… Six years ago, I wrote in these pages about the enduring prominence of Fortran for scientific computing and compared it with several other languages. I ended that article with a prediction that, in 10 years, a new language called Julia stood a good chance of becoming the one that scientists would turn to when tackling large-scale numerical problems. My prediction was not very accurate, though.
Panacea or pitfall?: The Hope, the Hype, the Promise, the Peril. That’s the subtitle of a National Academy of Medicine report on health and artificial intelligence. And it’s a good starting point to size up how software, machine learning and complex algorithms are changing the medical system in the middle of a pandemic.
Everybody celebrates the hope and promise; if you google “Covid and AI” you get something like 2.7 million hits.
But it’s easy to gloss over the hype and the peril. If AI is built on data scaffolding that is tainted, flawed or distorted by systematic bias — or just the wrong starting points — those inequities will be baked in and perpetuated.
On Nov. 10, the world’s latest Earth-observing satellite will launch from Vandenberg Air Force Base in California. As a historic U.S.-European partnership, the Sentinel-6 Michael Freilich spacecraft will begin a five-and-a-half-year prime mission to collect the most accurate data yet on global sea level and how our oceans are rising in response to climate change. The mission will also collect precise data of atmospheric temperature and humidity that will help improve weather forecasts and climate models.
The spacecraft is named after Dr. Michael Freilich, the former director of NASA’s Earth Science Division and a tireless advocate for advancing satellite measurements of the ocean. Sentinel-6 Michael Freilich builds on the heritage of the ESA (European Space Agency) Copernicus Sentinel-3 mission as well as the heritage and legacy of the U.S.-European TOPEX/Poseidon and Jason-1, 2, and 3 series of sea level observation satellites. Launched in 2016, Jason-3 is currently providing data initiated with the observations of TOPEX/Poseidon in 1992.
The data from these satellites has become the gold standard for sea level studies from space over the past 30 years. In 2025, Sentinel-6 Michael Freilich’s twin, Sentinel-6B, is scheduled to launch and advance these measurements for at least another half decade.
Cory Doctorow, 49, is a British-Canadian blogger, science fiction author and tech activist. He has worked for the Electronic Frontier Foundation and helped found the Open Rights Group – he is an advocate of liberalising copyright law. He has held various academic posts and is a visiting professor of the Open University. His latest novel, Attack Surface, was published earlier this month.
The protagonist in your new novel tries to offset her job at a tech company where she is working for a repressive regime by helping some of its targets evade detection. Do you think many Silicon Valley employees feel uneasy about their work?
Anyone who has ever fallen in love with technology knows the amount of control that it gives you. If you can express yourself well to a computer it will do exactly what you tell it to do perfectly, as many times as you want. Across the tech sector, there are a bunch of workers who are waking up and going: “How did I end up rationalising my love for technology and all the power it gives me to take away that power from other people?”
The Supreme Court’s eight justices on Wednesday seemed skeptical of Google’s argument that application programming interfaces (APIs) are not protected by copyright law. The high court was hearing oral arguments in Google’s decade-long legal battle with Oracle. Oracle argues that Google infringed its copyright in the Java programming language when it re-implemented Java APIs for use by Android app developers.
The stakes in the case are high for Google, which could owe Oracle billions of dollars in damages. More importantly, an Oracle win could reshape how copyright law treats APIs, giving incumbents the power to lock out competitors who want to build compatible software.
For decades prior to Oracle’s lawsuit, most people in the software industry assumed that APIs couldn’t be copyrighted. That meant a software company could re-implement the APIs of a competitor’s product in order to enable software, designed to work with the competitor’s product, to work with its own.
Grid, the company pushing new state-of-the-art standards in AI, announced today that it has raised $18.6 million in Series A funding led by Index Ventures with participation from Bain Capital Ventures and Firstminute. Grid enables companies of all sizes to train state-of-the-art AI models on hundreds of cloud GPUs and TPUs. Grid’s breakthrough technology enables machine learning engineers, data scientists and AI researchers to focus on solving problems and building products at the scale available to only the most sophisticated AI companies in the world.
“Grid is here to teach, train and supercharge machine learning engineers, data scientists and AI researchers, so they can work with maximum efficiency on exactly the way they want, instead of spending all their time managing hardware or solving engineering problems,” said Grid cofounder William Falcon. “Our main goal is for the product to be user friendly so companies don’t have to worry about the machines they are running or clusters, and instead can focus on delivering value for their customers.”
University of Washington researchers have created one potential solution: A 98 milligram sensor system — about one tenth the weight of a jellybean, or less than one hundredth of an ounce — that can ride aboard a small drone or an insect, such as a moth, until it gets to its destination. Then, when a researcher sends a Bluetooth command, the sensor is released from its perch and can fall up to 72 feet — from about the sixth floor of a building — and land without breaking. Once on the ground, the sensor can collect data, such as temperature or humidity, for almost three years.
The Walther Cancer Foundation will invest $11 million to advance collaborative cancer research at Indiana University and Purdue University by supporting scientists through bioinformatics — an increasingly critical aspect of their work.
Bioinformatics involves managing and analyzing the massive amounts of data generated by scientific research — turning data into knowledge that could lead to new cancer treatments.
“We hope this gift enables scientists at IU and Purdue to dig more deeply and refine their studies so they can point out new pathways to good patient outcomes in cancer,” said Tom Grein, president and CEO of the Walther Cancer Foundation. “Sometimes you have so much data, it’s hard to comprehend where it’s leading you. I hope the data-driven analysis will uncover nuggets of opportunity that would otherwise never be seen.”
Australia’s position as one of the world leaders in artificial intelligence (AI) and machine learning will be further boosted thanks to A$20 million towards a new national centre, to be based at the University of Adelaide.
The Centre for Augmented Reasoning is an investment by the Australian Government in people and research to make computers better at interacting with humans, so that all technology might be easier and safer to use.
The new centre builds on the expertise of the internationally regarded Australian Institute for Machine Learning (AIML) at the University of Adelaide, jointly established with the South Australian Government and based in Adelaide’s Lot Fourteen innovation precinct.
Immunity is a multifaceted phenomenon. For T cell-mediated memory responses to SARS-CoV-2, it is relevant to consider their impact both on COVID-19 disease severity and on viral spread in a population. Here, we reflect on the immunological and epidemiological aspects and implications of pre-existing cross-reactive immune memory to SARS-CoV-2, which largely originates from previous exposure to circulating common cold coronaviruses. We propose four immunological scenarios for the impact of cross-reactive CD4+ memory T cells on COVID-19 severity and viral transmission. For each scenario, we discuss its implications for the dynamics of herd immunity and on projections of the global impact of SARS-CoV-2 on the human population, and assess its plausibility. In sum, we argue that key potential impacts of cross-reactive T cell memory are already incorporated into epidemiological models based on data of transmission dynamics, particularly with regard to their implications for herd immunity. The implications of immunological processes on other aspects of SARS-CoV-2 epidemiology are worthy of future study.
Verizon Business brings 5G Ultra Wideband service to Innovation Hub at the University of Illinois Research Park
Innovators can now develop and test how 5G can enhance robotics, guided vehicles, manufacturing process automation, data analytics and more
Part of Verizon Business’ broader strategy to partner with customers, startups, universities and large enterprises to explore how 5G can disrupt and transform nearly every industry
We live in an age of manipulation. An extensive network of commercial surveillance tracks our every move and a fair number of our thoughts. That data is fed into sophisticated artificial intelligence and used by advertisers to hit us with just the right sales pitch, at just the right time, to get us to buy a toothbrush or sign up for a meal kit or donate to a campaign. The technique is called behavioral advertising, and it raises the frightening prospect that we’ve been made the subjects of a highly personalized form of mind control.
Or maybe that fear is precisely backwards. The real trouble with digital advertising, argues former Google employee Tim Hwang—and the more immediate danger to our way of life—is that it doesn’t work.
Hwang’s new book, Subprime Attention Crisis, lays out the case that the new ad business is built on a fiction. Microtargeting is far less accurate, and far less persuasive, than it’s made out to be, he says, and yet it remains the foundation of the modern internet
A team of UC Merced researchers is developing a model to recognize bird calls.
Recording devices called AudioMoths have been placed across bird habitat in Sonoma County. During two-week periods, the device wakes up every 10 minutes and records one minute of sound. Department of Computer Science and Engineering Professor Shawn Newsam and Electrical Engineering and Computer Science graduate student Shrishail “Shree” Baligar are using artificial intelligence (AI) to detect bird calls in the recordings.
Their model can detect 45 different species so far, and will be used to produce maps of where, when and how many species are present.
Usama Fayyad, a longtime leader in data science and AI, will be joining Northeastern as the inaugural executive director for the Institute of Experiential Artificial Intelligence.
“Usama will work with senior leadership to create a pioneering research hub that places human skills and intelligence at the forefront of artificial intelligence development, from the earliest design steps to final implementation,” wrote David Madigan, provost and senior vice president for academic affairs, in a letter welcoming Fayyad to the university.
Launched a year ago a year ago, the institute is university-wide in scope but will work especially closely with the Roux Institute in Portland, Maine.
Stanford University, Stanford Medicine, Scope blog
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Months ago, when the COVID-19 outbreak was declared a pandemic, Ariel Ganz, PhD, began searching for a way to help.
In her quest to contribute, Ganz, a postdoctoral scholar at Stanford Medicine, reached out to past and present mentors to connect with like-minded scientists who also wanted to make a difference in the COVID-19 pandemic.
In doing so, she spoke with James Phillips, a United Kingdom-based engineer at an organization called Helpful Engineering. He was considering the impact that open licensing might have on increasing testing capacity. Inspired by the idea, Ganz pursued something similar in the United States.
Online October 15, starting at 11 a.m. Eastern time. “Join POLITICO as it virtually convenes its 3rd AI Summit in the U.S., bringing together policymakers, federal officials, technologists, private-sector executives, scientists and advocates to explore the future of AI as the world tries to recover from the pandemic and as the U.S. also deals with social unrest and a presidential election.” [registration required]
The International Conference on Complex Networks and their Applications
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Online December 1-3. The conference “aims at bringing together researchers from different scientific communities working on areas related to complex networks.” [$$$]
Online October 14 starting at 3:30 p.m. Eastern time. “At the Town Hall, we will share a new Responsible Data Re-Use Framework informed by deliberations with NYC residents, data holders in the public and private sectors, and representatives from civic rights and advocacy organizations.” [free, registration required]
Data and machine learning technologies for the big data era have been developed in the last twenty years. Let’s review these two decades in terms of data processing and machine learning frameworks as well as machine learning platforms that have been spotted in the wild.
While ML platforms such as IBM’s SPSS and SAS have been around for decades, open-source data and machine learning technologies are a recent phenomenon. Modern machine learning (ML) and deep learning frameworks (for Python) have only been around for 10–15 years.
The associated end-to-end machine learning platforms have only popped up at tech companies in the last five years or so. No wonder frameworks and platforms have not yet converged towards a dominant design, especially since we have been able to witness advances in public clouds, containers and orchestration, DevOps in years since 2000. In other words, the entire cloud-native suite has not yet been around that long.