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Recent and forthcoming machine learning and AI seminars: January 2021 edition

AIHub

This post contains a list of the AI-related seminars that are scheduled to take place between now and the end of February 2021. We've also listed recent past seminars that are available for you to watch. All events detailed here are free and open for anyone to attend virtually. This list includes forthcoming seminars scheduled to take place between 15 January and 28 February. Zero-shot (human-AI) coordination (in Hanabi) and ridge rider Speaker: Jakob Foerster (Facebook, University of Toronto & Vector Institute) Organised by: University College London Zoom link is here.


AI-Powered Text From This Program Could Fool the Government

WIRED

In October 2019, Idaho proposed changing its Medicaid program. The state needed approval from the federal government, which solicited public feedback via Medicaid.gov. But half came not from concerned citizens or even internet trolls. They were generated by artificial intelligence. And a study found that people could not distinguish the real comments from the fake ones.


10 Intro Books On AI To Bring You Up To Speed

#artificialintelligence

Artificial Intelligence (AI) has come a long way over the past few years in simulating human intelligence. Today, AI is the lifeblood of almost every organisation cutting across sectors including, retail, financial, healthcare, among others. Here's an updated list of 10 best intro books on artificial intelligence geared towards AI enthusiasts. About: Mathematics and statistics are the backbone of artificial intelligence. This book is perfect for understanding the basics and the mathematics behind AI.


AI can determine a person's political affiliation based on their photo with 70% accuracy

Daily Mail - Science & tech

The Stanford research who made headlines in 2017 for designing an AI that uses'facial landmarks' to determine a person's sexual preference is back with what may be another controversial system. Dr. Michal Kosinski claims to have a facial recognition algorithm capable of identifying if a person is a liberal or conservative based on a single photo – and with over 70 percent accuracy. The technology, which builds on the 2017 AI, was trained with more than a million images from dating websites and Facebook and programmed to focus in on expressions and posture. Although Kosinski and his team were unable to pin down exact characteristics the algorithm associated with a political preference, but they did find some trends like head orientation and emotional expression in pictures. Some examples include people who looked directly at the camera were labeled as liberal and those showing disgust were judged as more conservative.


AIs that read sentences can also spot virus mutations

MIT Technology Review

In a study published in Science today, Berger and her colleagues pull several of these strands together and use NLP to predict mutations that allow viruses to avoid being detected by antibodies in the human immune system, a process known as viral immune escape. The basic idea is that the interpretation of a virus by an immune system is analogous to the interpretation of a sentence by a human. "It's a neat paper, building off the momentum of previous work," says Ali Madani, a scientist at Salesforce, who is using NLP to predict protein sequences. Berger's team uses two different linguistic concepts: grammar and semantics (or meaning). The genetic or evolutionary fitness of a virus--characteristics such as how good it is at infecting a host--can be interpreted in terms of grammatical correctness.


The language of a virus

Science

Uncovering connections between seemingly unrelated branches of science might accelerate research in one branch by using the methods developed in the other branch as stepping stones. On page 284 of this issue, Hie et al. ([ 1 ][1]) provide an elegant example of such unexpected connections. The authors have uncovered a parallel between the properties of a virus and its interpretation by the host immune system and the properties of a sentence in natural language and its interpretation by a human. By leveraging an extensive natural language processing (NLP) toolbox ([ 2 ][2], [ 3 ][3]) developed over the years, they have come up with a powerful new method for the identification of mutations that allow a virus to escape from recognition by neutralizing antibodies. In 1950, Alan Turing predicted that machines will eventually compete with men in “intellectual fields” and suggested that one possible way forward would be to build a machine that can be taught to understand and speak English ([ 4 ][4]). This was, and still is, an ambitious goal. It is clear that language grammar can provide a formal skeleton for building sentences, but how can machines be trained to infer the meanings? In natural language, there are many ways to express the same idea, and yet small changes in expression can often change the meaning. Linguistics developed a way of quantifying the similarity of meaning (semantics). Specifically, it was proposed that words that are used in the same context are likely to have similar meanings ([ 5 ][5], [ 6 ][6]). This distributional hypothesis became a key feature for the computational technique in NLP, known as word (semantic) embedding. The main idea is to characterize words as vectors that represent distributional properties in a large amount of language data and then embed these sparse, high-dimensional vectors into more manageable, low-dimensional space in a distance-preserving manner. By the distributional hypothesis, this technique should group words that have similar semantics together in the embedding space. Hie et al. proposed that viruses can also be thought to have a grammar and semantics. Intuitively, the grammar describes which sequences make specific viruses (or their parts). Biologically, a viral protein sequence should have all the properties needed to invade a host, multiply, and continue invading another host. Thus, in some way, the grammar represents the fitness of a virus. With enough data, current machine learning approaches can be used to learn this sequence-based fitness function. ![Figure][7] Predicting immune escape The constrained semantic change search algorithm obtains semantic embeddings of all mutated protein sequences using bidirectional long short-term memory (LSTM). The sequences are ranked according to the combined score of the semantic change (the distance of a mutation from the original sequence) and fitness (the probability that a mutation appears in viral sequences). GRAPHIC: V. ALTOUNIAN/SCIENCE But what would be the meaning (semantics) of a virus? Hie et al. suggested that the semantics of a virus should be defined in terms of its recognition by immune systems. Specifically, viruses with different semantics would require a different state of the immune system (for example, different antibodies) to be recognized. The authors hypothesized that semantic embeddings allow sequences that require different immune responses to be uncovered. In this context, words represent protein sequences (or protein fragments), and recognition of such protein fragments is the task performed by the immune system. To escape immune responses, viral genomes can become mutated so that the virus evolves to no longer be recognized by the immune system. However, a virus that acquires a mutation that compromises its function (and thus fitness) will not survive. Using the NLP analogy, immune escape will be achieved by the mutations that change the semantics of the virus while maintaining its grammaticality so that the virus will remain infectious but escape the immune system. On the basis of this idea, Hie et al. developed a new approach, called constrained semantic change search (CSCS). Computationally, the goal of CSCS is to identify mutations that confer high fitness and substantial semantic changes at the same time (see the figure). The immune escape scores are computed by combining the two quantities. The search algorithm builds on a powerful deep learning technique for language modeling, called long short-term memory (LSTM), to obtain semantic embeddings of all mutated sequences and rank the sequences according to their immune escape scores in the embedded space. The semantic changes correspond to the distance of the mutated sequences to the original sequence in the semantic embedding, and its “grammaticality” (or fitness) is estimated by the probability that the mutation appears in viral sequences. The immune escape scores can then be computed by simultaneously considering both the semantic distance and fitness probability. Hie et al. confirmed their hypothesis for the correspondence of grammaticality and semantics to fitness and immune response in three viral proteins: influenza A hemagglutinin (HA), HIV-1 envelope (Env), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Spike. For the analogy of semantics to immune response, they found that clusters of semantically similar viruses were in good correspondence with virus subtypes, host, or both, confirming that the language model can extract functional meanings from protein sequences. The clustering patterns also revealed interspecies transmissibility and antigenic similarity. The correspondence of grammaticality to fitness was assessed more directly by using deep mutational scans evaluated for replication fitness (for HA and Env) or binding (for Spike). The combined model was tested against experimentally verified mutations that allow for immue escape. Scoring each amino acid residue with CSCS, the authors uncovered viral protein regions that are significantly enriched with escape potential: the head of HA for influenza, the V1/V2 hypervariable regions for HIV Env, and the receptor-binding domain (RBD) and amino-terminal domain for SARS-CoV-2 Spike. The language of viral evolution and escape proposed by Hie et al. provides a powerful framework for predicting mutations that lead to viral escape. However, interesting questions remain. Further extending the natural language analogy, it is notable that individuals can interpret the same English sentence differently depending on their past experience and the fluency in the language. Similarly, immune response differs between individuals depending on factors such as past pathogenic exposures and overall “strength” of the immune system. It will be interesting to see whether the proposed approach can be adapted to provide a “personalized” view of the language of virus evolution. 1. [↵][8]1. B. Hie, 2. E. Zhong, 3. B. Berger, 4. B. Bryson , Science 371, 284 (2021). [OpenUrl][9][Abstract/FREE Full Text][10] 2. [↵][11]1. L. Yann, 2. Y. Bengio, 3. G. Hinton , Nature 521, 436 (2015). [OpenUrl][12][CrossRef][13][PubMed][14] 3. [↵][15]1. T. Young, 2. D. Hazarika, 3. S. Poria, 4. E. Cambria , IEEE Comput. Intell. Mag. 13, 55 (2018). [OpenUrl][16] 4. [↵][17]1. A. Turing , Mind LIX, 433 (1950). 5. [↵][18]1. Z. S. Harris , Word 10, 146 (1954). [OpenUrl][19][CrossRef][20][PubMed][21] 6. [↵][22]1. J. R. Firth , in Studies in Linguistic Analysis (1957), pp. 1–32. Acknowledgments: The authors are supported by the Intramural Research Programs of the National Library of Medicine at the National Institutes of Health, USA. [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #ref-5 [6]: #ref-6 [7]: pending:yes [8]: #xref-ref-1-1 "View reference 1 in text" [9]: {openurl}?query=rft.jtitle%253DScience%26rft.stitle%253DScience%26rft.aulast%253DHie%26rft.auinit1%253DB.%26rft.volume%253D371%26rft.issue%253D6526%26rft.spage%253D284%26rft.epage%253D288%26rft.atitle%253DLearning%2Bthe%2Blanguage%2Bof%2Bviral%2Bevolution%2Band%2Bescape%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.abd7331%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 [10]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjEyOiIzNzEvNjUyNi8yODQiO3M6NDoiYXRvbSI7czoyMjoiL3NjaS8zNzEvNjUyNi8yMzMuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9 [11]: #xref-ref-2-1 "View reference 2 in text" [12]: {openurl}?query=rft.jtitle%253DNature%26rft.volume%253D521%26rft.spage%253D436%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fnature14539%26rft_id%253Dinfo%253Apmid%252F26017442%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 [13]: /lookup/external-ref?access_num=10.1038/nature14539&link_type=DOI [14]: /lookup/external-ref?access_num=26017442&link_type=MED&atom=%2Fsci%2F371%2F6526%2F233.atom [15]: #xref-ref-3-1 "View reference 3 in text" [16]: {openurl}?query=rft.jtitle%253DIEEE%2BComput.%2BIntell.%2BMag.%26rft.volume%253D13%26rft.spage%253D55%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 [17]: #xref-ref-4-1 "View reference 4 in text" [18]: #xref-ref-5-1 "View reference 5 in text" [19]: {openurl}?query=rft.jtitle%253DWord%26rft.volume%253D10%26rft.spage%253D146%26rft_id%253Dinfo%253Adoi%252F10.1080%252F00437956.1954.11659520%26rft_id%253Dinfo%253Apmid%252F32513867%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 [20]: /lookup/external-ref?access_num=10.1080/00437956.1954.11659520&link_type=DOI [21]: /lookup/external-ref?access_num=32513867&link_type=MED&atom=%2Fsci%2F371%2F6526%2F233.atom [22]: #xref-ref-6-1 "View reference 6 in text"


Transmission heterogeneities, kinetics, and controllability of SARS-CoV-2

Science

A minority of people infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmit most infections. How does this happen? Sun et al. reconstructed transmission in Hunan, China, up to April 2020. Such detailed data can be used to separate out the relative contribution of transmission control measures aimed at isolating individuals relative to population-level distancing measures. The authors found that most of the secondary transmissions could be traced back to a minority of infected individuals, and well over half of transmission occurred in the presymptomatic phase. Furthermore, the duration of exposure to an infected person combined with closeness and number of household contacts constituted the greatest risks for transmission, particularly when lockdown conditions prevailed. These findings could help in the design of infection control policies that have the potential to minimize both virus transmission and economic strain. Science , this issue p. [eabe2424][1] ### INTRODUCTION The role of transmission heterogeneities in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) dynamics remains unclear, particularly those heterogeneities driven by demography, behavior, and interventions. To understand individual heterogeneities and their effect on disease control, we analyze detailed contact-tracing data from Hunan, a province in China adjacent to Hubei and one of the first regions to experience a SARS-CoV-2 outbreak in January to March 2020. The Hunan outbreak was swiftly brought under control by March 2020 through a combination of nonpharmaceutical interventions including population-level mobility restriction (i.e., lockdown), traveler screening, case isolation, contact tracing, and quarantine. In parallel, highly detailed epidemiological information on SARS-CoV-2–infected individuals and their close contacts was collected by the Hunan Provincial Center for Disease Control and Prevention. ### RATIONALE Contact-tracing data provide information to reconstruct transmission chains and understand outbreak dynamics. These data can in turn generate valuable intelligence on key epidemiological parameters and risk factors for transmission, which paves the way for more-targeted and cost-effective interventions. ### RESULTS On the basis of epidemiological information and exposure diaries on 1178 SARS-CoV-2–infected individuals and their 15,648 close contacts, we developed a series of statistical and computational models to stochastically reconstruct transmission chains, identify risk factors for transmission, and infer the infectiousness profile over the course of a typical infection. We observe overdispersion in the distribution of secondary infections, with 80% of secondary cases traced back to 15% of infections, which indicates substantial transmission heterogeneities. We find that SARS-CoV-2 transmission risk scales positively with the duration of exposure and the closeness of social interactions, with the highest per-contact risk estimated in the household. Lockdown interventions increase transmission risk in families and households, whereas the timely isolation of infected individuals reduces risk across all types of contacts. There is a gradient of increasing susceptibility with age but no significant difference in infectivity by age or clinical severity. Early isolation of SARS-CoV-2–infected individuals drastically alters transmission kinetics, leading to shorter generation and serial intervals and a higher fraction of presymptomatic transmission. After adjusting for the censoring effects of isolation, we find that the infectiousness profile of a typical SARS-CoV-2 patient peaks just before symptom onset, with 53% of transmission occurring in the presymptomatic phase in an uncontrolled setting. We then use these results to evaluate the effectiveness of individual-based strategies (case isolation and contact quarantine) both alone and in combination with population-level contact reductions. We find that a plausible parameter space for SARS-CoV-2 control is restricted to scenarios where interventions are synergistically combined, owing to the particular transmission kinetics of this virus. ### CONCLUSION There is considerable heterogeneity in SARS-CoV-2 transmission owing to individual differences in biology and contacts that is modulated by the effects of interventions. We estimate that about half of secondary transmission events occur in the presymptomatic phase of a primary case in uncontrolled outbreaks. Achieving epidemic control requires that isolation and contact-tracing interventions are layered with population-level approaches, such as mask wearing, increased teleworking, and restrictions on large gatherings. Our study also demonstrates the value of conducting high-quality contact-tracing investigations to advance our understanding of the transmission dynamics of an emerging pathogen. ![Figure][2] Transmission chains, contact patterns, and transmission kinetics of SARS-CoV-2 in Hunan, China, based on case and contact-tracing data from Hunan, China. (Top left) One realization of the reconstructed transmission chains, with a histogram representing overdispersion in the distribution of secondary infections. (Top right) Contact matrices of community, social, extended family, and household contacts reveal distinct age profiles. (Bottom) Earlier isolation of primary infections shortens the generation and serial intervals while increasing the relative contribution of transmission in the presymptomatic phase. A long-standing question in infectious disease dynamics concerns the role of transmission heterogeneities, which are driven by demography, behavior, and interventions. On the basis of detailed patient and contact-tracing data in Hunan, China, we find that 80% of secondary infections traced back to 15% of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) primary infections, which indicates substantial transmission heterogeneities. Transmission risk scales positively with the duration of exposure and the closeness of social interactions and is modulated by demographic and clinical factors. The lockdown period increases transmission risk in the family and households, whereas isolation and quarantine reduce risks across all types of contacts. The reconstructed infectiousness profile of a typical SARS-CoV-2 patient peaks just before symptom presentation. Modeling indicates that SARS-CoV-2 control requires the synergistic efforts of case isolation, contact quarantine, and population-level interventions because of the specific transmission kinetics of this virus. [1]: /lookup/doi/10.1126/science.abe2424 [2]: pending:yes


Learning the language of viral evolution and escape

Science

Viral mutations that evade neutralizing antibodies, an occurrence known as viral escape, can occur and may impede the development of vaccines. To predict which mutations may lead to viral escape, Hie et al. used a machine learning technique for natural language processing with two components: grammar (or syntax) and meaning (or semantics) (see the Perspective by Kim and Przytycka). Three different unsupervised language models were constructed for influenza A hemagglutinin, HIV-1 envelope glycoprotein, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein. Semantic landscapes for these viruses predicted viral escape mutations that produce sequences that are syntactically and/or grammatically correct but effectively different in semantics and thus able to evade the immune system. Science , this issue p. [284][1]; see also p. [233][2] The ability for viruses to mutate and evade the human immune system and cause infection, called viral escape, remains an obstacle to antiviral and vaccine development. Understanding the complex rules that govern escape could inform therapeutic design. We modeled viral escape with machine learning algorithms originally developed for human natural language. We identified escape mutations as those that preserve viral infectivity but cause a virus to look different to the immune system, akin to word changes that preserve a sentence’s grammaticality but change its meaning. With this approach, language models of influenza hemagglutinin, HIV-1 envelope glycoprotein (HIV Env), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Spike viral proteins can accurately predict structural escape patterns using sequence data alone. Our study represents a promising conceptual bridge between natural language and viral evolution. [1]: /lookup/doi/10.1126/science.abd7331 [2]: /lookup/doi/10.1126/science.abf6894


Three-quarters attack rate of SARS-CoV-2 in the Brazilian Amazon during a largely unmitigated epidemic

Science

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) incidence peaked in Manaus, Brazil, in May 2020 with a devastating toll on the city's inhabitants, leaving its health services shattered and cemeteries overwhelmed. Buss et al. collected data from blood donors from Manaus and São Paulo, noted when transmission began to fall, and estimated the final attack rates in October 2020 (see the Perspective by Sridhar and Gurdasani). Heterogeneities in immune protection, population structure, poverty, modes of public transport, and uneven adoption of nonpharmaceutical interventions mean that despite a high attack rate, herd immunity may not have been achieved. This unfortunate city has become a sentinel for how natural population immunity could influence future transmission. Events in Manaus reveal what tragedy and harm to society can unfold if this virus is left to run its course. Science , this issue p. [288][1]; see also p. [230][2] Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread rapidly in Manaus, the capital of Amazonas state in northern Brazil. The attack rate there is an estimate of the final size of the largely unmitigated epidemic that occurred in Manaus. We use a convenience sample of blood donors to show that by June 2020, 1 month after the epidemic peak in Manaus, 44% of the population had detectable immunoglobulin G (IgG) antibodies. Correcting for cases without a detectable antibody response and for antibody waning, we estimate a 66% attack rate in June, rising to 76% in October. This is higher than in São Paulo, in southeastern Brazil, where the estimated attack rate in October was 29%. These results confirm that when poorly controlled, COVID-19 can infect a large proportion of the population, causing high mortality. [1]: /lookup/doi/10.1126/science.abe9728 [2]: /lookup/doi/10.1126/science.abf7921


Behavioral convergence in humans and animals

Science

Over the 20th century, the social sciences developed without taking much notice of humans' nature as products of evolution. In the 1970s this attitude was challenged by behavioral biologists ([ 1 ][1], [ 2 ][2]) who asserted that general principles concerning the behavior of life forms must also be relevant to understanding human behavior. They argued that because human cognition and emotions had evolved by natural selection, these behavior-generating mechanisms should generally shape behavior so that it maximizes biological fitness. Not all social scientists agreed. Cultural anthropologists, in particular, were mostly aghast at the rigidly scientific and overtly biological nature of this perspective, viewing it as blatantly flawed ([ 3 ][3]). They claimed that differences between and within human societies were mainly due to variant cultural belief systems. On page 292 of this issue, Barsbai et al. ([ 4 ][4]) show that adaptation to local ecological conditions is an important determinant of variation in human behavior in traditional societies. The sample analyzed by Barsbai et al. consists of 339 hunter-gatherer societies that are most appropriate for comparison because their members' lives and livelihoods are intimately constrained by the natural world. The authors show that variation in hunter-gatherer patterns for 15 behavioral variables statistically converges on the same characteristics that are most common in birds and mammals in the same local regions of the world. These traits include diet composition, mobility patterns, paternal investment, divorce rates, social group size, and social stratification. In other words, in places where hunter-gatherers are more polygynous, there also tend to be more polygynous bird and mammal species. These patterns appear to be driven by ecological and habitat similarity, not by locational proximity per se. Not only are hunter-gatherers behaviorally similar in similar ecologies, but even mammals and birds in those ecologies tend to exhibit the same behavioral regularities as do the human populations. Hence, the study appears to validate the basic premise of the evolutionary perspective called “human behavioral ecology” ([ 5 ][5], [ 6 ][6]). However, it is a mistake to conclude from this that culture is unimportant. Beginning in the 1980s, researchers in human behavioral sciences developed a sophisticated, scientific, evolutionary theory accounting for the role of culture in human behavior ([ 7 ][7], [ 8 ][8]). These scientists provided both theoretical and empirical evidence that social learning was a prime determinant of human behavioral variation. The affective and cognitive mechanisms that underpin social learning are adaptations and are in large part responsible for our species' spectacular ecological success, but they also create historical patterns absent in other species and lead to outcomes not predicted by theories developed for noncultural creatures. Barsbai et al. show convincingly that ecological factors explain much variation in human behavior, but so too does cultural history. For example, Mathew and Perreault ([ 9 ][9]) studied the causes of variation among 172 native American groups in western North America. Like Barsbai et al. , they found that ecological factors explained a substantial amount of variation, particularly in behaviors related to subsistence and technology. But the variation in subsistence-related behaviors was equally well explained by the linguistic distance between groups, which proved to be an even better explanation than ecological factors for the variation in political organization, religious practice, and kinship organization. Moreover, the effect of cultural history seems to persist for hundreds or even thousands of years. Cultural evolution can also lead to outcomes not predicted by the evolutionary mechanisms applied to other species. Barsbai et al. show that variation in human residential group size has the same relationship to ecology as in other species. However, human foragers are much more cooperative than other primates ([ 10 ][10]), and sometimes they cooperate in groups numbering hundreds of individuals in communal foraging, construction of shared capital facilities, and warfare ([ 11 ][11]). No other vertebrate cooperates on these scales. Exactly why this is the case is controversial, but it seems likely that culturally transmitted social norms play an important role. Culture and genes are linked in a tight coevolutionary embrace, and this leads to complex patterns of genetic and cultural co adaptation. For example, Henrich has recently argued ([ 12 ][12]) that the extent to which people are embedded in networks of kin obligation is a function of both ecological factors (cooperative intensive agriculture) and cultural history (church edicts against kin marriage and collective property institutions), and that variations in the intensity of kin-network embeddedness ultimately transformed human psychology into that observed today in “WEIRD” (Western, educated, industrialized, rich, democratic) societies, where individualism is paramount, rather than the psychology of traditional societies, where collectivism and kin-group favoritism predominate. Experiments show that the cognitive, emotional, and psychological effects of these different cultural histories are profound, and imply that findings from Western modern societies may often be irrelevant to predicting behavior in non-Western and traditional societies. Likewise, the spread of monogamy in modern societies, despite increasing wealth stratification, appears to be a puzzle that requires both adaptive modeling ([ 13 ][13]) and a recognition that monogamous social norms substantially increased cooperation with societies, and this norm can spread by group competition and cultural imitation ([ 12 ][12]). Coevolution between genes and culture in different ecologies may lead to uniquely human patterns not anticipated by animal studies. These examples illustrate just how complex human behavioral studies will become when the social sciences fully integrate an adaptive evolutionary view with a view of human behavioral variation in terms of cultural social norms. 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