“Are vaccines safe for my baby?” wonders a new mother. After reading a few articles online that seem authoritative, she steps away from the computer and decides that there is not enough evidence to answer her question definitively. This scenario appears in the first of many vignettes in Science Denial that educational psychologists Gale Sinatra and Barbara Hofer use to confront a worrisome problem that extends beyond ideological science denial itself: the denial of science to those who seek credible information and who are often in great need of it. Most people who search for information online favor trusted, easy-to-find sources. What they encounter is a forum that offers a platform to anyone with an online marketing strategy. Sinatra and Hofer point to a rise in “the sophistication of those who wish to portray fiction as fact.” Herculean efforts are being made by determined lobbies to counter scientific sources and undermine public confidence in science itself, they note, and even websites run by government agencies can sometimes stray from scientific consensus. As intelligent virtual assistants become more widespread and the number of online information searches performed daily continues to rise, we become more and more tethered to an information source that can be as misleading as it is valuable. Sinatra and Hofer remind us that we are more vulnerable to misinformation than we may think. Those who craft messages that run counter to accepted science know that the layperson's understanding of science is limited. They know that people are quick to use simple heuristics and the opinions of those around them as substitutes for deeper investigations. Hearing the same message repeatedly and seeing a few friends nod their heads in agreement with it can make it seem more credible. Appeals to remain “fair and balanced” are sometimes used to convince people to give equal consideration to messages that fly in the face of scientific consensus. The authors join other psychologists who remind us that our own biases can prompt even the most prudent among us to dismiss scientific findings when they conflict with what we think we already know about the world. For example, drivers are known to remain confident in their ability to safely multitask behind the wheel, even when that ability has been measured and confirmed to be poor ([ 1 ][1]). And once our beliefs have been formulated, we often come to personally identify with them and regard negations of them as personal criticism. Sinatra and Hofer argue that the path toward a better future starts in schools, at the loftiest conceptual levels. Educators should strive to help kids form what they call a “science attitude”—one that places value on the truth, on hypotheses and theories that have a fair chance to be right or wrong, and most of all on evidence. A science attitude needs to be accompanied by science knowledge, including a familiarity with where good research can be found and a basic understanding of the methods used to evaluate scientific hypotheses, including practices such as peer review and replicability. School curricula, they argue, need to prepare some students to become producers of science and all students to become good consumers of science. But what can be done about the grown-ups who already hold beliefs that run counter to scientific consensus? The authors offer hope that reason can prevail. Experimental evidence suggests that strongly held beliefs in unsupported theories can be moderated or even overturned using refutational techniques that identify specific misconceptions, state that they are incorrect, and detail the reasons why. The authors describe their success with using these techniques to change, or at least moderate, strong negative opinions about genetically modified foods. Falling somewhere between academic and trade writing, Science Denial is filled with relatable scenarios, research studies, and helpful advice for individuals, educators, science communicators, and policy-makers. As social media discussions of science topics continue to proliferate and carefully reported coverage of science continues to decline, the authors warn readers to ready themselves for a future in which separating fact and fiction may be more difficult than ever. Their book offers abundant practical guidance to help us meet the challenge. 1. [↵][2]1. D. M. Sanbonmatsu, 2. D. L. Strayer, 3. N. Medeiros-Ward, 4. J. M. Watson , PLOS ONE 8, e54402 (2013). [OpenUrl][3][CrossRef][4][PubMed][5] [1]: #ref-1 [2]: #xref-ref-1-1 "View reference 1 in text" [3]: {openurl}?query=rft.jtitle%253DPLOS%2BONE%26rft.volume%253D8%26rft.spage%253De54402%26rft_id%253Dinfo%253Adoi%252F10.1371%252Fjournal.pone.0054402%26rft_id%253Dinfo%253Apmid%252F23372720%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [4]: /lookup/external-ref?access_num=10.1371/journal.pone.0054402&link_type=DOI [5]: /lookup/external-ref?access_num=23372720&link_type=MED&atom=%2Fsci%2F372%2F6549%2F1400.atom

David B. Wake, a pioneer in the fields of evolutionary morphology, evolutionary developmental biology (evo-devo), and organismal diversification, died on 29 April. He was 84. Wake was a career-long visionary in organismal biology who led evolutionary biologists to examine not only how organisms are different but also how they become different. As a graduate student, he set the framework for his career by detailing the evolutionary relationships and morphological diversity of salamanders. He then delved into functional morphology (how organismal structures work), evolutionary development (how developmental pathways influence diversification of form), and speciation (how species come to be). One of the most influential and integrative biodiversity scientists of his era, Dave was boundlessly curious about all aspects of evolution and unusually open-minded about new techniques and analyses. Dave was born on 8 June 1936 and raised in South Dakota. He attended Pacific Lutheran College in Tacoma, Washington, where he became fascinated by salamanders after uncovering some while looking for insects for an entomology course. After receiving his BA in biology in 1958, he joined the lab of herpetologist Jay Savage at the University of Southern California (USC). At USC, he met Marvalee Hendricks, a fellow graduate student and scholar of caecilians, another understudied group of amphibians. Dave and Marvalee married in 1962 and became collaborators in life and in science. Dave completed his MS in 1960 and his PhD in 1964, both in biology at USC. He joined the faculty at the University of Chicago for 5 years and then moved to the University of California, Berkeley (UC Berkeley), where he was director of the Museum of Vertebrate Zoology from 1971 to 1998 and professor of integrative biology until his retirement in 2003. Marvalee joined the faculty at UC Berkeley as a tenure-track professor soon after Dave. From the time I first heard of them, they were known as “the Wakes,” and for many of us, they were an early model for successful dual careers in academia. An unapologetic organismal biologist, Dave used salamanders as a model taxon (as opposed to a model organism) to ask questions, answer some, and rework others, creating a cycle of ever-deepening inquiry into their evolution. Rather than focusing on a single model organism or particular evolutionary mechanisms, Dave developed extensive knowledge of many salamander species, enough to use the whole taxon as a model platform to inform his many research foci. In doing so, Dave achieved an unprecedented level of integration across approaches to address evolutionary mechanisms and their consequences for diversification. His exemplary integration inspired a series of papers by James Griesemer in the field of history and philosophy of science. Dave's salamander research transcended boundaries of methodologies, specialties, lines of inquiry, and disciplines. He developed a distinctive form of scientific problem-solving and iterative questioning that synergistically increased our general understanding of evolution. Dave developed expertise in phylogenetics, morphology, development, ecology, neurobiology, behavior, and physiology, and his discoveries were groundbreaking in many fields. Coupling knowledge of adult morphology, ontogeny, embryology, function, and selection, he developed predictions about the retention and/or loss of morphological structures during development and became one of the first to frame these findings as part of the nascent field of evo-devo. By combining detailed spatial knowledge of morphological variation, biogeography, behavior, and genomics, he contributed a classic example of speciation in action with his exploration of the salamander ring species Ensatina . These discoveries, none of which could have been made without integrating approaches, span micro- to macroevolution and are now classics in evolutionary biology. His accomplishments led to many accolades, including election to the American Philosophical Society, the American Academy of Arts and Sciences, and the National Academy of Sciences. In the late 1980s, Dave was an early proponent of action in response to the alarming global decline in amphibians. He chaired the first Declining Amphibian Populations Task Force and raised awareness of the predicament posed to amphibians by anthropogenic changes in climate and landscapes. As with his own research, he promoted diverse approaches in finding the causes of this large-scale biodiversity loss, to the benefit of both the scientific community and amphibians. I joined the Wake lab in 1996 as a postdoc. It was an exciting time, with many field trips and countless discussions of species concepts, salamander tongues, and amphibian declines. Dave had a work ethic that amazed everyone and often left us challenging our expectations of ourselves. His leadership style emphasized showing, not telling. That work ethic resulted in some humorous “Wakeisms”—when lab members took vacations that he perceived as just a bit long, he began lab meetings by naming those who were absent and stating that “they must be gallivanting around the world.” To this day, former Wake lab members refer to vacationing as doing just that. Dave set high standards for research and expected us to meet them. He was honest in his delivery of criticism but somehow made it feel like it was for our own good. He was an unwavering supporter of those who worked with him and incredibly loyal to his students and colleagues. No matter how busy, he eagerly welcomed visiting students and early-career scientists, always showing interest in their stories and offering advice about their research and professional development. Nearly 15 years ago, at a symposium organized around the potential of new genomic approaches in herpetology, Dave regaled the audience with what he saw as the biggest questions still to be answered by integrating this new approach. He genuinely reveled in witnessing the advancement of science, not only through his own work but also through that of his lab members and anyone else who stepped up to the plate. He concluded with a typically positive outlook on science: “I only wish I had another 50 years to live, just to see what you are all going to discover!”

Artificial intelligence is getting a little smarter every day. Digital learning continues its expansion at all levels of education. Bots are designed to make our lives easier, more informative, and more interesting. Their machine learning capabilities make them a promising technology in education. The knowledge base of chatbots will only grow, and the bots themselves will be able to learn along with students.

A lip-reading app from Irish startup Liopa is said to represent a breakthrough in the field of visual speech recognition (VSR), which trains AI to read lips without any audio input. Liopa's product, SRAVI (Speech Recognition App for the Voice Impaired) is a communication aid for speech-impaired patients. It is likely to be the first lip-reading AI app available for public purchase, according to an account from Vice/Motherboard. Researchers driven by a range of potential commercial applications including surveillance tools have been working for years to teach computers to lip-read, and it has proven a challenging task. Liopa is working to certify SRAVI as a Class I medical device in Europe, hoping to complete the certification by August.

Results from the DOTS competition were released yesterday, after an intense month with teams from around the world designing new algorithms for robot swarms tasked with delivering emergency food parcels. The Scenario Increases in the number of emergency food parcels distributed by food banks have accelerated over the course of the coronavirus pandemic, particularly in those going to children. Robot swarms could help streamline the distribution of these emergency food parcels, while freeing up time for volunteers and workers to interface with the users and provide human contact. What if you could unbox a swarm of robots and immediately use them to power your organisation and transport needs? You could use them to organise the stock room of a small retail shop, or retrieve boxes in a pop-up distribution centre for school lunches.

This course will teach you foundations of deep learning and TensorFlow as well as prepare you to pass the TensorFlow Developer Certification exam (optional). Videos going through the rest of the notebooks (03 - 10) are available in the full course. New You can now read the full course as an online book! (note: this is a work in progress, but 95% of it should run fine) Check out the livestream Q&A celebrating the course launch on YouTube. Otherwise, many of them might be answered below. This table is the ground truth for course materials.

We present Brax, an open source library for rigid body simulation with a focus on performance and parallelism on accelerators, written in JAX. We present results on a suite of tasks inspired by the existing reinforcement learning literature, but remade in our engine. Additionally, we provide reimplementations of PPO, SAC, ES, and direct policy optimization in JAX that compile alongside our environments, allowing the learning algorithm and the environment processing to occur on the same device, and to scale seamlessly on accelerators. Finally, we include notebooks that facilitate training of performant policies on common OpenAI Gym MuJoColike tasks in minutes. Figure 1: The suite of examples environments included in the initial release of Brax. From left to right: ant, fetch, grasp, halfcheetah, and humanoid.

Despite the ubiquity of U-statistics in modern Probability and Statistics, their non-asymptotic analysis in a dependent framework may have been overlooked. In a recent work, a new concentration inequality for U-statistics of order two for uniformly ergodic Markov chains has been proved. In this paper, we put this theoretical breakthrough into action by pushing further the current state of knowledge in three different active fields of research. First, we establish a new exponential inequality for the estimation of spectra of trace class integral operators with MCMC methods. The novelty is that this result holds for kernels with positive and negative eigenvalues, which is new as far as we know. In addition, we investigate generalization performance of online algorithms working with pairwise loss functions and Markov chain samples. We provide an online-to-batch conversion result by showing how we can extract a low risk hypothesis from the sequence of hypotheses generated by any online learner. We finally give a non-asymptotic analysis of a goodness-of-fit test on the density of the invariant measure of a Markov chain. We identify some classes of alternatives over which our test based on the $L_2$ distance has a prescribed power.

Learning new tasks continuously without forgetting on a constantly changing data distribution is essential for real-world problems but extremely challenging for modern deep learning. In this work we propose HCL, a Hybrid generative-discriminative approach to Continual Learning for classification. We model the distribution of each task and each class with a normalizing flow. The flow is used to learn the data distribution, perform classification, identify task changes, and avoid forgetting, all leveraging the invertibility and exact likelihood which are uniquely enabled by the normalizing flow model. We use the generative capabilities of the flow to avoid catastrophic forgetting through generative replay and a novel functional regularization technique. For task identification, we use state-of-the-art anomaly detection techniques based on measuring the typicality of the model's statistics. We demonstrate the strong performance of HCL on a range of continual learning benchmarks such as split-MNIST, split-CIFAR, and SVHN-MNIST.

Matt is currently an Instructional Designer II at Orbis Education and a Part-Time Instructor at the University of Texas Rio Grande Valley. Previously he worked as a Learning Innovation Researcher with the UT Arlington LINK Research Lab. His work focuses on learning theory, Heutagogy, and learner agency. Matt holds a Ph.D. in Learning Technologies from the University of North Texas, a Master of Education in Educational Technology from UT Brownsville, and a Bachelors of Science in Education from Baylor University. His research interests include instructional design, learning pathways, sociocultural theory, heutagogy, virtual reality, and open networked learning.