucinates” proteins with new, stable structures.
Proteins, which are string-like molecules found in every cell, spontaneously fold into intricate three-dimensional shapes. These folded shapes are key to nearly every biological process, including cellular development, DNA repair, and metabolism. But the complexity of protein shapes makes them difficult to study. Biochemists often use computers to predict how protein strings, or sequences, might fold. In recent years, deep learning has revolutionized the accuracy of this work.
“For this project, we made up completely random protein sequences and introduced mutations into them until our neural network predicted that they would fold into stable structures,” said co-lead author Ivan Anishchenko, He is an acting instructor of biochemisty at the University of Washington School of Medicine and a researcher in David Baker’s laboratory at the UW Medicine Institute for Protein Design.
Nvidia wants to use this Million-X computing to build a second Earth. Unfortunately it’s not at the point where it could be for living on, but instead is intended as a climate model. Making an Earth 2 in Nvidia’s Omniverse will allow for climate change modelling at a more regional level. Being able to predict how our planet might change may just give us a leg up, or just be horrifying to watch in advance.
GlaxoSmithKline plc and the University of Oxford today announced a major five-year collaboration to establish the Oxford-GSK Institute of Molecular and Computational Medicine.
The new Institute, which will be based at the University of Oxford, aims to improve the success and speed of research and development of new medicines, building on insights from human genetics and using advanced technologies such as functional genomics and machine learning.
Gradient Flow newsletter, Assaf Araki and Ben Lorica
from
Companies that are able to use data securely will be well-positioned to build data and AI applications in the future.
The use of data within companies continues to grow exponentially. This comes at a time when data platforms and tools for analytics, data science, and AI continue to get simpler. As a result the number of data users and data applications are growing within organizations.
This growth in data usage comes at a time of heightened concern for data security and privacy. On the cybersecurity front, data breaches are at an alltime high. In addition to data breaches, users have different expectations for the security and privacy of the information they generate or share. There have also been increasing demands from regulators. Since the GDPR and the CCPA were implemented in 2018, companies have been forced to adhere to many more privacy regulations.
With a new data-focused approach, Norfolk officials hope they can learn more about exactly which litter is accumulating and where. Keep Norfolk Beautiful is a local branch of the national nonprofit Keep America Beautiful and is housed in the city’s public works department.
Norfolk was chosen by data science company Litterati to participate in a year-long City Fingerprint Project that will analyze the litter problem.
The Litterati phone application, which began in 2015, is free and allows users to take photos of of litter. Information including what the trash is and where it was found then gets fed into a dashboard. More useful trends will emerge as more data is collected.
The fingerprint project will include an enhanced version of the app. The grant is worth about $50,000 in services, [Sarah] Sterzing said.
University of Chicago, Pritzker School of Molecular Engineering
from
The Pritzker School of Molecular Engineering (PME) at the University of Chicago has announced the launch of a PhD in quantum science and engineering. The program aims to ready the next generation of scientists and engineers who will lead advancements in this rapidly growing field.
Quantum technology is poised to dramatically transform multiple industries, including information security, health care, sustainability, and finance. Facilitating those transformations will require a specialized workforce educated in aspects of applied physics, chemistry, computer science, electrical engineering, and materials science.
Pritzker Molecular Engineering has made this training a cornerstone of its educational offerings since the introduction of its PhD program in 2013. The quantum science and engineering degree builds on that foundation, providing students with the skills needed to create, manipulate, and apply quantum phenomena toward developing radical new technologies.
How does being a researcher in industry compare with being an academic? That’s a question explored in a series of articles pegged to Nature’s latest survey of salaries and job satisfaction, which concludes this week. The results make for sobering reading for academics, revealing a shift towards industry (see Nature 599, 519–521; 2021).
Scientists who work in industry are more satisfied and better paid than are colleagues in academia, according to the self-selected group of respondents, which comprised more than 3,200 working scientists, mostly from high-income countries. Two-thirds of respondents (65%) are in academia; 15% work in industry. Industry employs, on average, half of the researchers in these countries.
Another key finding, covered in this week’s piece on workplace diversity, is that 30% of respondents in academia reported workplace discrimination, harassment or bullying, compared with 15% of those in industry.
During these days of a hopefully declining pandemic, hiring new faculty has recently begun in earnest for many research universities. Hence, considering the most effective criteria for selecting new faculty is important, with long-term implications. So, what are the best criteria?
University of Pennsylvania, The Daily Pennsylvanian student newspaper, Erica Edman
from
A new initiative in Penn’s School of Arts and Sciences aims to centralize data science education and research across disciplines.
The program, named the Data Driven Discovery Initiative, hopes to provide a forum for interactions between faculty and students working across disciplinary boundaries to spark discoveries, according to Arts & Sciences News. As part of the initiative, the school will provide funding for postdocs to conduct data science research across fields including sociology, neuroscience, and astrophysics. The initiative also includes a program called Data Science for Social Good, which will provide grants to Penn faculty and students in partnership with Philadelphia or global agencies.
Walter H. and Leonore C. Annenberg Professor in the Natural Sciences Bhuvnesh Jain and Criminology Department Chair Greg Ridgeway spearheaded the initiative, which aims to fund several projects focused in Philadelphia and the Delaware Valley on local, national, and global issues.
A new report examining how big data research is pursued in academic contexts was released Tuesday by Ithaka S+R, a non-profit organization focused on helping the academic community use digital technologies to advance research and teaching.
The findings of the report, “Big Data Infrastructure at the Crossroads: Support Needs and Challenges for Universities,” were drawn from a partnership with librarians at more than 20 colleges and universities who conducted interviews with more than 200 faculty working across a variety of disciplines.
The report also studied methodologies, workflows, outputs and struggles confronted by big data researchers. Ithaka S+R said the report was intended to provide guidance to universities, funders and others focused on improving how institutions support big data research.
The work was done in a collaboration between the University of Oxford, the University of Sydney in Australia and DeepMind, Google’s artificial intelligence sister company.
While computers have long been used to generate data for mathematicians, the task of identifying interesting patterns has relied mainly on the intuition of the mathematicians themselves. However, it’s now possible to generate more data than any mathematician can reasonably expect to study in a lifetime. Which is where machine learning comes in.
A paper, published today in Nature, describes how DeepMind was set the task of discerning patterns and connections in the fields of knot theory and representation theory. To the surprise of the mathematicians, new connections were suggested; the mathematicians were then able to examine these connections and prove the conjecture suggested by the AI. These results suggest that machine learning can complement mathematical research, guiding intuition about a problem.
University of Washington, Allen School of Computer Science & Engineering
from
Last year, a group of technology leaders who have worked alongside Lazowska to boost the UW and greater Seattle as innovation hotspots came together to recognize his outsized impact. Peter Lee, corporate vice president of research and incubations at Microsoft, and Allen School alumnus Jeffrey Dean (Ph.D., ‘96), a Google Senior Fellow and senior vice president of Google Research and Google Health, hatched a plan to cement their friend and colleague’s legendary status to mark his 70th birthday. They teamed up with Brad Smith, president and vice chair of Microsoft, and Harry Shum, emeritus researcher at Microsoft, to make a combined $1 million gift to the UW. The purpose of their gift was to establish the Endowed Professorship in Computer Science & Engineering in Honor of Edward D. Lazowska to support the recruitment and retention of faculty who will advance the Allen School’s leadership in the field — and serve as a lasting tribute to how Lazowska has uplifted students, colleagues, and the entire computing community.
“In the two years since the onset of COVID-19, and one climate disaster after another, more and more people are experiencing anxiety,” says SFU visiting professor and social psychologist Gulnaz Anjum. “Our research appears to show that AI could provide a highly reliable measurement for recognizing the signs that someone is anxious.”
Anjum and collaborators Nida Saddaf Khan and Sayeed Ghani from the Institute of Business Administration in Karachi, Pakistan collected an extensive range of data from adult participants for their Human Activity Recognition (HAR) study. Participants performed a series of activities in a specific order while wearing sensors that recorded their movements.
The researchers created a dataset of activities of typical anxiety-displaying behaviours for the sensors to detect, including idle sitting, nail biting, knuckle cracking and hands tapping. Their behaviours were analyzed using deep learning algorithms and computational hybrid models.
SPONSORED CONTENT
The eScience Institute’s Data Science for Social Good program is now accepting applications for student fellows and project leads for the 2021 summer session. Fellows will work with academic researchers, data scientists and public stakeholder groups on data-intensive research projects that will leverage data science approaches to address societal challenges in areas such as public policy, environmental impacts and more. Student applications due 2/15 – learn more and apply here. DSSG is also soliciting project proposals from academic researchers, public agencies, nonprofit entities and industry who are looking for an opportunity to work closely with data science professionals and students on focused, collaborative projects to make better use of their data. Proposal submissions are due 2/22.
The primary difference between software engineering and data science is the reason for writing the code. In data science, the code serves only as a means for solving another problem. Our work focuses on exploring the data, drawing conclusions, generating new research questions, running experiments. Training a machine learning model is also an experiment, where we try to learn if the model would help make better predictions. Jupyter notebooks can be seen more as a laboratory journal than a production code. Sometimes the job is to write code, but I would argue that this is a different type of work.
