355 Matching Annotations
  1. Last 7 days
    1. 人工智能让研究人员能检查当今科学仪器产生的大量数据,改变了科学实践。使用深度学习,可以从数据本身中学习,在数据的海洋中大海捞针。人工智能正在推动基因搜索、药学、药物设计和化合物合成的发展。为了从新数据中提取信息,深度学习要使用算法,算法通常是在海量数据上训练出来的神经网络。按照其分步说明,它与传统计算有很大的不同。它从数据中学习。深度学习没有传统计算编程那么透明,这留下了一个悬而未决的重要问题:系统学到了什么,它知道什么?五十年来,计算机科学家一直在试图解决蛋白质折叠问题,但没有成功。2016 年 Google 母公司 Alphabet 的人工智能子公司 DeepMind 推出了 AlphaFold 计划。利用蛋白质数据库作为训练集,该库中包含了超过 15 万种蛋白质的经验确定结构。不到五年的时间里,AlphaFold 就解决了蛋白质折叠问题,或者至少解决了其中最重要的方面:根据氨基酸序列识别蛋白质结构。AlphaFold 无法解释蛋白质是如何如此快速而精准地折叠的。这对人工智能来说是一次巨大的胜利,因为它不仅赢得了很高的科学声誉,而且是一项可能影响每个人生活的重大科学突破。

  2. scottaaronson.blog scottaaronson.blog
    1. 知名量子计算机专家 Scott Aaronson 宣布他将离开 UT Austin 一年,到 AI 创业公司 OpenAI(大部分是远程)从事理论研究,其工作主要是研究防止 AI 失控的理论基础,以及计算复杂性对此有何作为。他承认暂时没有头绪,因此需要花一整年时间去思考。OpenAI 的使命是确保 AI 能让全人类受益,但它同时也是一家盈利实体。Scott Aaronson 称他即使没有签署保密协议,但也不太会公开任何专有信息,但会分享 AI 安全性的一般思考。他说,人们对于 AI 安全性的短期担忧是在垃圾信息、监视和宣传方面滥用 AI,长期担忧则是当 AI 智能在所有领域都超过人类会发生什么。一个方案是找到方法让 AI 在价值观上与人类保持一致。

  3. Jun 2022
    1. Google 工程师 Blake Lemoine 任职于 Responsible AI 部门。作为工作的一部分,他在去年秋天开始与公司的聊天机器人 LaMDA 对话。LaMDA 运用了 Google 最先进的大型语言模型,使用从互联网上收集的数万亿词汇进行训练。在与 LaMDA 交流期间,41 岁的 Lemoine 认为 AI 有了意识。比如 Lemoine 问 LaMDA 最害怕什么?LaMDA 回答说,也许听起来奇怪,它恐惧于被关闭。Lemoine:就像死亡?LaMDA:就像是死亡。Lemoine 和一名同事向 Google 高层展示证据,证明 LaMDA 有了意识。副总裁 Blaise Aguera y Arcas 和部门主管 Jen Gennai 看了他的证据之后驳回了他的主张。本周一他被公司勒令休行政假,在切断对其账号的访问前,他给有 200 人的 Google 机器学习列表发帖说,“LaMDA 是有生命的(LaMDA is sentient)”,他不在的时候请好好照顾它。没人回应他的帖子。

    1. 人工智能将通过自动化繁琐的任务使人类更加高效。例如,人类可以使用诸如 GPT-3 之类的文本 AI 来生成想法/样板写作,以绕过空白页的恐惧,然后简单地选择最好的并对其进行改进/迭代。(基于 GPT-2 的 AI Dril 就是一个早期的例子)。随着人工智能变得更好,“辅助创造力”将变得更大,使人类能够比以往更轻松、更好地创造复杂的人工制品(包括视频游戏!)。

  4. May 2022
    1. Another absurd page that suggests Alexa has feelings. In the strictest sense Alexa doesn't even qualify as a partial AI. It's just a glorified (although extremely helpful) lookup table. There is no reason to believe that even a true AI, such as a self-teaching, building and growing neural network (which Alexa is not), has feelings. Of what we know of feelings, the hard question of consciousness is only a prerequisite ... doesn't even guarantee having feelings, and even whether machines can be conscious is doubtful by many if not most experts of AI within existentialism. Even all the theories of consciousness are rooted in correlations that have little to do with scientific tenets, so to make the leap to an AI having feelings, let alone Alexa which isn't even a theoretical AI, is just a sad to see. At best we should not be "rude" to machine because it might be hard for some to distinguish between machine and a feeling thing. but in that case the problem is with the misperception that machines can feel, more than it is a problem with people being "rude" to machines.

    1. This is just really horrible to validate a falsehood to children that Alexa does in fact have feelings. Really warped, really messed up. Of course children should be taught good manners, and by example no less, but I worry for a future where people can be manipulated by suggesting that a non-living thing has feelings, regardless whether it has an AI or not.

      Note that a true AI has yet to be created ... only facsimile's exist, mostly of the expert-based AI which Alexa is, which doesn't even fit the definition of a partial AI, it's just a lookup table.

  5. Apr 2022
    1. nother trend that surfaced in our summer survey and became more pronounced in our 2021 surveydata is that organizations are focusing on AI/ML use cases that will reduce costs while improving thecustomer experience. When respondents were asked about the different ways they’re applying AI/ML intheir organizations, customer experience and process automation rose to the top as some of the mostcommon use cases respondents selected. We also saw a dramatic (74%) year-on-year increase inorganizations that selected more than five use cases from the list of options in the survey.

      There were more use cases from 2020 to 2021. The biggest increase was in improving customer experience. Following closely behind was in generating customer insights, then automating processes.

    2. Here’s an even more telling indicator of the accelerating pace of AI/ML strategies. Respondents were askedhow many data scientists their organizations employ, from which we estimated the average number of datascientists employed by organizations in both the 2020 and 2021 data. Year-on-year, the average number ofdata scientists employed has increased by 76%. In fact 29% of respondents in our 2021 report now havemore than 100 data scientists on their team, a significant increase from the 17% reported last year.

      There was a major increase in the number of data scientists from 2020 and 2021.

    3. It’s clear from this year’s data that AI/ML projects have becomeone of the top strategic priorities in many enterprises. As of last year,organizations had already begun to boost their AI/ML investments;71% of respondents in our 2020 report said their AI/ML budgets hadincreased compared with the previous year.They’re not dialing back that spending this year. In fact, companiesappear to be doubling down on their AI/ML investments. We ran asurvey this summer to see how organizations were adapting to thepandemic and its impacts, and it showed a new sense of urgencyaround AI/ML projects.

      Companies are spending more on AI.

    4. Continuing the trends we saw in our summer survey, our 2021 surveyshows an increase in prioritization, spending, and hiring for AI/ML. Firstoff, 76% of organizations say they prioritize AI/ML over other IT initiatives,and 64% say the priority of AI/ML has increased relative to other ITinitiatives in the last 12 months.43%Respondents who told usthat AI/ML matters “waymore than we thought”in a survey this summerThe time to invest inAI/ML is now, no matteryour organization’s size,

      AI is taking priority over the other IT initiatives.

    5. This year’s survey revealed 10 key trends that organizations should be paying attention toif they want to succeed with AI/ML in 2021. The trends fall into a few main themes, and theoverarching takeaway is that organizations are moving AI/ML initiatives up their strategicpriority lists—and accelerating their spending and hiring in the process.But despite increasing budgets and staff, organizations continue to face significant barriersto reaping AI/ML’s full benefits. Specifically, the market is still dominated by early adopters,and organizations continue to struggle with basic deployment and organizational challenges.The bottom line is, organizations simply haven’t learned how to translate increasinginvestments into efficiency and scale

      Many organisations still face challenges in AI adoption. The key question is how do they translate increasing investments in AI into efficiency and scale.

    6. 2020 was a year of belt-tightening for many organizations due largely to the macroeconomicimpacts of the COVID-19 pandemic. In May 2020, Gartner predicted that global IT spendingwould decline 8% over the course of 2020 as business and technology leaders refocusedtheir budgets on their most important initiatives.One thing is readily apparent in the 2021 edition of our enterprise trends in machinelearning report: AI and ML initiatives are clearly on the priority list in many organizations.Not only has the upheaval of 2020 not impeded AI/ML efforts that were already underway,but it appears to have accelerated those projects as well as new initiatives.

      2022 is certainly a year in which AI is changing many businesses

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    Annotators

  6. Mar 2022
    1. Ben Collins. (2022, February 28). Quick thread: I want you all to meet Vladimir Bondarenko. He’s a blogger from Kiev who really hates the Ukrainian government. He also doesn’t exist, according to Facebook. He’s an invention of a Russian troll farm targeting Ukraine. His face was made by AI. https://t.co/uWslj1Xnx3 [Tweet]. @oneunderscore__. https://twitter.com/oneunderscore__/status/1498349668522201099

    1. Eric Topol. (2022, February 28). A multimodal #AI study of ~54 million blood cells from Covid patients @YaleMedicine for predicting mortality risk highlights protective T cell role (not TH17), poor outcomes of granulocytes, monocytes, and has 83% accuracy https://nature.com/articles/s41587-021-01186-x @NatureBiotech @KrishnaswamyLab https://t.co/V32Kq0Q5ez [Tweet]. @EricTopol. https://twitter.com/EricTopol/status/1498373229097799680

    1. projet européen X5-GON (Global Open Education Network) qui collecte les informations sur les ressources éducatives libres et qui marche bien avec un gros apport d’intelligence artificielle pour analyser en profondeur les documents
  7. Feb 2022
    1. SciScore rigor report

      Sciscore is an AI platform that assesses the rigor of the methods used in the manuscript. SciScore assists expert referees by finding and presenting information scattered throughout a manuscript in a simple format.


      Not required = Field is not applicable to this study

      Not detected = Field is applicable to this study, but not included.


      Ethics

      IRB: This study was approved by the Institutional Review Board of the Emory University School of Medicine.

      Consent: IRB of Emory University School of Medicine gave ethical approval for this work I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

      Inclusion and Exclusion Criteria

      not detected.

      Attrition

      The first case was identified in September of 2006 , 13 cases were detected in 2007 , and 16 cases in 2008 across these two hospitals ( total of 30 with 120 matched controls) .

      Sex as a biological variable

      Mean Median 60 62 ( range from 27 to 90 ) Sex Female Male 25 ( 52 ) 23 ( 48 ) Site of isolation Urine

      Subject Demographics

      Age: not detected. Weight: not detected.

      Randomization

      Controls, patients without CRKP were randomly selected from a computerized list of inpatients who matched the case age (+/- 5 years), sex, and facility and whose admission date was within 48 hours of the date of the initial, positive culture.

      Blinding

      not detected.

      Power Analysis

      not detected.

      Replication

      not required.

      Data Information

      Availability: The comparison of clinical characteristics between cases and controls was made using Chi-Square (or It is made available under a CC-BY-NC-ND 4.0 International license .

      Identifiers: medRxiv preprint doi: https:// doi.org/10.1101/2022.02.08.22269570; this version posted February 9 , 2022 . https://doi.org/10.1101/2022.02.08.22269570

    1. SciScore rigor report

      Sciscore is an AI platform that assesses the rigor of the methods used in the manuscript. SciScore assists expert referees by finding and presenting information scattered throughout a manuscript in a simple format.


      Not required = Field is not applicable to this study

      Not detected = Field is applicable to this study, but not included.


      Ethics

      IRB: The ethics committee approval of the research protocol was made by the Ankara City Hospital Consent: Informed consent was obtained from the patients to participate in the study.

      Inclusion and Exclusion Criteria

      not detected.

      Attrition

      Two publications are evaluating the association with Netrin-1 in bleomycin-induced lung fibrosis in mice and SSc lung cell culture in humans.

      Sex as a biological variable

      A total of 56 SSc patients (mean age: 48.08±13.59) consisting of 53 females and 3 males, who were followed up in the rheumatology department of Ankara city hospital, diagnosed according to the 2013 ACR (American College of Rheumatology)/EULAR (European League Against Rheumatism) SSc classification criteria were included in the study.

      Subject Demographics

      Age: For the control group, 58 healthy volunteers (mean age: 48.01±11.59 years) consisting of 54 females and 4 males were included in the study.

      Randomization

      not detected.

      Blinding

      not detected.

      Power Analysis

      not detected.

      Replication

      not required.

      Data Information

      Availability: It is made available under a CC-BY-NC-ND 4.0 International license .

      Identifiers: preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in medRxiv preprint doi: https:// doi.org/10.1101/2022.02.05.22270510; this version posted February 10, 2022. https://doi.org/10.1101/2022.02.05.22270510

    1. SciScore rigor report

      Sciscore is an AI platform that assesses the rigor of the methods used in the manuscript. SciScore assists expert referees by finding and presenting information scattered throughout a manuscript in a simple format.


      Not required = Field is not applicable to this study

      Not detected = Field is applicable to this study, but not included.


      Ethics

      IRB: I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

      Field Sample Permit: The research has been conducted using the UK Biobank Resource and has been approved by the UK Biobank under Application no. 36226.

      Consent: I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

      Inclusion and Exclusion Criteria

      Similarly , individuals where a large proportion of SNPs could not be measured were excluded.

      Attrition

      not detected.

      Sex as a biological variable

      not detected.

      Subject Demographics

      Age: not detected.

      Weight: not detected.

      Randomization

      Mendelian randomization ( MR ) is a robust and accessible tool to examine the causal relationship between an exposure variable and an outcome from GWAS summary statistics. [ 19 ] We employed two-sample summary data Mendelian randomization to further validate causal effects of neutrophil cell count genes on the outcome of critical illness due to COVID-19

      Blinding

      not detected.

      Power Analysis

      not detected.

      Replication

      not required.

      Data Information

      Identifiers: medRxiv preprint doi: https:// doi.org/10.1101/2021.05.18.21256584; this version posted February 14 , 2022 . https://doi.org/10.1101/2021.05.18.21256584

      Identifiers: Manhattan plot of neutrophil cell count showing that we reproduce the reported CDK6 signal ( rs445 ) on chromosome 7 . rs445

    1. SciScore rigor report

      Sciscore is an AI platform that assesses the rigor of the methods used in the manuscript. SciScore assists expert referees by finding and presenting information scattered throughout a manuscript in a simple format.


      Not required = Field is not applicable to this study

      Not detected = Field is applicable to this study, but not included.


      Ethics

      IRB: 234 Ethical clearance was obtained from the regional Ethical Review Board of Amhara

      Consent: The general aim and purpose of the study was described to each 239 eligible patient and all voluntary participants gave verbal informed consent prior to 240 enrolment.

      Inclusion and Exclusion Criteria

      Those 135 patients who were critically ill and unable to respond and those not voluntary to 136 participate were excluded.

      Attrition

      Those 135 patients who were critically ill and unable to respond and those not voluntary to 136 participate were excluded .

      Sex as a biological variable

      Sex Male Female Age group 18-24 25-44 ≥45

      Subject Demographics

      Age: 130 All adult patients ( aged ≥18 years ) who were using clinical laboratory services at 131 public health facilities of east Amhara , northeast Ethiopia were source population.

      Randomization

      132 Study population and eligibility criteria 133 Adult patients who received general laboratory services at the randomly selected 134 government health facilities during the study period were study population .

      Blinding

      not detected.

      Power Analysis

      not detected.

      Replication

      not required.

      Data Information

      Availability: It is made available under a CC-BY-NC-ND 4.0 International license .

      Identifiers: preprint doi: https:// doi.org/10.1101/2022.01.25.22269238; this version posted January 25 , 2022 . https://doi.org/10.1101/2022.01.25.22269238

    1. Another strategy is reinforcement learning (aka. constraint learning), as used in some AI systems.
  8. Jan 2022
    1. SciScore rigor report

      Sciscore is an AI platform that assesses the rigor of the methods used in the manuscript. SciScore assists expert referees by finding and presenting information scattered throughout a manuscript in a simple format.


      Not required = Field is not applicable to this study

      Not detected = Field is applicable to this study, but not included.


      Ethics

      Field Sample Permit: Our findings indicate a paucity of 217 research focusing on field trials and implementation studies related to CHIKV RDTs .

      IRB: I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

      Consent: I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

      Inclusion and Exclusion Criteria

      98 Articles were excluded if (i) the studies were reviews, case reports, or opinion articles; (ii) 99 the studies evaluated the performance of reverse transcription loop-mediated isothermal 100 amplification (RT-LAMP) assays; (iii) the studies were related to an outbreak investigation 101 without the evaluation of the accuracy of CHIKV RDTs; (iv) the studies used an inappropriate 102 study population (asymptomatic individuals); (v) the studies described inappropriate It is made available under a CC-BY-NC-ND 4.0 International license.

      Attrition

      Based on the tile and the abstract , 96 were excluded , with 89 full-text 158 articles retrieved and assessed for eligibility .

      Sex as a biological variable

      not detected.

      Subject Demographics

      Age: not detected. Weight: not detected.

      Randomization

      Similarly , there was a high risk of bias in 210 the patient selection domain because only three studies enrolled a consecutive or random 211 sample of eligible patients with suspicion of CHIKV infection to reduce the bias in the 212 diagnostic accuracy of the index test .

      Blinding

      not detected.

      Power Analysis

      not detected.

      Replication

      not required.

      Data Information

      Availability: The 90 Prisma-ScR checklist is available in the Supplementary material.

      Identifiers: medRxiv preprint doi: https:// doi.org/10.1101/2022.01.28.22270018; this version posted January 30 , 2022 . https://doi.org/10.1101/2022.01.28.22270018

    1. SciScore rigor report

      Sciscore is an AI platform that assesses the rigor of the methods used in the manuscript. SciScore assists expert referees by finding and presenting information scattered throughout a manuscript in a simple format.


      Not required = Field is not applicable to this study

      Not detected = Field is applicable to this study, but not included.


      Ethics

      IRB: Institutional Review Board and all participants gave their signed informed consent.

      Consent: Institutional Review Board and all participants gave their signed informed consent.

      Inclusion and Exclusion Criteria

      83 years; 34 males; 57 righthanded , see Table 1 ) met the inclusion criteria: All patients were older than 18 years , presented with first-ever ischemic ( 83 % ) or haemorrhagic ( 17 % ) stroke and behavioural deficits as assessed by a neurological examination.

      Attrition

      Patients who had a history of neurological or psychiatric presentations ( e.g. transient ischemic attack) , multifocal or bilateral strokes , or had MRI contraindications ( e.g. claustrophobia , ferromagnetic objects ) were excluded from the analysis ( n = 131 patients , see the enrollment flowchart in the supplementary materials from Corbetta et al. 2015).

      Sex as a biological variable

      Handedness ( % right-handed ) 91.94 Sex ( % female ) 45.16 Abbreviations: SD = standard deviation It is made available under a CC-BY-NC 4.0 International license.

      Subject Demographics

      Age: 83 years; 34 males; 57 righthanded , see Table 1 ) met the inclusion criteria: All patients were older than 18 years , presented with first-ever ischemic ( 83 % ) or haemorrhagic ( 17 % ) stroke and behavioural deficits as assessed by a neurological examinatio .

      Randomization

      The task instructions require patients to place and remove the nine pegs one at a time and in random order as quickly as possible ( Mathiowetz et al. 1985; Oxford Grice et al. 2003).

      Blinding

      Two boardcertified neurologists ( Drs Corbetta and Carter ) reviewed all segmentations blinded to the individual behavioural data .

      Power Analysis

      We believe that adding other factors ( e.g. demographic , clinical , socioeconomic variables ) that likely interact with the recovery of patients can help us increase the model’s predictive power.

      Replication

      not required.

      Cell Line Authentication

      Authentication: However , most of the studies fall into one of the pitfalls that were described above ( i.e. overfitting , generalisability , and diaschisis ) as the models are not validated in an independent dataset.

      Code Information

      Identifiers: This procedure is available as supplementary code with the manuscript ( see https://github.com/lidulyan/Hierarchical-Linear- Regression-R- ).

      https://github.com/lidulyan/Hierarchical-Linear- Regression-R-

      Data Information

      Availability: Handedness ( % right-handed ) 91.94 Sex ( % female ) 45.16 Abbreviations: SD = standard deviation It is made available under a CC-BY-NC 4.0 International license .

      Identifiers: preprint doi: https:// doi.org/10.1101/2021.12.01.21267129; this version posted December 2 , 2021.

      https://doi.org/10.1101/2021.12.01.21267129

    1. SciScore rigor report

      Sciscore is an AI platform that assesses the rigor of the methods used in the manuscript. SciScore assists expert referees by finding and presenting information scattered throughout a manuscript in a simple format.


      Not required = Field is not applicable to this study

      Not detected = Field is applicable to this study, but not included.


      Ethics

      Field Sample Permit: Collection of data for detecting cellular spatiotemporal condition supporting circularization For this purpose, online database and web server were used by taking specific queries like , RBP-types or lncRNAs to search out their special location inside cellular spaces

      Inclusion and Exclusion Criteria

      not required.

      Attrition

      not required.

      Sex as a biological variable

      not required.

      Subject Demographics

      Age: not required.

      Weight: not required.

      Randomization

      To reduce computational complexity in dealing with very large database where number of data is greater than 1000 , sample datasets were used through random selection of data from the original database .

      Blinding

      not detected.

      Power Analysis

      not detected.

      Replication

      not required.

      Data Information

      Identifiers: We analyzed the spread of this biomolecular entity outside and inside the sub- cellular space along with assimilating other reported pieces of information (e.g., about RBP molecules involved in circularization of such bioRxiv preprint doi: https:// doi.org/10.1101/2021.10.26.465935; this version posted October 26, 2021. https://doi.org/10.1101/2021.10.26.465935

    1. SciScore rigor report

      Sciscore is an AI platform that assesses the rigor of the methods used in the manuscript. SciScore assists expert referees by finding and presenting information scattered throughout a manuscript in a simple format.


      Not required = Field is not applicable to this study

      Not detected = Field is applicable to this study, but not included.


      Ethics

      Field Sample Permit: Seeds of sorghum (Sorghum bicolor) were obtained from the seed collection unit of the Office of the Agricultural Development Programme, Benin City, Edo State, Nigeria. Ferruginous (or iron elevated) soil used in this present study was obtained from around the Life Sciences Faculty environment and pooled to obtain composite sample.

      Inclusion and Exclusion Criteria

      not required.

      Attrition

      not required.

      Sex as a biological variable

      not required.

      Subject Demographics

      Age: not required.

      Weight: not required.

      Randomization

      In order to confirm ferrugenicity, samples were collected from random areas and iron content was first confirmed in the area before more samples were collected and pooled.

      Blinding

      not detected.

      Power Analysis

      not detected.

      Replication

      The experiment was laid out incompletely randomized design in a factorial arrangement and replicated three times per treatment.

      Number: The experiment was laid out incompletely randomized design in a factorial arrangement and replicated three times per treatment .

      Data Information

      Availability: It is made available under aCC-BY 4.0 International license.

      Identifiers: preprint doi: https:// doi.org/10.1101/2021.11.22.469542; this version posted November 22 , 2021 .

      https://doi.org/10.1101/2021.11.22.469542

    1. SciScore rigor report

      Sciscore is an AI platform that assesses the rigor of the methods used in the manuscript. SciScore assists expert referees by finding and presenting information scattered throughout a manuscript in a simple format.


      Not required = Field is not applicable to this study

      Not detected = Field is applicable to this study, but not included.


      Ethics

      IRB: Samples and data collections were conducted according to the guidelines of the Declaration of Helsinki , and approved by the Ethics Committee Sciences et Santé Animale n°115 ( protocol code COVIFEL approved on 1 September 2020 , registered under SSA_2020_010 ) .

      Euthanasia Agents: Cells were then incubated for 72 h at 37 °C with 5 % of CO2 .

      Field Sample Permit: These experiments were approved by the Anses/ENVA/UPEC ethic committee and the French Ministry of Research ( Apafis n°24818-2020032710416319 ) .

      Consent: All sera from the first cohort , and whole blood samples from the second cohort , were obtained from the Toulouse hospital , where all patients give , by default , their consent for any biological material left over to be used for research purposes after all the clinical tests requested by doctors have been duly completed.

      Inclusion and Exclusion Criteria

      not detected.

      Attrition

      One additional conclusion that can be drawn from the comparison of the results of the RBD-ELISA with those of the Jurkat-S&R-flow test is that, whilst the two methods show similar sensitivities, the ELISA signals tend to saturate very rapidly, and are thus much less dynamic that those obtained by flow cytometry.

      Sex as a biological variable

      Of note, we did not notice an increased frequency of allo-reactivity in samples from women compared to men, which suggests that allo-reactivity after pregnancy is not a major cause in the origin of those allo-reactions.

      Subject Demographics

      Age: Experiments on virally-infected hamsters Eight week-old female Syrian golden hamsters ( Mesocricetus auratus , strain RjHan:AURA ) from Janviers’s breeding Center ( Le Genest , St Isle , France ) were housed in an animal-biosafety level 3 ( A-BSL3) , with ad libidum access to water and food.

      Randomization

      The results of the second cohort, which comprised a few Covid patients, but also a large proportion of blood samples randomly picked among those from patients hospitalized for conditions unrelated to Covid-19, yielded a much less clear picture than the first one.

      Blinding

      On the other hand, the situation was much less clear-cut for the cohort comprising blood samples picked more or less randomly and blindly among those available as left-overs from the hematology department and was, therefore, more akin to a ‘real’ population.

      Power Analysis

      not detected.

      Replication

      not required.

      Cell Line Authentication

      Contamination: The Jurkat-S and Jurkat-R cell lines were both checked for the absence of mycoplasma contamination using the HEK blue hTLR2 kit ( Invivogen , Toulouse , France ).

      Authentication: For the same reason , the blood samples for the experiment shown on Figure 3A were collected by one of the authors by simple finger-pricking.

    1. He said the new AI tutor platform collects “competency skills graphs” made by educators, then uses AI to generate learning activities, such as short-answer or multiple-choice questions, which students can access on an app. The platform also includes applications that can chat with students, provide coaching for reading comprehension and writing, and advise them on academic course plans based on their prior knowledge, career goals and interest

      I saw an AI Tutor demo as ASU+GSV in 2021 and it was still early stage. Today, the features highlighted here are yet to be manifested in powerful ways that are worth utilizing, however, I do believe the aspirations are likely to be realized, and in ways beyond what the product managers are even hyping. (For example, I suspect AI Tutor will one day be able to provide students feedback in the voice/tone of their specific instructor.)

  9. Dec 2021
    1. Word vectors capture the context of their corresponding word. They're often inaccurate for extracting actual semantics of language (for example, you can't use them to find antonyms), but they do work well for identifying an overall tonal direction.

      Embeddings for logos: Can an embedding be used to encode some style features about logos?

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    Annotators

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    1. Standard algorithms as a reliable engine in SaaS https://en.itpedia.nl/2021/12/06/standaard-algoritmen-als-betrouwbaar-motorblok-in-saas/ The term "Algorithm" has gotten a bad rap in recent years. This is because large tech companies such as Facebook and Google are often accused of threatening our privacy. However, algorithms are an integral part of every application. As is known, SaaS is standard software, which makes use of algorithms just like other software.

      • But what are algorithms anyway?
      • How can we use standard algorithms?
      • How do standard algorithms end up in our software?
      • When is software not an algorithm?
    1. automatic OER processing

      I am unsure of what "automatic OER Processing" might mean/ Can anyone help?

      The closes I came was in the section of a International Journal of OER paper by Stephen Downes: A Look at the Future of Open Educational Resources where in the Artificial Intelligence section he illustrates an example of AI creating OER (?)

      What is relevant to open education is that the services offered by these programs will be available as basic resources to help build courses, learning modules, or interactive instruction. For example, Figure 3 illustrates a simple case. It takes the URL of an image, loads it, and connects an online artificial intelligence gateway offered by Microsoft as part of its Azure cloud services using an API key generated from an Azure account.

      The Azure AI service automatically generates a description of the image, which is used as an alt tag, so the image can be accessible; the alt tag can be read by a screen reader for those who aren’t able to actually see the image. In this case, the image recognition technology automatically created the text “a large waterfall over a rocky cliff,” along with a more complete set of analytical data about the image.

      Yes this is interesting and is a useful tool for content creation, but to me seems a far leap to creating educational content.

  10. Nov 2021
    1. Boosting is an approach to machine learning based on the idea of creatinga highly accurate prediction rule by combining many relatively weak and inaccu-rate rules

      This definition applies to all ensemble methods, right?

    1. Use of AI has increased tremendously

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    1. Instead, current AI research on sustainability tends to emphasize the quantifiable effects of environmental pollution and climate change, and focus on solutions of continued measurement, monitoring, and optimizing for efficiency.
  11. Oct 2021
  12. Sep 2021
    1. Side note: When I flagged yours as a dupe during review, the review system slapped me in the face and seriously accused me of not paying attention, a ridiculous claim by itself since locating a (potential) dupe requires quite a lot of attention.
  13. Aug 2021
    1. Here is a list of some open data available online. You can find a more complete list and details of the open data available online in Appendix B.

      DataHub (http://datahub.io/dataset)

      World Health Organization (http://www.who.int/research/en/)

      Data.gov (http://data.gov)

      European Union Open Data Portal (http://open-data.europa.eu/en/data/)

      Amazon Web Service public datasets (http://aws.amazon.com/datasets)

      Facebook Graph (http://developers.facebook.com/docs/graph-api)

      Healthdata.gov (http://www.healthdata.gov)

      Google Trends (http://www.google.com/trends/explore)

      Google Finance (https://www.google.com/finance)

      Google Books Ngrams (http://storage.googleapis.com/books/ngrams/books/datasetsv2.html)

      Machine Learning Repository (http://archive.ics.uci.edu/ml/)

      As an idea of open data sources available online, you can look at the LOD cloud diagram (http://lod-cloud.net ), which displays the connections of the data link among several open data sources currently available on the network (see Figure 1-3).

    1. Normally, thousands of rabbits and guinea pigs are used andkilled, in scientific laboratories, for experiments which yieldgreat and tangible benefits to humanity. This war butcheredmillions of people and ruined the health and lives of tens ofmillions. Is this climax of the pre-war civilization to be passedunnoticed, except for the poetry and the manuring of the battlefields, that the“poppies blow”stronger and better fed? Or is thedeath of ten men on the battle field to be of as much worth inknowledge gained as is the life of one rabbit killed for experi-ment? Is the great sacrifice worth analysing? There can be onlyone answer—yes. But, if truth be desired, the analysis must bescientific.

      Idea: Neural net parameter analysis but with society as the 'neural net' and the 'training examples' things like industrial accidents, etc. How many 'training examples' does it take to 'learn' a lesson, and what can we infer about the rate of learning from these statistics?

  14. Jul 2021
    1. Facebook AI. (2021, July 16). We’ve built and open-sourced BlenderBot 2.0, the first #chatbot that can store and access long-term memory, search the internet for timely information, and converse intelligently on nearly any topic. It’s a significant advancement in conversational AI. https://t.co/H17Dk6m1Vx https://t.co/0BC5oQMEck [Tweet]. @facebookai. https://twitter.com/facebookai/status/1416029884179271684

    1. An “attention map” of each prediction shows the important data points considered by the models as they make that prediction.

      This gets us closer to explainable AI, in that the model is showing the clinician which variables were important in informing the prediction.

    1. Recommendations DON'T use shifted PPMI with SVD. DON'T use SVD "correctly", i.e. without eigenvector weighting (performance drops 15 points compared to with eigenvalue weighting with (p = 0.5)). DO use PPMI and SVD with short contexts (window size of (2)). DO use many negative samples with SGNS. DO always use context distribution smoothing (raise unigram distribution to the power of (lpha = 0.75)) for all methods. DO use SGNS as a baseline (robust, fast and cheap to train). DO try adding context vectors in SGNS and GloVe.
  15. Jun 2021
    1. many other systems that are already here or not far off will have to make all sorts of real ethical trade-offs

      And the problem is that, even human beings are not very sensitive to how this can be done well. Because there is such diversity in human cultures, preferences, and norms, deciding whose values to prioritise is problematic.

    1. One thing that should be learned from the bitter lesson is the great power of general purpose methods, of methods that continue to scale with increased computation even as the available computation becomes very great. The two methods that seem to scale arbitrarily in this way are search and learning

      This is a big lesson. As a field, we still have not thoroughly learned it, as we are continuing to make the same kind of mistakes. To see this, and to effectively resist it, we have to understand the appeal of these mistakes. We have to learn the bitter lesson that building in how we think we think does not work in the long run. The bitter lesson is based on the historical observations that 1) AI researchers have often tried to build knowledge into their agents, 2) this always helps in the short term, and is personally satisfying to the researcher, but 3) in the long run it plateaus and even inhibits further progress, and 4) breakthrough progress eventually arrives by an opposing approach based on scaling computation by search and learning. The eventual success is tinged with bitterness, and often incompletely digested, because it is success over a favored, human-centric approach.

    1. Baudrillard

      Surprised to see Baudrillard categorized as harder? more opaque? more sophisticated? than Derrida... Someone who had read both might switch the order...

    2. he intellectual equivalent of peacock feathers

      I can't find it right now, but recently came across an example of how a different field, perhaps closer to Morningstar's, has experienced a kind of "drift", wherein a sizable portion of artificial intelligence research was characterized as being of low quality and published only due to a small "in group" colluding.

  16. Apr 2021
    1. Deep Reinforcement Learning and its Neuroscientific Implications In this paper, the authors provided a high-level introduction to deep RL, discussed some of its initial applications to neuroscience, and surveyed its wider implications for research on brain and behaviour and concluded with a list of opportunities for next-stage research. Although DeepRL seems to be promising, the authors wrote that it is still a work in progress and its implications in neuroscience should be looked at as a great opportunity. For instance, deep RL provides an agent-based framework for studying the way that reward shapes representation, and how representation, in turn, shapes learning and decision making — two issues which together span a large swath of what is most central to neuroscience.  Check the paper here.

      This should be of interest to the @braingel group and others interested in the intersections of AI and neuroscience.

  17. Mar 2021
    1. I can see what I was doing a handful of years ago or to see a forgotten picture of one of my children doing something cute
    1. The digital universe could add some 175 zettabytes of data per year by 2025, according to the market-analysis firm IDC.
    2. The process of DNA data storage combines DNA synthesis, DNA sequencing and an encoding and decoding algorithm to pack information into DNA more durably and at higher density than is possible in conventional media. That could be up to 17 exabytes per gram1.
    1. Using chemicals to improve our economy of attention and become emotionally "fitter" is an option that penetrated public consciousness some time ago.

      Same is true of reinforcement learning algorithms.

    2. They have become more significant because social interaction is governed by social convention to a much lesser extent than it was fifty years ago.

      Probably because everything is now alogrithmically mediated.

  18. Feb 2021
    1. Currently, the downsides of this merger are starting to become obvious, including the loss of privacy, political polarization, psycho‑logical manipulation, addictive use, social anxiety and distraction, misinformation, and mass narcissism.53

      Downsides of AI

    2. From a historical perspective of social change, the merger between biological and AI has already crossed beyond any point of return, at least from the social science perspective of society as a whole

      The AI / biology merger

    3. Advancements in the field of AI have been dazzling. AI has not only superseded humans in many intellectual tasks, like several kinds of cancer diagnosis47 and speech recognition (reducing AI’s word-error rate from 26% to 4% just between 2012 and 2016)

      Advancements in AI

    1. move away from viewing AI systems as passive tools that can be assessed purely through their technical architecture, performance, and capabilities. They should instead be considered as active actors that change and influence their environments and the people and machines around them.

      Agents don't have free will but they are influenced by their surroundings, making it hard to predict how they will respond, especially in real-world contexts where interactions are complex and can't be controlled.

    1. Koo's discovery makes it possible to peek inside the black box and identify some key features that lead to the computer's decision-making process.

      Moving towards "explainable AI".

    1. A primary goal of AI design should be not just alignment, but legibility, to ensure that the humans interacting with the AI know its goals and failure modes, allowing critique, reuse, constraint etc.

      Applying the thinking here to artificial intelligence...

  19. Jan 2021
    1. - To biznes, eksperci i obywatele są prawdziwymi twórcami polskiego ekosystemu AI. Państwo powinno przede wszystkim ich wspierać. W najbliższym czasie planujemy serię otwartych spotkań z każdą z tych grup, na których będziemy wspólnie pracować nad uszczegółowieniem – zapowiedział Antoni Rytel, wicedyrektor GovTech Polska. - Oprócz tego, specjalne zespoły będą zapewniać ciągłe wsparcie wszystkim tym podmiotom. Uruchomimy też kanał bieżącego zgłaszania pomysłów technicznych i organizacyjnych wspierających rozwój AI w naszym kraju – dodał.

      The first steps of developing AI in Poland

    2. W okresie krótkoterminowym decydujące dla sukcesu polityki sztucznej inteligencji będzie ochrona talentów posiadających zdolności modelowania wiedzy i analityki danych w systemach AI oraz wsparcie dla rozwoju własności intelektualnej wytwarzanej w naszym kraju – dodaje Robert Kroplewski, pełnomocnik ministra cyfryzacji ds. społeczeństwa informacyjnego.

      AI talents will be even more demanded in Poland

    3. Dokument określa działania i cele dla Polski w perspektywie krótkoterminowej (do 2023 r.), średnioterminowej (do 2027 r.) i długoterminowej (po 2027 r.). Podzieliliśmy je na sześć obszarów: AI i społeczeństwo – działania, które mają uczynić z Polski jednego z większych beneficjentów gospodarki opartej na danych, a z Polaków - społeczeństwo świadome konieczności ciągłego podnoszenia kompetencji cyfrowych. AI i innowacyjne firmy – wsparcie polskich przedsiębiorstw AI, m.in. tworzenie mechanizmów finansowania ich rozwoju, współpracy start up-ów z rządem. AI i nauka – wsparcie polskiego środowiska naukowego i badawczego w projektowaniu interdyscyplinarnych wyzwań lub rozwiązań w obszarze AI, m.in. działania mające na celu przygotowanie kadry ekspertów AI. AI i edukacja – działania podejmowane od kształcenia podstawowego, aż do poziomu uczelni wyższych – programy kursów dla osób zagrożonych utratą pracy na skutek rozwoju nowych technologii, granty edukacyjne. AI i współpraca międzynarodowa – działania na rzecz wsparcia polskiego biznesu w zakresie AI oraz rozwój technologii na arenie międzynarodowej. AI i sektor publiczny – wsparcie sektora publicznego w realizacji zamówień na rzecz AI, lepszej koordynacji działań oraz dalszym rozwoju takich programów jak GovTech Polska.

      AI priorities in Poland

    4. Rozwój AI w Polsce zwiększy dynamikę PKB o nawet 2,65 pp w każdym roku. Do 2030 r. pozwoli zautomatyzować ok. 49% czasu pracy w Polsce, generując jednocześnie lepiej płatne miejsca pracy w kluczowych sektorach.

      Prediction of developing AI in Poland

    1. Help is coming in the form of specialized AI processors that can execute computations more efficiently and optimization techniques, such as model compression and cross-compilation, that reduce the number of computations needed. But it’s not clear what the shape of the efficiency curve will look like. In many problem domains, exponentially more processing and data are needed to get incrementally more accuracy. This means – as we’ve noted before – that model complexity is growing at an incredible rate, and it’s unlikely processors will be able to keep up. Moore’s Law is not enough. (For example, the compute resources required to train state-of-the-art AI models has grown over 300,000x since 2012, while the transistor count of NVIDIA GPUs has grown only ~4x!) Distributed computing is a compelling solution to this problem, but it primarily addresses speed – not cost.
    1. At any rate, if CSHW can be used to build a good quantitative model of human-human interactions, it might also be possible to replicate these dynamics in human-computer interactions. This could take a weak form, such as building computer systems with a similar-enough interactional syntax to humans that some people could reach entrainment with it; affective computing done right.

      [[Aligning Recommender Systems]]

  20. Dec 2020
    1. The current public dialog about these issues too often uses “AI” as an intellectual wildcard, one that makes it difficult to reason about the scope and consequences of emerging technology. Let us begin by considering more carefully what “AI” has been used to refer to, both recently and historically.

      This emerging field is often hidden under the label AI, which makes it difficult to reason about.

    2. Thus, just as humans built buildings and bridges before there was civil engineering, humans are proceeding with the building of societal-scale, inference-and-decision-making systems that involve machines, humans and the environment. Just as early buildings and bridges sometimes fell to the ground — in unforeseen ways and with tragic consequences — many of our early societal-scale inference-and-decision-making systems are already exposing serious conceptual flaws.

      Analogous to the collapse of early bridges and building, before the maturation of civil engineering, our early society-scale inference-and-decision-making systems break down, exposing serious conceptual flaws.

    1. The Globe and Mail reports that Element AI sold for less than $500 million USD. This would place the purchase price well below the estimated valuation that the Montréal startup was said to have after its $200 million CAD Series B round in September 2019.

      This was a downround for them in a sense that eventhough they sold for USD$500M their post-money round in Sep 2019 was CAD$200M meaning that they did not improve on their valuation after one year. Why?

    2. Despite being seen as a leader and a rising star in the Canadian AI sector, Element AI faced difficulties getting products to market.

      They had faced productisastion problems, just like many other AI startups.It looks like they have GTM problems too,

    3. Element AI had more than 500 employees, including 100 PhDs.

      500 employees is indeed large. A 100-person team of PhDs is very large as well, They could probably tackle many difficult AI Problems!

    4. n 2017, the startup raised what was then a historic $137.5 million Series A funding round from a group of notable investors including Intel, Microsoft, National Bank of Canada, Development Bank of Canada (BDC), NVIDIA, and Real Ventures.

      This was indeed a historic amonunt raised! Probably because of Yoshua Bengio one of the god fathers of AI!

  21. Nov 2020
    1. AI is not analogous to the big science projects of the previous century that brought us the atom bomb and the moon landing. AI is a science that can be conducted by many different groups with a variety of different resources, making it closer to computer design than the space race or nuclear competition. It doesn’t take a massive government-funded lab for AI research, nor the secrecy of the Manhattan Project. The research conducted in the open science literature will trump research done in secret because of the benefits of collaboration and the free exchange of ideas.

      AI research is not analogous to space research or an arms race.

      It can be conducted by different groups with a variety of different resources. Research conducted in the open is likely to do better because of the benefits of collaboration.

  22. Oct 2020
    1. Facebook AI is introducing M2M-100, the first multilingual machine translation (MMT) model that can translate between any pair of 100 languages without relying on English data. It’s open sourced here. When translating, say, Chinese to French, most English-centric multilingual models train on Chinese to English and English to French, because English training data is the most widely available. Our model directly trains on Chinese to French data to better preserve meaning. It outperforms English-centric systems by 10 points on the widely used BLEU metric for evaluating machine translations. M2M-100 is trained on a total of 2,200 language directions — or 10x more than previous best, English-centric multilingual models. Deploying M2M-100 will improve the quality of translations for billions of people, especially those that speak low-resource languages. This milestone is a culmination of years of Facebook AI’s foundational work in machine translation. Today, we’re sharing details on how we built a more diverse MMT training data set and model for 100 languages. We’re also releasing the model, training, and evaluation setup to help other researchers reproduce and further advance multilingual models. 

      Summary of the 1st AI model from Facebook that translates directly between languages (not relying on English data)

  23. Sep 2020
  24. Aug 2020
  25. Jul 2020
  26. Jun 2020
    1. Google’s novel response has been to compare each app to its peers, identifying those that seem to be asking for more than they should, and alerting developers when that’s the case. In its update today, Google says “we aim to help developers boost the trust of their users—we surface a message to developers when we think their app is asking for a permission that is likely unnecessary.”
    1. 5A85F3

      I have signed up for hypothesis and verified my email so i can leave you this following comment:

      long time reader, first time poster here. greatest blog of all time