The Radiological Society of North America (RSNA) is staying future-focused for its annual symposium in Chicago in November. According to a statement from the organization, machine learning and artificial intelligence (AI) will be playing an expanded role at this year’s conference.
After last year’s debut of the ML Pediatric Bone Challenge, during which more than 250 participants crafted algorithms to predict skeletal age using a dataset of pediatric hand x-rays, RSNA 18 will feature another challenge in which conference attendees will be asked to create machine learning algorithms to help detect pneumonia.
Curtis P. Langlotz, MD, PhD, a professor of radiology and biomedical informatics and the director of Stanford University’s Center for Artificial Intelligence in Medicine and Imaging, said last year’s machine learning challenge made an impact on the existing body of research for skeletal age prediction.
“Data scientists from around the world used the dataset to exceed the performance of previously published AI systems that automatically estimate bone age,” he said in a release from the RSNA.
LG Electronics has opened a new AI Research Lab in Toronto to supports its strategic dedication to AI as a transformative force.
This week’s opening sees it greatly expand its AI research capacity in North America as it looks to take a leading role in the technology’s implementation. The Lab is an extension of LG’s Silicon Valley AI Lab in Santa Clara, California.
Additionally, LG has signed a five-year, multi-million dollar research partnership with the University of Toronto, which is recognised worldwide for its AI and machine learning expertise.
Over the past couple of years, a group of organizations with a shared purpose—California Digital Library, Crossref, DataCite, and ORCID—invested our time and energy into launching the Org ID initiative, with the goal of defining requirements for an open, community-led organization identifier registry. The goal of our initiative has been to offer a transparent, accessible process that builds a better system for all of our communities. As the working group chair, I wanted to provide an update on this initiative and let you know where our efforts are headed.
On July 11, 2018, Twitter announced in a blog post they would be removing all “locked” accounts – those exhibiting suspicious or spammy behavior – from the service and warned that by doing a sweep of these accounts, many users would lose a pretty significant number of followers.
One of the metrics Craft tracks on companies is social media engagement, so we looked through our database before and after the purge to see which company Twitter accounts lost the most followers.
With over 350 million customers in 17 countries, Telefónica is one of the largest telecommunications companies in the world. But the Spanish-based organization wants to do more than connect people with mobile, landline, internet and pay TV services. It wants to make digital life easier for customers.
Founded in 1924, Telefónica has transformed into a modern, data-driven company in recent years, with major investments in infrastructure and technology. The upgrades enabled the company to launch Aura, an artificial intelligence-powered digital assistant that “learns the language of people so that they don’t have to learn the language of machines,” says Telefónica. It is available in Spain, Brazil, the United Kingdom, Germany, Argentina and Chile through mobile apps, webs and third-party channels including Facebook Messenger and Google Assistant.
The company will soon launch a smart device called Movistar Home that integrates Aura’s capabilities.
The US-based smart speaker company Sonos went public on Nasdaq today, launching an initial public offering valued at $16 per share. Hours later, its shares jumped over 25 percent for a high of $21, at the time of writing.
“We’re raising our profile a bit and stepping onto the bigger stage. I don’t think it could be better timed,” Sonos CEO Patrick Spence says in a phone call with The Verge. “It’s the next phase of us growing up as a company.”
Going public means Sonos will have more cash to take on smart speaker rivals. But as it goes head to head with tech giants like Apple, Amazon, and Google, it runs into the interesting conundrum that its rivals are also its much-needed allies.
While the premise of automated systems is that they will eventually prove to be more cost effective than human labour, at the early stage of developing new tech, the initial investment required is higher than just hiring humans to do the job.
“Developing the software can be quite expensive, and human labour is cheap,” says Robert Seamans, an associate professor at the NYU Stern School of Business.
Starting with humans doing the work “is the way companies should go,” says [Adam] Drake, who has worked on the development of data-driven solutions for industries including e-commerce, health care, and online travel.
Despite the advancements made in artificial intelligence so far, the Defense Advanced Research Projects Agency (DARPA) believes there is still more work to be done. DARPA is launching the Artificial Intelligence Exploration (AIE) program as part of its broader AI investment strategy.
“DARPA has established a streamlined process to push the state of the art in AI through regular and relatively short-term technology development projects,” said Peter Highnam, DARPA’s deputy director. “The intent is to get researchers on contract quickly to test the value and feasibility of innovative concepts. Where we’re successful, individual projects could lead to larger research and development programs spurring major AI breakthroughs.”
If Toronto is an artificial intelligence hub, MaRS is its beating heart. In the past few years, start-ups and tech giants have set up AI labs within its walls, and the world’s smartest researchers have turned down jobs at Stanford and MIT to move in. On June 27, AI enthusiasts got a rare glimpse of what those brainiacs are up to. During the MaRS AI Open House, a cadre of companies took over the building’s foyer to show how they’re using the technology to predict legal decisions (Blue J Legal), design HD maps (Ecopia), treat wounds (Swift Medical) and help retailers stock their shelves (Rubikloud). But the main draw was the chance to peer into the offices of MaRS’s buzziest tenants. The Vector Institute—a new lab co-founded by the “godfather of AI,” Geoffrey Hinton—showed off its sleek space. Autodesk, an architecture firm, toured guests around its AI-designed office. CIFAR, a research institute that leads the government’s Pan-Canadian AI Strategy, detailed projects it’s funded for nearly 400 scholars and advisors. And Borealis AI, an R&D arm of the Royal Bank of Canada, touted a computer program that can scan the news and predict what will show up in tomorrow’s paper. “Our entrepreneurs are literally inventing—or reinventing—the future,” said MaRS CEO Yung Wu. “These labs make AI dreams a reality.”
The Senate Intelligence Committee held a hearing on the use of social media misinformation campaigns by foreign actors. Members asked witnesses particularly about what they had observed and studied in terms of Russian attempts to manipulate social media platforms, not only to affect U.S. democracy and the political process but also businesses and global economic markets, among other targets. [video, 2:29:15]
If you were to drop Dave Imus anywhere in the United States, he could likely point out something unique in the landscape around him.
This winding road through flat farmland, 30 miles outside of Eugene, Oregon, is no different.
“This spot has the unique characteristic in that you can see all five of Oregon’s highest snow peaks in the Cascade Range,” Imus said pointing to the mountain range obscured by clouds and distance. “One thing I’ve learned is that all landforms, regardless of how subtle they are, have their own beauty and character.” [audio, 4:18]
Science has always relied on a combination of approaches to derive an answer or develop a theory. The seeds for Darwin’s theory of natural selection grew under a Herculean aggregation of observation, data, and experiment. The more recent confirmation of gravitational waves by the Laser Interferometer Gravitational-Wave Observatory (LIGO) was a decades-long interplay of theory, experiment, and computation.
Certainly, this idea was not lost on the U.S. Department of Energy’s (DOE) Argonne National Laboratory, which has helped advance the boundaries of high-performance computing technologies through the Argonne Leadership Computing Facility (ALCF).
Realizing the promise of exascale computing, the ALCF is developing the framework by which to harness this immense computing power to an advanced combination of simulation, data analysis, and machine learning. This effort will undoubtedly reframe the way science is conducted, and do so on a global scale.
Technology reporter Natasha Singer and Safiya Noble, author of the book Algorithms of Oppression, talk about what type of questions facial recognition technology brings up for tech creators, policy makers, and the general public. [audio, 17:00]
“The Electronic Frontier Foundation (EFF) is honored to announce the winners of its 2018 Pioneer Awards: fair use champion Stephanie Lenz, European digital rights leader Joe McNamee, and groundbreaking content moderation researcher Sarah T. Roberts. The ceremony will be held September 27th in San Francisco.” [$$]
While neural architecture search (NAS) has drawn increasing attention for automatically tuning deep neural networks, existing search algorithms usually suffer from expensive computational cost. Network morphism, which keeps the functionality of a neural network while changing its neural architecture, could be helpful for NAS by enabling a more efficient training during the search. However, network morphism based NAS is still computationally expensive due to the inefficient process of selecting the proper morph operation for existing architectures. As we know, Bayesian optimization has been widely used to optimize functions based on a limited number of observations, motivating us to explore the possibility of making use of Bayesian optimization to accelerate the morph operation selection process. In this paper, we propose a novel framework enabling Bayesian optimization to guide the network morphism for efficient neural architecture search by introducing a neural network kernel and a tree-structured acquisition function optimization algorithm. With Bayesian optimization to select the network morphism operations, the exploration of the search space is more efficient. Moreover, we carefully wrapped our method into an open-source software, namely Auto-Keras for people without rich machine learning background to use. Intensive experiments on real-world datasets have been done to demonstrate the superior performance of the developed framework over the state-of-the-art baseline methods.
Applied visualization researchers often work closely with domain collaborators to explore new and useful applications of visualization. The early stages of collaborations are typically time consuming for all stakeholders as researchers piece together an understanding of domain challenges from disparate discussions and meetings. A number of recent projects, however, report on the use of creative visualization-opportunities (CVO) workshops to accelerate the early stages of applied work, eliciting a wealth of requirements in a few days of focused work. Yet, there is no established guidance for how to use such workshops effectively. In this paper, we present the results of a 2-year collaboration in which we analyzed the use of 17 workshops in 10 visualization contexts. Its primary contribution is a framework for CVO workshops that: 1) identifies a process model for using workshops; 2) describes a structure of what happens within effective workshops; 3) recommends 25 actionable guidelines for future workshops; and 4) presents an example workshop and workshop methods. The creation of this framework exemplifies the use of critical reflection to learn about visualization in practice from diverse studies and experience.
The JPL Open Source Rover is an open source, build it yourself, scaled down version of the 6 wheel rover design that JPL uses to explore the surface of Mars. The Open Source Rover is designed almost entirely out of consumer off the shelf (COTS) parts. This project is intended to be a teaching and learning experience for those who want to get involved in mechanical engineering, software, electronics, or robotics.