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confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the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

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

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

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

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

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

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

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

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.

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.)

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

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?

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.

This definition applies to all ensemble methods, right?

Use of AI has increased tremendously

applications that can be built on the [[Alethea AI Protocol]].

Hoffman, R., Mueller, S., Klein, G., & Litman, J. (2021). Measuring Trust in the XAI Context. PsyArXiv. https://doi.org/10.31234/osf.io/e3kv9

Here is a link to another useful AI framework resource https://www.weforum.org/projects/model-ai-governance-framework

Why people believe Covid conspiracy theories: Could folklore hold the answer? | Coronavirus | The Guardian. (n.d.). Retrieved October 26, 2021, from https://www.theguardian.com/world/ng-interactive/2021/oct/26/why-people-believe-covid-conspiracy-theories-could-folklore-hold-the-answer

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).

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?

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

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

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.

Johnson, Khari. ‘AI Could Soon Write Code Based on Ordinary Language’. Wired. Accessed 21 June 2021. https://www.wired.com/story/ai-write-code-ordinary-language.

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.

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

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.

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

Writing with AI

Amazing combination to crack WiFis!

Same is true of reinforcement learning algorithms.

Probably because everything is now alogrithmically mediated.

ReconfigBehSci on Twitter: ‘RT @factmata: We are excited to launch of a side project we worked on this summer—Https://t.co/2yGSgkqzTG. We scan Twitter profiles with…’ / Twitter. (n.d.). Retrieved 6 March 2021, from https://twitter.com/SciBeh/status/1323664538777124867

ReconfigBehSci. (2020, November 9). Session 2: The policy interface followed with a really helpful presentation by Lindsey Pike, from Bristol, and then panel discussion with Mirjam Jenny (Robert Koch Insitute), Paulina Lang (UK Cabinet Office), Rachel McCloy (Reading Uni.), and Rene van Bavel (European Commission) [Tweet]. @SciBeh. https://twitter.com/SciBeh/status/1325795286065815552

Downsides of AI

The AI / biology merger

Advancements in AI

found on GitHub https://github.com/google/model_search

No YouTube yet so far.

Facial Recognition

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.

Moving towards "explainable AI".

Applying the thinking here to artificial intelligence...

The first steps of developing AI in Poland

AI talents will be even more demanded in Poland

AI priorities in Poland

Prediction of developing AI in Poland

Artificial Intelligence

[[Aligning Recommender Systems]]

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

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.

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?

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

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

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

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.

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

AI and control of Covid-19 coronavirus. (n.d.). Artificial Intelligence. Retrieved October 15, 2020, from https://www.coe.int/en/web/artificial-intelligence/ai-and-control-of-covid-19-coronavirus

;

DiResta, Renée. ‘The Supply of Disinformation Will Soon Be Infinite’. The Atlantic, 20 September 2020. https://www.theatlantic.com/ideas/archive/2020/09/future-propaganda-will-be-computer-generated/616400/.

Still an incredible headline...

Yang, Scott Cheng-Hsin, Chirag Rank, Jake Alden Whritner, Olfa Nasraoui, and Patrick Shafto. ‘Unifying Recommendation and Active Learning for Information Filtering and Recommender Systems’. Preprint. PsyArXiv, 25 August 2020. https://doi.org/10.31234/osf.io/jqa83.

Building the New Economy · Works in Progress. (n.d.). Works in Progress. Retrieved June 16, 2020, from https://wip.mitpress.mit.edu/new-economy

Kirkwood. I. (2020) HERE’S HOW #CDNTECH COMPANIES ARE PITCHING IN DURING COVID-19. Betakit. Retrieved from:https://betakit.com/heres-how-cdntech-companies-are-pitching-in-during-the-covid-19-outbreak/

Centre for Effective Altruism. (2020, June 13 & 14). EAGxVirtual 2020 Virtual Conference. https://www.youtube.com/playlist?list=PLwp9xeoX5p8NfF4UmWcwV0fQlSU_zpHqc

American Philosophical Society. (2020, June 08). Evidence Symposium. YouTube. https://www.youtube.com/playlist?list=PLoKwLGnyZL4Ds5cQo5muFMg8zKXK4KobH

Nesta, (2020, May 15). Invisible work: Nesta talks to John Howkins. https://www.nesta.org.uk/event/live-stream-invisible-work/

How COVID-19 revealed 3 critical AI procurement blindspots. (n.d.). World Economic Forum. Retrieved June 22, 2020, from https://www.weforum.org/agenda/2020/06/how-covid-19-revealed-3-critical-blindspots-ai-governance-procurement/

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

Kozyreva, A., Lewandowsky, S., & Hertwig, R. (2019, December 4). Citizens Versus the Internet: Confronting Digital Challenges With Cognitive Tools. https://doi.org/10.31234/osf.io/ky4x8

Gent, Edd. ‘Robots to the Rescue: How They Can Help During Coronavirus (and Future Pandemics)’. Singularity Hub (blog), 1 April 2020. https://singularityhub.com/2020/04/01/robots-to-the-rescue-how-they-can-help-during-coronavirus-and-future-pandemics/.

Young, V. A. (2020, May 20). Nearly Half Of The Twitter Accounts Discussing ‘Reopening America’ May Be Bots. Carnegie Mellon School of Computer Science. https://www.scs.cmu.edu/news/nearly-half-twitter-accounts-discussing-%E2%80%98reopening-america%E2%80%99-may-be-bots

Kurzweil, R. (2020 May 19). AI-Powered Biotech Can Help Deploy a Vaccine In Record Time. Wired. https://www.wired.com/story/opinion-ai-powered-biotech-can-help-deploy-a-vaccine-in-record-time/

Yang Yang: The Replicability of Scientific Findings Using Human and Machine Intelligence (Video). Metascience 2019 Symposium. https://www.metascience2019.org/presentations/yang-yang/

Yang, Y., Youyou, W., & Uzzi, B. (2020). Estimating the deep replicability of scientific findings using human and artificial intelligence. Proceedings of the National Academy of Sciences, 117(20), 10762–10768. https://doi.org/10.1073/pnas.1909046117