Human Activity Recognition (HAR) using wearable inertial measurement unit (IMU) sensors can revolutionize healthcare by enabling continual health monitoring, disease prediction, and routine recognition. Despite the high accuracy of Deep Learning (DL) HAR models, their robustness to real-world variabilities remains untested, as they have primarily been trained and tested on limited lab-confined data. In this study, we isolate subject, device, position, and orientation variability to determine their effect on DL HAR models and assess the robustness of these models in real-world conditions. We evaluated the DL HAR models using the HARVAR and REALDISP datasets, providing a comprehensive discussion on the impact of variability on data distribution shifts and changes in model performance. Our experiments measured shifts in data distribution using Maximum Mean Discrepancy (MMD) and observed DL model performance drops due to variability. We concur that studied variabilities affect DL HAR models differently, and there is an inverse relationship between data distribution shifts and model performance. The compounding effect of variability was analyzed, and the implications of variabilities in real-world scenarios were highlighted. MMD proved an effective metric for calculating data distribution shifts and explained the drop in performance due to variabilities in HARVAR and REALDISP datasets. Combining our understanding of variability with evaluating its effects will facilitate the development of more robust DL HAR models and optimal training techniques. Allowing Future models to not only be assessed based on their maximum F1 score but also on their ability to generalize effectively

Freezing of gait (FOG) is a debilitating feature of Parkinson's disease (PD), which is a cause of injurious falls among PD patients. Recent advances in wearable-based human activity recognition (HAR) technology have enabled the detection of FOG subtypes across benchmark datasets. Since FOG manifestation is heterogeneous, developing models that quantify FOG consistently across patients with varying demographics, FOG types, and PD conditions is important. Bias and fairness in FOG models remain understudied in HAR, with research focused mainly on FOG detection using single benchmark datasets. We evaluated the bias and fairness of HAR models for wearable-based FOG detection across demographics and PD conditions using multiple datasets and the effectiveness of transfer learning as a potential bias mitigation approach. Our evaluation using demographic parity ratio (DPR) and equalized odds ratio (EOR) showed model bias (DPR & EOR < 0.8) for all stratified demographic variables, including age, sex, and disease duration. Our experiments demonstrated that transfer learning from multi-site datasets and generic human activity representations significantly improved fairness (average change in DPR +0.027, +0.039, respectively) and performance (average change in F1-score +0.026, +0.018, respectively) across attributes, supporting the hypothesis that generic human activity representations learn fairer representations applicable to health analytics.

Due to the scarcity of labeled sensor data in HAR, prior research has turned to video data to synthesize Inertial Measurement Units (IMU) data, capitalizing on its rich activity annotations. However, generating IMU data from videos presents challenges for HAR in real-world settings, attributed to the poor quality of synthetic IMU data and its limited efficacy in subtle, fine-grained motions. In this paper, we propose Multi$^3$Net, our novel multi-modal, multitask, and contrastive-based framework approach to address the issue of limited data. Our pretraining procedure uses videos from online repositories, aiming to learn joint representations of text, pose, and IMU simultaneously. By employing video data and contrastive learning, our method seeks to enhance wearable HAR performance, especially in recognizing subtle activities.Our experimental findings validate the effectiveness of our approach in improving HAR performance with IMU data. We demonstrate that models trained with synthetic IMU data generated from videos using our method surpass existing approaches in recognizing fine-grained activities.

How can we ensure that Ubiquitous Computing (UbiComp) research outcomes are both ethical and fair? While fairness in machine learning (ML) has gained traction in recent years, fairness in UbiComp remains unexplored. This workshop aims to discuss fairness in UbiComp research and its social, technical, and legal implications. From a social perspective, we will examine the relationship between fairness and UbiComp research and identify pathways to ensure that ubiquitous technologies do not cause harm or infringe on individual rights. From a technical perspective, we will initiate a discussion on data practices to develop bias mitigation approaches tailored to UbiComp research. From a legal perspective, we will examine how new policies shape our community's work and future research. We aim to foster a vibrant community centered around the topic of responsible UbiComp, while also charting a clear path for future research endeavours in this field.

The Space-Air-Ground Integrated Network (SAGIN), integrating heterogeneous devices including low earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and ground users (GUs), holds significant promise for advancing smart city applications. However, resource management of the SAGIN is a challenge requiring urgent study in that inappropriate resource management will cause poor data transmission, and hence affect the services in smart cities. In this paper, we develop a comprehensive SAGIN system that encompasses five distinct communication links and propose an efficient cooperative multi-type multi-agent deep reinforcement learning (CMT-MARL) method to address the resource management issue. The experimental results highlight the efficacy of the proposed CMT-MARL, as evidenced by key performance indicators such as the overall transmission rate and transmission success rate. These results underscore the potential value and feasibility of future implementation of the SAGIN.

Energy efficiency and low latency are crucial requirements for designing wearable AI-empowered human activity recognition systems, due to the hard constraints of battery operations and closed-loop feedback. While neural network models have been extensively compressed to match the stringent edge requirements, spiking neural networks and event-based sensing are recently emerging as promising solutions to further improve performance due to their inherent energy efficiency and capacity to process spatiotemporal data in very low latency. This work aims to evaluate the effectiveness of spiking neural networks on neuromorphic processors in human activity recognition for wearable applications. The case of workout recognition with wrist-worn wearable motion sensors is used as a study. A multi-threshold delta modulation approach is utilized for encoding the input sensor data into spike trains to move the pipeline into the event-based approach. The spikes trains are then fed to a spiking neural network with direct-event training, and the trained model is deployed on the research neuromorphic platform from Intel, Loihi, to evaluate energy and latency efficiency. Test results show that the spike-based workouts recognition system can achieve a comparable accuracy (87.5\%) comparable to the popular milliwatt RISC-V bases multi-core processor GAP8 with a traditional neural network ( 88.1\%) while achieving two times better energy-delay product (0.66 \si{\micro\joule\second} vs. 1.32 \si{\micro\joule\second}).

Among the many adverse impacts of climate change in the most vulnerable countries, climate change–induced displacement increasingly caused by extreme weather events is a serious concern, particularly in densely populated Asian countries. Reports by the Intergovernmental Panel on Climate Change (IPCC) project a grim picture for South Asia, the most populous region on Earth, home to about one-quarter of global population, with the highest poverty incidence. A combination of poor socioeconomic indicators and increased frequency and intensity of cyclones and floods renders the region extremely vulnerable. Meanwhile, slow-onset climate hazards, such as sea level rise, salinity intrusion, water stress, and crop failures gradually turn into larger disasters. Within South Asia, Bangladesh stands as the most vulnerable: 4.1 million people were displaced as a result of climate disasters in 2019 (2.5% of the population), 13.3 million people could be displaced by climate change by 2050, and 18% of its coastland will remain inundated by 2080 ([ 1 ][1]). We describe how, faced with such natural and human-made adversities, Bangladesh can stand as a model of disaster management, adaptation, and resilience. The Paris Agreement goal of keeping the temperature rise at 1.5°C or well below 2°C compared to pre-industrial times may not be achieved, given the lack of ambitious mitigation. As a result, the number of people estimated to be displaced by slow-onset events will stand at ∼22.5 million by 2030 and ∼34.4 million by 2050 ([ 2 ][2]). A combination of sudden and slow-onset climate events, which affect all elements of the environment, becomes the main driver of environmental displacement. Migration is an adaptation strategy. An estimated half a million people move to Dhaka, the capital city of Bangladesh, each year. Migration of this magnitude presents a challenge for Bangladesh given its small land area (147,570 km2) and high population density (∼1100/km2). There is simply little space for retreat: Bangladesh's population is half that of the United States, living on ∼1.5% of the land area of the United States. Usually, three pathways can be discerned with respect to how displaced people are settled: autonomous relocation by displaced individuals (without much government support), government-supported temporary settlement, and planned relocation. In Bangladesh, the first option overwhelms, followed by efforts for temporary settlement, until the government rehabilitates their former residences. Planned relocation or managed retreat in response to climate change ([ 3 ][3]) is not yet happening widely because of space and resource constraints. ![Figure][4] Building migrant-friendly, climate-resilient cities in Bangladesh The map shows some activities being undertaken to build migrant-friendly and climate-resilient cities in Bangladesh. Descriptions of activities are based on publicly available information about the programs, and on discussions with representatives of the NGO BRAC. GRAPHIC: N. DESAI/ SCIENCE Since the founding of Bangladesh in 1971, and even earlier in Pakistan, government-planned relocation of people displaced by riverbank erosion has fueled ethnic conflicts in the Chittagong Hill Tracts in the southeast part of the country, because the move was not backed by consultations with tribal communities. About 100,000 of more than a million Rohingya refugees in Bangladesh, fleeing persecution in Myanmar, are being relocated to Bhasan Char, an island in the Bay of Bengal. In land-hungry Bangladesh, most of the 30+ such Chars/mudflats in the bay are already inhabited at different degrees by people displaced by riverbank erosion and climate change. Despite these odds, Bangladesh is a leader in economic growth among developing countries and in mainstreaming climate change into its development strategy. Partially in response to scientific findings, the National Strategy on the Management of Disaster and Climate Induced Internal Displacement (N SMDCIID) adopted in 2015 incorporated disaster risk reduction and rights-based approaches, so that vulnerable communities can enjoy their basic rights to livelihood, food, health, and housing. The Strategy is built on an integrated Displacement Management Framework, in line with the migration management cycle of the International Organization of Migration (IOM). This Framework elaborates responses during the three phases of mobility management: pre-displacement [disaster risk reduction (DRR)], displacement (emergency), and post-displacement (rehabilitation/relocation). Under the Strategy, the government has initiated support for livelihood opportunities, housing, and human development of displaced people in vulnerable hotspots. It is likely that the government-supported community mobilization and disaster management and DRR policies, both before and after adoption of this Strategy, were helpful in lessening the number of casualties from the supercyclone Amphan in May 2020. One way to address displacements under increasing urbanization across the world could be the establishment of peri-urban growth centers and transformation of cities and towns to be migrant-friendly. This option appears practicable for populous countries such as Bangladesh, having little space for retreat from vulnerable hotspots. To achieve this, institutional changes in a city need to be fostered by research, planning, design, and capacity building. Examples from cities such as Durban, Quito, Semarang, and Malé indicate that cities may need to develop general as well as sector-based strategies to manage effective climate change adaptation ([ 4 ][5]). This warrants the linking of adaptation planning and implementation to city priorities. Cities must have access to reliable information and opportunities to share experiences through local, regional, national, and international networks ([ 4 ][5]). National and local governments should develop migrant-friendly plans along three lines: building of resilient hardware, such as low-cost housing, industries for employment generation, and other infrastructure; software, such as legal, policy, and institutional frameworks; and “heart-ware”—the promotion of awareness, reflecting values and ethics. The basic parameters for safe and orderly movement for migrants are to ensure employment, social protection, access to education, housing, health services, utilities, etc. Although government support is important, engagement of the private sector, nongovernmental organizations (NGOs), civil society, and university-led research can strengthen municipal adaptation efforts. This is what the International Centre for Climate Change and Development (ICCCAD) in Bangladesh has been doing—to facilitate the transformation of smaller peripheral towns to be migrant-friendly as a climate adaptation strategy (see the figure). Our work has multiple purposes: to shift the tide of migration away from Dhaka and other large cities toward smaller towns, and to decentralize climate-resilient development and facilitate planning for basic services and amenities. In Bangladesh, a majority of those displaced by climate change prefer non-migration from their ancestral roots ([ 5 ][6]) if they are provided support for improving their livelihood, housing, etc. Settlement of displaced people in a town nearer to their ancestral home allows them to maintain psychological kinship and cultural comforts. On the basis of such local context and needs, each migrant-friendly town needs its own development and adaptation plans to address climate risks and economic opportunities. The NGO BRAC has initially identified about 20 towns and municipalities, considering their economic potential and climate stress, to determine whether they can absorb a sizeable number of displaced people. A number of satellite towns adjacent to economic hubs, such as relatively elevated sea and river ports and export processing zones (EPZs), can potentially employ millions of migrants. Investment in manufacturing and/or services is generating jobs through public, private, and community partnerships, such as private investments, government support, and microfinancing from BRAC and Grameen Bank. ICCCAD has formal agreements with many ministries and agencies including the Local Government Engineering Department (LGED), the agency for building and maintenance of rural infrastructure. ICCCAD has been working as an advisor and co-implementer of programs with all stakeholders, including mayors in two small towns in coastal Bangladesh, Mongla and Noapara (see the figure). It is helping town authorities in planning and implementing initiatives that are intended to be hospitable to incoming settlers, so that they can gradually be mainstreamed into citizenship ([ 6 ][7]). The process is based on a participatory, consultative process involving the municipal authorities, host community leaders, and settlers. The Strategy (NSMDCIID) includes options such as supporting livelihood for new settlers and skill development, both in displacement hotspots and in new settlements. Although these towns do not yet have adaptation plans as such, the programs consider risk-informed and socially conducive adaptation measures. BRAC with its Climate Bridge Fund is also currently implementing different programs in five cities: Khulna, Rajshahi, Satkhira, Barisal, and Sirajgonj. For programs under implementation in these cities, the target groups are incoming migrants, who crowd the slums. The activities undertaken in these cities are similar, with some specific activities in each town (see the figure). Most of the new settlers have moved from rural areas rendered inhospitable as a consequence of slow and sudden-onset climate impacts. ICCCAD started facilitating this program 3 years ago with a strategy of learning by doing. Among the lessons learned: (i) Vibrant economic activities in these rapidly growing towns are absorbing increasing numbers of migrants from vulnerable hotspots, and (ii) migrants with energy and agency are engaging themselves in different small businesses, with government support and microcredits from Grameen Bank and BRAC. The fact that an overwhelming share of those displaced by climate change around the world resettle internally indicates that adaptation in-country is the most viable option. The global community dealing with disaster displacement, including the United Nations Framework Convention on Climate Change (UNFCCC), primarily recommends this option. However, it requires adequate international support, which developed countries are obligated to deliver (with the language “shall provide”) under the UNFCCC and the Paris Agreement. Unfortunately, adaptation finance continues to remain the “poor cousin” of mitigation, the ratio remaining 20:80 despite repeated pledges by developed countries and agencies. For domestic resource mobilization, some countries (for example, Fiji) have introduced an adaptation levy on all goods and services produced and consumed in the country. There are limits to relocation in-country; sudden and slow-onset events sometimes trigger cross-border movement of individuals seeking jobs and protection. The UN Commission on Human Rights argues for looking at such mobility from a human-rights perspective (i.e., the space for realizing the basic human rights of livelihood, health, housing, etc.). Currently, those displaced by climate change suffer an international protection deficit, not qualifying as “refugees” under the 1951 Geneva Convention. Consideration of those displaced by climate change began in 2008 under the UNFCCC, with research and advocacy. The Cancun Adaptation Framework (Decision 1./CP16, paragraph 14f ) provides for different types of climate-induced human mobility (displacement, migration, and planned relocation), different scales of mobility (national, regional, and international), and different actions (research, cooperation, and coordination). This decision recognized migration as an adaptation strategy. The Nansen Initiative in 2011–2012 focused on promoting research and planned relocation. The Paris Agreement established a Task Force on Displacement under the Warsaw International Mechanism, with mandates to make recommendations for averting, minimizing, and addressing climate change–induced displacement. Finally, the Global Compact on Safe, Orderly, Regular and Responsive Migration was adopted in 2018 as the first multilateral framework to cooperate on migration, including in response to climate change. Many major countries and think tanks started looking at climate displacement through a lens of national security, with its characterization as a “threat multiplier,” and a number of nationally determined contributions under the Paris Agreement refer to those displaced by climate change as potentially fueling national and regional conflicts ([ 7 ][8]). However, climate security can be looked at either from a conflict perspective or from a lens of vulnerability-focused human and global security ([ 8 ][9]). The “conflict view” proponents call for closing the borders, but still the result of such a policy ends up being a humanitarian disaster, caused primarily by actions beyond the control of those being displaced or of their home countries. Should we see more of these displaced and disgruntled youth as victims in the hands of human traffickers? If not, we then argue—viewing this displacement in terms of vulnerability-focused human security—that planned relocation internationally can be an effective way forward under paragraph 14f of the Cancun agreement. As multilateral processes are typically very cumbersome and painstakingly slow, bilateral action can be more rapid and effective, and may then gradually feed into regional and global initiatives. For example, the Seasonal Migrant Worker Program in Australia and New Zealand, or New Zealand's Climate Visa Program ([ 9 ][10]), attract migrants from the Pacific Small Island States (although these initiatives are not solely meant for absorbing migrants displaced by climate change). Canada and the United States offered immigration opportunities to typhoon Haiyan victims, but these were based on kinship relations ([ 10 ][11]). Although the EU does not have a common policy, Finland and Sweden changed their earlier liberal policies on climate-induced displacement after the refugee influx from Syria ([ 11 ][12]). There are also provisions of circular migration, as between Spain and Colombia. The IOM continues recommending such migration between developed and developing countries as an adaptation response to climate-induced vulnerability. The Bangladesh Strategy recommends such options as well. Many developed countries already suffer from demographic deficits, with negative growth, and increasingly aging cohorts. The rhetoric in many of these counties, which often is anti-immigrant, cannot change the reality that these countries will need more and more young and skilled labor. Using projected needs of specific skills, developed countries could thus enter into bilateral agreements with climate-vulnerable countries, where those displaced by climate change may be trained in jointly supported educational and training institutions, either for permanent or for circular migration. For example, under the “Triple Win” program, Germany recruits nurses from Serbia, Bosnia-Herzegovina, and the Philippines to meet their nursing shortage, while reducing unemployment and contributing to economic development in the countries of origin ([ 12 ][13]). It is only just and fair for developed-country emitters of greenhouse gases to take some responsibility under Article 3.1 of the UNFCCC for their disproportionate contributions to generating this increasing number of people displaced by climate change. Lessons suggest that migration to rich countries can have strong positive impacts on labor market, GDP growth, and public revenue for host countries ([ 13 ][14], [ 14 ][15]). Mig ration is also typically positive for countries of origin, through remittance, transfer of technology, skills, domestic consumption and GDP growth, housing, children's education, and more. In 2017, low- and middle-income countries received more than $466 billion in remittances, three times the amount of official aid ([ 15 ][16]). This presents an important indicator of the effects that bilateral agreements on migration of climate-displaced people may have on promoting many different Sustainable Development Goals. Such migration should be framed as a win-win option, not as climate humanitarianism ([ 10 ][11]). The Bangladesh Strategy (NSMDCIID) argues for creating “opportunities for international labor migration by one or few members of families from the displacement hotspots” (p. 115). Older and underage family members and spouses can stay behind and rebuild their lives with remittance support. We believe this option of selective, not wholesale, relocation as a pragmatic policy can be scaled gradually, as warranted by projected demands of skills over time in developed countries. This relocation is based on bilateral planning and preparation, unlike the conventional, voluntary migration of skilled labor to industrial countries. This option is challenging, though mutually rewarding. However, acceptance of this proposal by Western democracies depends on whether they are ready to embrace and enjoy more of “smart/pooled” sovereignty, with enlightened self-interests under climate-induced vulnerability interdependence, rather than holding on to a centuries-old “Westphalian” model of a zero-sum game in global cooperation. Many have argued that with the increasing number of global commons problems, we now live in a positive-sum world. But such a paradigm shift warrants a vigorous campaign to raise awareness among citizens in industrial countries about the “new normal” of increasing extreme and ever-growing slow-onset events. Those citizens and politicians must face the lead and obligatory responsibility their countries have assumed under the international climate regime to support adaptation in vulnerable countries. Such awareness must confront and overcome the xenophobia and anti-immigration sentiments that often surface in many countries, inhibiting the enjoyment of mutual dividends, which can contribute to real and sustainable global peace and security. Successful implementation of the two options raised above (migrant-friendly towns and bilateral agreements for international migration) could help to germinate coordinated implementation, as stipulated in the Cancun agreement, of global policy frameworks on climate change (UNFCCC), disaster risk reduction (Sendai Framework), and human migration (Global Compact for Migration). As many ideas and actions on planned internal or international relocation of climate change–induced displacement are relatively new in the national and global policy domains, continued research and science-policy interface are essential in order to determine the feasibility, efficacy, and scalability of these options. 1. [↵][17]1. K. Rigaud et al ., “Groundswell: Preparing for Internal Climate Migration” (World Bank, 2018). 2. [↵][18]1. H. Singh, 2. J. Faleiro, 3. T. Anderson, 4. S. Vashist , “Costs of Climate Inaction Displacement and Distress Migration” (Actionaid, 2020). 3. [↵][19]1. J. Carmin, 2. D. Roberts, 3. I. Anguelovski , “Planning Climate Resilient Cities: Early Lessons from Early Adapters” (2011), pp. 5–8. 4. [↵][20]1. S. Weerasinghe et al ., “Planned Relocation, Disasters and Climate Change: Consolidating Good Practices and Preparing for the Future” (UNHCR, 2014). 5. [↵][21]1. B. Mallick, 2. K. G. Rogers, 3. Z. Sultana , Ambio 10.1007/s13280-021-01552-8 (2021). 6. [↵][22]1. S. S. Alam, 2. S. Huq, 3. F. Islam, 4. H. M. A. Hoque , “Building Climate-Resilient, Migrant-Friendly Cities and Towns” (International Centre for Climate Change and Development, 2018). 7. [↵][23]1. E. Wright, 2. D. Tänzler, 3. L. Rüttinger , “Migration, Environment and Climate Change: Responding via Climate Change Adaptation Policy” (German Environment Agency, 2020). 8. [↵][24]1. M. R. Khan , Toward a Binding Climate Change Adaptation Regime: A Proposed Framework (Routledge, 2014), chapter 6. 9. [↵][25]1. H. Dempster , “New Zealand's ‘Climate Refugee’ Visas: Lessons for the Rest of the World” (Centre for Global Development, Washington, DC, 2020). 10. [↵][26]1. D. M. S. Matias , Clim. Change 160, 143 (2020). [OpenUrl][27] 11. [↵][28]1. A. Kraler, 2. K. Caitlin, 3. M. Wagner , “Climate Change and Migration: Legal and Policy Challenges and Responses to Environmentally-Induced Migration” (European Union, 2020). 12. [↵][29]German Development Agency, “Sustainable Recruitment of Nurses (Triple Win)” (2019); [www.giz.de/en/worldwide/41533.html][30]. 13. [↵][31]1. E.-j. Quak , “The effects of economic integration of migrants on the economy of host countries” (Institute of Development Studies, London, 2016). 14. [↵][32]1. V. Grossmann , “How Immigration Affects Investment and Productivity in Host and Home Countries” (IZA, 2016); . 15. [↵][33]World Bank, “Record high remittances to low- and middle-income countries in 2017” (2018); [www.worldbank.org/en/news/press-release/2018/04/23/record-high-remittances-to-low-and-middle-income-countries-in-2017][34]. 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Last December, a conference of biologists gathered in Cancun, Mexico, to review a shocking finding. DeepMind, Alphabet's artificial intelligence lab and sister company to Google, had beat a roomful of biologists in a contest to predict the shape of a protein based on its genetic code. That might not sound monumental, but understanding the way proteins fold into three-dimensional shapes is crucial to helping create drugs, which often fight disease by latching onto proteins and altering the way they work in the body. DeepMind was able to predict these proteins' shapes with significantly more accuracy than the many esteemed academics and professionals at the conference. "It dawned on me that this is a field that people have been working in for decades," Mohammed AlQuraishi, a biologist and researcher at Harvard who participated in the contest, told Vox. "The fact that a new group could come in and do so well, so quickly--I felt bad because it demonstrated the structural inefficiency of academia."

Nature's spectacular inventiveness, reflected in the enormous diversity of form and function displayed by the biosphere, is a feature of life that distinguishes living most strongly from nonliving. It is, therefore, not surprising that this aspect of life should become a central focus of artificial life. We have known since Darwin that the diversity is produced dynamically, through the process of evolution; this has led life's creative productivity to be called Open-Ended Evolution (OEE) in the field. This article introduces the second of two special issues on current research in OEE and provides an overview of the contents of both special issues. Most of the work was presented at a workshop on open-ended evolution that was held as a part of the 2018 Conference on Artificial Life in Tokyo, and much of it had antecedents in two previous workshops on open-ended evolution at artificial life conferences in Cancun and York. We present a simplified categorization of OEE and summarize progress in the field as represented by the articles in this special issue.

It was with a strangely deflated feeling in his gut that Harvard biologist Mohammed AlQuraishi made his way to Cancun for a scientific conference in December. Strange because a major advance had just been made in his field, something that might normally make him happy. Deflated because the advance hadn't been made by him or by any of his fellow academic researchers. It had been made by a machine. DeepMind, an AI company that Google bought in 2014, had outperformed all the researchers who'd submitted entries to the Critical Assessment of Structure Prediction (CASP) conference, which is basically a fancy science contest for grown-ups.