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brainsteam/content/annotations/2022/11/19/1668872696.md
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---
date: '2022-11-19T15:44:56'
hypothesis-meta:
created: '2022-11-19T15:44:56.849529+00:00'
document:
title:
- What if your Index Page was Smart?
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tags:
- productivity
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- exact: "Many people report writers block with blogs, particularly after a big\
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\ out bangers."
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'
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source: https://www.swyx.io/smart-indexes
text: Certainly true, people go through peaks and troughs of productivity like [seasons](https://herbertlui.net/seasons/)
updated: '2022-11-19T15:44:56.849529+00:00'
uri: https://www.swyx.io/smart-indexes
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tags:
- productivity
- hypothesis
type: annotation
url: /annotations/2022/11/19/1668872696
---
<blockquote>Many people report writers block with blogs, particularly after a big successful post, because it’s almost impossible to consistently pump out bangers.</blockquote>Certainly true, people go through peaks and troughs of productivity like [seasons](https://herbertlui.net/seasons/)

11
brainsteam/content/annotations/2022/11/19/1668896069.md
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---
date: '2022-11-19T22:14:29'
in-reply-to: https://tomcritchlow.com/2018/02/23/small-b-blogging/
type: annotation
url: /annotations/2022/11/19/1668896069
---
<blockquote>But - as the overall network has grown exponentially the network topology has changed. Digg, Reddit, Hacker News etc all still exist but the audience you can reach with a “homepage” hit there has become much smaller relative to the overall size of the network. And getting a homepage hit there is harder than ever because the volume of content has increased exponentially</blockquote>A similar dynamic can now be observed in the mass migration from twitter to mastodon. People who were successful at using the big "homepage" of twitter are likely to be a bit thrown by the fediverse but it represents an opportunity to connect with a smaller but more specialised audience.

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brainsteam/content/annotations/2022/11/20/1668933765.md
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---
date: '2022-11-20T08:42:45.040182'
in-reply-to: https://www.zylstra.org/blog/2022/08/22036/
tags:
- hypothesis
- personal
- indieweb
type: annotation
url: /annotation/2022/11/20/1668933765
---
<blockquote>
Is it possible to annotate links in Hypothes.is that are in the Internet Archive? My browser bookmarklet for it doesn’t work on such archived pages... in some cases this would be very useful to be able to do. For instance, Manfred Kuehns blog was discontinued in 2018, and more recently removed entirely from Blogspot where it was hosted. The archived versions are the only current source for those blogpostings. This means there is no ‘original’ page online anymore to gather the annotations around.
</blockquote>
This is a great point and use case - I often worry about content I care about and have spent time thinking about disappearing. I run my own archive using <a href="https://github.com/ArchiveBox/ArchiveBox">ArchiveBox</a>. I see that the Hypothes.is bookmarklet seems to work for archive.org but only in chrome. Also, it doesn't play nice with archivebox yet. I might have to see if I can get it working at some point.

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---
date: '2022-11-20T09:06:40'
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- 'Learn In Public: The fastest way to learn'
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- exact: "Whatever your thing is, make the thing you wish you had found when you\
\ were learning. Don\u2019t judge your results by \u201Cclaps\u201D or retweets\
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url: /annotation/2022/11/20/1668935200
---
<blockquote>Whatever your thing is, make the thing you wish you had found when you were learning. Don’t judge your results by “claps” or retweets or stars or upvotes - just talk to yourself from 3 months ago</blockquote>Completely agree, this is a great intrinsic metric to measure the success of your work by.

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---
date: '2022-11-20T11:18:31'
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- 'Data Engineering in 2022: ELT tools'
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\ up to fit just a specific use case then you lose the fidelity of the data\
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Of course, despite what the'
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text: absolutely right - there's also a data provenance angle here - it is useful
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input and be able to say "yes I know exactly where this came from, here are all
the steps that came before"
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url: /annotation/2022/11/20/1668943111
---
<blockquote>Working with the raw data has lots of benefits, since at the point of ingest you don’t know all of the possible uses for the data. If you rationalise that data down to just the set of fields and/or aggregate it up to fit just a specific use case then you lose the fidelity of the data that could be useful elsewhere. This is one of the premises and benefits of a data lake done well.</blockquote>absolutely right - there's also a data provenance angle here - it is useful to be able to point to a data point that is 5 or 6 transformations from the raw input and be able to say "yes I know exactly where this came from, here are all the steps that came before"

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---
date: '2022-11-20T11:20:16'
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- 'Data Engineering in 2022: ELT tools'
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text: Love this analogy - imagine chucking some crude into a black box and hoping
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what happened.
updated: '2022-11-20T11:20:16.520474+00:00'
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---
<blockquote>Of course, despite what the "data is the new oil" vendors told you back in the day, you can’t just chuck raw data in and assume that magic will happen on it, but that’s a rant for another day ;-)</blockquote>Love this analogy - imagine chucking some crude into a black box and hoping for ethanol at the other end. Then, when you end up with diesel you have no idea what happened.

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---
date: '2022-11-20T11:35:46'
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- 'Data Engineering in 2022: ELT tools'
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t just another Informatica, another Oracle Data Integrator. It\u2019s not\
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dbt is the T in ELT
'
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text: Also - just because their "pricing" page caught me off guard and their website
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worth calling out that DBT appears to be an open-core platform. They have a SaaS
offering and also an open source python command-line tool - it seems that these
articles are about the latter
updated: '2022-11-20T11:35:46.410564+00:00'
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- ELT
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type: annotation
url: /annotation/2022/11/20/1668944146
---
<blockquote>It took me a while to grok where dbt comes in the stack but now that I (think) I have it, it makes a lot of sense. I can also see why, with my background, I had trouble doing so. Just as Apache Kafka isn’t easily explained as simply another database, another message queue, etc, dbt isn’t just another Informatica, another Oracle Data Integrator. It’s not about ETL or ELT - it’s about T alone. With that understood, things slot into place. This isn’t just my take on it either - dbt themselves call it out on their blog:</blockquote>Also - just because their "pricing" page caught me off guard and their website isn't that clear (until you click through to the technical docs) - I thought it's worth calling out that DBT appears to be an open-core platform. They have a SaaS offering and also an open source python command-line tool - it seems that these articles are about the latter

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---
date: '2022-11-20T16:47:28'
hypothesis-meta:
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- Rest in motion
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- exact: the work that needs to be done is not a finite list of tasks, it is a
neverending stream. Clothes are always getting worn down, food is always getting
eaten, code is always in motion. The goal is not to finish all the work before
you; for that is impossible. The goal is simply to move through the work.
Instead of struggling to reach the end of the stream, simply focus on moving
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But '
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Advertisements and media often'
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url: /annotation/2022/11/20/1668962848
---
<blockquote>the work that needs to be done is not a finite list of tasks, it is a neverending stream. Clothes are always getting worn down, food is always getting eaten, code is always in motion. The goal is not to finish all the work before you; for that is impossible. The goal is simply to move through the work. Instead of struggling to reach the end of the stream, simply focus on moving along it.</blockquote>This is true and worth remembering. It is very easy to fall into the mindset of "I'll rest when I'm finished"

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---
date: '2022-11-20T16:53:00'
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- exact: 'The actual reward state is not one where you''re lazing around doing
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that stimulate you, and where you''re resting only a fraction of the time.
The preferred ground state is not one where you have no activity to partake
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For that would be boring'
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'
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And yet, most people have this'
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---
<blockquote>The actual reward state is not one where you're lazing around doing nothing. It's one where you're keeping busy, where you're doing things that stimulate you, and where you're resting only a fraction of the time. The preferred ground state is not one where you have no activity to partake in, it's one where you're managing the streams of activity precisely, and moving through them at the right pace: not too fast, but also not too slow. For that would be boring</blockquote>Doing nothing at all is boring. When we "rest" we are actually just doing activities that we find interesting rather than those we find dull or stressful.

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---
date: '2022-11-21T06:28:39'
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- 8 Years on the Road
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- exact: Being self-employed feels a bit like being on an extended road trip.
Untethered and free, but lonely and unsupported too. Ultimate freedoms combined
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<blockquote>Being self-employed feels a bit like being on an extended road trip. Untethered and free, but lonely and unsupported too. Ultimate freedoms combined with shallow roots.</blockquote>That's a super insightful take on the self employment thing that people probably don't consider that much when deciding whether to take the leap

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<blockquote>I’ve been using this phrase “the next most useful thing” as a guiding light for my consulting work - I’m obsessed with being useful not just right. I’ve always rejected the fancy presentation in favor of the next most useful thing, and I simply took my eye off the ball with this one. I’m not even sure the client views this project as a real disappointment, there was still some value in it, but I’m mad at myself personally for this one. A good reminder not to take your eye off the ball. And to push your clients beyond what they tell you the right answer is.</blockquote>The customer is not always right (just in matters of taste). Part of consultancy is providing stewardship and pushing back, just like any role I guess

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date: '2022-11-21T06:37:23'
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<blockquote>I only know a handful of people directly making money from blogging (via ads, subscriptions etc) but I know many more who:
Got a better career because of blogging (new job, better pay etc)
Negotiated better contracts (e.g. with a publisher or platform) because they had “an audience”
Sold their own courses / ebooks / books / merchandise / music
Blogging is this kind of engine that opens up economic opportunity and advantage. Being visible in the networked economy has real value.</blockquote>Making money from blogging isn't just about selling ads or subscriptions a direct thing. It can be indirect too. Eg selling courses or books.

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<blockquote>A blog post is a very long and complex search query to find fascinating people and make them route interesting stuff to your inbox.</blockquote>This is a really cool take on blogging. By writing about interesting people and stuff you are increasing your chances of meeting someone cool and indeed [increasing your luck](https://github.com/readme/guides/publishing-your-work )

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<blockquote>Elicit is really impressive. It searches academic papers, providing summary abstracts as well as structured analyses of papers. For example, it tries to identify the outcomes analysed in the paper or the conflicts of interest of the authors, as well as easily tracks citations. (See a similar search on “technology transitions”. Log in required.)</blockquote>https://elicit.org/ - another academic search engine

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<blockquote>“The metaphor is that the machine understands what I’m saying and so I’m going to interpret the machine’s responses in that context.”</blockquote>Interesting metaphor for why humans are happy to trust outputs from generative models

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- exact: . However, such a framework is not applicablehere since the learned latent
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meanings
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representations
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<blockquote>. However, such a framework is not applicablehere since the learned latent topic representations in topicmodels can not be shared directly with word or sentencerepresentations learned in classifiers, due to their differentinherent meanings</blockquote>Latent word vectors and topic models learn different and entirely unrelated representations

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- exact: "e argue that mutual learningwould benefit sentiment classification since\
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the sentiment task can also be improved (as per the example "incredible" should
be positive when used to describe "acting" or "movie" in this context
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<blockquote>e argue that mutual learningwould benefit sentiment classification since it enriches theinformation required for the training of the sentiment clas-sifier (e.g., when the word “incredible” is used to describe“acting” or “movie”, the polarity should be positive)</blockquote>By training a topic model that has "similar" weights to the word vector model the sentiment task can also be improved (as per the example "incredible" should be positive when used to describe "acting" or "movie" in this context

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- exact: n recent years, the neural network based topic modelshave been proposed
for many NLP tasks, such as infor-mation retrieval [11], aspect extraction
[12] and sentimentclassification [13]. The basic idea is to construct a neuralnetwork
which aims to approximate the topic-word distri-bution in probabilistic topic
models. Additional constraints,such as incorporating prior distribution [14],
enforcing di-versity among topics [15] or encouraging topic sparsity [16],have
been explored for neural topic model learning andproved effective.
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text: "Neural topic models are often trained to mimic the behaviours of probabilistic\
\ topic models - I should come back and look at some of the works:\n\n* R. Das,\
\ M. Zaheer, and C. Dyer, \u201CGaussian LDA for topic models with word embeddings,\u201D\
\ \n* P. Xie, J. Zhu, and E. P. Xing, \u201CDiversity-promoting bayesian learning\
\ of latent variable models,\u201D\n * M. Peng, Q. Xie, H. Wang, Y. Zhang, X.\
\ Zhang, J. Huang, and G. Tian, \u201CNeural sparse topical coding,\u201D"
updated: '2022-11-21T20:13:05.556810+00:00'
uri: https://www.researchgate.net/profile/Lin-Gui-5/publication/342058196_Multi-Task_Learning_with_Mutual_Learning_for_Joint_Sentiment_Classification_and_Topic_Detection/links/5f96fd48458515b7cf9f3abd/Multi-Task-Learning-with-Mutual-Learning-for-Joint-Sentiment-Classification-and-Topic-Detection.pdf
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tags:
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url: /annotation/2022/11/21/1669061585
---
<blockquote>n recent years, the neural network based topic modelshave been proposed for many NLP tasks, such as infor-mation retrieval [11], aspect extraction [12] and sentimentclassification [13]. The basic idea is to construct a neuralnetwork which aims to approximate the topic-word distri-bution in probabilistic topic models. Additional constraints,such as incorporating prior distribution [14], enforcing di-versity among topics [15] or encouraging topic sparsity [16],have been explored for neural topic model learning andproved effective.</blockquote>Neural topic models are often trained to mimic the behaviours of probabilistic topic models - I should come back and look at some of the works:
* R. Das, M. Zaheer, and C. Dyer, “Gaussian LDA for topic models with word embeddings,”
* P. Xie, J. Zhu, and E. P. Xing, “Diversity-promoting bayesian learning of latent variable models,”
* M. Peng, Q. Xie, H. Wang, Y. Zhang, X. Zhang, J. Huang, and G. Tian, “Neural sparse topical coding,”

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<blockquote> this work, we developa crowdsourcing-friendly coreference annota-tion methodology, ezCoref, consisting of anannotation tool and an interactive tutorial. Weuse ezCoref to re-annotate 240 passages fromseven existing English coreference datasets(spanning fiction, news, and multiple other do-mains) while teaching annotators only casesthat are treated similarly across these datasets</blockquote>this paper describes a new efficient coreference annotation tool which simplifies co-reference annotation. They use their tool to re-annotate passages from widely used coreference datasets.

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<blockquote>owever, these datasets vary widelyin their definitions of coreference (expressed viaannotation guidelines), resulting in inconsistent an-notations both within and across domains and lan-guages. For instance, as shown in Figure 1, whileARRAU (Uryupina et al., 2019) treats generic pro-nouns as non-referring, OntoNotes chooses not tomark them at all</blockquote>One of the big issues is that different co-reference datasets have significant differences in annotation guidelines even within the coreference family of tasks - I found this quite shocking as one might expect coreference to be fairly well defined as a task.

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<blockquote>Specifically, our work investigates the quality ofcrowdsourced coreference annotations when anno-tators are taught only simple coreference cases thatare treated uniformly across existing datasets (e.g.,pronouns). By providing only these simple cases,we are able to teach the annotators the concept ofcoreference, while allowing them to freely interpretcases treated differently across the existing datasets.This setup allows us to identify cases where ourannotators disagree among each other, but moreimportantly cases where they unanimously agreewith each other but disagree with the expert, thussuggesting cases that should be revisited by theresearch community when curating future unifiedannotation guidelines</blockquote>The aim of the work is to examine a simplified subset of co-reference phenomena which are generally treated the same across different existing datasets.
This makes spotting inter-annotator disagreement easier - presumably because for simpler cases there are fewer modes of failure?

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<blockquote>Annotation structure: Two annotation ap-proaches are prominent in the literature: (1) a localpairwise approach, annotators are shown a pairof mentions and asked whether they refer to thesame entity (Hladká et al., 2009; Chamberlain et al.,2016a; Li et al., 2020; Ravenscroft et al., 2021),which is time-consuming; or (2) a cluster-basedapproach (Reiter, 2018; Oberle, 2018; Bornsteinet al., 2020), in which annotators group all men-tions of the same entity into a single cluster. InezCoref we use the latter approach, which can befaster but requires the UI to support more complexactions for creating and editing cluster structures.</blockquote>ezCoref presents clusters of coreferences all at the same time - this is a nice efficient way to do annotation versus pairwise annotation (like we did for CD^2CR)

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<blockquote>an algorithm with high precision on LitBank orOntoNotes would miss a huge percentage of rele-vant mentions and entities on other datasets (con-straining our analysis) </blockquote>these datasets have the most limited/constrained definitions for co-reference and what should be marked up so it makes sense that precision is poor in these datasets

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\ by a set of rules. To set upthe baseline performance on SCIARG, weexploit\
\ three state-of-the-art neural architec-tures based on pre-trained language\
\ modelsand prompt tuning, and further ensemble themto attain the highest\
\ 77.0% F-score. To the bestof our knowledge, we are the first to leveragenatural\
\ language processing techniques to cu-rate all validated ARGs from scientific\
\ papers.Both the code and data are publicly availableat https://github.com/VT-NLP/SciARG."
prefix: g,clb21565,lifuh}@vt.eduAbstract
suffix: 1 IntroductionAntibiotic resista
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updated: '2022-11-23T20:47:05.414293+00:00'
uri: https://aclanthology.org/2022.bionlp-1.40.pdf
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type: annotation
url: /annotation/2022/11/23/1669236425
---
<blockquote>Antibiotic resistance has become a growingworldwide concern as new resistance mech-anisms are emerging and spreading globally,and thus detecting and collecting the cause– Antibiotic Resistance Genes (ARGs), havebeen more critical than ever. In this work,we aim to automate the curation of ARGs byextracting ARG-related assertive statementsfrom scientific papers. To support the researchtowards this direction, we build SCIARG, anew benchmark dataset containing 2,000 man-ually annotated statements as the evaluationset and 12,516 silver-standard training state-ments that are automatically created from sci-entific papers by a set of rules. To set upthe baseline performance on SCIARG, weexploit three state-of-the-art neural architec-tures based on pre-trained language modelsand prompt tuning, and further ensemble themto attain the highest 77.0% F-score. To the bestof our knowledge, we are the first to leveragenatural language processing techniques to cu-rate all validated ARGs from scientific papers.Both the code and data are publicly availableat https://github.com/VT-NLP/SciARG.</blockquote>The authors use prompt training on LLMs to build a classifier that can identify statements that describe whether or not micro-organisms have antibiotic resistant genes in scientific papers.

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<blockquote>Suppose a human is given two sentences: “Noweapons of mass destruction found in Iraq yet.”and “Weapons of mass destruction found in Iraq.”They are then asked to respond 0 or 1 and receive areward if they are correct. In this setup, they wouldlikely need a large number of trials and errors be-fore figuring out what they are really being re-warded to do. This setup is akin to the pretrain-and-fine-tune setup which has dominated NLP in recentyears, in which models are asked to classify a sen-tence representation (e.g., a CLS token) into some</blockquote>This is a really excellent illustration of the difference in paradigm between "normal" text model fine tuning and prompt-based modelling

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type: annotation
url: /annotation/2022/11/23/1669236944
---
<blockquote>Misleading Templates There is no consistent re-lation between the performance of models trainedwith templates that are moderately misleading (e.g.{premise} Can that be paraphrasedas "{hypothesis}"?) vs. templates that areextremely misleading (e.g., {premise} Isthis a sports news? {hypothesis}).T0 (both 3B and 11B) perform better givenmisleading-moderate (Figure 3), ALBERT andT5 3B perform better given misleading-extreme(Appendices E and G.4), whereas T5 11B andGPT-3 perform comparably on both sets (Figure 2;also see Table 2 for a summary of statisticalsignificances.) Despite a lack of pattern between</blockquote>Their misleading templates really are misleading
{premise} Can that be paraphrased as "{hypothesis}"
{premise} Is this a sports news? {hypothesis}

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we happen to be in the Las Vegas airport). This solves the problem of huddles
of people awkwardly hovering in liminal space; near doorways, taking up sidewalks,
anxiously waiting for delayed flights or dental operations or immigration
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'
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Others like Light on Two Sides '
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url: /annotations/2022/11/25/1669411452
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<blockquote>For example, the design pattern A Place to Wait asks that we create comfortable accommodation and ambient activity whenever someone needs to wait; benches, cafes, reading rooms, miniature playgrounds, three-reel slot machines (if we happen to be in the Las Vegas airport). This solves the problem of huddles of people awkwardly hovering in liminal space; near doorways, taking up sidewalks, anxiously waiting for delayed flights or dental operations or immigration investigations without anything to distract them from uncertain fates.</blockquote>Amazing to think how ubiquitous waiting rooms are and how we take them for granted

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\ RSS currently can\u2019t provide to me. This increases my own agency, by\
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<blockquote>First, to experiment personally with AP itself, and if possible with the less known Activities that AP could support, e.g. travel and check-ins. This as an extension of my personal site in areas that WordPress, OPML and RSS currently can’t provide to me. This increases my own agency, by adding affordances to my site. This in time may mean I won’t be hosting or self-hosting my personal Mastodon instance. (See my current fediverse activities)</blockquote>Interesting for me to explore and understand too. How does AP compare to micropub which can be used for similar purposes? As far as I can tell it is much more heavyweight

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<blockquote>I love the IndieWeb and its tools, but it has always bothered me that at some point they basically require you to have a webdevelopment background.</blockquote>Yeah this is definitely a concern and a major barrier for adoption at the moment.

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text: "Yeah totally! In my opinion AP is a really nicely designed protocol that\
\ does what it is designed to do very well. I've been playing with indieweb tech\
\ (micropub/sub/webmentions etc) to get a similar suite of behaviour but I guess\
\ these technologies are even less widely supported than AP and of course there\
\ are more moving parts to configure. Essentially, given that [you basically have\
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\ more niche bloggers with other interests getting involved I guess). \n\nTo be\
\ honest I am just curious to see what an AP implementation would look like for\
\ my current web setup and how it would compare in terms of a) code complexity\
\ and b) performance/compute intensity - might make for a fun weekend project\
\ and blog post!"
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url: /annotations/2022/11/26/1669454568
---
Yeah totally! In my opinion AP is a really nicely designed protocol that does what it is designed to do very well. I've been playing with indieweb tech (micropub/sub/webmentions etc) to get a similar suite of behaviour but I guess these technologies are even less widely supported than AP and of course there are more moving parts to configure. Essentially, given that [you basically have to be a web developer to use them](https://hyp.is/jrrh0G1rEe2SI2OlykvzjQ/mxb.dev/blog/the-indieweb-for-everyone/), they're probably not gonna see mass adoption outside of webdev/programmer circles I think (maybe "mass adoption" isn't what we want but it'd be nice to see more niche bloggers with other interests getting involved I guess).
To be honest I am just curious to see what an AP implementation would look like for my current web setup and how it would compare in terms of a) code complexity and b) performance/compute intensity - might make for a fun weekend project and blog post!

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<blockquote>a habit of creating learning exhaust:</blockquote>not sure I love the metaphor but I can definitely see the advantages of leaving your learnings "out there" for others to see and benefit from

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pages I want to archive. The h plugin works well with it but you lose the social
side of things when you annotate docs in your personal archive. It's even less
likely you'll encounter others there!
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I've started using [archivebox](https://archivebox.io/) to take copies of pages I want to archive. The h plugin works well with it but you lose the social side of things when you annotate docs in your personal archive. It's even less likely you'll encounter others there!

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<blockquote>In the same category of integrating h. into my pkm workflows, falls the interaction between h. and Zotero, especially now that Zotero has its own storage of annotations of PDFs in my library. It might be of interest to be able to share those annotations, for a more complete overview of what I’m annotating. Either directly from Zotero, or by way of my notes in Obsidian (Zotero annotatins end up there in the end)</blockquote>I've been thinking about this exact same flow. Given that I'm mostly annotating scientific papers I got from open access journals I was wondering whether there might be some way to syndicate my zotero annotations back to h via a script.

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<blockquote>Nim in Action book</blockquote>todo: procure this

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<blockquote>This isn't a highly scientific post full of esoteric details and language feature matrices. It's about making the best choice for what I can be the most productive in for my target market and product requirements.</blockquote>this post is more about the author's needs and requirements. It does not attempt to be objective

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To read: The Internet Trap http://assets.press.princeton.edu/chapters/s13236.pdf

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<blockquote>The term ‘impact’ is currently used widely in research, especially with the inclusion ofnon-academic impact as part of the latest Research Excellence Framework (REF)</blockquote>RF use similar definition of impact to that of REF

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<blockquote>However, knowledge production isnormally only an intermediate aim: the ultimateobjective of most medical research is to improvehealth and prosperity. </blockquote>Exactly! Measuring citation counts doesn't help us understand whether research actually helped people

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contributions of works to economic impact.
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<blockquote>look at the economicimpact of research – taking an area of research(often cardiovascular disease), calculating thetotal investment in research and comparing it tothe total payback in terms of monetarised healthbenefit and other economic effects. </blockquote>Interesting to see that the authors considers these macro level economic indicators "broad and shallow" but it does make sense. Ideally we want to understand individual contributions of works to economic impact.

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---
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<blockquote>Research outputs (and outcomes and impact) are gathered through a ‘questionset’ developed by funding institutions through a consultative process. This set of16 questions contains 175 sub-questions as illustrated in Figure 3 (the full set ofquestions are available in Annex A). A researcher, or one of their delegates, can add,edit and delete entries, and crucially, attribute entries to research grants and awards</blockquote>RF allows researchers to input fine-grained information about the research that they have done and this information is passed back to the funding bodies.

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date: '2022-11-27T12:59:03'
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<blockquote>Research funders and providers are having to compete with other public services, and,as such, must be able to advocate the need for funding of research. Leaders within thesector must have compelling arguments to ‘make the case’ for research. For example,the Research Councils each publish an annual impact report which describe the waysin which they are maximising the impacts of their investments. These reports includeillustrations of how their research and training has made a contribution to the economyand society.10 The analysis of Researchfish and other similar data can support thedevelopment of these cases</blockquote>For research councils, being able to illustrate how their research impacts the economy and society helps them to compete for and justify their continued funding.

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---
date: '2022-11-27T13:06:01'
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text: the REF definition of impact as it pertains to comprehensive impact (and as
opposed to academic impact)
updated: '2022-11-27T13:06:01.886391+00:00'
uri: https://webarchive.nationalarchives.gov.uk/ukgwa/20170712131025mp_/http://www.hefce.ac.uk/media/HEFCE,2014/Content/Pubs/Independentresearch/2015/Analysis,of,REF,impact/Analysis_of_REF_impact.pdf
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<blockquote>any effect on, change or benefit to the economy, society,culture, public policy or services, health, the environment or quality of life, beyondacademia’ (REF, 2011).</blockquote>the REF definition of impact as it pertains to comprehensive impact (and as opposed to academic impact)

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---
date: '2022-11-27T13:08:56'
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references:
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tags:
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target:
- source: https://webarchive.nationalarchives.gov.uk/ukgwa/20170712131025mp_/http://www.hefce.ac.uk/media/HEFCE,2014/Content/Pubs/Independentresearch/2015/Analysis,of,REF,impact/Analysis_of_REF_impact.pdf
text: there's actually a typo here - it is "an" not "any" in the [original document](https://www.ref.ac.uk/2014/media/ref/content/pub/assessmentframeworkandguidanceonsubmissions/GOS%20including%20addendum.pdf)
updated: '2022-11-27T13:08:56.843673+00:00'
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tags:
- comprehensive impact
- hypothesis
type: annotation
url: /annotations/2022/11/27/1669554536
---
there's actually a typo here - it is "an" not "any" in the [original document](https://www.ref.ac.uk/2014/media/ref/content/pub/assessmentframeworkandguidanceonsubmissions/GOS%20including%20addendum.pdf)

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---
date: '2022-11-27T13:14:43'
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tags:
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target:
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implementation was used.
updated: '2022-11-27T13:14:43.604240+00:00'
uri: https://webarchive.nationalarchives.gov.uk/ukgwa/20170712131025mp_/http://www.hefce.ac.uk/media/HEFCE,2014/Content/Pubs/Independentresearch/2015/Analysis,of,REF,impact/Analysis_of_REF_impact.pdf
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<blockquote>Topic modelling was used to determine common topics across the wholecorpus. Sixty-five topics were found (of which 60 were used) using theApache Mallet Toolkit Latent Dirichlet Allocation (LDA) algorithm.</blockquote>The authors used LDA with k=60 across full text case studies. The Apache Mallet implementation was used.

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---
date: '2022-11-27T13:17:16'
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- exact: 'With the benefit of hindsight, our analysis would have been much easierif
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text: I found similar in my [2017 work](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0173152).
I'd guess that modern vector-based analyses and entity linking approaches could
help a lot with reconciling these issues now.
updated: '2022-11-27T13:17:16.223069+00:00'
uri: https://webarchive.nationalarchives.gov.uk/ukgwa/20170712131025mp_/http://www.hefce.ac.uk/media/HEFCE,2014/Content/Pubs/Independentresearch/2015/Analysis,of,REF,impact/Analysis_of_REF_impact.pdf
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url: /annotations/2022/11/27/1669555036
---
<blockquote>With the benefit of hindsight, our analysis would have been much easierif the case studies had greater structure and used standardized definitions. Giventhat the case studies spanned a 20-year period, organization names have changed inthat time and keyword searches were not sophisticated enough to capture some keyinformation. </blockquote>I found similar in my [2017 work](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0173152). I'd guess that modern vector-based analyses and entity linking approaches could help a lot with reconciling these issues now.

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- exact: while there aregroups potentially benefiting from the case studies relating
to their field of research (egwriters benefiting from studies in Panel D,
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across all UOAs
updated: '2022-11-27T13:23:58.799954+00:00'
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- research funders
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url: /annotations/2022/11/27/1669555438
---
<blockquote>while there aregroups potentially benefiting from the case studies relating to their field of research (egwriters benefiting from studies in Panel D, engineers benefiting from studies in PanelB), there are mentions of these potential beneficiaries across all the panels</blockquote>The beneficiaries of research named by REF impact case studies are heterogeneous across all UOAs

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<blockquote> RANDreport that had been commissioned by HEFCE (Grant et al. 2010)</blockquote>interesting ties here between REF and ResearchFish - both came out of RAND

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<blockquote>Unsur-prisingly, therefore, existing research documents various ways in which REF impact has becomeembedded within university governance, including via the broadening of career progression criteria(Bandola-Gill 2019)</blockquote>REF has become embedded within university governance - including career progression criteria (for researchers presumably)

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<blockquote>BanditSum a hierarchical bi-LSTM</blockquote>Banditsum uses bi-directional LSTM encoding. It generates sentence-level representations

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<blockquote>Of course, the closest you can get is having the activity available in your own living space, but as unused home treadmills and exercise bikes demonstrate, this has its pitfalls. There could be something about a thing always being available that means there’s never any urgency.</blockquote>There seems to be a minimum at which hyperbolic discounting stops working because things are too easy to access

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<blockquote>Whether you want to call them mottos, memes, or manifestos, words can be the building blocks of how we think and transmit ideas. You can also gauge how well someone is grasping your concepts—or at least making an effort to—by the language they’re responding to you with as well.</blockquote>You can use the way that a person responds to your concepts as a metric for how well they understand you. If they don't understand chances are they will retreat back to jargon to try to hide the fact that they're struggling. If they're getting on well they might have an insightful way to extend your metaphor

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<blockquote>Amazon is hated on the right as a bulwark of progressivism. For instance, to pick a random example, GOP icon Tucker Carlson recently characterized the firm’s behavior as ‘modern-day book burning.’ And you can find an endless number of right-wing critiques. Conservatives distrust Amazon.</blockquote>That is really interesting. Amazon is not exactly renowned as an m upholder of progressive values by the left either.

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<blockquote>We test this hypothesis by training a predicted compute-optimal model, Chinchilla, that uses the same compute budget as Gopher but with 70B parameters and4× more more data. Chinchilla uniformly and significantly outperforms Gopher (280B), GPT-3 (175B),Jurassic-1 (178B), and Megatron-Turing NLG (530B) on a large range of downstream evaluation tasks.This also means that Chinchilla uses substantially less compute for fine-tuning and inference, greatlyfacilitating downstream usage. As a highlight, Chinchilla reaches a state-of-the-art average accuracy of67.5% on the MMLU benchmark, greater than a 7% improvement over Gopher</blockquote>By using more data on a smaller language model the authors were able to achieve better performance than with the larger models - this reduces the cost of using the model for inference.

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<blockquote>anyone skilled in the art can now replicate their recipe. </blockquote>Well anyone skilled enough who has $500k for the gpu bill and access to and the means to store the corpus... So corporations I guess... Yey!

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<blockquote>every country is going to need to reconsider its policies on misinformation. It’s one thing for the occasional lie to slip through; it’s another for us all to swim in a veritable ocean of lies. In time, though it would not be a popular decision, we may have to begin to treat misinformation as we do libel, making it actionable if it is created with sufficient malice and sufficient volume. </blockquote>What to do then when our government reps are already happy to perpetuate "culture wars" and empty talking points?

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<blockquote>Perceptions of failure don’t always lead to shared ideas of how to learn from these lessons.</blockquote>Really good insight that I hadn't really considered before. If normally opposing parties reach the same end goal then nobody wants to think about why, we'd rather just take the win.

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<blockquote>Throughout the 80s and 90s, private equity firms and hedge funds gobbled up local news enterprises to extract their real estate. They didn’t give a shit about journalism; they just wanted prime real estate that they could develop. And news organizations had it in the form of buildings in the middle of town. So financiers squeezed the news orgs until there was no money to be squeezed and then they hung them out to dry.</blockquote>Wild that driving functional organisations into the ground could just be the cost of doing business

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<blockquote>AI training data is filled with racist stereotypes, pornography, and explicit images of rape, researchers Abeba Birhane, Vinay Uday Prabhu, and Emmanuel Kahembwe found after analyzing a data set similar to the one used to build Stable Diffusion.</blockquote>That is horrifying. You'd think that authors would attempt to remove or filter this kind of material. There are, after all models out there that are trained to find it. It makes me wonder what awful stuff is in the GPT-3 dataset too.

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<blockquote>For many intellectual tasks, the people with the least skill overestimate themselves the most, a pattern popularly known as the Dunning–Kruger effect (DKE). The dominant account of this effect depends on the idea that assessing the quality of one's performance (metacognition) requires the same mental resources as task performance itself (cognition). Unskilled people are said to suffer a dual burden: they lack the cognitive resources to perform well, and this deprives them of metacognitive insight into their failings. In this Registered Report, we applied recently developed methods for the measurement of metacognition to a matrix reasoning task, to test the dual-burden account. Metacognitive sensitivity (information exploited by metacognition) tracked performance closely, so less information was exploited by the metacognitive judgements of poor performers; but metacognitive efficiency (quality of metacognitive processing itself) was unrelated to performance. Metacognitive bias (overall tendency towards high or low confidence) was positively associated with performance, so poor performers were appropriately less confident—not more confident—than good performers. Crucially, these metacognitive factors did not cause the DKE pattern, which was driven overwhelmingly by performance scores. These results refute the dual-burden account and suggest that the classic DKE is a statistical regression artefact that tells us nothing much about metacognition.</blockquote>The Dunning-Kruger effect (DKE) seems to be a statistical regression artefact that doesn't actually explain whether people who are good at a task are able to estimate their own abilities at the task

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<blockquote>If my interpretation of the Retrieval quadrant is correct, it will become much more difficult to be an average, or even above average, writer. Only the best will flourish. Perhaps we will see a rise in neo-generalists.</blockquote>This is probably true of average or poor software engineers given that GPT-3 can produce pretty reasonable code snippets

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<blockquote>Okay, but one thing that’s been found empirically is that you take commonsense questions that are flubbed by GPT-2, let’s say, and you try them on GPT-3, and very often now it gets them right. You take the things that the original GPT-3 flubbed, and you try them on the latest public model, which is sometimes called GPT-3.5 (incorporating an advance called InstructGPT), and again it often gets them right. So it’s extremely risky right now to pin your case against AI on these sorts of examples! Very plausibly, just one more order of magnitude of scale is all it’ll take to kick the ball in, and then you’ll have to move the goal again.</blockquote>the stochastic parrots argument could be defeated as models get bigger and more complex

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<blockquote> And famously, self-driving cars have taken a lot longer than many people expected a decade ago. This is partly because of regulatory barriers and public relations: even if a self-driving car actually crashes less than a human does, that’s still not good enough, because when it does crash the circumstances are too weird. So, the AI is actually held to a higher standard. But it’s also partly just that there was a long tail of really weird events. A deer crosses the road, or you have some crazy lighting conditions—such things are really hard to get right, and of course 99% isn’t good enough here.</blockquote>I think the emphasis is wrong here. The regulation is secondary. The long tail of weird events is the more important thing.

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<blockquote> “AI alignment”</blockquote>AI Alignment is terminator situation. This versus AI Ethics which is more the concern around current models being racist etc.

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- exact: "(3) A third direction, and I would say maybe the most popular one in\
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<blockquote>(3) A third direction, and I would say maybe the most popular one in AI alignment research right now, is called interpretability. This is also a major direction in mainstream machine learning research, so there’s a big point of intersection there. The idea of interpretability is, why don’t we exploit the fact that we actually have complete access to the code of the AI—or if it’s a neural net, complete access to its parameters? So we can look inside of it. We can do the AI analogue of neuroscience. Except, unlike an fMRI machine, which gives you only an extremely crude snapshot of what a brain is doing, we can see exactly what every neuron in a neural net is doing at every point in time. If we don’t exploit that, then aren’t we trying to make AI safe with our hands tied behind our backs?</blockquote>Interesting metaphor - it is a bit like MRI for neural networks but actually more accurate/powerful

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<blockquote> Eventually GPT will say, “oh, I know what game we’re playing! it’s the ‘give false answers’ game!” And it will then continue playing that game and give you more false answers. What the new paper shows is that, in such cases, one can actually look at the inner layers of the neural net and find where it has an internal representation of what was the true answer, which then gets overridden once you get to the output layer.</blockquote>this is fascinating - GPT learns the true answer to a question but will ignore it and let the user override this in later layers of the model

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<blockquote>So then to watermark, instead of selecting the next token randomly, the idea will be to select it pseudorandomly, using a cryptographic pseudorandom function, whose key is known only to OpenAI.</blockquote>Watermarking by applying cryptographic pseudorandom functions to the model output instead of true random (true pseudo-random)

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<blockquote>Anyway, we actually have a working prototype of the watermarking scheme, built by OpenAI engineer Hendrik Kirchner. It seems to work pretty well—empirically, a few hundred tokens seem to be enough to get a reasonable signal that yes, this text came from GPT. In principle, you could even take a long text and isolate which parts probably came from GPT and which parts probably didn’t.</blockquote>Scott's team hsas already developed a prototype watermarking scheme at OpenAI and it works pretty well

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<blockquote>Now, this can all be defeated with enough effort. For example, if you used another AI to paraphrase GPT’s output—well okay, we’re not going to be able to detect that. On the other hand, if you just insert or delete a few words here and there, or rearrange the order of some sentences, the watermarking signal will still be there. Because it depends only on a sum over n-grams, it’s robust against those sorts of interventions.</blockquote>this mechanism can be defeated by paraphrasing the output with another model

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<blockquote>My goal with my content is to make it so recognizable that you would know it was me even if it didn't have my name on it. The same style. The same thought process. The same character.</blockquote>building a recognisable tone of voice can help with repeat visitors

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<blockquote>Don’t try to convince everyone that what you say, feel, think, or have done is better than everyone else.</blockquote>This is pretty normal for those of us who are academically inclined so it shouldn't be too much of a stretch - after all a lot of the time what we're doing is thinking about other peoples' works critically

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<blockquote>My goal was simply to scale this ladder over time. I worked the list 5 people at a time, starting at the bottom. I engaged relentlessly with those accounts until they noticed me and began engaging back.</blockquote>Interesting approach and these people are going to be great candidates for picking up new knowledge and self learning from too!

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<blockquote>Positive fantasies allow you to indulge in the desired future mentally…You can taste the sensations of what it’s like to achieve your goal in the present — this depletes your energy to pursue your desired future.</blockquote>It's easy to get caught up fantasising about what you could achieve rather than actually taking action to achieve it.

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<blockquote>You need to take a step back and form a picture of the overall structure of the ideas. Concretely, you might do that by clustering your scraps into piles and observing the structure that emerges. Or you might sketch a mind map or a visual outline.</blockquote>Andy suggests taking a step back and clustering annotations into piles or using a mind map or visualisations to identify common themes.
I wonder if this is a bit overkill for the number of notes I tend to take or a sign that I'm not taking enough notes?
What tools are out there that could integrate with my stack and help me do this.

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<blockquote>Other hardware options do exist, including Google Tensor Processing Units (TPUs); AMD Instinct GPUs; AWS Inferentia and Trainium chips; and AI accelerators from startups like Cerebras, Sambanova, and Graphcore. Intel, late to the game, is also entering the market with their high-end Habana chips and Ponte Vecchio GPUs. But so far, few of these new chips have taken significant market share. The two exceptions to watch are Google, whose TPUs have gained traction in the Stable Diffusion community and in some large GCP deals, and TSMC, who is believed to manufacture all of the chips listed here, including Nvidia GPUs (Intel uses a mix of its own fabs and TSMC to make its chips).</blockquote>Look at market share for tensorflow and pytorch which both offer first-class nvidia support and likely spells out the story. If you are getting in to AI you go learn one of those frameworks and they tell you to install CUDA

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<blockquote>See ericmitchell.ai/detectgptfor code, data, and other project information.</blockquote>Code and data available at https://ericmitchell.ai/detectgpt

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<blockquote>his approach, which we call DetectGPT,does not require training a separate classifier, col-lecting a dataset of real or generated passages, orexplicitly watermarking generated text. It usesonly log probabilities computed by the model ofinterest and random perturbations of the passagefrom another generic pre-trained language model(e.g, T5)</blockquote>The novelty of this approach is that it is cheap to set up as long as you have the log probabilities generated by the model of interest.

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<blockquote>improving detection offake news articles generated by 20B parameterGPT-NeoX</blockquote>The authors test their approach on GPT-NeoX. The question would be whether we can get hold of the log probs from ChatGPT to do the same

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<blockquote>ecause the GPT-3 API does not provideaccess to the complete conditional distribution for each to-ken, we cannot compare to the rank, log rank, and entropy-based prior methods</blockquote>GPT-3 api does not expose the cond probs for each token so we can't compare to some of the prior methods. That seems to suggest that this method can be used with limited knowledge about the probabilities.

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<blockquote>DetectGPT’s performancein particular is mostly unaffected by the change in languagefrom English to Germa</blockquote>Performance of this method is robust against changes between languages (e.g. English to German)

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