On Wednesday two influential members of the company’s Ethical AI team, Alex Hanna and Dylan Baker, departed in a very public fashion, dredging up a controversy Alphabet’s Google hoped was behind it.
It’s been more than a year since Google dismissed Timnit Gebru, the co-leader of its Ethical AI team, after she criticized its technology and diversity practices. (Gebru says she was fired, and Google contends she resigned.) Gebru, a prominent Black AI expert, has since moved on to found a research institute—DAIR, or Distributed Artificial Intelligence Research—that will examine technology’s potential harms from a variety of global perspectives. Hanna and Baker are her newest employees.
“I am quitting because I’m tired,” Hanna wrote in a Medium post.
World Economic Forum, Arunima Sarkar and Nikhil Malhotra
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Meta has promised a host of revolutionary uses of its supercomputer, from ultrafast gaming to instant and seamless translation of mind-bendingly large quantities of text, images and videos at once — think about a group of people simultaneously speaking different languages, and being able to communicate seamlessly. It could also be used to scan huge quantities of images or videos for harmful content, or identify one face within a huge crowd of people.
The computer will also be key in developing next-generation AI models, it will power the Metaverse, and it will be a foundation upon which future metaverse technologies can rely.
But the implications of all this power mean that there are serious ethical considerations for the use of Meta’s supercomputer, and for supercomputers more generally.
For Abhishek Gupta, founder and principal researcher of the Montreal AI Ethics Institute, it’s all that and more. The sheer number of sets of principles and guidelines that are out there that each try to segment or categorize this area into subdomains — sometimes overlapping, sometimes not — presents a challenge.
The Montreal AI Ethics Institute (MAIEI) is an international nonprofit organization democratizing AI ethics literacy. It aims to equip citizens concerned about artificial intelligence to take action, as its founders believe that civic competence is the foundation of change.
The institute’s State of AI Ethics Reports, published semi-annually, condense the top research & reporting around a set of ethical AI subtopics into one document. As the first of those reports for 2022 has just been released, VentureBeat picked some highlights from the almost 300 page document to discuss with Gupta.
The lack of consent or debriefing in online research has attracted widespread public distrust. How can designers createsystems to earn and maintain public trust in large-scale online research? Procedural theories inform processes thatenable individuals to make decisions about their participation. Substantive theories focus on the normative judgmentsthat researchers and participants make about specific studies in context. Informed by these theories, we designed Bartleby, a system for debriefing participants and eliciting their views about studies that involved them. We evaluated this system by using it to debrief thousands of participants in a series of observational and experimental studies on Twitter and Reddit. We find that Bartleby addresses procedural concerns by creating new opportunities for study participants to exercise autonomy. We also find that participants use Bartleby to contribute to substantive, value-driven conversations about participant voice and power. We conclude with a critical reflection on the strengths and limitations of re-usable software to satisfy values from both procedural and substantive ethical theories.
The America COMPETES Act is the latest iteration in a series of bills that would, among other things, increase funding to US science in an attempt to keep up with China’s growing influence on the world stage. Researchers say, in particular, that a budget increase slated in the bill for the US National Science Foundation (NSF), which funds a significant chunk of basic academic research in the country, is long overdue. Researchers are divided, however, over measures added to the bill to keep China at bay, including limits on some foreign funding and talent recruitment programmes.
The bill’s passage does not guarantee that more grant money will end up in the pockets of US-based researchers anytime soon: lawmakers in the House must now negotiate a compromise bill with their counterparts in the US Senate, which passed its own version of the legislation last year. And although both bills
Physicists and computer scientists have long been on the hunt for evidence of such “quantum speedups.” In a separate study, published in July, IBM researchers proved that quantum computers can learn to classify data in a task that is infeasible for any classical computer. The two studies tackle different branches of machine learning, but they reveal a similar story: Given the right circumstances, quantum machine learning can outmaneuver classical algorithms.
New research led by Negin Entezari, who recently received a doctoral degree in computer science at UC Riverside, Instacart collaborators, and her doctoral advisor Vagelis Papalexakis, brings a methodology called tensor decomposition—used by scientists to find patterns in massive volumes of data—into the world of commerce to recommend complementary products more carefully tailored to customer preferences.
Tensors can be pictured as multi-dimensional cubes and are used to model and analyze data with many different components, called multi-aspect data. Data closely related to other data can be connected in a cube arrangement and related to other cubes to uncover patterns in the data.
“Tensors can be used to represent customers’ shopping behaviors,” said Entezari. “Each mode of a 3-mode tensor can capture one aspect of a transaction. Customers form one mode of the tensor and the second and third mode captures product-to-product interactions by considering products co-purchased in a single transaction.”
$90M to fully-fund the outcomes-based formula, enabling a zero percent tuition increase for public universities
$200M for TCAT infrastructure investments to help double the skilled workforce by 2026
$72M to complete the Oak Ridge Innovation Institute, a partnership with the University of Tennessee to invest in data science and technology, advanced materials, and outcomes-based applications
$6M to establish the Institute of American Civics at the University of Tennessee
$50M to a Carnegie R1 research fund at the University of Memphis, matched by private donors, to sustain the University’s R1 designation
Western Washington University’s Computer Science Department is now offering a Bachelor of Science degree program in Data Science.
“Data has become increasingly important not only in technology-related fields, but practically every discipline and pursuit,” said WWU Assistant Professor of Computer Science Scott Wehrwein. “The Data Science program will provide students with the foundational knowledge and practical skills needed to analyze and draw insights from the data that they will inevitably encounter wherever their career path takes them.”
But now that Humboldt officially has the polytechnic designation, the real work begins, as the university looks to add a host of new academic programs, build out more than $680 million in infrastructure projects that will reshape campus and double its enrollment over the next seven years. It’s a transformational undertaking for the university that was founded as a teacher’s college in 1913 and known as Humboldt State Normal School, Humboldt State Teacher’s College, Humboldt State College and CSU Humboldt before eventually carrying the Humboldt State University moniker for nearly 50 years.
The National Institute of Standards and Technology brought on Mark Latonero as senior policy advisor for artificial intelligence, a spokesperson confirmed Thursday.
Latonero will coordinate with international policymakers and standards organizations to advise on the risks and impacts of AI.
The White House launched both the National AI Research Resource Task Force and National AI Advisory Committee in 2021 to strengthen the U.S.’s competitive position globally, but less attention has been paid to the problems with the technology.
U.S. Department of Defense, Defense Department News
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The Defense Department must become a digital and artificial intelligence-enabled enterprise capable of operating at the speed and scale necessary to preserve its military advantage, according to a memorandum issued by Deputy Secretary of Defense Kathleen H. Hicks.
The memorandum, published on defense.gov, outlines how the chief digital and artificial intelligence officer, or CDAO, is charged with making sure DOD supports such an enterprise. John Sherman, DOD chief information officer, will serve as the acting chief digital and artificial intelligence officer until the position is filled permanently.
When the human brain learns something new, it adapts. But when artificial intelligence learns something new, it tends to forget information it already learned.
As companies use more and more data to improve how AI recognizes images, learns languages and carries out other complex tasks, a paper published in Science this week shows a way that computer chips could dynamically rewire themselves to take in new data like the brain does, helping AI to keep learning over time.
“The brains of living beings can continuously learn throughout their lifespan. We have now created an artificial platform for machines to learn throughout their lifespan,” said Shriram Ramanathan, a professor in Purdue University’s School of Materials Engineering who specializes in discovering how materials could mimic the brain to improve computing.
Finnish satellite-imagery venture ICEYE has raised $136 million in new funds, the company announced on Thursday, as it looks to add to its spacecraft fleet in orbit and further develop its product line in natural catastrophe detection.
The latest round of venture capital fundraising, led by U.K. fund Seraphim Space, brings the total to $304 million since ICEYE’s founding seven years ago.
“This financing has really been built around the further growth of the natural catastrophe product line,” ICEYE CEO co-founder and CEO Rafal Modrzewski told CNBC, adding that “flood monitoring is really the prime product right now … [but] we want to cover wildfire, we want to cover wind, we want to cover hail.”
Columbia professor Johan de Jong has spent the last 15 years gathering the foundational theorems of algebraic geometry in one place. His creation, the Stacks Project, offers a new model for organizing and visualizing mathematical information.
Amazon Web Services continues to dominate the cloud market, with revenues growing 39.5 percent year-over-year in the last quarter, despite fears its sheer scale would make such growth hard to maintain.
Microsoft also saw its Azure revenue jump, but did not break out specific numbers. Analysts at Synergy found that the company was able to increase its market share, solidifying itself as the de facto second provider.
Trailing them in a respectable third place was Google, which is still losing billions to operate a cloud business.
Before buying tens of thousands of houses, Zillow tested its approach over three years achieving profits that emboldened Zillow to rapidly scale up its iBuying business. The answer is that home prices were moving up rapidly during that period. As a result, even if Zillow’s purchase was modestly over-priced, Zillow was able to make a profit when the house was sold some weeks later. However, when the market cooled down in 2021, Zillow’s disadvantage was starkly revealed.
In other words, the proverbial tide went out, and we found out that Zillow was … exposed.
When a real estate market cools down, many homeowners hold on to their home, giving the market a chance to recover. However, this wasn’t an option for Zillow due to the capital intensity of the iBuying business and the potential for increased losses if home prices declined further.
As more colleges and universities launch data science courses and programs, there is an increasing effort to initiate instructional programs in Big Data Analytics and Artificial Intelligence (AI). Here at Tuskegee University, the Caring Foundation of Blue Cross and Blue Shield of Alabama and Proctor & Gamble (P&G) have awarded the University a grant to provide STEM majors in the pre-med track with practical knowledge and robust training in emerging technologies.
Dr. Mohammed Qazi, associate dean for Research and Outreach in the College of Arts and Sciences, led the effort in developing and submitting grant proposals to the Caring Foundation and P&G. College of Arts and Sciences dean, Dr. Channa Prakash, served as co-author to the grant’s initiative.
“With the new funding, we plan to launch many instructional activities in these areas to empower our students with additional knowledge and expertise that would better prepare them for the 21st workforce, which demands familiarity with big data and artificial intelligence,” explained Prakash.
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.
LinkedIn, Engineering blog; Varun Saxena, Harikumar Velayutham, and Balamurugan Gangadharan
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LinkedIn is the largest global professional network and generates massive amounts of high-quality data. Our data infrastructure scales to store exabytes of data; data analysts, data scientists, and AI engineers then use this data to power several LinkedIn products and the platform as a whole, ranging from job recommendations to each member’s personalized feed. Over the last few years, data scientists and AI engineers at LinkedIn have been using various tools for interacting with data, via different query and storage engines, for exploratory data analysis, experimentation, and visualization. But we soon realized a need for building a unified “one-stop” data science platform that would centralize and serve the various needs of data scientists and AI engineers.
Our solution to this challenge was to build DARWIN: the Data Science and Artificial Intelligence Workbench at LinkedIn. DARWIN addresses similar use-cases as various popular data science platforms in the industry, and while it leverages the Jupyter ecosystem, we go beyond merely Jupyter notebooks to support the whole gamut of the needs of data scientists and AI engineers at LinkedIn. This blog post covers the motivation behind building DARWIN and the key capabilities we wanted to include in the platform. We also touch upon the foundations and key concepts, which serve as the base for various features provided by DARWIN. Lastly, we introduce the features we have added in DARWIN thus far, powering different use cases.
If you are passionate about #dataviz and intrigued by crowdsourced observation data on biodiversity from platforms like eBird or iNat, I’d love to chat with you,
Geospatial software has a fundamental inconsistency: which order we put longitude and latitude in. Below, a table of each format and technology’s decision, and below that, some explanations.
