The Centre for Big Data Statistics (CBDS) of Statistics Netherlands (CBS) is collaborating with the Leiden Centre of Data Science (LCDS) at Leiden University. Founder of the LCDS professor Jaap van den Herik explains why it is important to join forces: ‘Data science is multidisciplinary’.
One of the challenges confronting the data science industry is finding ways to make sophisticated analytics tools easier to use across cloud and data center platforms within multiple industries. The data layout matters, and Intel Corp. has issued the latest version of its Math Kernel Library in an effort to help developers leverage instruction sets and improve hardware or software performance.
The library is yet another step Intel has taken to expand the availability of tools and resources as it leverages the power of bringing the big data ecosystem and data sciences solutions together into a coherent whole. On the hardware side, Intel has introduced new tools to streamline software development on field programmable gate arrays. These powerful and versatile chips can process data at the edge and provide artificial intelligence support.
Franklin Resources, Inc. (NYSE:BEN), which operates as Franklin Templeton Investments, today announced the acquisition of Random Forest Capital, LLC (“Random Forest”), an investment firm with expertise in data science and non-bank marketplace lending. Following the acquisition, the Random Forest team will join the Franklin Templeton Fixed Income Group investment team. Terms of the transaction were not disclosed.
Jenny Johnson, president and COO of Franklin Templeton Investments, said, “We continue to make strategic investments and acquisitions in emerging investment-related technologies to augment and support Franklin Templeton’s global offerings. The Random Forest team will complement our existing fundamental fixed income research with their expertise in private lending and bring the capability to support the firm’s broader information technology and data science initiatives.”
“There are some really nuanced questions though about how to regulate, which I think are extremely interesting intellectually,” says Zuckerberg, who points to the bipartisan Honest Ads Act, cosponsored by senators Mark Warner, Amy Klobuchar, and John McCain, as an example of the sort of bill his company can get behind.
The Honest Ads Act, legislation that calls for increased transparency behind who pays for political ads online, makes for a convenient example, though, in part because Facebook has already implemented many of its provisions. The bill, introduced last October, also appears to have languished, making it a non-substantive threat. Meanwhile, critics say it wouldn’t have stopped Russian propagandists from flooding Facebook in the first place.
Besides, even the Honest Ads Act’s sponsors have noted that it addresses a very small piece of a very large problem.
A radiologist’s ability to make accurate diagnoses from high-quality diagnostic imaging studies directly impacts patient outcome. However, acquiring sufficient data to generate the best quality imaging comes at a cost – increased radiation dose for computed tomography (CT) and positron emission tomography (PET) or uncomfortably long scan times for magnetic resonance imaging (MRI). Now researchers with the Athinoula A. Martinos Center for Biomedical Imaging at Massachusetts General Hospital (MGH) have addressed this challenge with a new technique based on artificial intelligence and machine learning, enabling clinicians to acquire higher quality images without having to collect additional data. They describe the technique – dubbed AUTOMAP (automated transform by manifold approximation) – in a paper published today in the journal Nature.
“An essential part of the clinical imaging pipeline is image reconstruction, which transforms the raw data coming off the scanner into images for radiologists to evaluate,” says Bo Zhu, PhD, a research fellow in the MGH Martinos Center and first author of the Nature paper. “The conventional approach to image reconstruction uses a chain of handcrafted signal processing modules that require expert manual parameter tuning and often are unable to handle imperfections of the raw data, such as noise. We introduce a new paradigm in which the correct image reconstruction algorithm is automatically determined by deep learning artificial intelligence.
“With AUTOMAP, we’ve taught imaging systems to ‘see’ the way humans learn to see after birth, not through directly programming the brain but by promoting neural connections to adapt organically through repeated training on real-world examples,” Zhu explains. “This approach allows our imaging systems to automatically find the best computational strategies to produce clear, accurate images in a wide variety of imaging scenarios.”
AI can already dream up imaginary celebrities, so perhaps it can help the Army imagine revolutionary new engine parts or aircraft, too.
That’s the goal of a new project from the Defense Advanced Research Projects Agency (DARPA), the research wing of the US Defense Department. DARPA wants entrants to rethink the way complex components are designed by combining recent advances in machine learning with fundamental tenets of math and engineering.
No statistical model can truly capture the full impact of this effort – including the joy from years of quality life gained with a loved one, or how much pain and suffering would be avoided for millions of families across the country. But what we’re learning about the significant public health promise of this approach leaves me encouraged and optimistic. Our estimates underscore the tremendous opportunity to save so many lives if we come together and forge a new path forward to combat the overwhelming disease and death caused by cigarettes. And this unprecedented public health opportunity, contrasted against the cost of doing nothing, weighs heavily on me.
We’re at a crossroads when it comes to addressing nicotine addiction and smoking in this country – with important new tools to address this devastating public health burden. And although a potential nicotine product standard for cigarettes is the cornerstone of our approach, we also continue to push forward on additional pieces of the FDA’s multi-year plan designed to work in concert to better protect kids and significantly reduce tobacco-related disease and death. We said from the outset that ours was a comprehensive approach that requires us to pursue all of its parts in tandem.
Tempus, a Chicago startup led by Groupon co-founder Eric Lefkofsky, raised $80 million in a new funding round Tuesday, bringing the company’s total amount raised to $210 million. Investors in the startup’s latest round include new backers Kinship Trust Company and T. Rowe Price, along with existing investors New Enterprise Associates and Revolution Growth.
The latest funding round comes just six months after the startup raised a $70 million round. Crain’s puts Tempus’ valuation now at $1.1 billion, making the startup one of the city’s latest “unicorn” tech companies.
Washington University in St. Louis is set to become the second institution with a doctoral program dedicated to imaging science, with its inaugural class beginning in the fall. Mark Anastasio, PhD, the director of the new program, spoke with Health Imaging about the state of imaging education and incorporating artificial intelligence (AI) into its curriculum.
Health Imaging: Where did the idea for this PhD program start? How did it get off the ground?
Mark Anastasio, PhD: I’ve been interested in pushing imaging science as an independent academic discipline for a while, but I didn’t come up with this idea alone. There are giants in the field of imaging science who have advocated this for decades. At Washington University, it came about primarily from the dean of the engineering school, Aaron Bobick, who has a background in computer vision—so he’s familiar with imaging technologies. He and I decided this would be a great opportunity for Washington University to take leadership on the educational aspects of imaging science.
PLOS Biology [Uncorrected Proof]; John P. A. Ioannidis
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Meta-research is the study of research itself: its methods, reporting, reproducibility, evaluation, and incentives. Given that science is the key driver of human progress, improving the efficiency of scientific investigation and yielding more credible and more useful research results can translate to major benefits. The research enterprise grows very fast. Both new opportunities for knowledge and innovation and new threats to validity and scientific integrity emerge. Old biases abound, and new ones continuously appear as novel disciplines emerge with different standards and challenges. Meta-research uses an interdisciplinary approach to study, promote, and defend robust science. Major disruptions are likely to happen in the way we pursue scientific investigation, and it is important to ensure that these disruptions are evidence based.
Artificial intelligence could speed up metagenomic studies that look for species unknown to science.
Government Data Science News
In Israel, the National Digital Health plan will try to get 9 million patients to consent to sharing their medical records with researchers in academia and industry. Israeli Prime Minister Benyamin Netanyahu has said, “This is a huge asset,” valued at around $600 billion if his estimates are correct. The plan, as you may guess, is not without controversy from critics who worry that patient confidentiality will be compromised in a variety of ways.
Meanwhile, a sort of similar project in the US – the National Institutes of Health’s “All of Us” – received criticism for being too ambitious and too laden with bureaucracy to outshine smaller, nimbler projects. By comparison, the proposed Israeli project is 9 times bigger than the All of Us project’s goal of enrolling 1 million patients, though is less burdened by bureaucracy and electronic medical health synchronization problems because 98% of Israelis receive care from a single system.
China has an artificial intelligence plan that will roll out in stages. This is all totally normal for China, but it does raise questions about coordinated AI plans in the US or the lack thereof. The Chinese plan is in stage I and is focusing on autonomous vehicles, drones, service bots, AI in medical imaging, video image recognition, voice recognition, and automated translation. That’s actually a very good overview of the AI technologies most ready for prime time.
Ajit Pai, Chairman of the Federal Communications Commission is proposing to prohibit FCC funds from “being used to purchase equipment or services from any company that poses a national security threat to the integrity of communications networks or their supply chains.” It’s unclear which companies he has in mind, but Kaspersky Labs has felt the scorn of US federal agency purchasing regulators.
Elsewhere in related news, the Trump administration is looking into limiting the number of Chinese doctoral students who can enroll in US PhD programs. As it stands, “Chinese students receive 10 percent of all doctorates awarded in the U.S., most of them in science and engineering. Some 80 percent of Chinese doctoral holders stay in the U.S. and work after they earn their degrees. There are more Chinese engineers working on artificial intelligence at U.S. technology companies than in all of China.” If we lose Chinese students, we are not doing ourselves any favors. We need more talent, not less.
Twitter for science?? Yes, there is an app for that! And it is apparently not Twitter (Wait, it isn’t?). Polyplexus was developed with funds from the Defense Advanced Research Projects Agency to pump out 300 character abstracts and unite scientists. You can tell it’s for academics because the name adheres to a Latinate taxonomy, not the internet naming convention in which names must sound like they were generated by creative six year olds. I say this with immense love for six year olds of all creativity levels. Let’s all go experiment with what our tax dollars have bought us, science types. If my encouragement is not enough, maybe you want to win one of the $100,000 grants available to scientists who kick off projects from inspiring micropubs. Truth: you could get paid to procrastinate on science twitter.
Speaking of DARPA, they are also putting up cash to see if data science can lead to the redesign of the helicopter. Why not!
Also contrary to fears that the budget would kill the Wide Field Infrared Survey Telescope that is poised to discover the secrets of black holes, the telescope has been maintained in the budget. NASA’s budget is up overall, and does see the increase in planetary funding we expected (e.g. money for studying the moon and Mars).
Representative Elise Stefanik (R-NY) , chair of the House Armed Services Subcommittee on Emerging Threats and Capabilities, put forth legislation that would “develop a commission to review advances in AI, identify the nation’s AI needs and make recommendations to organize the federal government for the threat.” There is growing concern within the defense agencies that AI is the next greatest opportunity and biggest threat to the US. The shot has been heard, the AI race is on.
Stanford, CA April 5. “The Stanford Center for Legal Informatics will host the CodeX FutureLaw 2018, CodeX’s sixth annual conference focusing on how technology is changing the landscape of the legal profession, the law itself, and how these changes impact us all.” [$$$]
Ames, IA May 7-8, organized by the Department of Statistics at Iowa State University. “Goals of the conference include raising awareness about the importance of predictive inference and its applications, showcasing research of current and emerging leaders in the field, and encouraging interactions and collaborations among a diverse collection of scientists with complementary skills and abilities.” [$$$]
New York, NY April 26, organized by NYU Center for Urban Science + Progress. “Sensing the City will engage the local community in exploring overarching challenges and opportunities as urban sensing capabilities and ambitions continue to expand, inviting participants across New York to join sensing luminaries from across the USA for a blend of talks and panel discussions on three core themes.” [$$]
Mountain View, CA April 20, organized by SVAI and the Computer History Museum. “The program features two sessions. In the first session, presenters will give a short overview of their artificial intelligence and machine learning projects. In the second session, speakers will provide a more technical view of their work.” [free]
The 2018 Mortimer Spiegelman Award from the Applied Public Health Statistics Section of the American Public Health Association honors “a statistician below the age of 40 in the calendar year of the award who has made outstanding contributions to health statistics, especially public health statistics.” Deadline for nominations is April 6.
“Data Journeys is a podcast for aspiring Data Scientists by AJ Goldstein, where he interviews world-class Data Scientists about their learning journeys.”
Have you used Kaggle’s beta API to download data or make a competition submission? We’re pleased to announce version 1.1 of the API which includes new features for easily managing your datasets on Kaggle from the command line.
NVIDIA Highlights enables players to capture in-game moments automatically based on events occurring during gameplay. Highlights represents a key feature in NVIDIA’s ShadowPlay automated screen capture software. (You can check out more information about ShadowPlay here).
This blog introduces basic concepts a developer needs to implement the NVIDIA Highlights plugin for the Unity game engine.
“A pre-trained deep neural network making predictions on live camera input, trying to make sense of what it sees, in context of what it’s seen before. It can see only what it already knows, just like us.” [video, 3:02]
“I’m really excited to announce that we’ve launched a new blog, Data@Urban, that will give you a behind-the-scenes look into how Urban researchers use new technologies, create new data visualizations and tools, and use new data sources to do our work. It’s a place where you can learn not only what tools we are using at Urban–such as D3, Amazon Web Services, and more–but also how to use some of those tools.”