Northeastern is adding a campus in Vancouver to the university’s global network that will fill a demand for computer scientists on Canada’s west coast in its burgeoning high-tech industry. The Vancouver campus will offer degree programs to prepare students for careers in the age of artificial intelligence, including pathways that enable those with no high-tech background to pursue computing careers.
The Vancouver campus will be part of Northeastern’s global university system, which includes campuses in Boston, Seattle, the Bay Area, Charlotte, North Carolina, Toronto, and London.
Together with Crisis Text Line, Cornell researchers analyzed more than 1 million anonymized texts from nearly 3,500 counselors to better understand how counselor language use develops with experience. Their findings are included in “Finding Your Voice: the Linguistic Development of Mental Health Counselors,” which was presented at the Annual Meeting of the Association for Computational Linguistics, July 28-Aug. 2 in Florence, Italy.
Intel Corp on Tuesday unveiled its latest processor that will be its first using artificial intelligence (AI) and is designed for large computing centers.
The chip, developed at its development facility in Haifa, Israel, is known as Nervana NNP-I or Springhill and is based on a 10 nanometer Ice Lake processor that will allow it to cope with high workloads using minimal amounts of energy, Intel said.
Usage of UWB is growing fast with shipments of chips in the millions. Indeed, people in Europe and the US are already interacting with UWB without knowing it. If they own certain luxury cars, their key fobs already use UWB to securely unlock the vehicle. If they work in manufacturing, their factories are increasingly using UWB for tracking parts, tools and people. If they go to a museum, their electronic guides may be using UWB to enable precise guidance and commentary. These and other use cases are starting to show the utility of UWB. But, if you really want to see where UWB is going, and experience the myriad uses of the technology, there is only one country in the world to visit – China.
As in the West, in China, UWB is in factories. However, the scale of modernization and new factory building driven by the ‘Made in China 2025’ is huge. It dwarfs similar initiatives in other countries such as Industry 4.0 in Germany. Accurate location is at the core of the China initiative. With its class-leading capabilities, UWB is essential in factories across the country.
Hannah Fry, Associate Professor in the mathematics of cities at University College London, argues that mathematicians and data scientists need such an oath, just like medical doctors who swear to act only in their patients’ best interests.
“In medicine, you learn about ethics from day one. In mathematics, it’s a bolt-on at best. It has to be there from day one and at the forefront of your mind in every step you take,” Fry argued.
But is a tech version of the Hippocratic Oath really required? In medicine, these oaths vary between institutions, and have evolved greatly in the nearly 2,500 years of their history. Indeed, there is some debate around whether the oath remains relevant to practising doctors, particularly as it is the law, rather than a set of ancient Greek principles, by which they must ultimately abide.
When I picture drone-filled scenes of daily life in our not-so-distant future, my mind drifts to ultra-modern city centers and modern suburbs with autonomous delivery. I don’t think about farms. At least I didn’t until I visited one research project at the University of Kentucky.
Technology for farming in rural America is a very important piece of our future puzzle, and together a team of professors and student researchers are working to build an automated drone system that can monitor cattle health in the pasture.
The motivation for the project is backed by some pretty sobering stats. According to the team’s research, 2.5 million US cattle die every year from health issues, accounting for 60% of the cattle losses. Compare that to 220,000 lost to predators or other accidents and the stats make a strong case for paying more attention to cattle health.
For the better part of a decade, artificial intelligence has been propelled by a rocket fuel in seemingly endless supply. Deep learning, a method that allows machines to identify hidden patterns in data, has powered commercial applications like autonomous vehicles and voice assistants, and it’s potentially worth trillions of dollars a year, Kaveh reports.
But the rosy portrait of unstoppable progress belies a fear among some AI luminaries that things are not on the right path. In a new sort of resource curse, they say that deep learning has sucked energy away from other strains of inquiry without which AI may never approach even a child’s intellectual capabilities.
The big picture: For the past 5 years, Elon Musk and others have warned of a future disaster resulting from unchecked superintelligent AI. But today, much of the field is caught in a rather more elementary tug-of-war over which avenue will imbue AI even with the capacity for basic understanding.
Bots, trolls, state-run propaganda, information warfare and hate speech are some of the most pervasive ways that societal discourse is being warped in the modern era. Carnegie Mellon University today announced the creation of a new research center dedicated to the study of online disinformation and its effects on democracy, funded by a $5 million investment from the John S. and James L. Knight Foundation. The new center will bring together researchers from within the institution and across the country.
The Center for Informed Democracy and Social Cybersecurity (IDeaS) will study how disinformation is spread through online channels, such as social media, and address how to counter its effects to preserve and build an informed citizenry. Directed by Kathleen M. Carley, professor in the School of Computer Science’s Institute for Software Research, the center will take a multidisciplinary approach, engaging researchers from across the university to examine and develop responses to both technological and human facets of the issue. Douglas Sicker, head of Engineering and Public Policy in the College of Engineering, and David Danks, head of Philosophy in the Dietrich College of Humanities and Social Sciences, will be co-directors.
Every Wednesday afternoon, an alert flashes on the cellphones of about 50 teenagers in New York and Pennsylvania. Its questions are blunt: “In the past week, how often have you thought of killing yourself?” “Did you make a plan to kill yourself?” “Did you make an attempt to kill yourself?”
The 13- to 18-year-olds tap their responses, which are fed to a secure server. They have agreed, with their parents’ support, to something that would make many adolescents cringe: an around-the-clock recording of their digital lives. For 6 months, an app will gobble up nearly every data point their phones can offer, capturing detail and nuance that a doctor’s questionnaire cannot: their text messages and social media posts, their tone of voice in phone calls and facial expression in selfies, the music they stream, how much they move around, how much time they spend at home.
Science Advances;Jennifer F. Hoyal Cuthill, Nicholas Guttenberg, Sophie Ledger, Robyn Crowther and Blanca Huertas
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Traditional anatomical analyses captured only a fraction of real phenomic information. Here, we apply deep learning to quantify total phenotypic similarity across 2468 butterfly photographs, covering 38 subspecies from the polymorphic mimicry complex of Heliconius erato and Heliconius melpomene. Euclidean phenotypic distances, calculated using a deep convolutional triplet network, demonstrate significant convergence between interspecies co-mimics. This quantitatively validates a key prediction of Müllerian mimicry theory, evolutionary biology’s oldest mathematical model. Phenotypic neighbor-joining trees are significantly correlated with wing pattern gene phylogenies, demonstrating objective, phylogenetically informative phenome capture. Comparative analyses indicate frequency-dependent mutual convergence with coevolutionary exchange of wing pattern features. Therefore, phenotypic analysis supports reciprocal coevolution, predicted by classical mimicry theory but since disputed, and reveals mutual convergence as an intrinsic generator for the unexpected diversity of Müllerian mimicry. This demonstrates that deep learning can generate phenomic spatial embeddings, which enable quantitative tests of evolutionary hypotheses previously only testable subjectively.
Princeton University, School of Engineering and Applied Science
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Teenagers enjoy the products of artificial intelligence (AI) every day, whether taking a twisted selfie with a photo filter or listening to music with an automated streaming service. But not many high school students have used AI themselves to analyze human genetic variation or track deforestation in the Amazon.
These are two of the challenges undertaken by rising 11th graders as part of this summer’s Princeton AI4ALL program. With the ultimate goal of making AI technology more inclusive and beneficial to society, AI4ALL gives young women and others from underrepresented groups a chance to learn the basics of the field from an early age.
Root Insurance, a tech-enabled auto insurance upstart that lives among the legacy giants in Columbus, Ohio, recently raised around $350 million at a $3.5 billion valuation, Axios has learned from multiple sources.
Why it matters: VCs are valuing Root more as a tech company than as an insurer, arguably more because of high growth rates than the actual tech component. We’ve seen this elsewhere in the insurance sector (e.g., Lemonade), and in all sorts of other consumer-facing areas (eyeglasses, razors, mattresses, etc.). The the jury remains out on the sagacity of such classifications.
At each major point of the academic career path, there is significant hemorrhaging of female talent. In many countries of the Global North, women compose a little over half the undergraduate student body, which is only slightly more than the share of female doctoral students.1 It is after graduate school that the precipitous declines begin, as the number of women falls approximately ten percentage points each at the stages of assistant and associate professorship, so that finally the percentage of female full professors hovers around 32 percent.2 (In the European Union, the average share of full professors who are women is 21 percent.) This inverted pyramid is recognizable across academies in the Global North; even Scandinavia, despite its generous welfare states, conforms to the pattern. The few disciplines that boast large female faculties, such as education and foreign-language departments, tend to have the least prestige and are axed first during fiscal crises.3
Santa Clara, CA September 16-18. “Thanks to some high-profile IPOs, 2019 has been something of a breakout year for digital health. Sector luminaries will be flocking to Santa Clara, Calif., next month for HIMSS’ annual Health 2.0 Fall Conference to look back on the year and, more importantly, look ahead to the future.”
“Ryerson University and Maple Leaf Sports & Entertainment (MLSE) are now accepting applications for a second cohort of the Future of Sport Lab (FSL).” Deadline to apply is September 30.
Topic Extraction is an integral part of IE (Information Extraction) from Corpus of Text to understand what are all the key things the corpus is talking about. While this can be achieved naively using unigrams and bigrams, a more intelligent way of doing it with an algorithm called RAKE is what we’re going to see in this post.
Error analysis — the attempt to analyze when, how, and why machine-learning models fail — is a crucial part of the development cycle: Researchers use it to suggest directions for future improvement, and practitioners make deployment decisions based on it. Since error analysis profoundly determines the direction of subsequent actions, we cannot afford it to be biased or incomplete.
The purpose of this blog post is to introduce the reader to the tools of scientific machine learning, identify how they come together, and showcase the existing open source tools which can help one get started. We will be focusing on differentiable programming frameworks in the major languages for scientific machine learning: C++, Fortran, Julia, MATLAB, Python, and R.