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Complicated legacies: The human genome at 20

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Millions of people today have access to their personal genomic information. Direct-to-consumer services and integration with other “big data” increasingly commoditize what was rightly celebrated as a singular achievement in February 2001 when the first draft human genomes were published. But such remarkable technical and scientific progress has not been without its share of missteps and growing pains. Science invited the experts below to help explore how we got here and where we should (or ought not) be going. —Brad Wible By Kathryn Maxson Jones and Robert Cook-Deegan Sharing data can save lives. The “Bermuda Principles” for public data disclosure are a fundamental legacy of producing the first human reference DNA sequence during the Human Genome Project (HGP) ([ 1 ][1]). Since the 1990s, these principles have become a touchstone for open science. In February 1996, the leaders of the HGP gathered in Bermuda to discuss how to scale up production for a human reference DNA sequence. With some caveats, the consortium agreed that all sequencing centers would release their data online within 24 hours. Other examples of sharing data before publication existed, but most—such as the Protein Data Bank—restricted sharing of prepublication data to a small community of users, sometimes withholding data even after the related papers were published ([ 2 ][2]). At the time, the Bermuda Principles were distinctive in their aspiration that all HGP-funded sequences be released to anyone online within a day. Yet implementing this policy was hardly simple; the challenges that the HGP faced inform data sharing today ([ 3 ][3]). The Bermuda Principles required advocacy. This came from John Sulston and Robert Waterston, whose experiences with data sharing in Caenorhabditis elegans biology were the practical precedent for a radical idea. Context also mattered, and data release within 24 hours remained an aspirational ethos rather than a strict requirement. Flexibility allowed smaller centers to participate while also allowing the project to accommodate then-incompatible policies in Germany, France, Japan, and the United States. Finally, the policy required enforcement. Administrators from the HGP's largest patrons sent stern letters intended to make funders' policies conform to the Bermuda Principles, threatening expulsion from the international sequencing consortium. The Bermuda Principles have since been adapted to different communities and have served as an inspiration for many others ([ 4 ][4]). For example, rapid data sharing has been crucial in the current coronavirus crisis. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome was identified quickly and its sequence released on 10 January 2020, starting the clock on the development of vaccines and diagnostic tests. The COVID-19 Host Genetics Initiative disseminated data rapidly and openly, building on precedents such as the Global Initiative on Sharing All Influenza Data ([ 5 ][5], [ 6 ][6]). Of course, unfettered data sharing is not, and should not be, universal. Identifiable individual medical data, for instance, cannot be treated the same way as samples contributed to build a reference genome sequence. Many communities have adopted prepublication sharing strategies with considerable success, such as the various consortia for Alzheimer's research, the “open science” experiments at the Montreal Neurological Institute and the Mario Negri Institute, and the advances enabled by the Structural Genomics Consortium. The HGP set a high bar. Its core values of open science and rapid data flow persist, fomented by the urgency of rapid data sharing in biomedicine. By Charles N. Rotimi, Shawneequa L. Callier, Amy R. Bentley The long-term global impact of human genomics will be compromised, and our understanding of human history and biology hindered, if we continue to focus predominantly on individuals of European ancestry ([ 7 ][7]). Although we all share a recent common origin in Africa, and the genetic difference between any two individuals is small (0.1%), this translates to about 3 million points where individual genomes can vary, and the distribution of these human genetic variants (HGVs) is not random. It has long been understood that genomes (and exposures to key nongenetic factors) differ across ancestral and geographical backgrounds; nonetheless, genomics has largely focused on European-ancestry genomes. Presumably this is attributable to the availability of large, well-characterized datasets of European-ancestry individuals, academic and research networks that exclude and disadvantage underrepresented scholars ([ 8 ][8]), and the absence of publishing or funding motivation for large-scale genomics of diverse individuals. But diversity and representation are now being elevated from the purview of specialized research to a broad awareness across genomics. As this awareness develops, the field must grapple with understanding and communicating the implications: (i) Any two sub-Saharan Africans are more likely to be genetically different from each other than from an individual of European or Asian ancestry; (ii) a subset of HGVs can only be found in Africans because the small number of humans that left Africa about 100,000 years ago to populate the rest of the world carried a fraction of the variation that existed then; (iii) the African ecological environment has left its mark on human genomes (e.g., gene variants found to increase vulnerability to kidney failure) that are seen worldwide only in persons with ancestry from specific regions of Africa ([ 9 ][9]). Similarly, there are HGVs of health and historical importance that are rare or absent in African populations. For example, genomic regions harboring ancient DNA—the result of interbreeding with archaic human relatives (such as Neanderthals) in Asia, Europe, and the Americas—have biological functions , such as susceptibility to diabetes and viruses ([ 10 ][10]). For genomics-driven technologies and clinical and public health approaches to be deployed globally without exacerbating health inequalities, we must include individuals from diverse ancestral and geographical backgrounds. Growing prioritization of diverse populations in genomics research has begun to respond to these gaps. Programs such as TOPMed, All of Us, International Common Disease Alliance, Human Heredity and Health in Africa (H3Africa), Million Veteran Program, GenomeAsia, and the COVID global consortium contribute to advances in diversity and inclusion among research participants. The diversity of genomics researchers also merits continuing attention. The H3Africa initiative, for example, includes investments in training and infrastructure in each project, providing a blueprint for prioritizing capacity-building. The genomics community needs to value diverse samples in analyses and conclusions, as well as to focus resources on capacity-building and removing barriers to create a diverse workforce ([ 11 ][11]). By Hallam Stevens Over a few frenzied weeks in the middle of 2000, icing his wrists between coding sessions, Jim Kent, a graduate student at the University of California, Santa Cruz, created the first genome assembler software. GigAssembler pieced together the millions of fragments of DNA sequence generated at labs around the globe, literally making the human genome. At almost the same time, Celera Genomics acquired Paracel, a company that primarily designed software for intelligence gathering. Paracel owned specially designed text-matching hardware and software (the TRW Fast Data Finder) that was rapidly adapted for sniffing out genes within the vast spaces of the genome. Untangling the jumble of genomic letters required rapidly and accurately searching for a specified sequence within a very large space. This demanded new forms of training and disciplinary expertise. Physicists, mathematicians, and computer scientists brought methods such as linear programming, hashing, and hidden Markov models into biology. Since 2005, the Moore's Law–like growth of next-generation sequencing has generated everincreasing troves of data and required even faster algorithms for indexing and searching. Biology has borrowed “big data” methods from industry (e.g., Hadoop) but has also contributed to pushing the frontiers of computer science research (e.g., the Burrows-Wheeler transform) ([ 12 ][12]). The coalescence of bioinformatics and computational biology around algorithms has also given rise to new institutional forms and new markets for biomedicine. Statistically powered “data-driven biology” has configured an emerging medical-industrial complex that promises personalized and “precision” forms of diagnosis and treatment. Algorithmic pipelines that compare an individual's genotype to reference data generate a range of predictions about future health and risk. Direct-to-consumer genomics companies such as 23andMe now promise us healthier, happier, and longer ways of living via algorithms. This presents substantial challenges for privacy, data ownership, and algorithmic bias ([ 13 ][13]–[ 15 ][14]) that must be addressed if genomics is to avoid becoming a handmaiden of “surveillance capitalism” ([ 16 ][15]). Many tech companies have begun to look toward using machine learning to combine more and more biological data with other forms of personal data—where we go, what we buy, whom we associate with, what we like. The hopes for genomics have long been tempered by fears that the genome could reveal too much about ourselves, exposing us to new forms of discrimination, social division, or control. Algorithmic biology is depicting and predicting our bodies with growing accuracy, but it is also drawing biomedicine more closely into the orbits of corporate tech giants that are aggregating and attempting to monetize data. By Kathryn A. Phillips, Jeroen P. Jansen, Christopher F. Weyant Debates about precision medicine (PM), which uses genetic information to target interventions, commonly focus on whether we can “afford” PM ([ 17 ][16]), but focusing only on affordability, not also value, risks rejecting technologies that might make health care more efficient. Affordability is a question of whether we can pay for an intervention given its impact on budgets, whereas value can be measured by the health outcomes achieved per dollar spent for an intervention. Ideally, a PM intervention both saves money and improves outcomes; however, most health care interventions produce better outcomes at higher cost, and PM is no exception. By better distinguishing affordability and value, and by considering how we can address both, we can further the agenda of achieving affordable and valuable PM. The literature has generally not shown that PM is unaffordable or of low value; however, it has also not shown that PM is a panacea for reducing health care expenditures or always results in high-value care ([ 17 ][16]). Understanding PM affordability and value requires evidence on total costs and outcomes as well as potential cost offsets, but these data are difficult to capture because costs often occur up front while beneficial outcomes accrue over time ([ 18 ][17]). Also, PM could result in substantial downstream implications because of follow-up interventions, not only for patients but also for family members who may have inherited the same genetic condition. Emerging PM tests could be used for screening large populations and could include genome sequencing of all newborns, liquid biopsy testing to screen for cancers in routine primary care visits, and predictive testing for Alzheimer's disease in adults. These interventions may provide large benefits, but they are likely to require large up-front expenditures. Another complication is that many PM interventions measure multiple genes relevant to multiple conditions and provide myriad types of value, such as the personal value of this information to patients ([ 19 ][18]). Various methods have been developed for integrating affordability and value, but cost-effectiveness analyses often do not examine the budget impact, which can result in incomplete or contradictory conclusions ([ 20 ][19]). However, assessments that consider affordability and value simultaneously, such as those by the Institute for Clinical and Economic Review, are becoming more accepted by decisionmakers ([ 21 ][20]). The growing consideration of both affordability and value is less a result of methodological advances than of an increased focus on how to ensure sustainable and efficient health care (and the corresponding political will to do so). A positive consequence of this is an increase in research on how to best define and quantify affordability and value given the available data. PM is here to stay. However, it can only achieve its potential if it is both affordable and of high value. By Dorothy E. Roberts In the aftermath of the first publication of the human genome, researchers confirmed what many scholars had recognized for decades: that race is a social construct, not a natural division of human beings written in our genes ([ 22 ][21], [ 23 ][22]). Yet rather than hammer the final nail in the coffin, the human genome map sparked renewed interest in race-based genetic difference. The posting of recent genetic studies on white supremacist websites led the American Society of Human Genetics in 2018 to issue yet another statement denouncing genetics-based claims of racial purity as “scientifically meaningless,” while many geneticists failed to see how the biological concept of race was itself invented to support racism. None of this history has restrained the search for genetic differences between races and genetic explanations for various racial disparities (e.g., in COVID-19 outcomes), which in turn generates persistent public confusion about race and genetics. It is time to end the entanglement of race and genetics and to work toward a radically new understanding of human unity and diversity. There are two general approaches that can help guide innovative research questions and methods that no longer rely on invented racial classifications as if they were biological. First, genetic researchers should stop using race as a biological variable that can explain differences in health, disease, or responses to therapies ([ 24 ][23]). Treating race as a biological risk factor obscures how structural racism has biological effects and produces health disparities in racialized populations. Epigenetics offers promising models to investigate one pathway through which unequal social conditions get “embodied” or “under the skin” to generate disparate health outcomes. Still, researchers must use caution to avoid making deleterious epigenetic processes seem self-perpetuating and inevitable, taking attention away from structural inequities that caused the problem in the first place ([ 25 ][24]). Second, genetic researchers should stop using a white, European standard for human genetics and instead study a fuller range of human genetic variation. Projects dedicated to expanding genetic databases with DNA from groups on the African continent, for example, have shown that these populations are the most genetically diverse on Earth and refute the myth that there is a genetically distinguishable Black race ([ 26 ][25]). The aim of diversifying biomedical research should not be to find innate genetic differences between racial groups; rather, it should be to give persons from racialized populations equal access to the benefits of participating in highquality and ethical research (including clinical trials) and to give scientists a richer resource to understand human biology. In this way, genetic research can contribute to more individualized diagnoses and therapies that no longer rely on crude medical decisions based categorically on a patient's race. By Dina Zielinski and Yaniv Erlich In 2007, only two individuals had their full genome sequenced: Craig Venter and Jim Watson. Today, more than 30 million individuals have access to their detailed genomic datasets. This democratization of genomic data has helped to reunite families, fight racism, and promote genetic literacy ([ 27 ][26], [ 28 ][27]), but it has also enabled surveillance on a massive scale. The correlation of DNA variants between distant relatives means that relatively small databases can identify large parts of the population, including people who are not in the database ([ 29 ][28]). The high dimensionality of DNA data and linkage disequilibrium mean that efforts to obscure individual-level data, by pooling genomes or censoring parts of the genome, can fail unexpectedly ([ 30 ][29]). And with the advent of consumer genomics and third-party websites that allow participants to upload their genome data, it is increasingly easy to collect and access DNA data ([ 31 ][30]). We envision that the COVID-19 pandemic will accelerate genetic surveillance. People will likely see infectious disease surveillance, swabbing upon arrival, at border crossings, including airports. Governments can harness pandemic control infrastructure to build a DNA database of all arrivals. Such databases can identify a substantial portion of the visitor's home-country population because genetic re-identification is magnified through familial connections. But massive surveillance will not be restricted to government efforts. With the growing size of third-party genetic databases, essentially everyone with the right technical skills will be able to identify individuals. What are the implications of ubiquitous genetic surveillance? On the plus side, law enforcement agencies will be able to solve virtually all sexual assault cases. Screening at airports can help to reveal fraudulent identities, which is central in fighting human trafficking and espionage. However, the same technology can be used to target minorities or political opponents. The convergence of these applications underscores the importance of treading lightly with these new forensic superpowers. On the technical side, one theoretical mitigation option to limit such re-identification could include creating a trail that leads a genealogical tracing attempt to a fake identity. But this and other methods have yet to be investigated in a principled approach. Beyond technological countermeasures, the field needs guidelines concerning the use of genetic surveillance technologies. An important step is the interim policy laid out by the U.S. Department of Justice restricting forensic investigators' usage of third-party genetic databases to investigations of violent crimes, and only with sites that receive informed consent from users for such searches ([ 32 ][31]). Open public discussion is vital to further shape policies and expectations so as to harness the power of the genomic revolution for the benefit of the public. By Nanibaa' A. Garrison and Stephanie Russo Carroll Despite considerable advances in genomics research over the past two decades, Indigenous Peoples are incredibly underrepresented. Biological materials from Indigenous Peoples have been collected to study diseases, medical traits, and the origins of human populations, yet many studies have not benefited the participants or their communities. Some research has even created harms such as exacerbation of derogatory and detrimental stereotypes or challenges to cultural beliefs. Without productive relationships, Indigenous communities may not benefit from research in areas such as precision medicine and pharmacogenomics, and health disparities may remain unaddressed. Thus, many Indigenous Peoples are hesitant to participate in genomics research without extensive discussions and agreements to ensure that the results have individual and collective benefits, as well as to learn what happens to samples and how they are used ([ 33 ][32]). Indigenous scholars are developing guidance to address concerns and pave pathways for more equitable and beneficial research that aligns with the rights and interests of Indigenous Peoples ([ 34 ][33]). Culturally aligned research can increase Indigenous Peoples' participation in genomics research. The Summer Internship for Indigenous Peoples in Genomics (SING) trains and builds capacity for scientists and community members to shape research priorities of interest in their communities, and it has prompted the SING Consortium to develop a framework for ethical research engagement ([ 35 ][34]). The Center for the Ethics of Indigenous Genomic Research supports Indigenous-led research in biobanking and precision medicine that integrates sovereignty rights and Indigenous communities' ethical and cultural preferences. In Canada, Silent Genomes is creating an Indigenous Background Variant Library through close engagement with community and cultural advisors. Finally, in New Zealand, the Māori-developed Te Ara Tika framework integrates relationships, research design, cultural and social responsibility, justice, and equity as core interests for ethical genomic research with Māori people ([ 36 ][35]). Recognizing the need to foster self-determination and collective rights within open science and secondary use, the Global Indigenous Data Alliance's CARE Principles for Indigenous Data Governance (Collective Benefit, Authority to Control, Responsibility, and Ethics) complement the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) that make data machine-readable and usable in multiple contexts ([ 37 ][36], [ 38 ][37]). When operationalized together, CARE and FAIR enhance Indigenous leadership and innovation, leading to participatory governance and enabling opportunities for trust-building and accountability by incorporating Indigenous values and rights. For example, the creation of data standards and the use of Indigenous community-defined metadata can protect data while allowing them to be useful. The metadata become durable and persistent components of genomic information that provide guidance on future use, such as who has the authority to sanction that use, for what purposes, and to benefit whom ([ 34 ][33], [ 37 ][36]). An increased focus on rights and interests combined with enhanced engagement and capacity has the potential to reduce bias and produce more relevant and beneficial research for all. By Pilar N. Ossorio Polygenic risk scores (PRSs) are a rapidly emerging technology for aggregating the small effects of multiple polymorphisms across a person's genome into a single score. A PRS can be calculated for any phenotype for which genome-wide association data are available, usually by summing the weighted effect sizes of alleles ([ 39 ][38]). In medicine and public health, PRSs could be used for selecting therapies, initiating additional risk screening, or motivating behavior change. Whether they will be used in medicine depends on factors such as the degree to which they provide actionable risk information beyond that provided by clinical algorithms, the availability of information technology for calculating PRSs in clinical settings, and the availability of decision support tools. To date, PRSs have demonstrated moderate utility for complex medical phenotypes, including blood pressure, obesity, diabetes, depression, schizophrenia, and coronary heart disease. PRSs also highlight the complex intersection of race and ancestry in genomics. Substantiating and extending earlier work, a recent analysis showed that in 26 previous studies, PRSs performed significantly worse for people with predominantly African or South Asian ancestry than for people with predominantly European ancestry ([ 40 ][39], [ 41 ][40]). There was not enough data to assess performance for many groups (e.g., South East Asians, Pacific Islanders). Researchers have attributed this result to underrepresentation of non-European individuals and racial/ethnic minorities in datasets used to develop PRSs. Relative to people who are included in most genomic datasets, racial/ethnic minorities tend to have a greater portion of recent ancestry from places other than Europe. In response to the differential predictive power of PRSs, researchers have developed some PRSs specifically for people of predominantly African ancestry, and genome scientists are considering whether “ancestry-specific PRS are needed for every ethnic group…” ([ 42 ][41]). These developments occur as scholars of race call for an end to many uses of “race correction” in medicine ([ 43 ][42]). Appropriate attention to genetic ancestry's effects on PRSs can easily collapse into an ill-informed focus on race, without considering how social inequalities shape health and how race is an imperfect proxy for ancestry. Society needs a multidisciplinary approach for developing and implementing PRSs for diverse communities. Otherwise, ancestry-specific PRSs could reinvigorate people's misconceptions about human races as genetically distinct groups and encourage mistaken views that trait distribution between racial/ethnic groups is primarily caused by genetics ([ 39 ][38]). Such beliefs are central to white supremacy and racist medical practices. Injustice in science can occur because some groups of people are not included ([ 44 ][43]), but injustice can also result from inappropriate inclusion. By Yves Moreau and Maya Wang The use of DNA profiling for individual cases of law enforcement has helped to identify suspects and to exonerate the innocent. But retaining genetic materials in the form of national DNA databases, which have proliferated globally in the past two decades, raises important human rights questions. Landmark court decisions in Europe and in the United States set some limits on data collection and retention in DNA databases, such as restricting long-term retention of DNA profiles to people arrested for or convicted of a crime. But these decisions are far from the comprehensive regulations we need. Privacy rights are fundamental human rights. Around the world, the unregulated collection, use, and retention of DNA has become a form of genomic surveillance. Kuwait passed a now-repealed law mandating the DNA profiling of the entire population. In China, the police systematically collected blood samples from the Xinjiang population under the guise of a health program, and the authorities are working to establish a Y-chromosome DNA database covering the country's male population. Thailand authorities are establishing a targeted genetic database of Muslim minorities ([ 45 ][44]). Under policies set by the previous administration, the U.S. government has been indiscriminately collecting the genetic materials of migrants, including refugees, at the Mexican border. As the technology gets cheaper, and as the adoption of surveillance gets ever broader, there is an acute risk of pervasive genomic surveillance, not only by authoritarian regimes but also in democracies with weakening rights. But such a loss of autonomy and freedom is not inevitable. Governments should reform surveillance laws and draft comprehensive privacy protections that tightly regulate the collection, use, and retention of DNA and other biometric identifiers ([ 46 ][45]). They should ban such activities when they do not meet international human rights standards of lawfulness, proportionality, and necessity. They should develop a coordinated global regime of export control legislation, as well as sanctions akin to the U.S. Magnitsky Act, to hold businesses accountable that recklessly supply or market this technology for genomic surveillance. Journal editors and publishers should reassess hundreds of ethically suspect DNA-profiling publications—for example, publications co-authored by police forces involved in the persecution of the minorities studied ([ 47 ][46]) or lacking proper consent or ethical approval ([ 48 ][47]). Although there have been a few retractions ([ 47 ][46], [ 48 ][47]), such assessments should not be limited to the bureaucratic verification of informed consent and ethical approval documents; they also need to consider the basic ethical principles of beneficence, nonmaleficence, autonomy, justice, and faithfulness. 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The CRGGH is also supported by the National Institute of Diabetes and Digestive and Kidney Diseases. K.A.P. is a consultant to Illumina Inc. and was funded by NCI grant R01 CA221870 and NHGRI grant U01 HG009599. J.P.J. is a part-time salaried employee of the Precision Medicine Group. D.Z. and Y.E. hold equity in DNA.Land, a third-party genetic service. D.Z. is an employee of Cibiltech; Y.E. is an employee of Eleven Therapeutics, MyHeritage, and consultant to ArcBio. The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policies or positions of any of their employers. 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Canadian Regulators Say Clearview Violated Privacy Laws

WSJ.com: WSJD - Technology

Canadian regulators on Wednesday said facial-recognition-software company Clearview AI Inc. violated federal and provincial privacy laws in the country by offering its services there, though they acknowledged having limited enforcement powers in penalizing the New York-based company and others like it. Regulators said Clearview collected "highly sensitive biometric information without the knowledge or consent of individuals," affecting millions of Canadians. Clearview has a database of about 3 billion photos it scraped from the internet, allowing it to search for matches using facial recognition algorithms. The practices violated federal and provincial laws, regulators said, including in Quebec where express consent is required to use biometric data. Officials with four Canadian regulatory agencies said they completed an investigation into Clearview that began last February, finding that the company served 48 accounts for law enforcement agencies and other organizations across the country, including a paid subscription by the Royal Canadian Mounted Police.


Separation anxiety

Science

Phase separation, an idea about how cells organize their contents and functions into dropletlike compartments, has divided biologists. For 7 years as president of the Howard Hughes Medical Institute, Robert Tjian helped steer hundreds of millions of dollars to scientists probing provocative ideas that might transform biology and biomedicine. So the biochemist was intrigued a couple of years ago when his graduate student David McSwiggen uncovered data likely to fuel excitement about a process called phase separation, already one of the hottest concepts in cell biology. Phase separation advocates hold that proteins and other molecules self-organize into denser structures inside cells, like oil drops forming in water. That spontaneous sorting, proponents assert, serves as a previously unrecognized mechanism for arranging the cell's contents and mustering the molecules necessary to trigger key cellular events. McSwiggen had found hints that phase separation helps herpesviruses replicate inside infected cells, adding to claims that the process plays a role in functions as diverse as switching on genes, anchoring the cytoskeleton, and repairing damaged DNA. “It's pretty clear this process is at play throughout the cell,” says biophysicist Clifford Brangwynne of Princeton University. The pharmaceutical industry is as excited as some academic researchers, given studies linking phase separation to cancer, amyotrophic lateral sclerosis (ALS), diabetes, and other diseases. Dewpoint Therapeutics, a startup pursuing medical treatments targeting cellular droplets, recently signed development deals worth more than $400 million with pharma giants Merck and Bayer. And three other companies looking to exploit the process opened their doors late last year. Reflecting that enthusiasm, Science picked phase separation as a runner-up in its 2018 Breakthrough of the Year issue. Tjian says he was agnostic at first about the importance of the process. But after McSwiggen's findings inspired him and colleagues to look more closely at the range of claims, the researchers began to have doubts. Tjian and a camp of similarly skeptical biologists now argue that the evidence that liquidlike condensates arise naturally in cells is largely qualitative and obtained with techniques that yield equivocal results—in short, they believe much of the research is shoddy. Moreover, the contention that those intracellular droplets perform important roles “has gone from hypothetical to established dogma with no data,” says Tjian, who stepped down as president of Howard Hughes in 2016 and now co-directs a lab at the University of California (UC), Berkeley. “That to me is so perverse and destructive to the scientific discovery process.” Although proponents of phase separation bridle at some of those criticisms, many scientists agree that the research requires a jolt of rigor. “I don't think the whole field is bunk,” says biophysicist Stephanie Weber of McGill University. “But we do need to be more careful” in identifying instances of phase separation in cells and ascribing functions to them. The process may be less important than many scientists now assert, adds quantitative cell biologist Amy Gladfelter of the University of North Carolina, Chapel Hill. Some researchers, she says, have tried to make it “the answer to everything.” PHASE SEPARATION COULD ANSWER a fundamental question that has nagged biologists for more than 100 years: How do cells arrange their contents so that the molecules necessary to carry out a particular job are in the right place at the right time? One obvious way is with internal membranes, such as those fencing off the Golgi bodies and mitochondria. Yet many other well-known cellular structures, including the nucleolus—an organelle within the nucleus—and the RNA-processing Cajal bodies, lack membranes. Phase separation is an appealing answer. Many proteins sport sticky patches that attract other proteins of the same or a different type. Test tube studies have shown that under certain conditions, such as when protein concentration climbs above a certain level, the molecules may begin to huddle, forming dropletlike condensates. Researchers understand the mechanics best for proteins, but nucleic acids such as RNA could also aggregate with proteins. If the process happens in the cell, it could generate and maintain organelles and permit unique functions. “It's a principle that could explain how many things in the cell and nucleus are organized,” says biophysicist Mustafa Mir of the University of Pennsylvania, who as a postdoc once worked with Tjian. Although biologists mooted a role for intracellular droplets as far back as the 1890s, evidence that they are vital began to coalesce a little over 10 years ago. Brangwynne, then a postdoc at the Max Planck Institute of Molecular Cell Biology and Genetics, was tracing P granules, flecks of protein and RNA that, in nematode embryos, mark the cells that go on to produce sperm and eggs. To observe the granules' movements, Brangwynne squeezed worm gonads that harbor the structures between two microscope cover slips. Under pressure, P granules responded not like solids but like liquids, flowing along the surface of the nucleus and dripping off, he and colleagues reported in Science in 2009. The granules' watery behavior “was mind-blowing. It was so different than anything in cells,” says Weber, a former postdoc of Brangwynne's. In 2012, Brangwynne and colleagues saw similar fluid features in the nucleolus, a dense mix of proteins, RNA, and DNA that manufactures ribosomes, the cell's protein factories. The same year, biophysicist Michael Rosen of the University of Texas Southwestern Medical Center and colleagues showed that three proteins that collaborate to organize part of the cytoskeleton form liquid droplets in a test tube solution. They found that the process speeds the assembly of one type of skeletal fiber in vitro—and might do the same in the cell. Scientists have since reported dozens of examples of cellular structures that are round, prone to fuse, and tend to bead on or flow across surfaces—hallmarks of droplets formed by phase separation (see graphic, p. 338). To confirm that a molecular gathering in a cell is a liquid and not something more solid, scientists often deploy a technique called fluorescence recovery after photobleaching (FRAP). Using a cell that contains fluorescent proteins, researchers zap the region in question with a laser to darken the molecules and then trace how long the fluorescence takes to diffuse back in from other parts of the cell. A liquid, which the fluorescent proteins easily penetrate, should light up more quickly than a solid. Another test involves applying 1,6-hexanediol, a compound that fractures some of the molecular interactions that hold droplets together, to determine whether the structure dissolves. Rosen notes that three papers published last year in Cell offer some of the strongest evidence for phase separation in cells. One, from Brangwynne's lab, showed a particular protein had to reach a threshold concentration in cells to allow formation of stress granules—organelles that pop up during hard times and have been attributed to phase separation. The other two studies also identified threshold conditions for phase separation. Because a threshold is an attribute of the process, the studies provide “good but not perfect data that these structures are going through phase separation,” Rosen says. Many researchers are now convinced that phase separation explains many aspects of cell organization and function. Several research groups have reported that the mechanism helps convene the hundreds of proteins that carry out transcription, the process of reading DNA to produce the RNA instructions for making proteins. Similar molecular corralling may underlie functions including memory in fruit flies, immune cells' responses to pathogens, DNA silencing, transmission of nerve impulses across synapses, and reproduction of SARS-CoV-2, the pandemic coronavirus. Conversely, phase separation may cause disease when it goes awry. In 2018, for example, biophysicist Tanja Mittag of St. Jude Children's Research Hospital and colleagues revealed that mutations that promote several kinds of tumors disrupt the ability of the protein SPOP, which helps eliminate proteins that spur growth of cancer cells, to form droplets in test tube solutions. The researchers proposed that phase separation is key to SPOP's cleanup function in cells, and thwarting it allows cancer-promoting proteins to accumulate. Faulty phase separation could also spur damage by aiding the formation of the toxic intracellular inclusions, or protein globs, that amass in neurodegenerative illnesses such as ALS, Alzheimer's disease, and Parkinson's disease. For example, in some ALS patients the protein FUS is mutated and forms inclusions in their neurons. In the test tube, the mutated protein condenses into droplets that then morph into furry knots of fibers resembling the inclusions. In 2018, biochemist Dorothee Dormann of the Ludwig Maximilian University of Munich and colleagues discovered a possible reason: The mutated version of FUS shrugs off a protein bodyguard that prevents the normal variety from undergoing phase separation and clumping in the test tube. YET THAT SATISFYING PICTURE may be growing murky as more researchers have raised doubts about phase separation. In 2019, for instance, scientists organized a debate at Wiston House, a posh 16th century manor south of London, in part to mull whether the process helped control gene activity. About 30 participants hashed over the evidence that the process occurs in cells with the help of “free-flowing champagne,” recalls Mir, one of the presenters. The group's conclusion, he says, was that the support for many putative cases of phase separation in cells is shaky. Tjian, who was not at the meeting, came around to a similar conclusion because of new data from McSwiggen. McSwiggen's early evidence showed that in herpesvirus-infected cells, the replication compartments—clusters of protein and DNA that help produce new copies of the pathogen—are round and merge with each other, suggesting they result from phase separation. After tracking individual proteins within cells, though, McSwiggen and colleagues determined the molecules diffuse just as fast through the compartments as through the rest of the nucleus. In a true condensate, molecular crowding should have hindered diffusion. Other researchers found the negative evidence compelling when it was published later in 2019, soon after the Wiston House debate. The study is “a really important cautionary tale,” Weber says. The results spurred Tjian, McSwiggen, Mir, and Xavier Darzacq, a cell biologist who co-directs the UC Berkeley lab with Tjian, to scrutinize the phase separation literature. Later that year, in a December 2019 issue of Genes and Development , they published a scathing review of 33 studies that claimed to detect the process in cells. Tjian says he was “really disappointed by the quality of the papers.” The evidence, he and his co-authors wrote, was “often phenomenological and inadequate to discriminate between phase separation and other possible mechanisms.” Too often, he and the other review authors asserted, researchers looking for phase separation rely on qualitative indicators—shape, for example—rather than quantitative data. Moreover, because many intracellular structures possibly formed by phase separation are so small, they are near what's known as the diffraction limit of traditional light microscopes. As a result, the structures may look like fuzzy orbs, but their real shape isn't discernible. Tjian and colleagues also chastised researchers for often assuming the protein concentration in a cell is high enough to trigger phase separation, instead of actually measuring it. Overinterpretation “is rampant” in this type of research, Tjian says. The scientists questioned the FRAP measurements that underpin many claims of phase separation. In the hands of different scientists, the group noted, FRAP recovery rates for the same molecule can range from less than 1 second to several minutes, indicating the technique is too variable to confirm phase separation. Darzacq adds that FRAP “only shows you have a liquid. You have liquid everywhere in the cell.” Many of the congregations that researchers have identified with FRAP or other techniques are probably transient collections of molecules that only last a few seconds, Darzacq and Tjian say. ![Figure][1] Dropping inCREDITS: (GRAPHIC) V. ALTOUNIAN/ SCIENCE ; (IMAGE) C. BRANGWYNNE ET AL., SCIENCE , 324, 5935, 1729 (2020) The review was “an invitation for all of us to proceed with a more careful and thoughtful in-depth analysis of cellular condensates,” says molecular biophysicist Sua Myong of Johns Hopkins University. Although some scientists have been meticulous, “it has not been true of the field,” Rosen adds. Brangwynne says he, too, sees value in the critique. “I agree that we need quantitative approaches.” For example, he concurs that researchers need to be more rigorous when interpreting imaging results so that “every diffraction-limited blob” isn't declared an example of phase separation. Other recent papers have also raised doubts about cases of phase separation. In 2019 in Non-Coding RNA , Weber and a co-author weighed the support for phase separation in the cell nucleus and concluded that solid data back its role in forming three structures, including the nucleolus, but not two other structures commonly attributed to the process. And in April 2020 in Molecular Cell , biophysicist Fabian Erdel of the Center for Integrative Biology in Toulouse, France, and colleagues published a new investigation of heterochromatin—silenced regions of the genome in which DNA coils tightly with various proteins. Previous work suggested phase separation of the intracellular protein HP1 helped stretches of heterochromatin bunch up. But Erdel's team discovered that HP1 didn't form stable liquid droplets in mouse cells and that the size of the densely packed DNA regions didn't depend on the amount of the protein. Brangwynne and other researchers argue that even if some individual findings cited by Tjian and colleagues remain in dispute, the field is making progress toward more solid results. To provide some of the rigor of test tube studies, he and his team have developed a technique for seeding cells with what they call corelets, combinations of molecular fragments that cluster when exposed to light. The corelets trigger droplet formation in cells, allowing the researchers to more precisely probe what protein concentrations are necessary for phase separation and which parts of the molecule are required for the behavior. Even Tjian and colleagues give the approach high marks. Mir, who has been skeptical of much of the evidence for phase separation, agrees that the field seems to be moving away from the “everything is phase separation” stage to a more nuanced discussion of the formation and functions of condensates. “It's like any supertrendy thing in science. The noise subsides, and you are left with the truth.” To get to that truth, however, researchers “desperately need” new tools and a better understanding of the basic rules for how condensates form in cells, Gladfelter says. Scientists also need patience, she says, noting the field “tried to grow up and answer everything really fast.” But she's confident researchers will eventually sort out the real importance of phase separation in cells. “Give us time. We'll get there.” [1]: pending:yes


These Were Our Favorite Tech Stories ... :: Human Robots#

#artificialintelligence

This time last year we were commemorating the end of a decade and looking ahead to the next one. Enter the year that felt like a decade all by itself: 2020. News written in January, the before-times, feels hopelessly out of touch with all that came after. Stories published in the early days of the pandemic are, for the most part, similarly naive. The year’s news cycle was swift and brutal, ping-ponging from pandemic to extreme social and political tension, whipsawing economies, and natural disasters. Hope. Despair. Loneliness. Grief. Grit. More hope. Another lockdown. It’s been a hell of a year. Though 2020 was dominated by big, hairy societal change, science and technology took significant steps forward. Researchers singularly focused on the pandemic and collaborated on solutions to a degree never before seen. New technologies converged to deliver vaccines in record time. The dark side of tech, from biased algorithms to the threat of omnipresent surveillance and corporate control of artificial intelligence, continued to rear its head. Meanwhile, AI showed uncanny command of language, joined Reddit threads, and made inroads into some of science’s grandest challenges. Mars rockets flew for the first time, and a private company delivered astronauts to the International Space Station. Deprived of night life, concerts, and festivals, millions traveled to virtual worlds instead. Anonymous jet packs flew over LA. Mysterious monoliths appeared and disappeared worldwide. It was all, you know, very 2020. For this year’s (in-no-way-all-encompassing) list of fascinating stories in tech and science, we tried to select those that weren’t totally dated by the news, but rose above it in some way. So, without further ado: This year’s picks. How Science Beat the Virus Ed Yong | The Atlantic “Much like famous initiatives such as the Manhattan Project and the Apollo program, epidemics focus the energies of large groups of scientists. …But ‘nothing in history was even close to the level of pivoting that’s happening right now,’ Madhukar Pai of McGill University told me. … No other disease has been scrutinized so intensely, by so much combined intellect, in so brief a time.” ‘It Will Change Everything’: DeepMind’s AI Makes Gigantic Leap in Solving Protein Structures Ewen Callaway | Nature “In some cases, AlphaFold’s structure predictions were indistinguishable from those determined using ‘gold standard’ experimental methods such as X-ray crystallography and, in recent years, cryo-electron microscopy (cryo-EM). AlphaFold might not obviate the need for these laborious and expensive methods—yet—say scientists, but the AI will make it possible to study living things in new ways.” OpenAI’s Latest Breakthrough Is Astonishingly Powerful, But Still Fighting Its Flaws James Vincent | The Verge “What makes GPT-3 amazing, they say, is not that it can tell you that the capital of Paraguay is Asunción (it is) or that 466 times 23.5 is 10,987 (it’s not), but that it’s capable of answering both questions and many more beside simply because it was trained on more data for longer than other programs. If there’s one thing we know that the world is creating more and more of, it’s data and computing power, which means GPT-3’s descendants are only going to get more clever.” Artificial General Intelligence: Are We Close, and Does It Even Make Sense to Try? Will Douglas Heaven | MIT Technology Review “A machine that could think like a person has been the guiding vision of AI research since the earliest days—and remains its most divisive idea. …So why is AGI controversial? Why does it matter? And is it a reckless, misleading dream—or the ultimate goal?” The Dark Side of Big Tech’s Funding for AI Research Tom Simonite | Wired “Timnit Gebru’s exit from Google is a powerful reminder of how thoroughly companies dominate the field, with the biggest computers and the most resources. …[Meredith] Whittaker of AI Now says properly probing the societal effects of AI is fundamentally incompatible with corporate labs. ‘That kind of research that looks at the power and politics of AI is and must be inherently adversarial to the firms that are profiting from this technology.’i” We’re Not Prepared for the End of Moore’s Law David Rotman | MIT Technology Review “Quantum computing, carbon nanotube transistors, even spintronics, are enticing possibilities—but none are obvious replacements for the promise that Gordon Moore first saw in a simple integrated circuit. We need the research investments now to find out, though. Because one prediction is pretty much certain to come true: we’re always going to want more computing power.” Inside the Race to Build the Best Quantum Computer on Earth Gideon Lichfield | MIT Technology Review “Regardless of whether you agree with Google’s position [on ‘quantum supremacy’] or IBM’s, the next goal is clear, Oliver says: to build a quantum computer that can do something useful. …The trouble is that it’s nearly impossible to predict what the first useful task will be, or how big a computer will be needed to perform it.” The Secretive Company That Might End Privacy as We Know It Kashmir Hill | The New York Times “Searching someone by face could become as easy as Googling a name. Strangers would be able to listen in on sensitive conversations, take photos of the participants and know personal secrets. Someone walking down the street would be immediately identifiable—and his or her home address would be only a few clicks away. It would herald the end of public anonymity.” Wrongfully Accused by an Algorithm Kashmir Hill | The New York Times “Mr. Williams knew that he had not committed the crime in question. What he could not have known, as he sat in the interrogation room, is that his case may be the first known account of an American being wrongfully arrested based on a flawed match from a facial recognition algorithm, according to experts on technology and the law.” Predictive Policing Algorithms Are Racist. They Need to Be Dismantled. Will Douglas Heaven | MIT Technology Review “A number of studies have shown that these tools perpetuate systemic racism, and yet we still know very little about how they work, who is using them, and for what purpose. All of this needs to change before a proper reckoning can take pace. Luckily, the tide may be turning.” The Panopticon Is Already Here Ross Andersen | The Atlantic “Artificial intelligence has applications in nearly every human domain, from the instant translation of spoken language to early viral-outbreak detection. But Xi [Jinping] also wants to use AI’s awesome analytical powers to push China to the cutting edge of surveillance. He wants to build an all-seeing digital system of social control, patrolled by precog algorithms that identify potential dissenters in real time.” The Case For Cities That Aren’t Dystopian Surveillance States Cory Doctorow | The Guardian “Imagine a human-centered smart city that knows everything it can about things. It knows how many seats are free on every bus, it knows how busy every road is, it knows where there are short-hire bikes available and where there are potholes. …What it doesn’t know is anything about individuals in the city.” The Modern World Has Finally Become Too Complex for Any of Us to Understand Tim Maughan | OneZero “One of the dominant themes of the last few years is that nothing makes sense. …I am here to tell you that the reason so much of the world seems incomprehensible is that it is incomprehensible. From social media to the global economy to supply chains, our lives rest precariously on systems that have become so complex, and we have yielded so much of it to technologies and autonomous actors that no one totally comprehends it all.” The Conscience of Silicon Valley Zach Baron | GQ “What I really hoped to do, I said, was to talk about the future and how to live in it. This year feels like a crossroads; I do not need to explain what I mean by this. …I want to destroy my computer, through which I now work and ‘have drinks’ and stare at blurry simulations of my parents sometimes; I want to kneel down and pray to it like a god. I want someone—I want Jaron Lanier—to tell me where we’re going, and whether it’s going to be okay when we get there. Lanier just nodded. All right, then.” Yes to Tech Optimism. And Pessimism. Shira Ovide | The New York Times “Technology is not something that exists in a bubble; it is a phenomenon that changes how we live or how our world works in ways that help and hurt. That calls for more humility and bridges across the optimism-pessimism divide from people who make technology, those of us who write about it, government officials and the public. We need to think on the bright side. And we need to consider the horribles.” How Afrofuturism Can Help the World Mend C. Brandon Ogbunu | Wired “…[W. E. B. DuBois’] ‘The Comet’ helped lay the foundation for a paradigm known as Afrofuturism. A century later, as a comet carrying disease and social unrest has upended the world, Afrofuturism may be more relevant than ever. Its vision can help guide us out of the rubble, and help us to consider universes of better alternatives.” Wikipedia Is the Last Best Place on the Internet Richard Cooke | Wired “More than an encyclopedia, Wikipedia has become a community, a library, a constitution, an experiment, a political manifesto—the closest thing there is to an online public square. It is one of the few remaining places that retains the faintly utopian glow of the early World Wide Web.” Can Genetic Engineering Bring Back the American Chestnut? Gabriel Popkin | The New York Times Magazine “The geneticists’ research forces conservationists to confront, in a new and sometimes discomfiting way, the prospect that repairing the natural world does not necessarily mean returning to an unblemished Eden. It may instead mean embracing a role that we’ve already assumed: engineers of everything, including nature.” At the Limits of Thought David C. Krakauer | Aeon “A schism is emerging in the scientific enterprise. On the one side is the human mind, the source of every story, theory, and explanation that our species holds dear. On the other stand the machines, whose algorithms possess astonishing predictive power but whose inner workings remain radically opaque to human observers.” Is the Internet Conscious? If It Were, How Would We Know? Meghan O’Gieblyn | Wired “Does the internet behave like a creature with an internal life? Does it manifest the fruits of consciousness? There are certainly moments when it seems to. Google can anticipate what you’re going to type before you fully articulate it to yourself. Facebook ads can intuit that a woman is pregnant before she tells her family and friends. It is easy, in such moments, to conclude that you’re in the presence of another mind—though given the human tendency to anthropomorphize, we should be wary of quick conclusions.” The Internet Is an Amnesia Machine Simon Pitt | OneZero “There was a time when I didn’t know what a Baby Yoda was. Then there was a time I couldn’t go online without reading about Baby Yoda. And now, Baby Yoda is a distant, shrugging memory. Soon there will be a generation of people who missed the whole thing and for whom Baby Yoda is as meaningless as it was for me a year ago.” Digital Pregnancy Tests Are Almost as Powerful as the Original IBM PC Tom Warren | The Verge “Each test, which costs less than $5, includes a processor, RAM, a button cell battery, and a tiny LCD screen to display the result. …Foone speculates that this device is ‘probably faster at number crunching and basic I/O than the CPU used in the original IBM PC.’ IBM’s original PC was based on Intel’s 8088 microprocessor, an 8-bit chip that operated at 5Mhz. The difference here is that this is a pregnancy test you pee on and then throw away.” The Party Goes on in Massive Online Worlds Cecilia D’Anastasio | Wired “We’re more stand-outside types than the types to cast a flashy glamour spell and chat up the nearest cat girl. But, hey, it’s Final Fantasy XIV online, and where my body sat in New York, the epicenter of America’s Covid-19 outbreak, there certainly weren’t any parties.” The Facebook Groups Where People Pretend the Pandemic Isn’t Happening Kaitlyn Tiffany | The Atlantic “Losing track of a friend in a packed bar or screaming to be heard over a live band is not something that’s happening much in the real world at the moment, but it happens all the time in the 2,100-person Facebook group ‘a group where we all pretend we’re in the same venue.’ So does losing shoes and Juul pods, and shouting matches over which bands are the saddest, and therefore the greatest.” Did You Fly a Jetpack Over Los Angeles This Weekend? Because the FBI Is Looking for You Tom McKay | Gizmodo “Did you fly a jetpack over Los Angeles at approximately 3,000 feet on Sunday? Some kind of tiny helicopter? Maybe a lawn chair with balloons tied to it? If the answer to any of the above questions is ‘yes,’ you should probably lay low for a while (by which I mean cool it on the single-occupant flying machine). That’s because passing airline pilots spotted you, and now it’s this whole thing with the FBI and the Federal Aviation Administration, both of which are investigating.” Image Credit: Thomas Kinto / Unsplash Continue reading →


Outwitting the Grim Reaper - Issue 94: Evolving

Nautilus

Some evolutionary biologists say that after we pass reproductive age, nature, like a cat who's been fed, is done with us. The bodily systems that thrived and repaired themselves to ensure that we pass on healthy genes cease to function well and leave us to slink to the finish line the best we can. Neuroscientist Daniel Levitin, author of this year's Successful Aging, says "that's not an unreasonable interpretation," but he doesn't settle for the view that aging after 40 is a long and listless mosey to the grave. Levitin, 62, emeritus professor of psychology and neuroscience at McGill University, has lit up readers' minds with his books on the joys of music, This Is Your Brain on Music and The World in Six Songs. But unlike an aging rocker playing his hits on an oldies tour, Levitin has remained fresh as a writer on the brain, exploring, in The Organized Mind, how to navigate our way through information overload to sane shores.


AI Debate 2: Night of a thousand AI scholars

ZDNet

Gary Marcus, top, hosted presentations by sixteen AI scholars on what AI needs to "move forward." A year ago, Gary Marcus, a frequent critic of deep learning forms of AI, and Joshua Bengio, a leading proponent of deep learning, faced off in a two-hour debate about AI at Bengio's MILA institute headquarters in Montreal. Wednesday evening, Marcus was back, albeit virtually, to open what is now the second installment of what has become a planned annual debate on AI, under the title "AI Debate 2: Moving AI Forward." Vincent Boucher, president of the organization Montreal.AI, who had helped to organize last year's debate, opened the proceedings, before passing the mic to Marcus as moderator. Marcus said 3,500 people had pre-registered for the evening, and at the start, 348 people were live on FaceBook. Last year's debate had 30,000 by the end of the night, noted Marcus. Bengio was not in attendance, but the evening featured presentations from sixteen scholars: Ryan Calo, Yejin Choi, Daniel Kahneman, Celeste Kidd, Christof Koch, Luis Lamb, Fei-Fei Li, Adam Marblestone, Margaret Mitchell, Robert Osazuwa Ness, Judea Pearl, Francesco Rossi, Ken Stanley, Rich Sutton, Doris Tsao and Barbara Tversky. "The point is to represent a diversity of views," said Marcus, promising a three hours that might be like "drinking from a firehose."


A quick tour of what you missed at the NeurIPS 2020 AI conference

ZDNet

A panel talk Friday afternoon brought together AI scholars Gary Marcus, Yoshua Bengio, Daniel Kahneman, Luis Lamb, and moderator Francesca Rossi, for a spirited discussion of where machines and humans differ in their processing of abstract thought, logic, reason and many, many related questions. The crown jewel of AI conferences each year is the NeurIPS conference, which is regularly over-subscribed, and which usually takes place in pretty cities such as Montreal, Vancouver, and Barcelona. This year, the event was fully virtual because of the pandemic. While not as scenic, it was a well-organized, very rich six days of poster sessions, oral presentations, tutorials, workshops, symposia, invited talks, and some virtual wine and cheese thrown in, ending this past Friday, December 11th. They even managed to do some neat things with poster sessions. The whole conference as made possible via the open-source software Miniconf, along with use of Zoom and RocketChat. This is by no means a comprehensive survey.


News Analysis: ServiceNow Gets Serious About AI With Element AI Acquisition

#artificialintelligence

On November 30th, ServiceNow announced an estimated $500 (CAD) million acquisition of Montreal, Canada based startup, Element AI. Key products include an AI-assisted insurance underwriting workflow software known as Underwriting Partner and a data set management platform for manufacturers known as Knowledge Scout. In a blog post, Element AI"s co-founder and CEO noted "Element AI will help ServiceNow deliver workflows that learn more efficiently from smaller datasets, improve the quality of existing AI capabilities like content and language understanding, and expand new capabilities like image recognition and cognitive search." Gagné also pointed out this level of integration would help users "summarize information, make predictions and recommendations, and automate repetitive tasks," As ServiceNow's fourth AI acquisition in 2020, CEO Bill McDermott and Chief AI Officer, Vijay K Narayanan, has doubled down on the investment and future of the platform with the build out of Now Intelligence capabilities along with the acquisitions of Loom Systems, Passage AI, and Sweagle. For ServiceNow, that is on the quest to become the universal workflow platform for the enterprise, it is the game changer that will attract CxOs to its platform.


Mila, IBM collaborating on open-source AI and machine learning project

#artificialintelligence

Quebec Artificial Intelligence Institute (Mila) and IBM have teamed up to accelerate artificial intelligence (AI) and machine learning research using open-source technology. Mila and IBM have been collaborating since early 2020 on a project that is meant to make a key component of AI, known as hyperparameter optimization, more accessible. The organizations claim that this would improve machine learning model performances and pinpoint within the'black box' of AI where models need work. "A collaboration with…IBM is a great opportunity to accelerate the development of an open-source solution…initiated at Mila." – Yoshua Bengio, Mila The two organizations are looking to integrate the Quebec institute's open-source software, Oríon, with IBM's Watson Machine Learning Accelerator, an AI model training and inference tool that the tech giant offers to businesses. The overall goal, they claim, is to "improve the development, deployment, and ongoing management of complex AI and deep learning models, as well as to make tools more accessible to a larger base of scientists, engineers, and developers through automation."


Faster, Smaller and More Accurate Edge AI Using Deeplite and Andes Technology Software + Hardware

#artificialintelligence

MONTREAL, CANADA and HSINCHU, TAIWAN – December 3, 2020 – The push for low-power and low-latency deep learning models, computing hardware, and systems for artificial intelligence (AI) inference on edge devices continues to create exciting new opportunities. There has been unprecedented interest from industry stakeholders in the development of hardware and software solutions for on-device deep learning, also called Edge AI. This has already begun to yield progress on hallmark applications such as keyword spotting in audio classification, anomaly detection and, in this case, person detection in computer vision applications. Specifically, tinyML, the branch of machine learning tailored to ultra-low power systems, holds tremendous promise. The efficiency of proposed solutions (milliwatt or even microwatt power consumption) and vast applicability and deployment of such devices in real-world settings will lead to over 100 billion IoT sensors and devices expected to ship over the next 5 years 1.